{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perkenalan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1515,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n=================================================\\nM2\\n\\nNama  : Ryan Trisnadi\\nBatch : HCK-17\\n\\nProyek ini dibuat untuk mengilustrasikan dan memproyek kedepan harga rumah di kota Melbourne, Australia. Kita akan menguji dengan Linear regression\\ndengan beberapa test algorithma seperti SVM, Decision Tree, Random Forest, dan Boosting. Oleh karena itu, kita akan memilih variable \"price\" atau harga\\nrumah sebagai target. Dengan mengunakan Pipeline di setiap proses dari Feature Engineering, kita bisa membuat proses lebih efisien dan mencegah data leak. \\nDataset tersebut memiliki beberapa faktor seperti alamat, kode pos, harga rumah, lokasi lingkungan, kamar, kamar mandi, garasi, besar tanah, umur dan \\nlain-lain. Dibawah kita akan coba menjawab teori kita dari regresi tersebut:\\n\\nPertanyaan (objective):\\n-Dengan Linear Regression, bisakah kita prediksi harga rumah bisa naik atau turun dari rata-rata?\\n-Kolom Numerikal apa saja yang memiliki dampak paling besar terhadap harga rumah?\\n-Apakah ada dampak signifikan antara harga rumah yang dekat CBD (Central Business District) dengan yang jauh? \\n\\nProses pe-modelan akan dillustrasikan dengan beberapa diagram di EDA terhadap pola terhadap faktor yang memiliki relasi atau inferensi terhadap harga \\nrumah. Dengan pembuatan analis tersebut, kita akan bisa membuat business recommendation terhadap pembeli rumah atau perusahaan yang tertarik untuk\\ninvestasi di properti Melbourne, Australia. \\n\\n=================================================\\n'"
      ]
     },
     "execution_count": 1515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "=================================================\n",
    "M2\n",
    "\n",
    "Nama  : Ryan Trisnadi\n",
    "Batch : HCK-17\n",
    "\n",
    "Proyek ini dibuat untuk mengilustrasikan dan memproyek kedepan harga rumah di kota Melbourne, Australia. Kita akan menguji dengan Linear regression\n",
    "dengan beberapa test algorithma seperti SVM, Decision Tree, Random Forest, dan Boosting. Oleh karena itu, kita akan memilih variable \"price\" atau harga\n",
    "rumah sebagai target. Dengan mengunakan Pipeline di setiap proses dari Feature Engineering, kita bisa membuat proses lebih efisien dan mencegah data leak. \n",
    "Dataset tersebut memiliki beberapa faktor seperti alamat, kode pos, harga rumah, lokasi lingkungan, kamar, kamar mandi, garasi, besar tanah, umur dan \n",
    "lain-lain. Dibawah kita akan coba menjawab teori kita dari regresi tersebut:\n",
    "\n",
    "Pertanyaan (objective):\n",
    "-Dengan Linear Regression, bisakah kita prediksi harga rumah bisa naik terus?\n",
    "-Kolom Numerikal apa saja yang memiliki dampak paling besar terhadap harga rumah?\n",
    "-Apakah ada dampak signifikan antara harga rumah yang dekat CBD (Central Business District) dengan yang jauh? \n",
    "\n",
    "Proses pe-modelan akan dillustrasikan dengan beberapa diagram di EDA terhadap pola terhadap faktor yang memiliki relasi atau inferensi terhadap harga \n",
    "rumah. Dengan pembuatan analis tersebut, kita akan bisa membuat business recommendation terhadap pembeli rumah atau perusahaan yang tertarik untuk\n",
    "investasi di properti Melbourne, Australia. \n",
    "\n",
    "=================================================\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latar Belakang"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita adalah sebuah analyst yang bekerja di sebuah perusahaan Real Estate Management Firm di Australia yang harus menganalisa harga rumah di setiap daerah di kota Melbourne. Tujuan kita untuk melihat relasi dari setiap faktor-faktor yang akan berguna terhadap pembeli rumah atau perusahaan di satu tahun kemudian. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conceptual Problems\n",
    "### Jawab pertanyaan berikut:\n",
    "\n",
    "Jelaskan latar belakang adanya bagging dan cara kerja bagging !\n",
    "\n",
    "Jelaskan perbedaan cara kerja algoritma Random Forest dengan algoritma boosting yang Anda pilih !\n",
    "\n",
    "Jelaskan apa yang dimaksud dengan Cross Validation !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1516,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Library\n",
    "# Library Dataframe\n",
    "import pandas as pd\n",
    "# Library Numerical Data\n",
    "import numpy as np\n",
    "# Library Statistic\n",
    "from scipy import stats\n",
    "from scipy.stats import uniform\n",
    "\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "\n",
    "# Library Data Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "from sklearn.metrics import silhouette_score,silhouette_samples\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "\n",
    "# Library Outlier Handling\n",
    "from feature_engine.outliers import Winsorizer\n",
    "# Library Correlation\n",
    "from scipy.stats import kendalltau, pearsonr, spearmanr\n",
    "\n",
    "# Save Model\n",
    "import pickle\n",
    "import joblib\n",
    "import json\n",
    "\n",
    "# To Ignore Warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Import Library\n",
    "# Library Dataframe\n",
    "import pandas as pd\n",
    "# Library Numerical Data\n",
    "import numpy as np\n",
    "# Library Statistic\n",
    "from scipy import stats\n",
    "from scipy.stats import uniform\n",
    "\n",
    "# Library Data Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Library Preprocessing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "# Library Machine Learning Model\n",
    "from sklearn.metrics.pairwise import manhattan_distances\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Library Model Evaluation\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score , classification_report , confusion_matrix, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from scipy.stats import uniform, randint\n",
    "\n",
    "# Library Outlier Handling\n",
    "from feature_engine.outliers import Winsorizer\n",
    "# Library Correlation\n",
    "from scipy.stats import pearsonr, kendalltau, spearmanr\n",
    "\n",
    "\n",
    "# Model Evaluation\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "# Save Model\n",
    "import pickle\n",
    "import joblib\n",
    "import json\n",
    "\n",
    "# To Ignore Warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder,OrdinalEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita pertama mau impor modul yang akan digunakan untuk menganalisa data dari CSV tersedia. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1517,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>3/12/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>4/06/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>3/30 Shaftsbury St</td>\n",
       "      <td>2</td>\n",
       "      <td>u</td>\n",
       "      <td>451000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>RT</td>\n",
       "      <td>24/09/2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7520</td>\n",
       "      <td>144.9540</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>211 Bell St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>635000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Brad</td>\n",
       "      <td>25/02/2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7404</td>\n",
       "      <td>144.9559</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>15 Hardwick St</td>\n",
       "      <td>5</td>\n",
       "      <td>h</td>\n",
       "      <td>1020000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Raine</td>\n",
       "      <td>25/02/2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7492</td>\n",
       "      <td>144.9690</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>6 Hardwick St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>967000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>25/02/2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7494</td>\n",
       "      <td>144.9681</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>32A Kelson St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1025000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>25/02/2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7408</td>\n",
       "      <td>144.9558</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Suburb             Address  Rooms Type      Price Method SellerG  \\\n",
       "0     Abbotsford        85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "1     Abbotsford     25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "2     Abbotsford        5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "3     Abbotsford    40 Federation La      3    h   850000.0     PI  Biggin   \n",
       "4     Abbotsford         55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "...          ...                 ...    ...  ...        ...    ...     ...   \n",
       "1995      Coburg  3/30 Shaftsbury St      2    u   451000.0      S      RT   \n",
       "1996      Coburg         211 Bell St      2    h   635000.0      S    Brad   \n",
       "1997      Coburg      15 Hardwick St      5    h  1020000.0     PI   Raine   \n",
       "1998      Coburg       6 Hardwick St      3    h   967000.0      S  Nelson   \n",
       "1999      Coburg       32A Kelson St      3    h  1025000.0     VB  Jellis   \n",
       "\n",
       "            Date  Distance  Postcode  ...  Bathroom  Car  Landsize  \\\n",
       "0      3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0   \n",
       "1      4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0   \n",
       "2      4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0   \n",
       "3      4/03/2017       2.5    3067.0  ...       2.0  1.0      94.0   \n",
       "4      4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0   \n",
       "...          ...       ...       ...  ...       ...  ...       ...   \n",
       "1995  24/09/2016       7.8    3058.0  ...       1.0  1.0       0.0   \n",
       "1996  25/02/2017       7.8    3058.0  ...       2.0  3.0     339.0   \n",
       "1997  25/02/2017       7.8    3058.0  ...       2.0  4.0     381.0   \n",
       "1998  25/02/2017       7.8    3058.0  ...       1.0  1.0     169.0   \n",
       "1999  25/02/2017       7.8    3058.0  ...       1.0  3.0     336.0   \n",
       "\n",
       "      BuildingArea  YearBuilt  CouncilArea Lattitude  Longtitude  \\\n",
       "0              NaN        NaN        Yarra  -37.7996    144.9984   \n",
       "1             79.0     1900.0        Yarra  -37.8079    144.9934   \n",
       "2            150.0     1900.0        Yarra  -37.8093    144.9944   \n",
       "3              NaN        NaN        Yarra  -37.7969    144.9969   \n",
       "4            142.0     2014.0        Yarra  -37.8072    144.9941   \n",
       "...            ...        ...          ...       ...         ...   \n",
       "1995          72.0     1970.0     Moreland  -37.7520    144.9540   \n",
       "1996         149.0     1920.0     Moreland  -37.7404    144.9559   \n",
       "1997         286.0     1985.0     Moreland  -37.7492    144.9690   \n",
       "1998           NaN        NaN     Moreland  -37.7494    144.9681   \n",
       "1999         112.0     1950.0     Moreland  -37.7408    144.9558   \n",
       "\n",
       "                 Regionname Propertycount  \n",
       "0     Northern Metropolitan        4019.0  \n",
       "1     Northern Metropolitan        4019.0  \n",
       "2     Northern Metropolitan        4019.0  \n",
       "3     Northern Metropolitan        4019.0  \n",
       "4     Northern Metropolitan        4019.0  \n",
       "...                     ...           ...  \n",
       "1995  Northern Metropolitan       11204.0  \n",
       "1996  Northern Metropolitan       11204.0  \n",
       "1997  Northern Metropolitan       11204.0  \n",
       "1998  Northern Metropolitan       11204.0  \n",
       "1999  Northern Metropolitan       11204.0  \n",
       "\n",
       "[2000 rows x 21 columns]"
      ]
     },
     "execution_count": 1517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Replace 'path/to/your/csvfile.csv' with the actual path to your CSV file\n",
    "file_path = '/Users/ryantrisnadi/Desktop/first_project1/p1-ftds017-hck-m2-ryantrisnadi/melb_data.csv'\n",
    "\n",
    "# Read the CSV file into a DataFrame\n",
    "data = pd.read_csv(file_path, nrows=2000)\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ini kita extract data CSV tentang Melbourne Housing Snapshot. Disini kita mau coba manipulasi data seperti delete\n",
    "duplicate atau Null data jadi extraksi terhadap numerikal kolom lebih jelas untuk dibaca sistem. Juga, kita mau delete beberapa kolom seperti nanti karena dataset memiliki kolom-kolom yang tidak diperlukan untuk analisa ini.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1518,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>3/12/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>4/06/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>129 Charles St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>941000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>7/05/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8041</td>\n",
       "      <td>144.9953</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>124 Yarra St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1876000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>7/05/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8024</td>\n",
       "      <td>144.9993</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>98 Charles St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1636000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>8/10/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8060</td>\n",
       "      <td>144.9954</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>6/241 Nicholson St</td>\n",
       "      <td>1</td>\n",
       "      <td>u</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>8/10/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8008</td>\n",
       "      <td>144.9973</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>10 Valiant St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1097000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>8/10/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8010</td>\n",
       "      <td>144.9989</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb             Address  Rooms Type      Price Method SellerG  \\\n",
       "0  Abbotsford        85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "1  Abbotsford     25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "2  Abbotsford        5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "3  Abbotsford    40 Federation La      3    h   850000.0     PI  Biggin   \n",
       "4  Abbotsford         55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "5  Abbotsford      129 Charles St      2    h   941000.0      S  Jellis   \n",
       "6  Abbotsford        124 Yarra St      3    h  1876000.0      S  Nelson   \n",
       "7  Abbotsford       98 Charles St      2    h  1636000.0      S  Nelson   \n",
       "8  Abbotsford  6/241 Nicholson St      1    u   300000.0      S  Biggin   \n",
       "9  Abbotsford       10 Valiant St      2    h  1097000.0      S  Biggin   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "1  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "2  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "3  4/03/2017       2.5    3067.0  ...       2.0  1.0      94.0           NaN   \n",
       "4  4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0         142.0   \n",
       "5  7/05/2016       2.5    3067.0  ...       1.0  0.0     181.0           NaN   \n",
       "6  7/05/2016       2.5    3067.0  ...       2.0  0.0     245.0         210.0   \n",
       "7  8/10/2016       2.5    3067.0  ...       1.0  2.0     256.0         107.0   \n",
       "8  8/10/2016       2.5    3067.0  ...       1.0  1.0       0.0           NaN   \n",
       "9  8/10/2016       2.5    3067.0  ...       1.0  2.0     220.0          75.0   \n",
       "\n",
       "   YearBuilt  CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "0        NaN        Yarra  -37.7996    144.9984  Northern Metropolitan   \n",
       "1     1900.0        Yarra  -37.8079    144.9934  Northern Metropolitan   \n",
       "2     1900.0        Yarra  -37.8093    144.9944  Northern Metropolitan   \n",
       "3        NaN        Yarra  -37.7969    144.9969  Northern Metropolitan   \n",
       "4     2014.0        Yarra  -37.8072    144.9941  Northern Metropolitan   \n",
       "5        NaN        Yarra  -37.8041    144.9953  Northern Metropolitan   \n",
       "6     1910.0        Yarra  -37.8024    144.9993  Northern Metropolitan   \n",
       "7     1890.0        Yarra  -37.8060    144.9954  Northern Metropolitan   \n",
       "8        NaN        Yarra  -37.8008    144.9973  Northern Metropolitan   \n",
       "9     1900.0        Yarra  -37.8010    144.9989  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "0        4019.0  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "3        4019.0  \n",
       "4        4019.0  \n",
       "5        4019.0  \n",
       "6        4019.0  \n",
       "7        4019.0  \n",
       "8        4019.0  \n",
       "9        4019.0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 1518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan tampilkan 10 data yang diatas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1519,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>144 The Avenue</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>23/04/2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7537</td>\n",
       "      <td>144.9766</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>2 Franklin St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1008000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>24/09/2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7505</td>\n",
       "      <td>144.9725</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>3/20 Loch St</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>599000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Ray</td>\n",
       "      <td>24/09/2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7474</td>\n",
       "      <td>144.9621</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>50a Rennie St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>860000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>24/09/2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7509</td>\n",
       "      <td>144.9696</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>1 Shaftsbury St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>990000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Grantham</td>\n",
       "      <td>24/09/2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7537</td>\n",
       "      <td>144.9532</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>3/30 Shaftsbury St</td>\n",
       "      <td>2</td>\n",
       "      <td>u</td>\n",
       "      <td>451000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>RT</td>\n",
       "      <td>24/09/2016</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7520</td>\n",
       "      <td>144.9540</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>211 Bell St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>635000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Brad</td>\n",
       "      <td>25/02/2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7404</td>\n",
       "      <td>144.9559</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>15 Hardwick St</td>\n",
       "      <td>5</td>\n",
       "      <td>h</td>\n",
       "      <td>1020000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Raine</td>\n",
       "      <td>25/02/2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7492</td>\n",
       "      <td>144.9690</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>6 Hardwick St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>967000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>25/02/2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7494</td>\n",
       "      <td>144.9681</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Coburg</td>\n",
       "      <td>32A Kelson St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1025000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>25/02/2017</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3058.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Moreland</td>\n",
       "      <td>-37.7408</td>\n",
       "      <td>144.9558</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Suburb             Address  Rooms Type      Price Method   SellerG  \\\n",
       "1990  Coburg      144 The Avenue      4    h  1250000.0     VB    Nelson   \n",
       "1991  Coburg       2 Franklin St      2    h  1008000.0      S    Jellis   \n",
       "1992  Coburg        3/20 Loch St      2    t   599000.0      S       Ray   \n",
       "1993  Coburg       50a Rennie St      2    h   860000.0      S    Nelson   \n",
       "1994  Coburg     1 Shaftsbury St      4    h   990000.0     SP  Grantham   \n",
       "1995  Coburg  3/30 Shaftsbury St      2    u   451000.0      S        RT   \n",
       "1996  Coburg         211 Bell St      2    h   635000.0      S      Brad   \n",
       "1997  Coburg      15 Hardwick St      5    h  1020000.0     PI     Raine   \n",
       "1998  Coburg       6 Hardwick St      3    h   967000.0      S    Nelson   \n",
       "1999  Coburg       32A Kelson St      3    h  1025000.0     VB    Jellis   \n",
       "\n",
       "            Date  Distance  Postcode  ...  Bathroom  Car  Landsize  \\\n",
       "1990  23/04/2016       7.8    3058.0  ...       3.0  1.0     515.0   \n",
       "1991  24/09/2016       7.8    3058.0  ...       1.0  3.0     457.0   \n",
       "1992  24/09/2016       7.8    3058.0  ...       2.0  1.0      76.0   \n",
       "1993  24/09/2016       7.8    3058.0  ...       1.0  1.0     483.0   \n",
       "1994  24/09/2016       7.8    3058.0  ...       1.0  1.0     488.0   \n",
       "1995  24/09/2016       7.8    3058.0  ...       1.0  1.0       0.0   \n",
       "1996  25/02/2017       7.8    3058.0  ...       2.0  3.0     339.0   \n",
       "1997  25/02/2017       7.8    3058.0  ...       2.0  4.0     381.0   \n",
       "1998  25/02/2017       7.8    3058.0  ...       1.0  1.0     169.0   \n",
       "1999  25/02/2017       7.8    3058.0  ...       1.0  3.0     336.0   \n",
       "\n",
       "      BuildingArea  YearBuilt  CouncilArea Lattitude  Longtitude  \\\n",
       "1990         249.0     1900.0     Moreland  -37.7537    144.9766   \n",
       "1991          89.0     1940.0     Moreland  -37.7505    144.9725   \n",
       "1992           NaN        NaN     Moreland  -37.7474    144.9621   \n",
       "1993           NaN        NaN     Moreland  -37.7509    144.9696   \n",
       "1994         142.0     1930.0     Moreland  -37.7537    144.9532   \n",
       "1995          72.0     1970.0     Moreland  -37.7520    144.9540   \n",
       "1996         149.0     1920.0     Moreland  -37.7404    144.9559   \n",
       "1997         286.0     1985.0     Moreland  -37.7492    144.9690   \n",
       "1998           NaN        NaN     Moreland  -37.7494    144.9681   \n",
       "1999         112.0     1950.0     Moreland  -37.7408    144.9558   \n",
       "\n",
       "                 Regionname Propertycount  \n",
       "1990  Northern Metropolitan       11204.0  \n",
       "1991  Northern Metropolitan       11204.0  \n",
       "1992  Northern Metropolitan       11204.0  \n",
       "1993  Northern Metropolitan       11204.0  \n",
       "1994  Northern Metropolitan       11204.0  \n",
       "1995  Northern Metropolitan       11204.0  \n",
       "1996  Northern Metropolitan       11204.0  \n",
       "1997  Northern Metropolitan       11204.0  \n",
       "1998  Northern Metropolitan       11204.0  \n",
       "1999  Northern Metropolitan       11204.0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 1519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan tampilkan 10 data yang dibawah."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1520,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 1520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita lihat beberapa baris kebawah. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1521,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 1521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Juga akan lihat beberapa kolom-kolom di dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1522,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Suburb         2000 non-null   object \n",
      " 1   Address        2000 non-null   object \n",
      " 2   Rooms          2000 non-null   int64  \n",
      " 3   Type           2000 non-null   object \n",
      " 4   Price          2000 non-null   float64\n",
      " 5   Method         2000 non-null   object \n",
      " 6   SellerG        2000 non-null   object \n",
      " 7   Date           2000 non-null   object \n",
      " 8   Distance       2000 non-null   float64\n",
      " 9   Postcode       2000 non-null   float64\n",
      " 10  Bedroom2       2000 non-null   float64\n",
      " 11  Bathroom       2000 non-null   float64\n",
      " 12  Car            2000 non-null   float64\n",
      " 13  Landsize       2000 non-null   float64\n",
      " 14  BuildingArea   1131 non-null   float64\n",
      " 15  YearBuilt      1285 non-null   float64\n",
      " 16  CouncilArea    2000 non-null   object \n",
      " 17  Lattitude      2000 non-null   float64\n",
      " 18  Longtitude     2000 non-null   float64\n",
      " 19  Regionname     2000 non-null   object \n",
      " 20  Propertycount  2000 non-null   float64\n",
      "dtypes: float64(12), int64(1), object(8)\n",
      "memory usage: 328.3+ KB\n",
      "None\n",
      "             Rooms         Price     Distance     Postcode     Bedroom2  \\\n",
      "count  2000.000000  2.000000e+03  2000.000000  2000.000000  2000.000000   \n",
      "mean      3.002000  1.238507e+06     9.493200  3115.856000     2.959500   \n",
      "std       0.974921  6.944251e+05     3.220832    57.870431     0.962976   \n",
      "min       1.000000  1.310000e+05     2.500000  3018.000000     0.000000   \n",
      "25%       2.000000  7.547500e+05     6.400000  3056.000000     2.000000   \n",
      "50%       3.000000  1.094500e+06    10.500000  3124.000000     3.000000   \n",
      "75%       4.000000  1.526125e+06    11.700000  3165.000000     4.000000   \n",
      "max       8.000000  5.525000e+06    13.900000  3206.000000     9.000000   \n",
      "\n",
      "          Bathroom        Car      Landsize  BuildingArea    YearBuilt  \\\n",
      "count  2000.000000  2000.0000   2000.000000   1131.000000  1285.000000   \n",
      "mean      1.599000     1.6210    464.456000    165.328028  1960.619455   \n",
      "std       0.756628     0.9337   1707.748707    236.814642    36.341266   \n",
      "min       0.000000     0.0000      0.000000      1.000000  1880.000000   \n",
      "25%       1.000000     1.0000    175.000000     97.000000  1930.000000   \n",
      "50%       1.000000     2.0000    384.500000    133.000000  1960.000000   \n",
      "75%       2.000000     2.0000    633.250000    193.000000  1995.000000   \n",
      "max       7.000000     8.0000  75100.000000   6791.000000  2018.000000   \n",
      "\n",
      "         Lattitude   Longtitude  Propertycount  \n",
      "count  2000.000000  2000.000000    2000.000000  \n",
      "mean    -37.837618   145.010596    7092.795000  \n",
      "std       0.062271     0.075563    2910.961351  \n",
      "min     -37.937600   144.807500    2211.000000  \n",
      "25%     -37.897725   144.961025    4605.000000  \n",
      "50%     -37.840650   145.022400    6923.000000  \n",
      "75%     -37.777800   145.071800   10579.000000  \n",
      "max     -37.716400   145.137300   11918.000000  \n"
     ]
    }
   ],
   "source": [
    "# Check the structure and basic statistics of the data\n",
    "print(data.info())\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ini untuk chek tipe dari semua kolom di data CSV. Karena tujuan kita untuk prediksi harga rumah, kelihatannya data yang object bisa di-drop sekaligus sebelum data di train. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1523,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n",
       "       'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',\n",
       "       'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude',\n",
       "       'Longtitude', 'Regionname', 'Propertycount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lihat semua kolom dari table tersebut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1524,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb             0\n",
       "Address            0\n",
       "Rooms              0\n",
       "Type               0\n",
       "Price              0\n",
       "Method             0\n",
       "SellerG            0\n",
       "Date               0\n",
       "Distance           0\n",
       "Postcode           0\n",
       "Bedroom2           0\n",
       "Bathroom           0\n",
       "Car                0\n",
       "Landsize           0\n",
       "BuildingArea     869\n",
       "YearBuilt        715\n",
       "CouncilArea        0\n",
       "Lattitude          0\n",
       "Longtitude         0\n",
       "Regionname         0\n",
       "Propertycount      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disini kelihatan dua kolom \"BuildingArea\" dan \"YearBuilt\" yang Null atau kosong. Kita mau drop data tersebut jadi training data model lebih efisien dan tidak merusak disibusi data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1525,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1526,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan melihat beberapa duplicate yang total di dataset. Kelihatannya tidak ada. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1527,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3/12/2016\n",
       "1        4/02/2016\n",
       "2        4/03/2017\n",
       "3        4/03/2017\n",
       "4        4/06/2016\n",
       "           ...    \n",
       "1995    24/09/2016\n",
       "1996    25/02/2017\n",
       "1997    25/02/2017\n",
       "1998    25/02/2017\n",
       "1999    25/02/2017\n",
       "Name: Date, Length: 2000, dtype: object"
      ]
     },
     "execution_count": 1527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita mau ganti 'Date' tanggal jadi bisa ganti ke \"datetime\" dengan pandas module. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1528,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n",
       "       'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',\n",
       "       'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude',\n",
       "       'Longtitude', 'Regionname', 'Propertycount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1529,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Date'] = pd.to_datetime(data['Date'], format='%d/%m/%Y')\n",
    "#data.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "masukan 'date' ke dataframe jadi bukan object atau string lagi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2016-12-03</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2016-02-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>2016-06-04</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb           Address  Rooms Type      Price Method SellerG  \\\n",
       "0  Abbotsford      85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "1  Abbotsford   25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "2  Abbotsford      5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "3  Abbotsford  40 Federation La      3    h   850000.0     PI  Biggin   \n",
       "4  Abbotsford       55a Park St      4    h  1600000.0     VB  Nelson   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0 2016-12-03       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "1 2016-02-04       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "2 2017-03-04       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "3 2017-03-04       2.5    3067.0  ...       2.0  1.0      94.0           NaN   \n",
       "4 2016-06-04       2.5    3067.0  ...       1.0  2.0     120.0         142.0   \n",
       "\n",
       "   YearBuilt  CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "0        NaN        Yarra  -37.7996    144.9984  Northern Metropolitan   \n",
       "1     1900.0        Yarra  -37.8079    144.9934  Northern Metropolitan   \n",
       "2     1900.0        Yarra  -37.8093    144.9944  Northern Metropolitan   \n",
       "3        NaN        Yarra  -37.7969    144.9969  Northern Metropolitan   \n",
       "4     2014.0        Yarra  -37.8072    144.9941  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "0        4019.0  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "3        4019.0  \n",
       "4        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 1530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cek setiap kolom jika Date sudah berubah. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1531,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Masukan data ke dataframe. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1532,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 1532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Suburb'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita mau cek unique values dari dari setiap values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1533,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data['Price'], kde=True)\n",
    "plt.title('Distribution of House Prices in Melbourne')\n",
    "plt.xlabel('Price in Millions of AU$')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk menganalisa harga rumah di kota Melbourne, Australia, kita akan mau lihat distribusinya dengan histogram. Kelihatan dari diagram diatas bahwa harga rumah cenderung berkelompokan di sekitar AU$1 Juta. Harga rumah jarang diatas AU$2 Juta walaupun ada beberapa harga rumah yang jauh lebih mahal di sisi kanan grafik. Skew dari distribusi jadi positif karena di data sample tersebut, lebih banyak rumah lebih murah daripada yang mahal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1534,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(data['Distance'], data['Price'], alpha=0.5)\n",
    "plt.title('Distance vs. Price')\n",
    "plt.xlabel('Distance')\n",
    "plt.ylabel('Price')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sekarang kita mau lihat dengan scatterplot salah satu faktor terbesar terhadap harga rumah \"distance\" atau kejauhan dari pusat kota \"CBD\". Biasanya harga rumah jauh lebih mahal per meter persegi jika lokasinya lebih dekat ke pusat kota daripada yang dipingir kota. Dari datset tersebut, kelihatannya rumah-rumah yang di dalam sample cukup seimbang dengan harganya, dekat maupun jauh dari pusat. \n",
    "\n",
    "Observasi yang cukup menarik adalah harga rumah tidak jauh terlalu murah jika lokasinya lebih jauh dari pusat. Kelihatan diatas ada konsentrasi padat di sisi kanan diagram yang merepresentasikan harga rumah antara AU$ 1 sampai 2 Juta. Bisa dibilang harga rumah sangat rata dan seimbang di seluruh kota Melbourne. Kita nanti mau uji jika kejauhan rumah \"distance\" adalah faktor yang signifikan.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1535,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(data['YearBuilt'], data['Price'], alpha=0.5)\n",
    "plt.title('Yearbuilt vs. Price')\n",
    "plt.xlabel('Yearbuilt')\n",
    "plt.ylabel('Price')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Faktor yang juga berdampak terhadap harga rumah adalah tahun pembangunan rumah. Bisasanya rumah lebih mahal jika masih baru dan umurnya kurang dari 10 tahun. Kelihatan di diagram bahwa walaupun rumah-rumah di sample banyak yang dibawah 25 tahun (dari tahun 2000-an), Juga ada banyah rumah yang historis dibangun sebelum tahun 1980-an. Ini artinya rumah-rumah yang di pasar, pernah dijual dan ditangilkan memiliki umur yang sangat beragam, bukan saja rumah yang baru saja yang akan dijual.\n",
    "\n",
    "Diagram diatas mengatakan bahwa susah untuk mengumpulkan kelompok rumah yang termasuk baru atau tua, karena rumah di kota Melbourne sangat banyak tegantung lingkungannya. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1536,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(data['Landsize'], data['Price'], alpha=0.5)\n",
    "plt.title('Land size vs. Price')\n",
    "plt.xlabel('Land size')\n",
    "plt.ylabel('Price')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita expektasinya rumah yang memiliki lahan yang lebih besar pasti juga lebih mahal. Diatas diagram kelihatan ada beberapa outlier sample yang terletak di sisi kanan and kiri atas diagram. Rumah-rumah di sample cenderung memiliki ukuran tanah dibawah 5000 square feet, dan harganya bisa menonjol sampai AU$ 5 Juta per rumah. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1537,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(data['BuildingArea'], data['Price'], alpha=0.5)\n",
    "plt.title('Building Area vs. Price')\n",
    "plt.xlabel('Building Area')\n",
    "plt.ylabel('Price')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk harga besar luas bangunan \"Building Area\" memiliki trend yang hampir sama dengan \"Land size\". Korelasi antar harga dan luas bangunan sangat positif dan curam di diagram, artinya luas area adalah salah satu faktor yang berdampak kuat terhadap harga rumah dan bisa prediksi harga di masa depan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1538,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data['Rooms'], kde=True)\n",
    "plt.title('Distribution of Rooms of Houses in Melbourne')\n",
    "plt.xlabel('Price in Millions of AU$')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sekarang juga mau lihat distribusi rumah-rumah dengan histogram yang memiliki beberapa banyak banyak kamar. Kelihatan banyak rumah rata-rata memiliki 2 sampai 4 kamar di sample. Karena kebanyakan rumah memiliki tiga kamar, ada bias di dataset yang akan cenderung merepresentasikan rumah tiga-kamar daripada yang lain. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1539,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(data['Rooms'], data['Price'], alpha=0.5)\n",
    "plt.title('Rooms vs. Price')\n",
    "plt.xlabel('Rooms')\n",
    "plt.ylabel('Price')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Terlihat bahwa rumah yang memiliki lebih banyak kamar pasti lebih mahal, walaupun rumah yang tiga dan empat-kamar memiliki range yang cukup besar di dataset. Karena tadi terlihat frequency rumah tiga kamar sangat banyak, maka ruangan \"rooms\" bisa saja tidak proporsional dengan harganya.\n",
    "\n",
    "Artinya bisa saja rumah dengan lebih sedikit kamar memiliki harga yang bisa lebih tinggi daripada rumah yang banyak kamar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1540,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Suburb         2000 non-null   object        \n",
      " 1   Address        2000 non-null   object        \n",
      " 2   Rooms          2000 non-null   int64         \n",
      " 3   Type           2000 non-null   object        \n",
      " 4   Price          2000 non-null   float64       \n",
      " 5   Method         2000 non-null   object        \n",
      " 6   SellerG        2000 non-null   object        \n",
      " 7   Date           2000 non-null   datetime64[ns]\n",
      " 8   Distance       2000 non-null   float64       \n",
      " 9   Postcode       2000 non-null   float64       \n",
      " 10  Bedroom2       2000 non-null   float64       \n",
      " 11  Bathroom       2000 non-null   float64       \n",
      " 12  Car            2000 non-null   float64       \n",
      " 13  Landsize       2000 non-null   float64       \n",
      " 14  BuildingArea   1131 non-null   float64       \n",
      " 15  YearBuilt      1285 non-null   float64       \n",
      " 16  CouncilArea    2000 non-null   object        \n",
      " 17  Lattitude      2000 non-null   float64       \n",
      " 18  Longtitude     2000 non-null   float64       \n",
      " 19  Regionname     2000 non-null   object        \n",
      " 20  Propertycount  2000 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(12), int64(1), object(7)\n",
      "memory usage: 328.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1541,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FHY+n9vPzk6QMYSAxMVHXr193mle5cmX5+/vf9hKfkJAQ1a1bVwsWLHDqc9++fVq9evVtg5srtG7dWj4+PpoxY4bT2ZHPPvtMV65c0UMPPeSYV7lyZW3bts1xiaN0456OU6dOOfV58eJFp2kfHx/VrFlThmHIbrfLy8tLjzzyiL755hvt27cvQ02ZPcL77yIjI+Xp6ak33ngjw1mW3Jzh8vLyyrDe4sWLM9xf44o+HnnkEV24cCHDOJD+r/ZbHWNlypRRixYt9PHHHys6OjrD+lntt1u5dOlShtrTw0v6sXurmjKTnfcnO9sEYG6cCQKQ761cudJxxuTcuXP68ssvdfjwYY0ePVoBAQGSpM6dO6tly5Z65ZVXdPz4cdWpU0erV6/Wd999p6FDh6py5cqSpLfeeku7d+/W2rVr5e/vr7vvvluvv/66Xn31VXXv3j1boSA5OVmtWrXSY489poMHD+qjjz5SkyZN9PDDD99ynX/961/6+OOP1bt3b+3cuVMVK1bUkiVL9NNPP2n69OmOs1e+vr6qWbOmvv76a1WtWlUlS5ZU7dq1lZKS4thmzZo15e3trW+//VYxMTF64oknblvv5MmT1aFDBz3wwAPq27ev4xHZxYoVc/r7MHmhdOnSGjNmjMaPH6/27dvr4Ycfduyz++67z+kx588995yWLFmi9u3b67HHHtPRo0e1cOFCx3uXrm3btgoODlbjxo0VFBSk/fv364MPPtBDDz3k2I/vvvuu1q9fr4YNG+r5559XzZo1FRsbq127dmnNmjWKjY29Zc3h4eF65ZVX9Oabb6pp06bq1q2brFarduzYobJly2rChAk52gedOnVynHls1KiRfvvtN33xxRcZzm65oo+ePXvq3//+t4YPH67t27eradOmSkhI0Jo1a9S/f3916dLllsdY7dq19eGHH6pJkyaKiIjQ888/r0qVKikmJkZbt27V6dOnM/27RFlZsGCBPvroI3Xt2lWVK1fW1atX9emnnyogIMAx3m5X082y8/5kZ5sATM4NT6QDgGzJ7BHZRYoUMerWrWvMmjXL6XHFhmEYV69eNYYNG2aULVvWsFgsRpUqVYzJkyc72u3cudPw9vY2Bg0a5LReSkqKcd999xlly5Y1Ll26lGU9GzduNP71r38ZJUqUMIoWLWr06NHD6fHThpHxsc6GYRgxMTFGnz59jFKlShk+Pj5GRESEMW/evAzb+fnnn4169eoZPj4+jscGX7hwwRgwYIBRvXp1w2azGcWKFTMaNmxo/Oc//8nWvlyzZo3RuHFjw9fX1wgICDA6d+5s/PHHH05tcvOI7JsfQZzex/r1653mf/DBB0b16tUNi8ViBAUFGf369ct0X0+ZMsUoV66cYbVajcaNGxu//vprhn358ccfG82aNTMCAwMNq9VqVK5c2XjxxReNK1euOPUVExNjDBgwwAgNDTUsFosRHBxstGrVyvjkk0+yfH2GceNx0/fcc49htVqNEiVKGM2bNzeioqIcyytUqGA89NBDGda7ud7r168bI0aMMEJCQgxfX1+jcePGxtatWzO0u93+z24fhmEYiYmJxiuvvGKEhYU5Xnf37t2No0ePOtpkdoylO3r0qNGzZ08jODjYsFgsRrly5YxOnToZS5YscbS51fv/92Xpj8jetWuX8eSTTxp33XWXYbVajTJlyhidOnUyfv31V6f1blXTzY/ITne79ye72wRgXh6GkYtz+wBgQvPnz1efPn20Y8cO1a9f393lAACAXOKeIAAAAACmQggCAAAAYCqEIAAAAACmwj1BAAAAAEyFM0EAAAAATIUQBAAAAMBUCvQfS01LS9OZM2fk7+8vDw8Pd5cDAAAAwE0Mw9DVq1dVtmxZeXre/lxPgQ5BZ86cUWhoqLvLAAAAAJBPnDp1SuXLl79tmwIdgvz9/SXdeKEBAQFursac7Ha7Vq9erbZt28pisbi7HMAtGAcA4wBgDLhfXFycQkNDHRnhdgp0CEq/BC4gIIAQ5CZ2u11+fn4KCAhgwMO0GAcA4wBgDOQf2blNhgcjAAAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAA/AOpqanauHGjNm3apI0bNyo1NdXdJQEAgCwQggAgl5YuXarw8HC1adNGU6dOVZs2bRQeHq6lS5e6uzQAAHAbhCAAyIWlS5eqe/fuioiI0ObNm/XVV19p8+bNioiIUPfu3QlCAADkY4QgAMih1NRUjRgxQp06ddKyZcvUsGFD+fr6qmHDhlq2bJk6deqkkSNHcmkcAAD5FCEIAHJo8+bNOn78uF5++WV5ejr/M+rp6akxY8bo2LFj2rx5s5sqBAAAt0MIAoAcio6OliTVrl070+Xp89PbAQCA/IUQBAA5FBISIknat29fpsvT56e3AwAA+QshCAByqGnTpqpYsaLeeecdpaWlOS1LS0vThAkTFBYWpqZNm7qpQgAAcDuEIADIIS8vL02ZMkXLly9XZGSktm3bpmvXrmnbtm2KjIzU8uXL9d5778nLy8vdpQIAgEx4u7sAACiIunXrpiVLlmjEiBFq1qyZY35YWJiWLFmibt26ubE6AABwO4QgAMilbt26qUuXLlq/fr1WrlypDh06qGXLlpwBAgAgnyMEAcA/4OXlpebNmyshIUHNmzcnAAEAUABwTxAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhAAAAAAUyEEAQAAADAVQhByLTk5WTNmzNAnn3yiGTNmKDk52d0lAXdcamqqNm7cqE2bNmnjxo1KTU11d0kAgDuMz4KCx60hKDU1Va+99prCwsLk6+urypUr680335RhGO4sC9kwatQo2Ww2jRw5UitWrNDIkSNls9k0atQod5cG3DFLly5VeHi42rRpo6lTp6pNmzYKDw/X0qVL3V0aAOAO4bOgYHJrCJo4caJmzZqlDz74QPv379fEiRM1adIkzZw5051lIQujRo3S5MmTFRgYqNmzZ2vevHmaPXu2AgMDNXnyZIIQTGHp0qXq3r27IiIitHnzZn311VfavHmzIiIi1L17dz78AMAE+CwouDwMN5526dSpk4KCgvTZZ5855j3yyCPy9fXVwoULs1w/Li5OxYoV05UrVxQQEJCXpeJ/JScny2azKTAwUKdPn5ZhGFqxYoU6duwoDw8PlS9fXhcvXlRCQoJ8fHzcXS6QJ1JTUxUeHq6IiAgtW7ZMqampjnHg5eWlyMhI7du3T4cPH5aXl5e7ywXuCLvd7hgHFovF3eUAeY7PgvwnJ9nA+w7VlKlGjRrpk08+0aFDh1S1alXt2bNHW7Zs0dSpUzNtn5SUpKSkJMd0XFycpBv/8Nrt9jtSs9nNnDlTKSkpGj9+vAzDcOx3u90ui8WisWPHqn///po5c6YGDx7s5mqBvLFx40YdP35cn3/+uVJTU53GgSS9+OKLatasmdavX6/mzZu7s1Tgjrl5HACFHZ8F+U9O/v1xawgaPXq04uLiVL16dXl5eSk1NVVvv/22evTokWn7CRMmaPz48Rnmr169Wn5+fnldLiStW7dOkmS1WrVixQrH/KioKElSkSJFHO3Cw8PvfIHAHbBp0yZJ0unTp3Xx4kXH/PRxcO3aNUnSypUrlZCQcOcLBNwofRwAhR2fBflPYmJittu6NQT95z//0RdffKEvv/xStWrV0u7duzV06FCVLVtWvXr1ytB+zJgxGj58uGM6Li5OoaGhatu2LZfD3SFHjhzRihUrlJSUpI4dO8putysqKkpt2rSRxWLRnDlzJEkPPvigOnbs6OZqgbxhs9k0depUlS9fXg0bNswwDrZt2yZJ6tChA7/9g2ncPA6Awo7Pgvwn/Sqx7HDrPUGhoaEaPXq0BgwY4Jj31ltvaeHChTpw4ECW63NP0J3HPUEA14EDmeGeIJgNnwX5T06ygVufDpeYmChPT+cSvLy8lJaW5qaKkBUfHx8NGzZMMTExKl++vObMmaPY2FjNmTNH5cuXV0xMjIYNG0YAQqHm5eWlKVOmaPny5YqMjNS2bdt07do1bdu2TZGRkVq+fLnee+89PvQAoBDjs6Bgc+vlcJ07d9bbb7+tu+66S7Vq1dJ///tfTZ06Vc8++6w7y0IWJk2aJEmaNm2a+vfv75jv7e2tF1980bEcKMy6deumJUuWaMSIEWrWrJljflhYmJYsWaJu3bq5sToAwJ3AZ0HB5dbL4a5evarXXntN3377rc6dO6eyZcvqySef1Ouvv56tMwlcDudeycnJmjlzptatW6cHH3xQgwYN4gwQTCc1NVXr16/XypUr1aFDB7Vs2ZLf+sGUuBwOZsZnQf6Qk2zg1hD0TxGC3I8PPYBxAEiMA4Ax4H4F5p4gAAAAALjTCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAAAATIUQBAAAAMBUCEEAAOAfSU5O1owZM/TJJ59oxowZSk5OdndJwB2VmpqqjRs3atOmTdq4caNSU1PdXRKyQAgCAAC5NmrUKNlsNo0cOVIrVqzQyJEjZbPZNGrUKHeXBtwRS5cuVXh4uNq0aaOpU6eqTZs2Cg8P19KlS91dGm6DEAQAAHJl1KhRmjx5sgIDAzV79mzNmzdPs2fPVmBgoCZPnkwQQqG3dOlSde/eXREREdq8ebO++uorbd68WREREerevTtBKB/zMAzDcHcRuRUXF6dixYrpypUrCggIcHc5pmS327VixQp17NhRFovF3eUAbsE4gBklJyfLZrMpMDBQp0+flmEYjnHg4eGh8uXL6+LFi0pISJCPj4+7ywVcLjU1VeHh4YqIiNCyZcuUmprqGANeXl6KjIzUvn37dPjwYXl5ebm7XFPISTbwvkM1wc0SExN14MABl/cbfy1JP/92VCVK/aqivlaX91+9enX5+fm5vF+YE+MAcJ2PPvpIKSkpeuutt+Tt7S273e5Y5u3trTfeeEMvvPCCPvroIw0dOtR9hQJ5ZPPmzTp+/Li++uoreXp6Ot0H5OnpqTFjxqhRo0bavHmzWrRo4b5CkSlCkEkcOHBA9erVy7P+J+VRvzt37tS9996bR73DbBgHgOscPXpUktSpU6dMl6fPT28HFDbR0dGSpNq1a2e6PH1+ejvkL4Qgk6hevbp27tzp8n4PRl/W8MW/aeqjEaoWUtzl/VevXt3lfcK8GAeA61SuXFmStHz5cj333HMZli9fvtypHVDYhISESJL27dun+++/P8Pyffv2ObVD/sI9QfhHdp+4qMhZ27Ss3/2qWyHQ3eUAbsE4gBlxTxDMjnuC8p+cZAOeDgcAAHLMx8dHw4YNU0xMjMqXL685c+YoNjZWc+bMUfny5RUTE6Nhw4YRgFBoeXl5acqUKVq+fLkiIyO1bds2Xbt2Tdu2bVNkZKSWL1+u9957jwCUT3E5HAAAyJVJk27cCTdt2jT179/fMd/b21svvviiYzlQWHXr1k1LlizRiBEj1KxZM8f8sLAwLVmyRN26dXNjdbgdQhAAAMi1SZMm6a233tLMmTO1bt06Pfjggxo0aBBngGAa3bp1U5cuXbR+/XqtXLlSHTp0UMuWLTkDlM8RggAAwD/i4+OjwYMHKzw8nL+XBVPy8vJS8+bNlZCQoObNmxOACgDuCQIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKoQgAAAAAKZCCAIAAABgKt7uLgAAABRsycnJmjlzptatW6cjR45o0KBB8vHxcXdZwB1z9uxZ1a1bV7GxsSpZsqR2796t4OBgd5eF23D7maC//vpLTz/9tAIDA+Xr66uIiAj9+uuv7i4LAABkw6hRo2Sz2TRy5EitWLFCI0eOlM1m06hRo9xdGnBH2Gw2hYSEKCYmRna7XTExMQoJCZHNZnN3abgNt54JunTpkho3bqyWLVtq5cqVKl26tA4fPqwSJUq4sywAAJANo0aN0uTJkxUUFKTx48fLarUqKSlJY8eO1eTJkyVJkyZNcnOVQN6x2WxKTEyUJFWsWFHdu3fXkiVLdPz4cSUmJspmsykhIcHNVSIzbj0TNHHiRIWGhmrevHlq0KCBwsLC1LZtW1WuXNmdZQEAgCwkJydr2rRpCgoK0unTp/Xss8+qRIkSevbZZ3X69GkFBQVp2rRpSk5OdnepQJ44e/asIwBdunRJhw4dUpMmTXTo0CFdunRJkpSYmKizZ8+6s0zcglvPBH3//fdq166dHn30UW3cuFHlypVT//799fzzz2faPikpSUlJSY7puLg4SZLdbpfdbr8jNcNZSkqK47+8BzArxgHMaObMmUpJSdH48eNlGIbj2Lfb7bJYLBo7dqz69++vmTNnavDgwW6uFnC9unXrSrpxBshmszmNAZvNpgoVKujEiROqW7euTp065cZKzSMnn8FuDUF//vmnZs2apeHDh+vll1/Wjh07NHjwYPn4+KhXr14Z2k+YMEHjx4/PMH/16tXy8/O7EyXjJqfiJclb27Zt01/73F0N4B6MA5jRunXrJElWq1UrVqxwzI+KipIkFSlSxNEuPDz8zhcI5LHY2FhJUvfu3TMdA127dtX06dMVGxvrtBx5J/3MXHa4NQSlpaWpfv36eueddyRJ99xzj/bt26fZs2dnGoLGjBmj4cOHO6bj4uIUGhqqtm3bKiAg4I7Vjf+z52Ss9Nuvuv/++1XnrpLuLgdwC8YBzOjIkSNasWKFkpKS1LFjR9ntdkVFRalNmzayWCyaM2eOJOnBBx9Ux44d3Vwt4HolS5ZUTEyMlixZonfeeSfDGBgyZIijHWPgzki/Siw73BqCQkJCVLNmTad5NWrU0DfffJNpe6vVKqvVmmG+xWKRxWLJkxpxe97e3o7/8h7ArBgHMKNBgwZp9OjRGjt2rPr27es49i0Wizw8PDR+/Hh5e3tr0KBBjAsUSrt371ZISIiOHz+uhIQEx9PgLBaLEhISdOLECUc7xsCdkZP97NYHIzRu3FgHDx50mnfo0CFVqFDBTRUBAIDs8PHx0bBhwxQTE6Py5ctrzpw5io2N1Zw5c1S+fHnFxMRo2LBh/L0gFFrBwcGO2zFKlCihKlWqaMOGDapSpYrjScd+fn78vaB8yq1ngoYNG6ZGjRrpnXfe0WOPPabt27frk08+0SeffOLOsgAAQDakP/562rRp6t+/v2O+t7e3XnzxRR6PjUIv/QxQYmKiTpw4oenTpzuW+fn58XjsfMytZ4Luu+8+ffvtt/rqq69Uu3Ztvfnmm5o+fbp69OjhzrIAAEA2TZo0SQkJCXrvvffUsWNHvffee0pISCAAwTQSEhIUHR2toKAgWSwWBQUFKTo6mgCUz7n1TJAkderUSZ06dXJ3GQAAIJd8fHw0ePBghYeHq2PHjtz/ANMJDg7WqVOntGLFCsZAAeHWM0EAAAAAcKcRggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYSq5C0J9//unqOgAAAADgjshVCAoPD1fLli21cOFCXb9+3dU1AQAAAECeyVUI2rVrl+6++24NHz5cwcHBeuGFF7R9+3ZX1wYAAAAALperEFS3bl29//77OnPmjObOnavo6Gg1adJEtWvX1tSpU3X+/HlX1wkAAAAALvGPHozg7e2tbt26afHixZo4caKOHDmikSNHKjQ0VD179lR0dLSr6gQAAPnU2bNnFRoaqkcffVShoaE6e/asu0sC7qjY2FjVrVtXzzzzjOrWravY2Fh3l4Qs/KMQ9Ouvv6p///4KCQnR1KlTNXLkSB09elRRUVE6c+aMunTp4qo6AQBAPmSz2RQSEqKYmBjZ7XbFxMQoJCRENpvN3aUBd0RwcLACAwP1xx9/6OrVq/rjjz8UGBio4OBgd5eG28hVCJo6daoiIiLUqFEjnTlzRv/+97914sQJvfXWWwoLC1PTpk01f/587dq1y9X1AgCAfMJmsykxMVGSVLFiRY0cOVIVK1aUJCUmJhKEUOgFBwcrJiZGktSwYUONHz9eDRs2lCTFxMQQhPKxXIWgWbNm6amnntKJEye0bNkyderUSZ6ezl2VKVNGn332mUuKBAAA+cvZs2cdAejSpUs6dOiQmjRpokOHDunSpUuSbgQhLo1DYRUbG+sIQFevXtXmzZtVp04dbd68WVevXpV0IwhxaVz+lKsQdPjwYY0ZM0YhISG3bOPj46NevXrlujAAAJB/1a1bV5IUFham4sWLOy0rXry4KlSo4NQOKGyaN28uSbr//vtVtGhRp2VFixZVgwYNnNohf8lVCJo3b54WL16cYf7ixYu1YMGCf1wUAADI3y5fvixJmjRpUqbL33nnHad2QGFz5swZSdLbb7+d6fI33njDqR3yl1yFoAkTJqhUqVIZ5pcpU8bxjx4AACi80s/+jBo1KtPlL7/8slM7oLApW7asJOmVV17JdPnrr7/u1A75S65C0MmTJxUWFpZhfoUKFXTy5Ml/XBQAAMjfdu/eLUk6duxYhrM9ly9f1okTJ5zaAYXNxo0bJUnbtm1TfHy807L4+Hht377dqR3yl1yFoDJlymjv3r0Z5u/Zs0eBgYH/uCgAAJC/BQcHy8/PT5JUokQJValSRRs2bFCVKlVUokQJSZKfnx9Px0KhVbJkSQUFBUmS/P391bhxY+3atUuNGzeWv7+/JCkoKEglS5Z0Z5m4Be/crPTkk09q8ODB8vf3V7NmzSTdSLlDhgzRE0884dICAQBA/pSQkOB4TPaJEyc0ffp0xzI/Pz8lJCS4rzjgDjh79qzjMdk7duzQjh07HMuCgoJ4OmI+lqszQW+++aYaNmyoVq1aydfXV76+vmrbtq0efPBB7gkCAMBEEhISFB0draCgIFksFgUFBSk6OpoABNM4e/asLl68qJo1a8rf3181a9bUxYsXCUD5XK7OBPn4+Ojrr7/Wm2++qT179sjX11cRERGOx2ECAADzCA4O1qlTp7RixQp17NhRFovF3SUBd1TJkiW1e/duxkABkqsQlK5q1aqqWrWqq2oBAAAAgDyXqxCUmpqq+fPna+3atTp37pzS0tKclq9bt84lxQEAAACAq+UqBA0ZMkTz58/XQw89pNq1a8vDw8PVdQEAAABAnshVCFq0aJH+85//qGPHjq6uBwAAAADyVK6eDufj46Pw8HBX1wIAAAAAeS5XIWjEiBF6//33ZRiGq+sBAAAAgDyVq8vhtmzZovXr12vlypWqVatWhscALl261CXFAQAAAICr5SoEFS9eXF27dnV1LQAAAACQ53IVgubNm+fqOgAAAADgjsjVPUGSlJKSojVr1ujjjz/W1atXJUlnzpxRfHy8y4oDAAAAAFfL1ZmgEydOqH379jp58qSSkpLUpk0b+fv7a+LEiUpKStLs2bNdXScAAAAAuESuzgQNGTJE9evX16VLl+Tr6+uY37VrV61du9ZlxQEAAACAq+XqTNDmzZv1888/y8fHx2l+xYoV9ddff7mkMAAAAADIC7k6E5SWlqbU1NQM80+fPi1/f/9/XBQAAAAA5JVchaC2bdtq+vTpjmkPDw/Fx8dr7Nix6tixo6tqAwAAAACXy9XlcFOmTFG7du1Us2ZNXb9+XU899ZQOHz6sUqVK6auvvnJ1jQAAAADgMrkKQeXLl9eePXu0aNEi7d27V/Hx8erbt6969Ojh9KAEAAAAAMhvchWCJMnb21tPP/20K2sBAAAAgDyXqxD073//+7bLe/bsmatiAAAAACCv5SoEDRkyxGnabrcrMTFRPj4+8vPzIwQBAAAAyLdy9XS4S5cuOf3Ex8fr4MGDatKkCQ9GAAAAAJCv5SoEZaZKlSp69913M5wlAgAAAID8xGUhSLrxsIQzZ864sksAAAAAcKlc3RP0/fffO00bhqHo6Gh98MEHaty4sUsKAwAAAIC8kKsQFBkZ6TTt4eGh0qVL68EHH9SUKVNcURcAAAAA5IlchaC0tDRX1wEAAAAAd4RL7wkCAAAAgPwuV2eChg8fnu22U6dOzc0mAAAAACBP5CoE/fe//9V///tf2e12VatWTZJ06NAheXl56d5773W08/DwcE2VAAAAAOAiuQpBnTt3lr+/vxYsWKASJUpIuvEHVPv06aOmTZtqxIgRLi0SAAAAAFwlV/cETZkyRRMmTHAEIEkqUaKE3nrrLZ4OBwAAACBfy1UIiouL0/nz5zPMP3/+vK5evfqPiwIAAACAvJKrENS1a1f16dNHS5cu1enTp3X69Gl988036tu3r7p16+bqGgEAAADAZXJ1T9Ds2bM1cuRIPfXUU7Lb7Tc68vZW3759NXnyZJcWCAAAAACulKsQ5Ofnp48++kiTJ0/W0aNHJUmVK1eWzWZzaXEAAAAA4Gr/6I+lRkdHKzo6WlWqVJHNZpNhGK6qCwAAFBBbt26Vj4+PIiMj5ePjo61bt7q7JOCOat26tdMYaN26tbtLQhZyFYIuXryoVq1aqWrVqurYsaOio6MlSX379s3147HfffddeXh4aOjQoblaHwAA3HkeHh5q1KiR07xGjRrxtwJhGh4eHlq7dq3TvLVr1zIG8rlchaBhw4bJYrHo5MmT8vPzc8x//PHHtWrVqhz3t2PHDn388ce6++67c1MOAABwg5u/5LVo0eK2y4HCJqtjnDGQf+UqBK1evVoTJ05U+fLlneZXqVJFJ06cyFFf8fHx6tGjhz799FOnvzsEAADyr79f8nbw4EElJydr6NChSk5O1sGDBzNtBxQmf7/k7YUXXlBycrKWLVum5ORkvfDCC5m2Q/6RqwcjJCQkOJ0BShcbGyur1ZqjvgYMGKCHHnpIrVu31ltvvXXbtklJSUpKSnJMx8XFSZLsdrvjKXWFwfGLCUpISnV3Gdly6OwVp/8WBDarlyoG8hCP/I5xkHcYA3CFv18CFxYW5vgcttvtCgsLc2qXnJx8x+sD8trfL4GbOXOm0xiYOXOmPv74Y0e7wvQ9NT/LyX7OVQhq2rSp/v3vf+vNN9+UdONUX1pamiZNmqSWLVtmu59FixZp165d2rFjR7baT5gwQePHj88wf/Xq1ZmGsoLo3DXp7d25elvcatS3+91dQo68UjdFZXzdXQVuhXGQ9xgDcJUWLVpoxYoVjumoqChJN8LPzz//LElOy4HCKLMxcKvlyDuJiYnZbuth5OKRbvv27VOrVq107733at26dXr44Yf1+++/KzY2Vj/99JMqV66cZR+nTp1S/fr1FRUV5bgXqEWLFqpbt66mT5+e6TqZnQkKDQ3VhQsXFBAQkNOXkS/9fiZOkbO26b3uEQovnf9/U5twPUmrNu9Q+6b3yVYkZ2cB3eHI+QSNXPKblvW7X7XKFo5jpjBiHOQdxgBcxcfHx/H/ycnJstvtioqKUps2bWSxWDIsBwobxkD+ExcXp1KlSunKlStZZoNc/aq1du3aOnTokD744AP5+/srPj5e3bp104ABAxQSEpKtPnbu3Klz587p3nvvdcxLTU3Vpk2b9MEHHygpKUleXl5O61it1kwvt7NYLLJYLLl5KfmOt/eNt6R6SDHVLlfMzdVkzW6368IBqUGl0gXiPUjfv97e3gWiXrNiHOQdxgBc5eeff3ZcEnfs2DHHJXAWi0XHjh1zasexhsKoVatWjkviBg0apJkzZ0q6MQYGDRrk1I4xcGfkZD/nOATZ7Xa1b99es2fP1iuvvJLT1R1atWql3377zWlenz59VL16db300ksZAhAAAMg/HnjgAcf/V6tWTdKNS+AiIyNv2Q4oTNasWeN4+tvHH3/suAcos3bIf3IcgiwWi/bu3fuPN+zv76/atWs7zbPZbAoMDMwwHwAA5D+GYTg9Ajj9HqC/LwcKs5vHQGbLkT/l6hHZTz/9tD777DNX1wIAAAoYwzAyhJ+ff/6ZL38wDcMw1KpVK6d5rVq1Ygzkc7m6JyglJUVz587VmjVrVK9ePdlszjcuT506NVfFbNiwIVfrAQAA93nggQeUnJysFStWqGPHjtz/ANNZs2aN7HY7Y6AAyVEI+vPPP1WxYkXt27fP8UCDQ4cOObXhL+MCAAAAyM9yFIKqVKmi6OhorV+/XpL0+OOPa8aMGQoKCsqT4gAAAADA1XJ0T9DN1zauXLlSCQkJLi0IAAAAAPJSrh6MkI4bvgAAAAAUNDkKQR4eHhnu+eEeIAAAAAAFSY7uCTIMQ71795bVapUkXb9+Xf/zP/+T4elwS5cudV2FAAAAAOBCOQpBvXr1cpp++umnXVoMAAAAAOS1HIWgefPm5VUdAAAAAHBH/KMHIwAAAABAQUMIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApuLt7gKQkYd3nI7FHZRnkaLuLiVLKSkpOpNyRvtj98vbO/8fTsfi4uXhHefuMpANjIO8wRhAYmKiDhw44PJ+468l6effjqpEqV9V1Nfq0r6rV68uPz8/l/YJ8yqIY0BiHLha/v60NilL8V/08vZ33F1Gjny06iN3l5BtluKtJHV0dxnIAuMg7zAGzO3AgQOqV69envU/KQ/63Llzp+6999486BlmVBDHgMQ4cDVCUD5kv9xQUx56SpXLFIzfgP+05Sc1btI43/8GXJKOnovX4C+OursMZAPjIG8wBlC9enXt3LnT5f0ejL6s4Yt/09RHI1QtpLhL+65evbpL+4O5FcQxIDEOXC1/f1qblJESoLCAaqoZWMzdpWTJbrfrmPcx1ShZQxaLxd3lZCnt+hUZKefdXQaygXGQNxgD8PPzy5PfJnueuCjr5muqUbuO6lYIdHn/gKswBiDxYAQAAAAAJkMIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApuLWEDRhwgTdd9998vf3V5kyZRQZGamDBw+6syQAAAAAhZxbQ9DGjRs1YMAAbdu2TVFRUbLb7Wrbtq0SEhLcWRYAAACAQszbnRtftWqV0/T8+fNVpkwZ7dy5U82aNXNTVQAAAAAKM7eGoJtduXJFklSyZMlMlyclJSkpKckxHRcXJ0my2+2y2+15X+AdcPXajde352SsUlJS3FxN1hKuJ+nX81KpP8/LVsTq7nKydOT8jbOMKSkpheaYKYwYB3mHMVCwHL+YoISkVHeXkS2Hzl5x+m9+Z7N6qWKgzd1lIBt2nTmh6Kvn3V1Glk5cjJdnkb+08fguHb9a1N3lZEuIf2ndW7aCu8twmZx8rnkYhmHkYS3ZlpaWpocffliXL1/Wli1bMm0zbtw4jR8/PsP8L7/8Un5+fnld4h2xNcZDi/70cncZhd4rdVNUxtfdVeBWGAd5jzGQ/527Jr29O1/9rrLQYRzkf+euSZNPr5e19Fp3l1IoJZ1vpRfLtyw04yAxMVFPPfWUrly5ooCAgNu2zTchqF+/flq5cqW2bNmi8uXLZ9omszNBoaGhunDhQpYvtKCITUjWmv3nVKm0Tb6W/P8l8NDZKxr17X5N6lpDVYOLubucbOG3f/kf4yBvMQYKht/PxCly1ja91z1C4aXz//uVcD1JqzbvUPum9xWIM6Ijl/ymZf3uV62yheP7Q2H1+5k4df10tYa3C1Joifz9Tf1acrJ+3vWHGt1bU74+Pu4uJ0unLl3T1B9j9O3zbQvNOIiLi1OpUqWyFYLyxa+YBg4cqOXLl2vTpk23DECSZLVaZbVm/IfVYrHIYrHkZYl3TFBxi3o8EObuMnKsanAx1a0Q6O4yUEgwDgDJ2/vGR3T1kGKqXS7/h2u73a4LB6QGlUrn+8/k9H3r7e2d72s1O29vbxkpAWpZqV6+Hwd2u11+py6rY0SjAnFc7fvriqakbClU4yAnr8OtIcgwDA0aNEjffvutNmzYoLCwgvelBwAAAEDB4tYQNGDAAH355Zf67rvv5O/vr7Nnz0qSihUrJl/f/H3KEwAAAEDB5Na/EzRr1ixduXJFLVq0UEhIiOPn66+/dmdZAAAAAAoxt18OBwAAAAB3klvPBAEAAADAnUYIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAAAAApuLt7gJwZyQmJurAgQMu7/dg9GUlnT2i/ft8lXaxuMv7r169uvz8/FzeLwDkd9fsqfLwjlPUkZ06FlfU3eVk6VpSsjafP6Ok33+Rr9XH3eXc1qnYRHl4x7m7DGTDNXuqJGnfX1fcXEnWEq4l6dfzUvCJS7L5Wt1dTpaOnIt3dwluRQgyiQMHDqhevXp51v9TC/Km3507d+ree+/Nm84BIB87ei5eluK/aO7xtdJxd1eTTRZpwx53F5E9luKtZLM+7O4ykIWj//tFffTS39xcSXZ56/MjO9xdRI7YrOaMA+Z81SZUvXp17dy50+X9xl9L0g/rt+qhlg+oaB781qN69eou7xMACoK2tYJ11d5bJQKekNU7/1+9fvxCvKatPaJhrcJVsVT+P3NV1j9IYaVs7i4DWWhbK1iSVLlMUflavNxcze0djL6iEUt+05TuEaoWUszd5WSLzept2nFACDIJPz+/PDmjYrfbdenCOT3QoL4sFovL+wcAsypp89Hzjeu6u4xs233ioqZcv6amd9VR3QqB7i4HhURJm4+eaHCXu8vIlpSUFElS5dI21S5XMEKQmeX/Xy0BAAAAgAsRggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYSr4IQR9++KEqVqyoIkWKqGHDhtq+fbu7SwIAAABQSLk9BH399dcaPny4xo4dq127dqlOnTpq166dzp075+7SAAAAABRCbg9BU6dO1fPPP68+ffqoZs2amj17tvz8/DR37lx3lwYAAACgEPJ258aTk5O1c+dOjRkzxjHP09NTrVu31tatWzO0T0pKUlJSkmM6Li5OkmS322W32/O+YGSQvt/Z/ygIEhMTdfDgQZf3eyj6ipLOHtG+3T5Kjinm8v6rVasmPz8/l/cLcyqI44AxAFcqiGNAYhxkR06+j7o1BF24cEGpqakKCgpymh8UFKQDBw5kaD9hwgSNHz8+w/zVq1dzULhZVFSUu0sAsnT06FGNGDEiz/p/ZkHe9DtlyhRVrlw5bzqH6RTEccAYgCsVxDEgMQ6yIzExMdtt3RqCcmrMmDEaPny4YzouLk6hoaFq27atAgIC3FiZedntdkVFRalNmzayWCzuLge4rcTERDVp0sTl/cZfS9KPm3eoXdP7VNTX6vL++e0fXKkgjgPGAFypII4BiXGQHelXiWWHW0NQqVKl5OXlpZiYGKf5MTExCg4OztDearXKas14UFksFr6AuxnvAQqCYsWKqUGDBi7v12636+rlWDVtdD/jAPke4wBmxxgovHKy3936YAQfHx/Vq1dPa9eudcxLS0vT2rVr9cADD7ixMgAAAACFldsvhxs+fLh69eql+vXrq0GDBpo+fboSEhLUp08fd5cGAAAAoBByewh6/PHHdf78eb3++us6e/as6tatq1WrVmV4WAIAAAAAuILbQ5AkDRw4UAMHDnR3GQAAAABMwO1/LBUAAAAA7iRCEAAAAABTIQQBAAAAMBVCEAAAAABTIQQBAAAAMBVCEAAAAABTIQQBAAAAMBVCEAAAAABTIQQBAAAAMBVCEAAAAABTIQQBAAAAMBVCEAAAAABTIQQBAAAAMBVvdxfwTxiGIUmKi4tzcyXmZbfblZiYqLi4OFksFneXA7gF4wBgHACMAfdLzwTpGeF2CnQIunr1qiQpNDTUzZUAAAAAyA+uXr2qYsWK3baNh5GdqJRPpaWl6cyZM/L395eHh4e7yzGluLg4hYaG6tSpUwoICHB3OYBbMA4AxgHAGHA/wzB09epVlS1bVp6et7/rp0CfCfL09FT58uXdXQYkBQQEMOBheowDgHEAMAbcK6szQOl4MAIAAAAAUyEEAQAAADAVQhD+EavVqrFjx8pqtbq7FMBtGAcA4wBgDBQsBfrBCAAAAACQU5wJAgAAAGAqhCAAAAAApkIIAgAAAGAqhCAAuEnv3r0VGRnp7jKAfMPDw0PLli1zdxkA4DKEIBPp3bu3PDw85OHhIYvForCwMI0aNUrXr193d2lArvz9mPbw8FBgYKDat2+vvXv3uru0PDVhwgTdd9998vf3V5kyZRQZGamDBw+6uyy4mSvGw7hx41S3bt28KxIowM6ePatBgwapUqVKslqtCg0NVefOnbV27Vp3l4ZcIASZTPv27RUdHa0///xT06ZN08cff6yxY8e6uywg19KP6ejoaK1du1be3t7q1KlTnm4zOTk5T/vPysaNGzVgwABt27ZNUVFRstvtatu2rRISEtxaF9zPHeMhM4ZhKCUl5Y5vF8grx48fV7169bRu3TpNnjxZv/32m1atWqWWLVtqwIABueozNTVVaWlpLq4U2UUIMhmr1arg4GCFhoYqMjJSrVu3VlRUlCQpKSlJgwcPVpkyZVSkSBE1adJEO3bscFp/48aNatCggaxWq0JCQjR69GinD7oWLVpo0KBBGjp0qEqUKKGgoCB9+umnSkhIUJ8+feTv76/w8HCtXLnSsc6lS5fUo0cPlS5dWr6+vqpSpYrmzZt3Z3YICrz0Yzo4OFh169bV6NGjderUKZ0/f16SdOrUKT322GMqXry4SpYsqS5duuj48eOO9VNTUzV8+HAVL15cgYGBGjVqlG7+ywEtWrTQwIEDNXToUJUqVUrt2rWTlPV4yGpMbdiwQR4eHvrxxx91zz33yNfXVw8++KDOnTunlStXqkaNGgoICNBTTz2lxMREx3qrVq1S7969VatWLdWpU0fz58/XyZMntXPnzrzYxShAshoPL730kqpWrSo/Pz9VqlRJr732mux2uyRp/vz5Gj9+vPbs2eM4mzR//nxH3xcuXFDXrl3l5+enKlWq6Pvvv3csSz+WV65cqXr16slqtWrLli1u+1wBXK1///7y8PDQ9u3b9cgjj6hq1aqqVauWhg8frm3btkmSpk6dqoiICNlsNoWGhqp///6Kj4939DF//nwVL15c33//vWrWrCmr1aqTJ0+66yWZHiHIxPbt26eff/5ZPj4+kqRRo0bpm2++0YIFC7Rr1y6Fh4erXbt2io2NlST99ddf6tixo+677z7t2bNHs2bN0meffaa33nrLqd8FCxaoVKlS2r59uwYNGqR+/frp0UcfVaNGjbRr1y61bdtWzzzzjONL3WuvvaY//vhDK1eu1P79+zVr1iyVKlXqzu4MFArx8fFauHChwsPDFRgYKLvdrnbt2snf31+bN2/WTz/9pKJFi6p9+/aOszlTpkzR/PnzNXfuXG3ZskWxsbH69ttvM/S9YMEC+fj46KefftLs2bOzNR6yGlPpxo0bpw8++EA///yzI7RNnz5dX375pX744QetXr1aM2fOvOXrvnLliiSpZMmSrtiNKCRuHg+S5O/vr/nz5+uPP/7Q+++/r08//VTTpk2TJD3++OMaMWKEatWq5Tib9Pjjjzv6Gz9+vB577DHt3btXHTt2VI8ePTIcy6NHj9a7776r/fv36+6773bb5wrgSrGxsVq1apUGDBggm82WYXnx4sUlSZ6enpoxY4Z+//13LViwQOvWrdOoUaOc2iYmJmrixImaM2eOfv/9d5UpU+ZOvARkxoBp9OrVy/Dy8jJsNpthtVoNSYanp6exZMkSIz4+3rBYLMYXX3zhaJ+cnGyULVvWmDRpkmEYhvHyyy8b1apVM9LS0hxtPvzwQ6No0aJGamqqYRiG0bx5c6NJkyaO5SkpKYbNZjOeeeYZx7zo6GhDkrF161bDMAyjc+fORp8+ffL0taNw+vsxbbPZDElGSEiIsXPnTsMwDOPzzz/PcMwmJSUZvr6+xo8//mgYhmGEhIQ4jnHDMAy73W6UL1/e6NKli2Ne8+bNjXvuucdp21mNh+yMqfXr1xuSjDVr1jjaTJgwwZBkHD161DHvhRdeMNq1a5fpPkhNTTUeeugho3HjxtnebyicshoPmZk8ebJRr149x/TYsWONOnXqZGgnyXj11Vcd0/Hx8YYkY+XKlYZh/N+xvGzZMqc27vpcAVzpl19+MSQZS5cuzdF6ixcvNgIDAx3T8+bNMyQZu3fvdnWJyAVv90QvuEvLli01a9YsJSQkaNq0afL29tYjjzyivXv3ym63q3Hjxo62FotFDRo00P79+yVJ+/fv1wMPPCAPDw9Hm8aNGys+Pl6nT5/WXXfdJUm6++67Hcu9vLwUGBioiIgIx7ygoCBJ0rlz5yRJ/fr10yOPPOL4bV5kZKQaNWqUdzsBhUr6MS3duLTyo48+UocOHbR9+3bt2bNHR44ckb+/v9M6169f19GjR3XlyhVFR0erYcOGjmXe3t6qX79+hkvi6tWr5zSd1Xi4fPlylmMq3d/HTFBQkONSpb/P2759e6avf8CAAdq3b5+2bNly2/0Ec7jdeKhQoYK+/vprzZgxQ0ePHlV8fLxSUlIUEBCQrb7/fpzabDYFBAQ4/h1PV79+fcf/Hz161G2fK4Ar3fx5cCtr1qzRhAkTdODAAcXFxSklJUXXr19XYmKi/Pz8JEk+Pj5OxzPchxBkMjabTeHh4ZKkuXPnqk6dOvrss8903333uWwbFovFaTr9aXR/n5bkuBmwQ4cOOnHihFasWKGoqCi1atVKAwYM0HvvveeymlB4/f2YlqQ5c+aoWLFi+vTTTxUfH6969erpiy++yLBe6dKlc7ydvHLz+MhsDGV28+zAgQO1fPlybdq0SeXLl8+z+lBw3G48PPTQQ+rRo4fGjx+vdu3aqVixYlq0aJGmTJmSrb6zc1zm1TjJ6ecK4EpVqlSRh4eHDhw4cMs2x48fV6dOndSvXz+9/fbbKlmypLZs2aK+ffsqOTnZEYJ8fX2dQj/ch3uCTMzT01Mvv/yyXn31VVWuXNlxv0M6u92uHTt2qGbNmpKkGjVqaOvWrU6/Efnpp5/k7+//j7+AlS5dWr169dLChQs1ffp0ffLJJ/+oP5iXh4eHPD09de3aNd177706fPiwypQpo/DwcKefYsWKqVixYgoJCdEvv/ziWD8lJSVbDxjIajxkZ0zllmEYGjhwoL799lutW7dOYWFh/6g/FF5/Hw8///yzKlSooFdeeUX169dXlSpVdOLECaf2Pj4+Sk1Ndcm23f25ArhKyZIl1a5dO3344YeZPoXz8uXL2rlzp9LS0jRlyhTdf//9qlq1qs6cOeOGapFdhCCTe/TRR+Xl5aVZs2apX79+evHFF7Vq1Sr98ccfev7555WYmKi+fftKuvFklFOnTmnQoEE6cOCAvvvuO40dO1bDhw+Xp2fuD6XXX39d3333nY4cOaLff/9dy5cvV40aNVz1ElHIJSUl6ezZszp79qz279+vQYMGKT4+Xp07d1aPHj1UqlQpdenSRZs3b9axY8e0YcMGDR48WKdPn5YkDRkyRO+++66WLVumAwcOqH///rp8+XKW281qPNhstizHVG4NGDBACxcu1Jdffil/f3/H67927do/6hcF3+3GQ5UqVXTy5EktWrRIR48e1YwZMzI8BKRixYo6duyYdu/erQsXLigpKSnXtWRnDOTV5wrgah9++KFSU1PVoEEDffPNNzp8+LD279+vGTNm6IEHHlB4eLjsdrtmzpypP//8U59//rlmz57t7rJxG1wOZ3Le3t4aOHCgJk2apGPHjiktLU3PPPOMrl69qvr16+vHH39UiRIlJEnlypXTihUr9OKLL6pOnToqWbKk+vbtq1dfffUf1eDj46MxY8bo+PHj8vX1VdOmTbVo0SJXvDyYwKpVqxQSEiLpxpOvqlevrsWLF6tFixaSpE2bNumll15St27ddPXqVZUrV06tWrVy3AcxYsQIRUdHq1evXvL09NSzzz6rrl27Op64divZGQ/vvvvubcdUbqXf85H+GtPNmzdPvXv3/kd9o2DLajwMGzZMAwcOVFJSkh566CG99tprGjdunGP9Rx55REuXLlXLli11+fLlf3xMZTUG8upzBXC1SpUqadeuXXr77bcdnxulS5dWvXr1NGvWLNWpU0dTp07VxIkTNWbMGDVr1kwTJkxQz5493V06bsHDyO7dXgAAAABQCHCuGQAAAICpEIIAAAAAmAohCAAAAICpEIIAAAAAmAohCAAAAICpEIIAAAAAmAohCAAAAICpEIIAAAAAmAohCADgNi1atNDQoUPdXQYAwGQIQQAA9e7dWx4eHvLw8JCPj4/Cw8P1xhtvKCUlxd2l3VF/3w+Z/VSsWNHdJQIAXIAQBACQJLVv317R0dE6fPiwRowYoXHjxmny5Mm57i85OdmF1d0Z77//vqKjox0/kjRv3jzH9I4dO9xcIQDAFQhBAABJktVqVXBwsCpUqKB+/fqpdevW+v777yVlftlaZGSkevfu7ZiuWLGi3nzzTfXs2VMBAQH617/+JUn66aef1KJFC/n5+alEiRJq166dLl265FgvLS1No0aNUsmSJRUcHKxx48Y5bWfq1KmKiIiQzWZTaGio+vfvr/j4eMfyEydOqHPnzipRooRsNptq1aqlFStWOJbv27dPHTp0UNGiRRUUFKRnnnlGFy5cyHQfFCtWTMHBwY4fSSpevLiCg4P18ssvq0+fPk7t7Xa7ypQpo88++8yxnwYOHKiBAweqWLFiKlWqlF577TUZhuFYJykpSSNHjlS5cuVks9nUsGFDbdiw4TbvDADA1QhBAIBM+fr65vhsznvvvac6derov//9r1577TXt3r1brVq1Us2aNbV161Zt2bJFnTt3VmpqqmOdBQsWyGaz6ZdfftGkSZP0xhtvKCoqyrHc09NTM2bM0O+//64FCxZo3bp1GjVqlGP5gAEDlJSUpE2bNum3337TxIkTVbRoUUnS5cuX9eCDD+qee+7Rr7/+qlWrVikmJkaPPfZYjvfHc889p1WrVjnOEEnS8uXLlZiYqMcff9zp9Xh7e2v79u16//33NXXqVM2ZM8exfODAgdq6dasWLVqkvXv36tFHH1X79u11+PDhHNcEAMglAwBger169TK6dOliGIZhpKWlGVFRUYbVajVGjhxpGIZhNG/e3BgyZIjTOl26dDF69erlmK5QoYIRGRnp1ObJJ580GjdufMvtNm/e3GjSpInTvPvuu8946aWXbrnO4sWLjcDAQMd0RESEMW7cuEzbvvnmm0bbtm2d5p06dcqQZBw8ePCW20gnyfj2228d0zVr1jQmTpzomO7cubPRu3dvp9dTo0YNIy0tzTHvpZdeMmrUqGEYhmGcOHHC8PLyMv766y+n7bRq1coYM2ZMlvUAAFzD290hDACQPyxfvlxFixaV3W5XWlqannrqqQyXpmWlfv36TtO7d+/Wo48+ett17r77bqfpkJAQnTt3zjG9Zs0aTZgwQQcOHFBcXJxSUlJ0/fp1JSYmys/PT4MHD1a/fv20evVqtW7dWo888oijzz179mj9+vWOM0N/d/ToUVWtWjVHr++5557TJ598olGjRikmJkYrV67UunXrnNrcf//98vDwcEw/8MADmjJlilJTU/Xbb78pNTU1w3aTkpIUGBiYo1oAALlHCAIASJJatmypWbNmycfHR2XLlpW39/99RHh6ejrd1yLduB/mZjabzWna19c3y+1aLBanaQ8PD6WlpUmSjh8/rk6dOqlfv356++23VbJkSW3ZskV9+/ZVcnKy/Pz89Nxzz6ldu3b64YcftHr1ak2YMEFTpkzRoEGDFB8fr86dO2vixIkZthsSEpJlbTfr2bOnRo8era1bt+rnn39WWFiYmjZtmu314+Pj5eXlpZ07d8rLy8tpWWZBDQCQN7gnCAAg6UaACQ8P11133eUUgCSpdOnSTvfCpKamat++fVn2effdd2vt2rW5rmnnzp1KS0vTlClTdP/996tq1ao6c+ZMhnahoaH6n//5Hy1dulQjRozQp59+Kkm699579fvvv6tixYoKDw93+rk5sGVHYGCgIiMjNW/ePM2fPz/DgxIk6ZdffnGa3rZtm6pUqSIvLy/dc889Sk1N1blz5zLUk/4gBgBA3iMEAQCy9OCDD+qHH37QDz/8oAMHDqhfv366fPlyluuNGTNGO3bsUP/+/bV3714dOHBAs2bNuuXT2W4WHh4uu92umTNn6s8//9Tnn3+u2bNnO7UZOnSofvzxRx07dky7du3S+vXrVaNGDUk3HpoQGxurJ598Ujt27NDRo0f1448/qk+fPk4PZ8iJ5557TgsWLND+/fvVq1evDMtPnjyp4cOH6+DBg/rqq680c+ZMDRkyRJJUtWpV9ejRQz179tTSpUt17Ngxbd++XRMmTNAPP/yQq3oAADlHCAIAZOnZZ59Vr1691LNnTzVv3lyVKlVSy5Yts1yvatWqWr16tfbs2aMGDRrogQce0HfffZfhTNOt1KlTR1OnTtXEiRNVu3ZtffHFF5owYYJTm9TUVA0YMEA1atRQ+/btVbVqVX300UeSpLJly+qnn35Samqq2rZtq4iICA0dOlTFixeXp2fuPgJbt26tkJAQtWvXTmXLls2wvGfPnrp27ZoaNGigAQMGaMiQIY7HhUs3/u5Qz549NWLECFWrVk2RkZHasWOH7rrrrlzVAwDIOQ/j5ou8AQDALcXHx6tcuXKaN2+eunXr5rSsRYsWqlu3rqZPn+6e4gAA2cKDEQAAyIa0tDRduHBBU6ZMUfHixfXwww+7uyQAQC4RggAAyIaTJ08qLCxM5cuX1/z587N9SR8AIP/hcjgAAAAApsKDEQAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKkQggAAAACYCiEIAAAAgKn8f0m+JRJo9JbxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "data[['Rooms','Bedroom2', 'Bathroom', 'Car']].boxplot()\n",
    "plt.title('Box plots of house characteristics')\n",
    "plt.xlabel('Purchase Type')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk Box plot, diagram mengatakan rumah rumah di Melbourne mempunyai rata-rata 3 kamar total, 3 kamar tidur, 1 kamar mandi, dan 2 mobil garasi. Ada beberapa sample karakteristik rumah yang mempunyai outlier besar seperti kamar mandi atau garasi karena ada sekelompok rumah-rumahan yang memiliki fitur lebih mewah dari yang lain. Diagram outlier adalah justifikasi terhadap variansi harga rumah.\n",
    "\n",
    "Segala-galanya, rumah-rumah tersebut jarang memiliki fitur yang sangat beda sekali atau variansi besar. Oleh karena itu bisa lebih mudah prediksi rumah apa saja yang dimaukan pembeli atau investor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1542,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1250x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data, vars=['Price', 'Distance', 'Rooms', 'Bathroom', 'Landsize'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dengan SNS plot, kita mau coba cari linear relationship antar beberapa faktor harga rumah. Walaupun semua faktor ada dampak positif terhadap harga, Landsize memiliki trend yang sangat datar dan faktor lain lebih tersebar diseluruh graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1543,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "gdf = gpd.GeoDataFrame(data, geometry=gpd.points_from_xy(data.Longtitude, data.Lattitude))\n",
    "gdf.plot(column='Price', cmap='coolwarm', legend=True)\n",
    "plt.title('House Prices by Location')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Peta kota Melbourne: \n",
    "\n",
    "https://www.google.com/maps/place/Melbourne+VIC,+Australia/@-37.9715652,144.7235004,108702m/data=!3m2!1e3!4b1!4m6!3m5!1s0x6ad646b5d2ba4df7:0x4045675218ccd90!8m2!3d-37.8136276!4d144.9630576!16zL20vMGNoZ3pt?entry=ttu\n",
    "\n",
    "Dengan fitur GeoDataframe, kita bisa melihat lokasi setiap datapoint rumah di topografi kota Melbourne. Bisa terlihat rumah-rumahnya dari koordinasi Longitude dan Latitude sangat berkelompokan di satu daerah dari yang lain. \n",
    "\n",
    "Harga rumah yang rata-rata paling mahal terletak di sisi kanan bawah peta, dan yang paling murah di sisi kiri atas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1544,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Group by neighborhood and calculate the average price\n",
    "neighborhood_avg_prices = data.groupby('Suburb')['Price'].mean()\n",
    "\n",
    "# Sort by average price (optional)\n",
    "neighborhood_avg_prices = neighborhood_avg_prices.sort_values(ascending=False)\n",
    "\n",
    "# Plot the data\n",
    "plt.figure(figsize=(12, 6))\n",
    "neighborhood_avg_prices.plot(kind='bar')\n",
    "plt.title('Average House Prices by Neighborhood')\n",
    "plt.xlabel('Neighborhood')\n",
    "plt.ylabel('Average Price')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk lihat harga rata-rata untuk setiap perumahan lingkungan, kita akan membuat bar chart untuk memperbandingkan harga dari satu lokasi ke yang lain. Variasi dari satu perumahan seperti \"Canterbury\" memiliki harga rumah yang hampir AU$ 2 Juta, dan yang paling murah itu \"Braybrook\" yang cuman AU$ 750 Ribu. \n",
    "\n",
    "Dengan perbedaan harga antar perumahan sampai 2.67x lipat dari yang mahal dan murah, maka variansi sangat signifikan di kota Melbourne dan ada stratifikasi kelas sosial yang memisah orang yang pemilik rumah biasa dan yang paling mampu.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1545,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Group by neighborhood and calculate the average price\n",
    "neighborhood_avg_prices = data.groupby('Regionname')['Price'].mean()\n",
    "\n",
    "# Sort by average price (optional)\n",
    "neighborhood_avg_prices = neighborhood_avg_prices.sort_values(ascending=False)\n",
    "\n",
    "# Plot the data\n",
    "plt.figure(figsize=(12, 6))\n",
    "neighborhood_avg_prices.plot(kind='bar')\n",
    "plt.title('Average House Prices by Neighborhood')\n",
    "plt.xlabel('Neighborhood')\n",
    "plt.ylabel('Average Price')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tadi dari peta kelihatan bahwa daerah selatan memiliki rumah-rumah yang paling mahal, dan yang di daerah barat yang paling murah. Bisa saja perumahan yang paling murah berkelompokan di sisi barat, dan pemilik rumah yang lebih mampu tinggal di dearah selatan dekat pantai.\n",
    "\n",
    "Karena jarak dari daerah selatan dan CBD cukup jauh, maka tidak bisa dibilang bahwa \"distance\" adalah faktor yang paling pengaruh terhadap harga rumah. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1546,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Suburb         2000 non-null   object        \n",
      " 1   Address        2000 non-null   object        \n",
      " 2   Rooms          2000 non-null   int64         \n",
      " 3   Type           2000 non-null   object        \n",
      " 4   Price          2000 non-null   float64       \n",
      " 5   Method         2000 non-null   object        \n",
      " 6   SellerG        2000 non-null   object        \n",
      " 7   Date           2000 non-null   datetime64[ns]\n",
      " 8   Distance       2000 non-null   float64       \n",
      " 9   Postcode       2000 non-null   float64       \n",
      " 10  Bedroom2       2000 non-null   float64       \n",
      " 11  Bathroom       2000 non-null   float64       \n",
      " 12  Car            2000 non-null   float64       \n",
      " 13  Landsize       2000 non-null   float64       \n",
      " 14  BuildingArea   1131 non-null   float64       \n",
      " 15  YearBuilt      1285 non-null   float64       \n",
      " 16  CouncilArea    2000 non-null   object        \n",
      " 17  Lattitude      2000 non-null   float64       \n",
      " 18  Longtitude     2000 non-null   float64       \n",
      " 19  Regionname     2000 non-null   object        \n",
      " 20  Propertycount  2000 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(12), int64(1), object(7)\n",
      "memory usage: 328.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1547,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.set_index('Date', inplace=True)\n",
    "data['Price'].resample('M').mean().plot(figsize=(14, 8))\n",
    "plt.title('House Price Trends Over Time')\n",
    "plt.ylabel('Average Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk melihat trend harga rumah di seluruh kota dengan time-series, kita buat line graph. Kelihatan di diagram ada \"missing values\" di dataset karena tidak semua penjual akan ingat taruh data rumah. Kita akan coba \"smoothen\" grafik tersebut jadi garis-nya akan terhubung dengan jelas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1548,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the exponential moving average with a span of your choice, e.g., 3 months\n",
    "ema = data['Price'].resample('M').mean().ewm(span=3, adjust=False).mean()\n",
    "\n",
    "# Plot the smoothed time series using EMA\n",
    "plt.figure(figsize=(14, 8))\n",
    "plt.plot(ema, label='3-Month EMA', color='green')\n",
    "plt.title('House Price Trends Over Time (Smoothed with EMA)')\n",
    "plt.ylabel('Average Price')\n",
    "plt.xlabel('Date')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tadi data diubah dengan \"3-month average\", atau melihat harga tiga bulan kebelakang. Teknik tersebut digunakan untuk mencoba mendapatkan harga rata-rata untuk missing values, dan karena ada \"lag\" 3 bulan maka kurva lebih halus. \n",
    "\n",
    "Dari diagram, kelihatan harga rumah telah naik cepat dari awal tahun 2016, terus berpuncak di tengan 2016-an. Waktu masuk bulan pertama-tama tahun 2017, harga rumah berpuncak lagi dengan harga max AU$ 1.35 Juta. Ini bisa dibilang harga rumah di Melbourne itu sangat seasonal, dan setiap 3 sampai 6 bulan trend harga rumah susah diprediksi karena pola konsumer bisa beli dan jual cepat sekali. \n",
    "\n",
    "Pada tahun 2017, kelihatan harga rumah telah menurun dari AU$ 1.35 sampai 1.3 Juta saja. Ini bisakah menjadi trend temporary atau juga bisa lebih jangkau panjang / permanent. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1549,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x='Type', y='Price', data=data)\n",
    "plt.title('House Prices by Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nLegend:\\n\\nType: br - bedroom(s); \\nh - house,cottage,villa, semi,terrace; \\nu - unit, duplex; \\nt - townhouse; \\ndev site - development site; \\no res - other residential.\\n'"
      ]
     },
     "execution_count": 1550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Legend:\n",
    "\n",
    "Type: br - bedroom(s); \n",
    "h - house,cottage,villa, semi,terrace; \n",
    "u - unit, duplex; \n",
    "t - townhouse; \n",
    "dev site - development site; \n",
    "o res - other residential.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disini bisa di-observasi bahwa harga rumah \"house/cottage/villa/terrace\" lebih mahal pada rata-ratanya daripada \"unit\" atau \"townhouse\". Bisa saja harga rumah termasuk kelas Mansion atau rumah yang sangat mewah seperti Bungalow, dan ini akan mengangkat harga rumah sebagai total.\n",
    "\n",
    "Bisa terlihat banyak outlier diatas IQR boundaries untuk tipe \"H\" atau \"house\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(x='Method', y='Price', data=data)\n",
    "plt.title('House Prices by Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1552,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nLegend:\\n\\nMethod: \\nS - property sold; \\nSP - property sold prior; \\nPI - property passed in; \\nPN - sold prior not disclosed; \\nSN - sold not disclosed; \\nNB - no bid; \\nVB - vendor bid; \\nW - withdrawn prior to auction; \\nSA - sold after auction; \\nSS - sold after auction price not disclosed. \\nN/A - price or highest bid not available.\\n'"
      ]
     },
     "execution_count": 1552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Legend:\n",
    "\n",
    "Method: \n",
    "S - property sold; \n",
    "SP - property sold prior; \n",
    "PI - property passed in; \n",
    "PN - sold prior not disclosed; \n",
    "SN - sold not disclosed; \n",
    "NB - no bid; \n",
    "VB - vendor bid; \n",
    "W - withdrawn prior to auction; \n",
    "SA - sold after auction; \n",
    "SS - sold after auction price not disclosed. \n",
    "N/A - price or highest bid not available.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Juga bisa dibandingkan harga rumah yang dijual dengan beberapa cara metode. Jika dilihat rata-rata kelompok rumah yang dijual pakai metode beda-beda, kelihatan cara vendor bid atau lelang memiliki harga tertinggi. Ini bisakah karena lelang atau \"Auction\" cenderung mendorong harga lebih tinggi dari penjualan rumah biasa, juga untuk \"PI\" atau \"Property Passed In\" juga memiliki harga yang lebih tinggi pada rata-ratanya. \n",
    "\n",
    "Ini artinya rumah yang dijual baru tidak selalu yang paling mahal, maka rumah yang dijual di second-hand-market bisa lebih mahal dari harga pasaran. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1553,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 1553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Suburb'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1554,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n",
       "       'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car', 'Landsize',\n",
       "       'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude', 'Longtitude',\n",
       "       'Regionname', 'Propertycount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1555,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count occurrences of each suburb\n",
    "suburb_counts = data['Suburb'].value_counts()\n",
    "\n",
    "# Display the top N suburbs (e.g., top 10) if there are too many suburbs\n",
    "top_n = 10\n",
    "suburb_counts_top_n = suburb_counts.head(top_n)\n",
    "\n",
    "# If there are too many suburbs, group the remaining into an \"Other\" category\n",
    "if len(suburb_counts) > top_n:\n",
    "    suburb_counts_top_n['Other'] = suburb_counts[top_n:].sum()\n",
    "\n",
    "# Data for the pie chart\n",
    "labels = suburb_counts_top_n.index\n",
    "sizes = suburb_counts_top_n.values\n",
    "\n",
    "# Plotting the pie chart\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140, colors=plt.cm.tab20.colors)\n",
    "plt.title('Distribution of Suburbs')\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Terakhir, kita mau lihat mana lingkungan perumahan yang paling besar direpresentasikan di dataset. Kelihatan bahwa dari setiap \"Suburb\" sangat rata dalam jumlah rumahnya ke yang lain, ini artinya dataset sangat seimbang untuk pilih rumahnya dari bermacam tempat. Rata rata setiap lingkungan memiliki 5 atau 6% jumlah rumah dari total, jadi tidak terlalu bias terhadap satu daerah saja.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1556,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-03</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-04</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-04</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-04</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Suburb           Address  Rooms Type      Price Method  \\\n",
       "Date                                                                     \n",
       "2016-12-03  Abbotsford      85 Turner St      2    h  1480000.0      S   \n",
       "2016-02-04  Abbotsford   25 Bloomburg St      2    h  1035000.0      S   \n",
       "2017-03-04  Abbotsford      5 Charles St      3    h  1465000.0     SP   \n",
       "2017-03-04  Abbotsford  40 Federation La      3    h   850000.0     PI   \n",
       "2016-06-04  Abbotsford       55a Park St      4    h  1600000.0     VB   \n",
       "\n",
       "           SellerG  Distance  Postcode  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "Date                                                                        \n",
       "2016-12-03  Biggin       2.5    3067.0       2.0       1.0  1.0     202.0   \n",
       "2016-02-04  Biggin       2.5    3067.0       2.0       1.0  0.0     156.0   \n",
       "2017-03-04  Biggin       2.5    3067.0       3.0       2.0  0.0     134.0   \n",
       "2017-03-04  Biggin       2.5    3067.0       3.0       2.0  1.0      94.0   \n",
       "2016-06-04  Nelson       2.5    3067.0       3.0       1.0  2.0     120.0   \n",
       "\n",
       "            BuildingArea  YearBuilt CouncilArea  Lattitude  Longtitude  \\\n",
       "Date                                                                     \n",
       "2016-12-03           NaN        NaN       Yarra   -37.7996    144.9984   \n",
       "2016-02-04          79.0     1900.0       Yarra   -37.8079    144.9934   \n",
       "2017-03-04         150.0     1900.0       Yarra   -37.8093    144.9944   \n",
       "2017-03-04           NaN        NaN       Yarra   -37.7969    144.9969   \n",
       "2016-06-04         142.0     2014.0       Yarra   -37.8072    144.9941   \n",
       "\n",
       "                       Regionname  Propertycount  \n",
       "Date                                              \n",
       "2016-12-03  Northern Metropolitan         4019.0  \n",
       "2016-02-04  Northern Metropolitan         4019.0  \n",
       "2017-03-04  Northern Metropolitan         4019.0  \n",
       "2017-03-04  Northern Metropolitan         4019.0  \n",
       "2016-06-04  Northern Metropolitan         4019.0  "
      ]
     },
     "execution_count": 1556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1557,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n",
       "       'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car', 'Landsize',\n",
       "       'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude', 'Longtitude',\n",
       "       'Regionname', 'Propertycount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1557,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1558,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2000 entries, 2016-12-03 to 2017-02-25\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Suburb         2000 non-null   object \n",
      " 1   Address        2000 non-null   object \n",
      " 2   Rooms          2000 non-null   int64  \n",
      " 3   Type           2000 non-null   object \n",
      " 4   Price          2000 non-null   float64\n",
      " 5   Method         2000 non-null   object \n",
      " 6   SellerG        2000 non-null   object \n",
      " 7   Distance       2000 non-null   float64\n",
      " 8   Postcode       2000 non-null   float64\n",
      " 9   Bedroom2       2000 non-null   float64\n",
      " 10  Bathroom       2000 non-null   float64\n",
      " 11  Car            2000 non-null   float64\n",
      " 12  Landsize       2000 non-null   float64\n",
      " 13  BuildingArea   1131 non-null   float64\n",
      " 14  YearBuilt      1285 non-null   float64\n",
      " 15  CouncilArea    2000 non-null   object \n",
      " 16  Lattitude      2000 non-null   float64\n",
      " 17  Longtitude     2000 non-null   float64\n",
      " 18  Regionname     2000 non-null   object \n",
      " 19  Propertycount  2000 non-null   float64\n",
      "dtypes: float64(12), int64(1), object(7)\n",
      "memory usage: 328.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1559,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the original DataFrame\n",
    "data_mod = data.copy()\n",
    "\n",
    "#del data_mod['Date']\n",
    "del data_mod['Suburb'] #delete karena bukan ordinal, tidak bisa buat inferensi yang efektif\n",
    "del data_mod['Address'] #delete karena bukan ordinal, tidak bisa buat inferensi yang efektif\n",
    "del data_mod['Type'] #delete karena bukan ordinal, tidak bisa buat inferensi yang efektif\n",
    "del data_mod['Method'] #delete karena bukan ordinal, tidak bisa buat inferensi yang efektif\n",
    "del data_mod['SellerG'] #penjualnya tidak terlalu penting untuk analisa\n",
    "del data_mod['Postcode'] #delete karena bukan ordinal, tidak bisa buat inferensi yang efektif\n",
    "del data_mod['Bedroom2'] #ini double sample dari yang \"Rooms\", tidak perlu data extra \n",
    "del data_mod['CouncilArea'] #delete karena bukan ordinal, tidak bisa buat inferensi yang efektif\n",
    "del data_mod['Lattitude'] #tidak digunakan lagi untuk analisa\n",
    "del data_mod['Longtitude'] #tidak digunakan lagi untuk analisa\n",
    "del data_mod['Regionname'] #delete karena bukan ordinal, tidak bisa buat inferensi yang efektif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sekarang kita mau delete kolom yang tidak digunakan untuk analisa kita terhadap harga rumah. Kebanyakan data di-analisa tadi memiliki data tipe kategorikal dan \"ordinal\", jadi kita tidak bisa urutkan mana yang lebih berguna atau penting. Ini akan susah untuk model untuk mengolah data karena kolom numerikal akan paling digunakan untuk linear regression.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1560,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Modified DataFrame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-03</th>\n",
       "      <td>2</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-04</th>\n",
       "      <td>2</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-04</th>\n",
       "      <td>3</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-04</th>\n",
       "      <td>3</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-04</th>\n",
       "      <td>4</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-24</th>\n",
       "      <td>2</td>\n",
       "      <td>451000.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-25</th>\n",
       "      <td>2</td>\n",
       "      <td>635000.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-25</th>\n",
       "      <td>5</td>\n",
       "      <td>1020000.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-25</th>\n",
       "      <td>3</td>\n",
       "      <td>967000.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-25</th>\n",
       "      <td>3</td>\n",
       "      <td>1025000.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rooms      Price  Distance  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "Date                                                                            \n",
       "2016-12-03      2  1480000.0       2.5       1.0  1.0     202.0           NaN   \n",
       "2016-02-04      2  1035000.0       2.5       1.0  0.0     156.0          79.0   \n",
       "2017-03-04      3  1465000.0       2.5       2.0  0.0     134.0         150.0   \n",
       "2017-03-04      3   850000.0       2.5       2.0  1.0      94.0           NaN   \n",
       "2016-06-04      4  1600000.0       2.5       1.0  2.0     120.0         142.0   \n",
       "...           ...        ...       ...       ...  ...       ...           ...   \n",
       "2016-09-24      2   451000.0       7.8       1.0  1.0       0.0          72.0   \n",
       "2017-02-25      2   635000.0       7.8       2.0  3.0     339.0         149.0   \n",
       "2017-02-25      5  1020000.0       7.8       2.0  4.0     381.0         286.0   \n",
       "2017-02-25      3   967000.0       7.8       1.0  1.0     169.0           NaN   \n",
       "2017-02-25      3  1025000.0       7.8       1.0  3.0     336.0         112.0   \n",
       "\n",
       "            YearBuilt  Propertycount  \n",
       "Date                                  \n",
       "2016-12-03        NaN         4019.0  \n",
       "2016-02-04     1900.0         4019.0  \n",
       "2017-03-04     1900.0         4019.0  \n",
       "2017-03-04        NaN         4019.0  \n",
       "2016-06-04     2014.0         4019.0  \n",
       "...               ...            ...  \n",
       "2016-09-24     1970.0        11204.0  \n",
       "2017-02-25     1920.0        11204.0  \n",
       "2017-02-25     1985.0        11204.0  \n",
       "2017-02-25        NaN        11204.0  \n",
       "2017-02-25     1950.0        11204.0  \n",
       "\n",
       "[2000 rows x 9 columns]"
      ]
     },
     "execution_count": 1560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\nModified DataFrame:\")\n",
    "data_mod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lihat dataframe baru kita. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Train Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1561,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Define features and target variable\n",
    "X = data_mod.drop('Price', axis=1)\n",
    "y = data_mod['Price'] #yang di prediksi\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=47)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan coba split-train test untuk menguji dataset yang baru dimodifikasi. Kita akan ambil test size 20%, jadi training dataset akan ambil 80% data yang pertama. Untuk target variable, kita pastikan pilih \"price\" untuk dipisahkan dari kolom lain. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1562,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-28</th>\n",
       "      <td>3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>3280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-18</th>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3052.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-10</th>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-30</th>\n",
       "      <td>4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>2211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-18</th>\n",
       "      <td>2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-30</th>\n",
       "      <td>2</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-11</th>\n",
       "      <td>3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-17</th>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>6567.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-04</th>\n",
       "      <td>8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-30</th>\n",
       "      <td>3</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10579.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1600 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rooms  Distance  Bathroom  Car  Landsize  BuildingArea  YearBuilt  \\\n",
       "Date                                                                            \n",
       "2016-08-28      3       3.3       2.0  2.0     147.0         134.0     1890.0   \n",
       "2016-06-18      3      11.0       1.0  1.0     227.0           NaN        NaN   \n",
       "2016-09-10      2       7.8       1.0  1.0       0.0          80.0     1965.0   \n",
       "2016-07-30      4       6.4       2.0  3.0     589.0         210.0     1920.0   \n",
       "2016-06-18      2      13.9       1.0  2.0     678.0           NaN        NaN   \n",
       "...           ...       ...       ...  ...       ...           ...        ...   \n",
       "2016-07-30      2       9.7       0.0  0.0    1611.0           NaN        NaN   \n",
       "2017-02-11      3       5.2       2.0  3.0     177.0           NaN        NaN   \n",
       "2016-09-17      4       5.9       2.0  2.0     597.0         180.0     1939.0   \n",
       "2017-03-04      8       7.8       2.0  4.0     983.0           NaN        NaN   \n",
       "2016-07-30      3      11.2       2.0  2.0     266.0           NaN     2000.0   \n",
       "\n",
       "            Propertycount  \n",
       "Date                       \n",
       "2016-08-28         3280.0  \n",
       "2016-06-18         3052.0  \n",
       "2016-09-10        11204.0  \n",
       "2016-07-30         2211.0  \n",
       "2016-06-18        10969.0  \n",
       "...                   ...  \n",
       "2016-07-30         5682.0  \n",
       "2017-02-11        11918.0  \n",
       "2016-09-17         6567.0  \n",
       "2017-03-04        11204.0  \n",
       "2016-07-30        10579.0  \n",
       "\n",
       "[1600 rows x 8 columns]"
      ]
     },
     "execution_count": 1562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Di dataset X_train kelihatan ada beberapa Null atau missing values, kita harus rapihkan nanti sebelum di Feature Engineering. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1563,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2016-08-28    1740000.0\n",
       "2016-06-18     968000.0\n",
       "2016-09-10     434000.0\n",
       "2016-07-30    1950000.0\n",
       "2016-06-18    1120000.0\n",
       "                ...    \n",
       "2016-07-30    1010000.0\n",
       "2017-02-11     810000.0\n",
       "2016-09-17    1480000.0\n",
       "2017-03-04    2250000.0\n",
       "2016-07-30    1600000.0\n",
       "Name: Price, Length: 1600, dtype: float64"
      ]
     },
     "execution_count": 1563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y_train memiliki variable \"price\". "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1564,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.fillna(y_train.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1565,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_train.fillna(y_train.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1566,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_clean = y_train.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1567,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_clean = y_test.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1568,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.fillna(X_train.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1569,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = X_train.fillna(X_train.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1570,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_clean = X_train.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1571,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_clean = X_test.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan coba buang Null data dan masukan fungsi \"fill\" jadi tidak ada yang kosong. Ini akan penting untuk model yang akan membaca kolom-kolom dataset X dan y. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1572,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1572,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1573,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rooms            0\n",
       "Distance         0\n",
       "Bathroom         0\n",
       "Car              0\n",
       "Landsize         0\n",
       "BuildingArea     0\n",
       "YearBuilt        0\n",
       "Propertycount    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kelihatan tidak ada Null values lagi. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1574,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-28</th>\n",
       "      <td>3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>3280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-18</th>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>3052.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-10</th>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-30</th>\n",
       "      <td>4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>2211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-18</th>\n",
       "      <td>2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>10969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2016-07-30</th>\n",
       "      <td>2</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>5682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-11</th>\n",
       "      <td>3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>11918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-17</th>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>6567.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-04</th>\n",
       "      <td>8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>983.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>11204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-30</th>\n",
       "      <td>3</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10579.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1600 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rooms  Distance  Bathroom  Car  Landsize  BuildingArea  YearBuilt  \\\n",
       "Date                                                                            \n",
       "2016-08-28      3       3.3       2.0  2.0     147.0         134.0     1890.0   \n",
       "2016-06-18      3      11.0       1.0  1.0     227.0         132.0     1960.0   \n",
       "2016-09-10      2       7.8       1.0  1.0       0.0          80.0     1965.0   \n",
       "2016-07-30      4       6.4       2.0  3.0     589.0         210.0     1920.0   \n",
       "2016-06-18      2      13.9       1.0  2.0     678.0         132.0     1960.0   \n",
       "...           ...       ...       ...  ...       ...           ...        ...   \n",
       "2016-07-30      2       9.7       0.0  0.0    1611.0         132.0     1960.0   \n",
       "2017-02-11      3       5.2       2.0  3.0     177.0         132.0     1960.0   \n",
       "2016-09-17      4       5.9       2.0  2.0     597.0         180.0     1939.0   \n",
       "2017-03-04      8       7.8       2.0  4.0     983.0         132.0     1960.0   \n",
       "2016-07-30      3      11.2       2.0  2.0     266.0         132.0     2000.0   \n",
       "\n",
       "            Propertycount  \n",
       "Date                       \n",
       "2016-08-28         3280.0  \n",
       "2016-06-18         3052.0  \n",
       "2016-09-10        11204.0  \n",
       "2016-07-30         2211.0  \n",
       "2016-06-18        10969.0  \n",
       "...                   ...  \n",
       "2016-07-30         5682.0  \n",
       "2017-02-11        11918.0  \n",
       "2016-09-17         6567.0  \n",
       "2017-03-04        11204.0  \n",
       "2016-07-30        10579.0  \n",
       "\n",
       "[1600 rows x 8 columns]"
      ]
     },
     "execution_count": 1574,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cek lagi jika masih ada yang Null."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1575,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Making data and columns for normal distribution\n",
    "data_normal = []\n",
    "column_normal = []\n",
    "\n",
    "# Making data and columns for skewed distribution\n",
    "data_skewed = []\n",
    "column_skewed = []\n",
    "\n",
    "# For loop in every numerical column to filter the data distribution into either normally distributed or skewed columns\n",
    "for num in X_train_clean:\n",
    "    skewness = data_mod[num].skew()\n",
    "\n",
    "    # If the data is normally distributed\n",
    "    if skewness <= 0.5 and skewness >= -0.5:\n",
    "        column_normal.append(num)\n",
    "        data_normal.append([num, skewness])\n",
    "\n",
    "    # If the data has low negative skewness\n",
    "    elif skewness < -1:\n",
    "        column_skewed.append(num)\n",
    "        data_skewed.append([num, skewness, 'high'])\n",
    "\n",
    "    # If the data has low positive skewness\n",
    "    elif skewness > 1:\n",
    "        column_skewed.append(num)\n",
    "        data_skewed.append([num, skewness, 'high'])\n",
    "\n",
    "    # If the data has moderate negative skewness\n",
    "    elif skewness <= -0.5 and skewness > -1:\n",
    "        column_skewed.append(num)\n",
    "        data_skewed.append([num, skewness, 'low'])\n",
    "\n",
    "    # If the data has moderate positive skewness\n",
    "    elif skewness >= 0.5 and skewness < 1:\n",
    "        column_skewed.append(num)\n",
    "        data_skewed.append([num, skewness, 'low'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita mau perbedakan variable yang memiliki skew normal dan yang tidak normal. Ini akan dimasukan kedalam Pipeline dengan fungsi Windsorizer. Kita mau cek jika skew lebih atau kurang dari +-0.5, jadi bisa diperdakan skew yang \"low\" atau \"high\".  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1576,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normal_distribution</th>\n",
       "      <th>skewness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distance</td>\n",
       "      <td>-0.402170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>-0.268293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Propertycount</td>\n",
       "      <td>0.219209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  normal_distribution  skewness\n",
       "0            Distance -0.402170\n",
       "1           YearBuilt -0.268293\n",
       "2       Propertycount  0.219209"
      ]
     },
     "execution_count": 1576,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Showing normally distributed columns\n",
    "pd.DataFrame(data=data_normal, columns=['normal_distribution', 'skewness'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kelihatan kolom \"Distance\", \"YearBuilt\", dan \"PropertyCount\" memiliki skew yang normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1577,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>skewed_distribution</th>\n",
       "      <th>skewness</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rooms</td>\n",
       "      <td>0.521267</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bathroom</td>\n",
       "      <td>1.527916</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Car</td>\n",
       "      <td>1.288397</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Landsize</td>\n",
       "      <td>41.832733</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BuildingArea</td>\n",
       "      <td>21.284326</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  skewed_distribution   skewness  rate\n",
       "0               Rooms   0.521267   low\n",
       "1            Bathroom   1.527916  high\n",
       "2                 Car   1.288397  high\n",
       "3            Landsize  41.832733  high\n",
       "4        BuildingArea  21.284326  high"
      ]
     },
     "execution_count": 1577,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Showing skewed columns\n",
    "pd.DataFrame(data=data_skewed, columns=['skewed_distribution', 'skewness', 'rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Banyak variable di dataset yang memiliki skew yang tinggi daripada yang rendah. Ini artinya dataset memiliki skew yang cenderung positif. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1578,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Distance', 'YearBuilt', 'Propertycount']"
      ]
     },
     "execution_count": 1578,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1579,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Rooms', 'Bathroom', 'Car', 'Landsize', 'BuildingArea']"
      ]
     },
     "execution_count": 1579,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_skewed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sudah kelihatan kolom terpisah antara yang normal dan skewed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1580,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column Name</th>\n",
       "      <th>Correlation Coefficient</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rooms</td>\n",
       "      <td>0.658610</td>\n",
       "      <td>1.329766e-199</td>\n",
       "      <td>Significant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Distance</td>\n",
       "      <td>-0.043032</td>\n",
       "      <td>8.529951e-02</td>\n",
       "      <td>Not Significant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bathroom</td>\n",
       "      <td>0.515079</td>\n",
       "      <td>4.040223e-109</td>\n",
       "      <td>Significant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Car</td>\n",
       "      <td>0.386978</td>\n",
       "      <td>2.617203e-58</td>\n",
       "      <td>Significant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Landsize</td>\n",
       "      <td>0.571496</td>\n",
       "      <td>2.132200e-139</td>\n",
       "      <td>Significant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BuildingArea</td>\n",
       "      <td>0.544412</td>\n",
       "      <td>3.843652e-124</td>\n",
       "      <td>Significant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>-0.166582</td>\n",
       "      <td>2.014439e-11</td>\n",
       "      <td>Significant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Propertycount</td>\n",
       "      <td>-0.021870</td>\n",
       "      <td>3.819876e-01</td>\n",
       "      <td>Not Significant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Column Name  Correlation Coefficient        P-value      Correlation\n",
       "0          Rooms                 0.658610  1.329766e-199      Significant\n",
       "1       Distance                -0.043032   8.529951e-02  Not Significant\n",
       "2       Bathroom                 0.515079  4.040223e-109      Significant\n",
       "3            Car                 0.386978   2.617203e-58      Significant\n",
       "4       Landsize                 0.571496  2.132200e-139      Significant\n",
       "5   BuildingArea                 0.544412  3.843652e-124      Significant\n",
       "6      YearBuilt                -0.166582   2.014439e-11      Significant\n",
       "7  Propertycount                -0.021870   3.819876e-01  Not Significant"
      ]
     },
     "execution_count": 1580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the correlation between numerical columns and Y Train using pearsonr and spearmanr correlation\n",
    "p_values = []\n",
    "interpretation = []\n",
    "cols = []\n",
    "corr = []\n",
    "selected_num_cols = []\n",
    "\n",
    "for col in X_train.columns:\n",
    "  if abs(X_train[col].skew()) < 0.5:\n",
    "    #For Normally Distributed Columns\n",
    "    corr_coef, p_value = pearsonr(X_train[col], y_train_clean)\n",
    "\n",
    "    p_values.append(p_value)\n",
    "    cols.append(col)\n",
    "    corr.append(corr_coef)\n",
    "\n",
    "    if p_value < 0.05:\n",
    "      interpretation.append('Significant')\n",
    "      selected_num_cols.append(col)\n",
    "    else :\n",
    "      interpretation.append('Not Significant')\n",
    "  else:\n",
    "    #For Skewed Columns\n",
    "    corr_coef, p_value = spearmanr(X_train[col], y_train_clean)\n",
    "\n",
    "    p_values.append(p_value)\n",
    "    cols.append(col)\n",
    "    corr.append(corr_coef)\n",
    "\n",
    "    if p_value < 0.05:\n",
    "      interpretation.append('Significant')\n",
    "      selected_num_cols.append(col)\n",
    "    else :\n",
    "      interpretation.append('Not Significant')\n",
    "\n",
    "pd.DataFrame({'Column Name':cols,\n",
    "              'Correlation Coefficient' : corr,\n",
    "              'P-value':p_values,\n",
    "              'Correlation': interpretation })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tadi kita lihat data yang skew normal dan skewed, sekarang kita mau uji pake Pearson dan Spearman terhadap data yang kita ubah. Karena dataset tidak mengandung kategorikal data, kita tidak akan gunakan Kendall Tau untuk analisa ini. \n",
    "\n",
    "Bisa terlihat ada dua kolom, \"Distance\" dan \"PropertyCount\", yang tidak signifikan di analisa ini karena P-value mereka diatas 0.05. Ini artinya kita fail to reject Null mereka, dan tidak bisa bilang data tersebut itu independen.\n",
    "\n",
    "Dari semua faktor lain, semua memiliki coefficient yang positif. Ini artinya setiap kali jumlah/nilai \"Rooms\" atau \"Bathrooms\" lebih tinggi, maka harga rumah akan lebih tinggi. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1581,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Rooms', 'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'YearBuilt']\n"
     ]
    }
   ],
   "source": [
    "# Show selected columns based on the correlation test\n",
    "\n",
    "print(selected_num_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lihat kolom yang diuji signifikan. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1582,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-28</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-18</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-10</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1965.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-30</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-18</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1960.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rooms  Bathroom  Car  Landsize  BuildingArea  YearBuilt\n",
       "Date                                                               \n",
       "2016-08-28      3       2.0  2.0     147.0         134.0     1890.0\n",
       "2016-06-18      3       1.0  1.0     227.0         132.0     1960.0\n",
       "2016-09-10      2       1.0  1.0       0.0          80.0     1965.0\n",
       "2016-07-30      4       2.0  3.0     589.0         210.0     1920.0\n",
       "2016-06-18      2       1.0  2.0     678.0         132.0     1960.0"
      ]
     },
     "execution_count": 1582,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Updating Numerical and Categorical Columns\n",
    "\n",
    "X_train_num = X_train[selected_num_cols]\n",
    "\n",
    "X_train_num.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan taruh kolom baru di variable baru."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1583,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Distance', 'YearBuilt', 'Propertycount']"
      ]
     },
     "execution_count": 1583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1584,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Rooms', 'Bathroom', 'Car', 'Landsize', 'BuildingArea']"
      ]
     },
     "execution_count": 1584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1585,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['YearBuilt']\n"
     ]
    }
   ],
   "source": [
    "# Example list of column names\n",
    "#column_normal = ['Distance', 'YearBuilt', 'Propertycount']\n",
    "\n",
    "# Specify the column you want to keep\n",
    "columns_to_keep = ['YearBuilt']\n",
    "\n",
    "# Filter the original list to keep only 'YearBuilt'\n",
    "column_normal_filtered = [col for col in column_normal if col in columns_to_keep]\n",
    "\n",
    "print(column_normal_filtered)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buat variable baru untuk keluarkan kolom yang tidak signifikan. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1586,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_columns = column_normal_filtered + column_skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1587,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['YearBuilt', 'Rooms', 'Bathroom', 'Car', 'Landsize', 'BuildingArea']"
      ]
     },
     "execution_count": 1587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gabungkan dari kolom skewed dan yang telah difilter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1588,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from feature_engine.outliers import Winsorizer\n",
    "import numpy as np\n",
    "\n",
    "# Define numerical features\n",
    "numeric_features = ['Rooms', 'Distance', 'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'YearBuilt', 'Propertycount']\n",
    "categorical_features = []  # Add categorical features if any\n",
    "\n",
    "# # Define columns for Winsorizer\n",
    "# column_normal_filtered = ['Rooms', 'Distance', 'Bathroom']  # Adjust as needed\n",
    "# column_skewed = ['Car', 'Landsize', 'BuildingArea', 'YearBuilt', 'Propertycount']  # Adjust as needed\n",
    "\n",
    "# Define preprocessing steps for numeric data\n",
    "numeric_transformer = Pipeline(\n",
    "    steps=[\n",
    "        (\"outlier_gaussian\", \n",
    "            Winsorizer(capping_method='gaussian',\n",
    "                       tail='both',\n",
    "                       fold=3,\n",
    "                       variables=column_normal,\n",
    "                       missing_values='ignore')),\n",
    "        (\"outlier_iqr\", \n",
    "            Winsorizer(capping_method='iqr', \n",
    "                       tail='both', \n",
    "                       fold=1.5,\n",
    "                       variables=column_skewed)),\n",
    "        ('imputer', SimpleImputer(strategy='median')),\n",
    "        (\"scaler\", StandardScaler())\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Define the ColumnTransformer\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', 'passthrough', categorical_features)  # 'passthrough' for categorical features\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk Pipeline ini, kita akan masukan semua Numerikal values kedalam list numeric_features untuk memperbedakannya dengan yang kategorik (jika ada). Kemudian, kita gunakan fungsi Windsorizer capping method dengan dua cara, Gaussian dan IQR, untuk handle outliers di dataset. \n",
    "\n",
    "Data dipisah dengan Gaussian yang akan akan handle normal distribution, dan IQR akan handle yang tidak normal. Untuk test ini, kita akan menggunakan semua kolom dari X_train original karena kita mau melihat dampak \"Distance\" dan \"PropertyCount\" yang tidak terlalu signifikan, walupun harusnya dibuang dari kolom. Kita akan gunakan SimpleImputer untuk buang missing values dan juga di Scale dengan StandardScaler untuk bisa diproses machine learning. \n",
    "\n",
    "Semua data akan diubah didalam ColumnTransformer yang akan bisa di-modelkan secara efisien. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1589,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "\n",
    "\n",
    "# Assuming 'preprocessor' is your preprocessing pipeline\n",
    "\n",
    "# Pipeline for Linear Regression\n",
    "lr_pipeline = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"linear_regression\", LinearRegression())\n",
    "])\n",
    "\n",
    "# Pipeline for SVM\n",
    "svm_pipeline = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"svm\", SVC(kernel='linear'))\n",
    "])\n",
    "\n",
    "# Pipeline for Decision Tree\n",
    "dt_pipeline = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"decision_tree\", DecisionTreeClassifier())\n",
    "])\n",
    "\n",
    "# Pipeline for Random Forest\n",
    "rf_pipeline = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"random_forest\", RandomForestClassifier())\n",
    "])\n",
    "\n",
    "# Pipeline for Gradient Boosting\n",
    "gb_pipeline = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"gradient_boosting\", GradientBoostingClassifier())\n",
    "])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Masukan data yang di transform ke lima algorithma untuk melihat korelasi dan relationship antar kolom di dataset. Kita akan gunakan Linear Regression, SVM linear, Decision Tree, Random Forest, dan Gradient Boosting untuk melihat bagaimana dataset bisa menjelasi korelasi antar faktor-faktor terhadap harga rumah. \n",
    "\n",
    "Note: Tidak akan gunakan KNN karena datanya linear regresi. Juga gunakan Confusion matrix tidak sesuai untuk test-test tersebut. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1590,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "\n",
    "\n",
    "# Assuming 'preprocessor' is your preprocessing pipeline\n",
    "\n",
    "# Pipeline for Linear Regression\n",
    "clf1 = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"regressor\", LinearRegression())\n",
    "])\n",
    "\n",
    "# Pipeline for SVM\n",
    "clf2 = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"svm\", SVC(kernel='linear'))\n",
    "])\n",
    "\n",
    "# Pipeline for Decision Tree\n",
    "clf3 = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"decision_tree\", DecisionTreeClassifier())\n",
    "])\n",
    "\n",
    "# Pipeline for Random Forest\n",
    "clf4 = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"random_forest\", RandomForestClassifier())\n",
    "])\n",
    "\n",
    "# Pipeline for Gradient Boosting\n",
    "clf5 = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"gradient_boosting\", GradientBoostingClassifier())\n",
    "])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan label setiap Pipeline dengan \"clf\" jadi lebih efisien."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1591,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-38 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-38 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-38 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-38 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-38 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-38 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-38 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-38 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-38 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-38 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-38 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-38 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-38 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-38 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-38 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-38 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-38 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-38 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-38 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-38\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                                   Winsorizer(fold=3,\n",
       "                                                                              missing_values=&#x27;ignore&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Distance&#x27;,\n",
       "                                                                                         &#x27;YearBuilt&#x27;,\n",
       "                                                                                         &#x27;Propertycount&#x27;])),\n",
       "                                                                  (&#x27;outlier_iqr&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              fold=1.5,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Rooms&#x27;,\n",
       "                                                                                         &#x27;Bathroom&#x27;,\n",
       "                                                                                         &#x27;Car&#x27;,\n",
       "                                                                                         &#x27;Landsize&#x27;,\n",
       "                                                                                         &#x27;BuildingArea&#x27;])),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Rooms&#x27;, &#x27;Distance&#x27;,\n",
       "                                                   &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                                   &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;,\n",
       "                                                   &#x27;YearBuilt&#x27;,\n",
       "                                                   &#x27;Propertycount&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])),\n",
       "                (&#x27;gradient_boosting&#x27;, GradientBoostingClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-274\" type=\"checkbox\" ><label for=\"sk-estimator-id-274\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                                   Winsorizer(fold=3,\n",
       "                                                                              missing_values=&#x27;ignore&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Distance&#x27;,\n",
       "                                                                                         &#x27;YearBuilt&#x27;,\n",
       "                                                                                         &#x27;Propertycount&#x27;])),\n",
       "                                                                  (&#x27;outlier_iqr&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              fold=1.5,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Rooms&#x27;,\n",
       "                                                                                         &#x27;Bathroom&#x27;,\n",
       "                                                                                         &#x27;Car&#x27;,\n",
       "                                                                                         &#x27;Landsize&#x27;,\n",
       "                                                                                         &#x27;BuildingArea&#x27;])),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Rooms&#x27;, &#x27;Distance&#x27;,\n",
       "                                                   &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                                   &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;,\n",
       "                                                   &#x27;YearBuilt&#x27;,\n",
       "                                                   &#x27;Propertycount&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])),\n",
       "                (&#x27;gradient_boosting&#x27;, GradientBoostingClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-275\" type=\"checkbox\" ><label for=\"sk-estimator-id-275\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                  Winsorizer(fold=3,\n",
       "                                                             missing_values=&#x27;ignore&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Distance&#x27;,\n",
       "                                                                        &#x27;YearBuilt&#x27;,\n",
       "                                                                        &#x27;Propertycount&#x27;])),\n",
       "                                                 (&#x27;outlier_iqr&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             fold=1.5,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Rooms&#x27;,\n",
       "                                                                        &#x27;Bathroom&#x27;,\n",
       "                                                                        &#x27;Car&#x27;,\n",
       "                                                                        &#x27;Landsize&#x27;,\n",
       "                                                                        &#x27;BuildingArea&#x27;])),\n",
       "                                                 (&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Rooms&#x27;, &#x27;Distance&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                  &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;, &#x27;YearBuilt&#x27;,\n",
       "                                  &#x27;Propertycount&#x27;]),\n",
       "                                (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-276\" type=\"checkbox\" ><label for=\"sk-estimator-id-276\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Rooms&#x27;, &#x27;Distance&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;, &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;, &#x27;YearBuilt&#x27;, &#x27;Propertycount&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-277\" type=\"checkbox\" ><label for=\"sk-estimator-id-277\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Winsorizer</label><div class=\"sk-toggleable__content fitted\"><pre>Winsorizer(fold=3, missing_values=&#x27;ignore&#x27;, tail=&#x27;both&#x27;,\n",
       "           variables=[&#x27;Distance&#x27;, &#x27;YearBuilt&#x27;, &#x27;Propertycount&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-278\" type=\"checkbox\" ><label for=\"sk-estimator-id-278\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Winsorizer</label><div class=\"sk-toggleable__content fitted\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, fold=1.5, tail=&#x27;both&#x27;,\n",
       "           variables=[&#x27;Rooms&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;, &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-279\" type=\"checkbox\" ><label for=\"sk-estimator-id-279\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-280\" type=\"checkbox\" ><label for=\"sk-estimator-id-280\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-281\" type=\"checkbox\" ><label for=\"sk-estimator-id-281\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-282\" type=\"checkbox\" ><label for=\"sk-estimator-id-282\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-283\" type=\"checkbox\" ><label for=\"sk-estimator-id-283\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('outlier_gaussian',\n",
       "                                                                   Winsorizer(fold=3,\n",
       "                                                                              missing_values='ignore',\n",
       "                                                                              tail='both',\n",
       "                                                                              variables=['Distance',\n",
       "                                                                                         'YearBuilt',\n",
       "                                                                                         'Propertycount'])),\n",
       "                                                                  ('outlier_iqr',\n",
       "                                                                   Winsorizer(capping_method='iqr',\n",
       "                                                                              fold=1.5,\n",
       "                                                                              tail='both',\n",
       "                                                                              variables=['Rooms',\n",
       "                                                                                         'Bathroom',\n",
       "                                                                                         'Car',\n",
       "                                                                                         'Landsize',\n",
       "                                                                                         'BuildingArea'])),\n",
       "                                                                  ('imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Rooms', 'Distance',\n",
       "                                                   'Bathroom', 'Car',\n",
       "                                                   'Landsize', 'BuildingArea',\n",
       "                                                   'YearBuilt',\n",
       "                                                   'Propertycount']),\n",
       "                                                 ('cat', 'passthrough', [])])),\n",
       "                ('gradient_boosting', GradientBoostingClassifier())])"
      ]
     },
     "execution_count": 1591,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.fit(X_train, y_train)\n",
    "clf2.fit(X_train, y_train)\n",
    "clf3.fit(X_train, y_train)\n",
    "clf4.fit(X_train, y_train)\n",
    "clf5.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disini kita akan proses data X_train dan y_train kedalam setiap ke-lima algorithma. Kita akan gunakan data original karena tanpa nge-drop duplicate atau Null, kita bisa lihat korelasi yang mentah. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1592,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['YearBuilt', 'Rooms', 'Bathroom', 'Car', 'Landsize', 'BuildingArea']"
      ]
     },
     "execution_count": 1592,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1593,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [ 132170.09243312 -129259.49333926  200375.86912614   36395.34308928\n",
      "  195031.94791959  106469.09015283  -90136.58042171   12972.88208969]\n",
      "Intercept: 1247863.1099999999\n"
     ]
    }
   ],
   "source": [
    "# Assuming clf1 is your linear regression pipeline already fitted\n",
    "\n",
    "# Accessing coefficients\n",
    "coefficients = clf1.named_steps['regressor'].coef_\n",
    "print(\"Coefficients:\", coefficients)\n",
    "\n",
    "# Accessing intercept\n",
    "intercept = clf1.named_steps['regressor'].intercept_\n",
    "print(\"Intercept:\", intercept)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1594,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nNumerical (Coefficients):\\n    'Rooms','Distance','Bathroom','Car',\\n    'Landsize','BuildingArea','YearBuilt','Propertycount'\\n\\nTarget:\\n    'Price'\\n\""
      ]
     },
     "execution_count": 1594,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Numerical (Coefficients):\n",
    "    'Rooms','Distance','Bathroom','Car',\n",
    "    'Landsize','BuildingArea','YearBuilt','Propertycount'\n",
    "\n",
    "Target:\n",
    "    'Price'\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk Linear correlation kita bisa lihat Numerikal koloms ynag memiliki positif (atau negatif) coefficient terhadap target \"price\". Keliahtan bahwa \"Bathroom\" dan \"Landsize\" memiliki dampak terbesar terhadap harga rumah di Melbourne. Ini interpretasinya setiap unit kamar mandi atau luas tanah rumah naik, maka harga rumah akan naik rata-rata AU$ 200 Ribu. \n",
    "\n",
    "Intercept regression memiliki nilai sekitar 1.2 Juta, ini adalah harga rumah jika faktor lain tidak berdampak terhadapmnya. Ini artinya pada dasarnya harga rumah di Melbourne sudah diatas AU$ 1.2 Juta rata-rata tanpa tambahan kamar atau lahan besar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1595,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    131000.0       1.00      1.00      1.00         1\n",
      "    145000.0       0.50      1.00      0.67         1\n",
      "    240000.0       0.50      1.00      0.67         1\n",
      "    250000.0       0.00      0.00      0.00         1\n",
      "    252000.0       0.00      0.00      0.00         1\n",
      "    255000.0       0.00      0.00      0.00         1\n",
      "    260000.0       0.00      0.00      0.00         1\n",
      "    265000.0       1.00      1.00      1.00         1\n",
      "    270000.0       0.00      0.00      0.00         1\n",
      "    272500.0       0.00      0.00      0.00         1\n",
      "    275500.0       0.00      0.00      0.00         1\n",
      "    279500.0       0.00      0.00      0.00         1\n",
      "    280000.0       0.00      0.00      0.00         2\n",
      "    285000.0       0.00      0.00      0.00         1\n",
      "    288000.0       0.50      1.00      0.67         1\n",
      "    290000.0       0.38      1.00      0.55         3\n",
      "    292000.0       0.00      0.00      0.00         1\n",
      "    300000.0       0.00      0.00      0.00         2\n",
      "    323000.0       1.00      1.00      1.00         1\n",
      "    324000.0       0.00      0.00      0.00         1\n",
      "    330000.0       0.00      0.00      0.00         1\n",
      "    340000.0       0.33      1.00      0.50         3\n",
      "    345000.0       0.00      0.00      0.00         1\n",
      "    350000.0       0.00      0.00      0.00         1\n",
      "    351000.0       0.00      0.00      0.00         1\n",
      "    360000.0       0.00      0.00      0.00         1\n",
      "    365000.0       0.00      0.00      0.00         1\n",
      "    370000.0       0.22      0.33      0.27         6\n",
      "    380000.0       0.11      0.50      0.18         6\n",
      "    385000.0       0.00      0.00      0.00         1\n",
      "    390000.0       0.25      0.80      0.38         5\n",
      "    400000.0       0.00      0.00      0.00         3\n",
      "    400500.0       0.00      0.00      0.00         1\n",
      "    403000.0       0.00      0.00      0.00         1\n",
      "    406000.0       0.00      0.00      0.00         1\n",
      "    410000.0       0.00      0.00      0.00         1\n",
      "    411000.0       0.00      0.00      0.00         1\n",
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      "   2350000.0       1.00      1.00      1.00         1\n",
      "   2360000.0       1.00      1.00      1.00         1\n",
      "   2375000.0       0.00      0.00      0.00         3\n",
      "   2400000.0       0.00      0.00      0.00         4\n",
      "   2410000.0       0.00      0.00      0.00         1\n",
      "   2425000.0       1.00      1.00      1.00         1\n",
      "   2435000.0       0.00      0.00      0.00         1\n",
      "   2450000.0       0.00      0.00      0.00         2\n",
      "   2500000.0       0.50      0.50      0.50         2\n",
      "   2504000.0       0.00      0.00      0.00         1\n",
      "   2517500.0       0.50      1.00      0.67         1\n",
      "   2520000.0       0.00      0.00      0.00         2\n",
      "   2525000.0       0.00      0.00      0.00         1\n",
      "   2550000.0       0.25      0.50      0.33         4\n",
      "   2570000.0       0.00      0.00      0.00         2\n",
      "   2575000.0       0.00      0.00      0.00         1\n",
      "   2600000.0       0.50      0.50      0.50         4\n",
      "   2608000.0       0.25      1.00      0.40         1\n",
      "   2614000.0       0.00      0.00      0.00         1\n",
      "   2615000.0       1.00      1.00      1.00         1\n",
      "   2620000.0       0.00      0.00      0.00         2\n",
      "   2625000.0       0.00      0.00      0.00         1\n",
      "   2640000.0       0.00      0.00      0.00         1\n",
      "   2650000.0       0.00      0.00      0.00         1\n",
      "   2665000.0       0.00      0.00      0.00         1\n",
      "   2700000.0       0.33      0.33      0.33         3\n",
      "   2718000.0       1.00      1.00      1.00         1\n",
      "   2730000.0       0.33      0.50      0.40         2\n",
      "   2740000.0       0.00      0.00      0.00         1\n",
      "   2750000.0       1.00      0.33      0.50         3\n",
      "   2800000.0       0.50      0.20      0.29         5\n",
      "   2801000.0       0.00      0.00      0.00         1\n",
      "   2810000.0       0.00      0.00      0.00         2\n",
      "   2820000.0       1.00      1.00      1.00         1\n",
      "   2830000.0       0.00      0.00      0.00         1\n",
      "   2840000.0       1.00      0.50      0.67         2\n",
      "   2860000.0       1.00      1.00      1.00         1\n",
      "   2900000.0       0.00      0.00      0.00         4\n",
      "   2905000.0       1.00      1.00      1.00         1\n",
      "   2910000.0       0.00      0.00      0.00         1\n",
      "   2930000.0       0.50      1.00      0.67         1\n",
      "   2950000.0       0.60      0.50      0.55         6\n",
      "   3000000.0       1.00      0.50      0.67         2\n",
      "   3020000.0       0.00      0.00      0.00         1\n",
      "   3050000.0       1.00      1.00      1.00         1\n",
      "   3105000.0       1.00      1.00      1.00         1\n",
      "   3180000.0       0.40      1.00      0.57         2\n",
      "   3207000.0       1.00      1.00      1.00         1\n",
      "   3225000.0       0.50      0.50      0.50         2\n",
      "   3230000.0       1.00      1.00      1.00         1\n",
      "   3250000.0       1.00      1.00      1.00         1\n",
      "   3270000.0       1.00      1.00      1.00         1\n",
      "   3290000.0       1.00      1.00      1.00         1\n",
      "   3295888.0       0.50      1.00      0.67         1\n",
      "   3300000.0       1.00      0.50      0.67         2\n",
      "   3320000.0       0.50      1.00      0.67         1\n",
      "   3360000.0       1.00      1.00      1.00         1\n",
      "   3400000.0       0.67      0.67      0.67         3\n",
      "   3401000.0       0.00      0.00      0.00         1\n",
      "   3510000.0       1.00      1.00      1.00         1\n",
      "   3512500.0       0.00      0.00      0.00         1\n",
      "   3550000.0       1.00      0.50      0.67         2\n",
      "   3600000.0       0.00      0.00      0.00         1\n",
      "   3625000.0       0.25      1.00      0.40         1\n",
      "   3660000.0       0.00      0.00      0.00         1\n",
      "   3695000.0       0.00      0.00      0.00         1\n",
      "   3700000.0       0.67      1.00      0.80         2\n",
      "   3755000.0       0.00      0.00      0.00         1\n",
      "   3775000.0       1.00      1.00      1.00         1\n",
      "   3800000.0       1.00      1.00      1.00         1\n",
      "   3900000.0       1.00      0.50      0.67         2\n",
      "   4000000.0       0.50      0.67      0.57         3\n",
      "   4250000.0       1.00      1.00      1.00         1\n",
      "   4350000.0       0.00      0.00      0.00         1\n",
      "   4735000.0       1.00      1.00      1.00         1\n",
      "   4760000.0       0.00      0.00      0.00         1\n",
      "   5525000.0       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           0.26      1600\n",
      "   macro avg       0.25      0.26      0.24      1600\n",
      "weighted avg       0.24      0.26      0.22      1600\n",
      "\n",
      "Classification Report for Test Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    131000.0       1.00      1.00      1.00         1\n",
      "    145000.0       0.50      1.00      0.67         1\n",
      "    240000.0       0.50      1.00      0.67         1\n",
      "    250000.0       0.00      0.00      0.00         1\n",
      "    252000.0       0.00      0.00      0.00         1\n",
      "    255000.0       0.00      0.00      0.00         1\n",
      "    260000.0       0.00      0.00      0.00         1\n",
      "    265000.0       1.00      1.00      1.00         1\n",
      "    270000.0       0.00      0.00      0.00         1\n",
      "    272500.0       0.00      0.00      0.00         1\n",
      "    275500.0       0.00      0.00      0.00         1\n",
      "    279500.0       0.00      0.00      0.00         1\n",
      "    280000.0       0.00      0.00      0.00         2\n",
      "    285000.0       0.00      0.00      0.00         1\n",
      "    288000.0       0.50      1.00      0.67         1\n",
      "    290000.0       0.38      1.00      0.55         3\n",
      "    292000.0       0.00      0.00      0.00         1\n",
      "    300000.0       0.00      0.00      0.00         2\n",
      "    323000.0       1.00      1.00      1.00         1\n",
      "    324000.0       0.00      0.00      0.00         1\n",
      "    330000.0       0.00      0.00      0.00         1\n",
      "    340000.0       0.33      1.00      0.50         3\n",
      "    345000.0       0.00      0.00      0.00         1\n",
      "    350000.0       0.00      0.00      0.00         1\n",
      "    351000.0       0.00      0.00      0.00         1\n",
      "    360000.0       0.00      0.00      0.00         1\n",
      "    365000.0       0.00      0.00      0.00         1\n",
      "    370000.0       0.22      0.33      0.27         6\n",
      "    380000.0       0.11      0.50      0.18         6\n",
      "    385000.0       0.00      0.00      0.00         1\n",
      "    390000.0       0.25      0.80      0.38         5\n",
      "    400000.0       0.00      0.00      0.00         3\n",
      "    400500.0       0.00      0.00      0.00         1\n",
      "    403000.0       0.00      0.00      0.00         1\n",
      "    406000.0       0.00      0.00      0.00         1\n",
      "    410000.0       0.00      0.00      0.00         1\n",
      "    411000.0       0.00      0.00      0.00         1\n",
      "    415000.0       0.00      0.00      0.00         1\n",
      "    416000.0       0.00      0.00      0.00         1\n",
      "    420000.0       0.50      0.33      0.40         3\n",
      "    421500.0       0.00      0.00      0.00         1\n",
      "    426000.0       0.00      0.00      0.00         1\n",
      "    428000.0       1.00      1.00      1.00         1\n",
      "    430000.0       0.00      0.00      0.00         1\n",
      "    432000.0       0.00      0.00      0.00         1\n",
      "    434000.0       0.00      0.00      0.00         1\n",
      "    435000.0       0.12      0.33      0.18         3\n",
      "    437000.0       0.00      0.00      0.00         1\n",
      "    440000.0       0.11      0.40      0.17         5\n",
      "    441000.0       0.00      0.00      0.00         1\n",
      "    442000.0       0.00      0.00      0.00         2\n",
      "    447000.0       0.00      0.00      0.00         1\n",
      "    450000.0       0.18      0.50      0.27         4\n",
      "    451000.0       0.00      0.00      0.00         1\n",
      "    455000.0       0.00      0.00      0.00         2\n",
      "    457000.0       0.00      0.00      0.00         1\n",
      "    460000.0       0.00      0.00      0.00         2\n",
      "    462500.0       0.00      0.00      0.00         1\n",
      "    465000.0       1.00      0.33      0.50         3\n",
      "    466000.0       0.00      0.00      0.00         1\n",
      "    467000.0       0.00      0.00      0.00         1\n",
      "    468000.0       1.00      1.00      1.00         1\n",
      "    470000.0       1.00      1.00      1.00         2\n",
      "    471000.0       0.00      0.00      0.00         1\n",
      "    473000.0       0.00      0.00      0.00         1\n",
      "    474000.0       0.00      0.00      0.00         1\n",
      "    484000.0       0.00      0.00      0.00         1\n",
      "    485000.0       1.00      1.00      1.00         2\n",
      "    486500.0       0.00      0.00      0.00         1\n",
      "    487500.0       1.00      1.00      1.00         1\n",
      "    495000.0       0.25      0.40      0.31         5\n",
      "    497000.0       0.00      0.00      0.00         1\n",
      "    497500.0       1.00      1.00      1.00         1\n",
      "    500000.0       0.11      0.50      0.18         8\n",
      "    501000.0       1.00      1.00      1.00         1\n",
      "    502000.0       0.00      0.00      0.00         2\n",
      "    502500.0       0.00      0.00      0.00         1\n",
      "    503000.0       0.00      0.00      0.00         1\n",
      "    505000.0       1.00      1.00      1.00         1\n",
      "    510000.0       0.18      0.67      0.29         3\n",
      "    512000.0       0.00      0.00      0.00         1\n",
      "    515000.0       0.00      0.00      0.00         1\n",
      "    516000.0       0.00      0.00      0.00         1\n",
      "    517500.0       0.00      0.00      0.00         1\n",
      "    520000.0       0.20      0.67      0.31         3\n",
      "    520500.0       0.00      0.00      0.00         1\n",
      "    521500.0       0.00      0.00      0.00         1\n",
      "    522500.0       0.00      0.00      0.00         1\n",
      "    523000.0       0.00      0.00      0.00         1\n",
      "    525000.0       1.00      1.00      1.00         1\n",
      "    525500.0       0.50      1.00      0.67         1\n",
      "    526000.0       0.00      0.00      0.00         1\n",
      "    530000.0       0.00      0.00      0.00         3\n",
      "    535000.0       0.00      0.00      0.00         1\n",
      "    536000.0       0.00      0.00      0.00         1\n",
      "    537000.0       0.00      0.00      0.00         1\n",
      "    537500.0       0.00      0.00      0.00         1\n",
      "    540000.0       0.00      0.00      0.00         3\n",
      "    542000.0       0.00      0.00      0.00         1\n",
      "    545000.0       0.00      0.00      0.00         2\n",
      "    546000.0       1.00      1.00      1.00         1\n",
      "    550000.0       0.00      0.00      0.00         4\n",
      "    558000.0       0.00      0.00      0.00         1\n",
      "    563000.0       0.00      0.00      0.00         1\n",
      "    565000.0       0.00      0.00      0.00         2\n",
      "    570000.0       0.00      0.00      0.00         2\n",
      "    570500.0       0.00      0.00      0.00         1\n",
      "    572000.0       1.00      1.00      1.00         1\n",
      "    575000.0       0.00      0.00      0.00         2\n",
      "    577000.0       0.00      0.00      0.00         1\n",
      "    579000.0       0.00      0.00      0.00         1\n",
      "    580000.0       0.50      0.33      0.40         3\n",
      "    585000.0       0.00      0.00      0.00         4\n",
      "    586000.0       0.00      0.00      0.00         1\n",
      "    588000.0       0.00      0.00      0.00         2\n",
      "    590000.0       0.25      0.50      0.33         6\n",
      "    592500.0       1.00      0.50      0.67         2\n",
      "    598000.0       0.20      0.67      0.31         3\n",
      "    599000.0       0.00      0.00      0.00         1\n",
      "    600000.0       0.00      0.00      0.00         4\n",
      "    601000.0       0.00      0.00      0.00         2\n",
      "    602500.0       0.00      0.00      0.00         1\n",
      "    603000.0       0.00      0.00      0.00         1\n",
      "    605000.0       0.17      0.17      0.17         6\n",
      "    605100.0       0.50      1.00      0.67         1\n",
      "    608000.0       0.00      0.00      0.00         1\n",
      "    610000.0       0.00      0.00      0.00         4\n",
      "    615000.0       0.00      0.00      0.00         1\n",
      "    616000.0       0.00      0.00      0.00         1\n",
      "    620000.0       0.29      0.33      0.31         6\n",
      "    622000.0       1.00      1.00      1.00         1\n",
      "    623000.0       0.00      0.00      0.00         1\n",
      "    625000.0       0.00      0.00      0.00         4\n",
      "    627000.0       0.50      1.00      0.67         1\n",
      "    627500.0       0.00      0.00      0.00         1\n",
      "    630000.0       0.00      0.00      0.00         1\n",
      "    632500.0       0.00      0.00      0.00         1\n",
      "    633000.0       0.00      0.00      0.00         1\n",
      "    634000.0       0.00      0.00      0.00         1\n",
      "    635000.0       0.00      0.00      0.00         4\n",
      "    636000.0       0.00      0.00      0.00         3\n",
      "    638000.0       0.00      0.00      0.00         1\n",
      "    640000.0       0.10      0.17      0.12         6\n",
      "    641000.0       1.00      1.00      1.00         2\n",
      "    645000.0       0.00      0.00      0.00         3\n",
      "    650000.0       0.09      0.25      0.13         8\n",
      "    651000.0       0.00      0.00      0.00         1\n",
      "    653000.0       0.00      0.00      0.00         1\n",
      "    655000.0       0.50      1.00      0.67         1\n",
      "    656000.0       0.00      0.00      0.00         2\n",
      "    657000.0       0.00      0.00      0.00         1\n",
      "    659500.0       0.00      0.00      0.00         1\n",
      "    660000.0       0.17      0.25      0.20         4\n",
      "    662000.0       0.00      0.00      0.00         2\n",
      "    665000.0       0.00      0.00      0.00         1\n",
      "    667000.0       0.00      0.00      0.00         1\n",
      "    670000.0       0.00      0.00      0.00         5\n",
      "    671000.0       0.00      0.00      0.00         1\n",
      "    675000.0       0.50      0.33      0.40         3\n",
      "    678000.0       0.00      0.00      0.00         2\n",
      "    680000.0       0.14      0.14      0.14         7\n",
      "    681000.0       1.00      1.00      1.00         1\n",
      "    683000.0       0.00      0.00      0.00         2\n",
      "    683500.0       0.00      0.00      0.00         1\n",
      "    685000.0       1.00      0.33      0.50         3\n",
      "    686000.0       0.00      0.00      0.00         1\n",
      "    689000.0       1.00      1.00      1.00         1\n",
      "    690000.0       0.00      0.00      0.00         2\n",
      "    690500.0       0.00      0.00      0.00         1\n",
      "    691500.0       0.00      0.00      0.00         1\n",
      "    692000.0       0.00      0.00      0.00         1\n",
      "    693000.0       0.00      0.00      0.00         1\n",
      "    695000.0       0.00      0.00      0.00         1\n",
      "    696000.0       1.00      1.00      1.00         1\n",
      "    700000.0       0.13      0.62      0.22        13\n",
      "    702000.0       0.50      0.50      0.50         2\n",
      "    704000.0       0.00      0.00      0.00         1\n",
      "    705000.0       1.00      0.20      0.33         5\n",
      "    706000.0       0.00      0.00      0.00         1\n",
      "    708000.0       0.00      0.00      0.00         1\n",
      "    710000.0       0.22      0.50      0.31         4\n",
      "    711000.0       0.00      0.00      0.00         1\n",
      "    712000.0       0.00      0.00      0.00         1\n",
      "    715000.0       0.00      0.00      0.00         2\n",
      "    716000.0       0.00      0.00      0.00         1\n",
      "    720000.0       0.00      0.00      0.00         2\n",
      "    722000.0       0.00      0.00      0.00         1\n",
      "    722500.0       0.00      0.00      0.00         1\n",
      "    725000.0       0.00      0.00      0.00         5\n",
      "    729000.0       0.00      0.00      0.00         1\n",
      "    730000.0       0.20      0.43      0.27         7\n",
      "    732000.0       0.00      0.00      0.00         1\n",
      "    735000.0       0.40      0.67      0.50         3\n",
      "    736000.0       0.00      0.00      0.00         1\n",
      "    737000.0       0.00      0.00      0.00         2\n",
      "    740000.0       1.00      0.25      0.40         4\n",
      "    742500.0       0.00      0.00      0.00         1\n",
      "    743000.0       1.00      1.00      1.00         1\n",
      "    745000.0       0.00      0.00      0.00         1\n",
      "    746000.0       0.00      0.00      0.00         1\n",
      "    750000.0       0.40      0.20      0.27        10\n",
      "    751000.0       0.00      0.00      0.00         1\n",
      "    752000.0       0.00      0.00      0.00         3\n",
      "    753000.0       0.00      0.00      0.00         1\n",
      "    754000.0       0.00      0.00      0.00         2\n",
      "    755000.0       1.00      1.00      1.00         1\n",
      "    756000.0       1.00      1.00      1.00         1\n",
      "    760000.0       0.33      0.40      0.36         5\n",
      "    761000.0       0.50      0.33      0.40         3\n",
      "    762000.0       0.00      0.00      0.00         1\n",
      "    763000.0       0.00      0.00      0.00         1\n",
      "    765000.0       1.00      0.25      0.40         4\n",
      "    770000.0       0.00      0.00      0.00         5\n",
      "    772000.0       0.33      0.50      0.40         2\n",
      "    772500.0       1.00      1.00      1.00         1\n",
      "    773000.0       1.00      1.00      1.00         1\n",
      "    775000.0       0.00      0.00      0.00         2\n",
      "    780000.0       0.25      0.20      0.22         5\n",
      "    781000.0       0.00      0.00      0.00         1\n",
      "    785000.0       0.00      0.00      0.00         1\n",
      "    786000.0       0.00      0.00      0.00         1\n",
      "    790000.0       0.20      0.14      0.17         7\n",
      "    791000.0       0.50      1.00      0.67         1\n",
      "    795000.0       0.00      0.00      0.00         2\n",
      "    797500.0       0.00      0.00      0.00         2\n",
      "    800000.0       0.08      0.14      0.11         7\n",
      "    801000.0       0.00      0.00      0.00         3\n",
      "    804000.0       0.00      0.00      0.00         1\n",
      "    805000.0       0.17      0.12      0.14         8\n",
      "    806000.0       1.00      0.50      0.67         2\n",
      "    807000.0       1.00      1.00      1.00         1\n",
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      "   1630000.0       0.00      0.00      0.00         2\n",
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      "   1742000.0       1.00      0.50      0.67         2\n",
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      "   1760000.0       0.00      0.00      0.00         1\n",
      "   1761000.0       1.00      1.00      1.00         1\n",
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      "   1790000.0       0.00      0.00      0.00         2\n",
      "   1800000.0       0.15      0.33      0.21        12\n",
      "   1801000.0       0.00      0.00      0.00         2\n",
      "   1802100.0       0.00      0.00      0.00         1\n",
      "   1802500.0       1.00      1.00      1.00         1\n",
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      "   1850000.0       0.33      0.33      0.33         6\n",
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      "   1865000.0       0.00      0.00      0.00         1\n",
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      "   1875000.0       1.00      0.33      0.50         3\n",
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      "   1880000.0       0.00      0.00      0.00         1\n",
      "   1890000.0       1.00      1.00      1.00         1\n",
      "   1900000.0       0.23      0.58      0.33        12\n",
      "   1905000.0       1.00      0.50      0.67         2\n",
      "   1910000.0       1.00      0.33      0.50         3\n",
      "   1912000.0       0.00      0.00      0.00         1\n",
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      "   1916000.0       0.00      0.00      0.00         1\n",
      "   1918000.0       1.00      1.00      1.00         1\n",
      "   1920000.0       0.33      0.50      0.40         2\n",
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      "   1950000.0       0.25      0.33      0.29         3\n",
      "   1955000.0       0.00      0.00      0.00         1\n",
      "   1960000.0       0.00      0.00      0.00         2\n",
      "   1975000.0       0.00      0.00      0.00         1\n",
      "   2000000.0       0.22      0.57      0.31        14\n",
      "   2005000.0       0.50      1.00      0.67         1\n",
      "   2015000.0       1.00      1.00      1.00         1\n",
      "   2020000.0       0.00      0.00      0.00         1\n",
      "   2030000.0       0.00      0.00      0.00         2\n",
      "   2050000.0       0.67      0.50      0.57         4\n",
      "   2068000.0       0.00      0.00      0.00         1\n",
      "   2070000.0       0.50      1.00      0.67         1\n",
      "   2090000.0       0.00      0.00      0.00         1\n",
      "   2100000.0       0.40      0.33      0.36         6\n",
      "   2105000.0       0.00      0.00      0.00         1\n",
      "   2120000.0       0.00      0.00      0.00         2\n",
      "   2125000.0       0.00      0.00      0.00         2\n",
      "   2130000.0       1.00      0.50      0.67         2\n",
      "   2140000.0       1.00      1.00      1.00         1\n",
      "   2145000.0       1.00      1.00      1.00         1\n",
      "   2150000.0       0.25      0.33      0.29         3\n",
      "   2155000.0       0.00      0.00      0.00         2\n",
      "   2170000.0       1.00      0.50      0.67         2\n",
      "   2175000.0       1.00      1.00      1.00         1\n",
      "   2177000.0       0.00      0.00      0.00         1\n",
      "   2200000.0       0.33      0.40      0.36         5\n",
      "   2205000.0       0.00      0.00      0.00         2\n",
      "   2210000.0       0.00      0.00      0.00         1\n",
      "   2215000.0       0.00      0.00      0.00         1\n",
      "   2220000.0       0.00      0.00      0.00         1\n",
      "   2228000.0       0.00      0.00      0.00         1\n",
      "   2230000.0       1.00      1.00      1.00         1\n",
      "   2250000.0       0.27      0.60      0.38         5\n",
      "   2255000.0       0.00      0.00      0.00         1\n",
      "   2266000.0       0.00      0.00      0.00         1\n",
      "   2275000.0       0.00      0.00      0.00         2\n",
      "   2300000.0       1.00      1.00      1.00         1\n",
      "   2310000.0       0.00      0.00      0.00         1\n",
      "   2325000.0       1.00      1.00      1.00         1\n",
      "   2330000.0       0.00      0.00      0.00         1\n",
      "   2350000.0       1.00      1.00      1.00         1\n",
      "   2360000.0       1.00      1.00      1.00         1\n",
      "   2375000.0       0.00      0.00      0.00         3\n",
      "   2400000.0       0.00      0.00      0.00         4\n",
      "   2410000.0       0.00      0.00      0.00         1\n",
      "   2425000.0       1.00      1.00      1.00         1\n",
      "   2435000.0       0.00      0.00      0.00         1\n",
      "   2450000.0       0.00      0.00      0.00         2\n",
      "   2500000.0       0.50      0.50      0.50         2\n",
      "   2504000.0       0.00      0.00      0.00         1\n",
      "   2517500.0       0.50      1.00      0.67         1\n",
      "   2520000.0       0.00      0.00      0.00         2\n",
      "   2525000.0       0.00      0.00      0.00         1\n",
      "   2550000.0       0.25      0.50      0.33         4\n",
      "   2570000.0       0.00      0.00      0.00         2\n",
      "   2575000.0       0.00      0.00      0.00         1\n",
      "   2600000.0       0.50      0.50      0.50         4\n",
      "   2608000.0       0.25      1.00      0.40         1\n",
      "   2614000.0       0.00      0.00      0.00         1\n",
      "   2615000.0       1.00      1.00      1.00         1\n",
      "   2620000.0       0.00      0.00      0.00         2\n",
      "   2625000.0       0.00      0.00      0.00         1\n",
      "   2640000.0       0.00      0.00      0.00         1\n",
      "   2650000.0       0.00      0.00      0.00         1\n",
      "   2665000.0       0.00      0.00      0.00         1\n",
      "   2700000.0       0.33      0.33      0.33         3\n",
      "   2718000.0       1.00      1.00      1.00         1\n",
      "   2730000.0       0.33      0.50      0.40         2\n",
      "   2740000.0       0.00      0.00      0.00         1\n",
      "   2750000.0       1.00      0.33      0.50         3\n",
      "   2800000.0       0.50      0.20      0.29         5\n",
      "   2801000.0       0.00      0.00      0.00         1\n",
      "   2810000.0       0.00      0.00      0.00         2\n",
      "   2820000.0       1.00      1.00      1.00         1\n",
      "   2830000.0       0.00      0.00      0.00         1\n",
      "   2840000.0       1.00      0.50      0.67         2\n",
      "   2860000.0       1.00      1.00      1.00         1\n",
      "   2900000.0       0.00      0.00      0.00         4\n",
      "   2905000.0       1.00      1.00      1.00         1\n",
      "   2910000.0       0.00      0.00      0.00         1\n",
      "   2930000.0       0.50      1.00      0.67         1\n",
      "   2950000.0       0.60      0.50      0.55         6\n",
      "   3000000.0       1.00      0.50      0.67         2\n",
      "   3020000.0       0.00      0.00      0.00         1\n",
      "   3050000.0       1.00      1.00      1.00         1\n",
      "   3105000.0       1.00      1.00      1.00         1\n",
      "   3180000.0       0.40      1.00      0.57         2\n",
      "   3207000.0       1.00      1.00      1.00         1\n",
      "   3225000.0       0.50      0.50      0.50         2\n",
      "   3230000.0       1.00      1.00      1.00         1\n",
      "   3250000.0       1.00      1.00      1.00         1\n",
      "   3270000.0       1.00      1.00      1.00         1\n",
      "   3290000.0       1.00      1.00      1.00         1\n",
      "   3295888.0       0.50      1.00      0.67         1\n",
      "   3300000.0       1.00      0.50      0.67         2\n",
      "   3320000.0       0.50      1.00      0.67         1\n",
      "   3360000.0       1.00      1.00      1.00         1\n",
      "   3400000.0       0.67      0.67      0.67         3\n",
      "   3401000.0       0.00      0.00      0.00         1\n",
      "   3510000.0       1.00      1.00      1.00         1\n",
      "   3512500.0       0.00      0.00      0.00         1\n",
      "   3550000.0       1.00      0.50      0.67         2\n",
      "   3600000.0       0.00      0.00      0.00         1\n",
      "   3625000.0       0.25      1.00      0.40         1\n",
      "   3660000.0       0.00      0.00      0.00         1\n",
      "   3695000.0       0.00      0.00      0.00         1\n",
      "   3700000.0       0.67      1.00      0.80         2\n",
      "   3755000.0       0.00      0.00      0.00         1\n",
      "   3775000.0       1.00      1.00      1.00         1\n",
      "   3800000.0       1.00      1.00      1.00         1\n",
      "   3900000.0       1.00      0.50      0.67         2\n",
      "   4000000.0       0.50      0.67      0.57         3\n",
      "   4250000.0       1.00      1.00      1.00         1\n",
      "   4350000.0       0.00      0.00      0.00         1\n",
      "   4735000.0       1.00      1.00      1.00         1\n",
      "   4760000.0       0.00      0.00      0.00         1\n",
      "   5525000.0       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           0.26      1600\n",
      "   macro avg       0.25      0.26      0.24      1600\n",
      "weighted avg       0.24      0.26      0.22      1600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Assuming clf2 is your SVM pipeline already fitted\n",
    "# Making predictions\n",
    "y_pred_train_svc = clf2.predict(X_train)\n",
    "y_pred_test_svc = clf2.predict(X_test)\n",
    "\n",
    "# Printing classification reports\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(\"Classification Report for Training Set:\")\n",
    "print(classification_report(y_train_clean, y_pred_train_svc))\n",
    "\n",
    "print(\"Classification Report for Test Set:\")\n",
    "print(classification_report(y_test_clean, y_pred_test_svc))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Untuk SVC linear, kelihatan bahwa test memiliki akurasi yang rendah. Artinya model tidak terlalu sensitif untuk diprediksi dengan SVC, dan lebih cocok gunakan Linear regression. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1596,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set Evaluation (SVC):\n",
      "accuracy: 0.2556\n",
      "precision (weighted avg): 0.2396\n",
      "recall (weighted avg): 0.2556\n",
      "f1-score (weighted avg): 0.2154\n",
      "\n",
      "Test Set Evaluation (SVC):\n",
      "accuracy: 0.2556\n",
      "precision (weighted avg): 0.2396\n",
      "recall (weighted avg): 0.2556\n",
      "f1-score (weighted avg): 0.2154\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, precision_recall_fscore_support\n",
    "\n",
    "# Compute metrics for training set\n",
    "train_accuracy = accuracy_score(y_train_clean, y_pred_train_svc)\n",
    "train_precision, train_recall, train_f1, _ = precision_recall_fscore_support(y_train_clean, y_pred_train_svc, average='weighted')\n",
    "\n",
    "# Compute metrics for test set\n",
    "test_accuracy = accuracy_score(y_test_clean, y_pred_test_svc)\n",
    "test_precision, test_recall, test_f1, _ = precision_recall_fscore_support(y_test_clean, y_pred_test_svc, average='weighted')\n",
    "\n",
    "# Print formatted evaluation\n",
    "print(\"Train Set Evaluation (SVC):\")\n",
    "print(f\"accuracy: {train_accuracy:.4f}\")\n",
    "print(f\"precision (weighted avg): {train_precision:.4f}\")\n",
    "print(f\"recall (weighted avg): {train_recall:.4f}\")\n",
    "print(f\"f1-score (weighted avg): {train_f1:.4f}\")\n",
    "\n",
    "print(\"\\nTest Set Evaluation (SVC):\")\n",
    "print(f\"accuracy: {test_accuracy:.4f}\")\n",
    "print(f\"precision (weighted avg): {test_precision:.4f}\")\n",
    "print(f\"recall (weighted avg): {test_recall:.4f}\")\n",
    "print(f\"f1-score (weighted avg): {test_f1:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bisa konfirmasi bahwa test akurasi, precision, recall, dan f1 sangat rendah. Ini bisakah karena dataset telah overfit atau underfit. Kita harus lihat test lain untuk lihat kenapa evaluation train dan test memiliki performa yang tidak akurat. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1599,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score for Training Set (Decision Tree): 0.9681213404650905\n",
      "F1-score for Test Set (Decision Tree): 0.9681213404650905\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming clf3 is your Decision Tree pipeline already fitted\n",
    "# Visualizing the Decision Tree with limited depth\n",
    "plt.figure(figsize=(30, 15))\n",
    "X_train_columns = X_train_clean.columns  # Assuming X_train_clean is your preprocessed training data\n",
    "tree.plot_tree(clf3.named_steps['decision_tree'], feature_names=X_train_columns, filled=True, fontsize=10, max_depth=3)\n",
    "plt.show()\n",
    "\n",
    "# Calculating F1-score\n",
    "y_pred_train_dt = clf3.predict(X_train_clean)\n",
    "y_pred_test_dt = clf3.predict(X_test_clean)\n",
    "\n",
    "f1_train = f1_score(y_train_clean, y_pred_train_dt, average='weighted')\n",
    "f1_test = f1_score(y_test_clean, y_pred_test_dt, average='weighted')\n",
    "\n",
    "print(\"F1-score for Training Set (Decision Tree):\", f1_train)\n",
    "print(\"F1-score for Test Set (Decision Tree):\", f1_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan buat decision tree untuk melihata jalur proses linear relationship terhadap target variable. Bisa kelihatan Decision Tree tidak cocok untuk tugas-nya karena array/list yang diambil semua numerikal dan bukan kategorikal, jadi tidak bisa diklasifikasan ke beberapa bagian tertentu.\n",
    "\n",
    "Juga terlihat F1-score yang sangat tinggi, ini artinya ada masalah overfitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1600,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import graphviz\n",
    "\n",
    "# Assuming clf3 is your Decision Tree pipeline already fitted\n",
    "# Generate DOT data from the decision tree\n",
    "dot_data = tree.export_graphviz(clf3.named_steps['decision_tree'], out_file=None,\n",
    "                                feature_names=X_train_clean.columns,\n",
    "                                filled=True, rounded=True, special_characters=True)\n",
    "\n",
    "# Create a graph from DOT data\n",
    "graph = graphviz.Source(dot_data)\n",
    "\n",
    "# Display the decision tree\n",
    "#graph.render('decision_tree', format='png', cleanup=True)  # Optional: Save the tree to a file\n",
    "#graph.view()  # Display in a viewer (opens in a new window)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1601,
   "metadata": {},
   "outputs": [],
   "source": [
    "#graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan coba lihat grafik alternatif dengan X_train data. Data sangat kompleks karena tidak sesuai untuk tugas linear regression. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1602,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for Training Set (Random Forest):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    131000.0       1.00      1.00      1.00         1\n",
      "    145000.0       1.00      1.00      1.00         1\n",
      "    240000.0       1.00      1.00      1.00         1\n",
      "    250000.0       0.00      0.00      0.00         1\n",
      "    252000.0       0.50      1.00      0.67         1\n",
      "    255000.0       0.00      0.00      0.00         1\n",
      "    260000.0       0.00      0.00      0.00         1\n",
      "    265000.0       1.00      1.00      1.00         1\n",
      "    270000.0       0.50      1.00      0.67         1\n",
      "    272500.0       0.50      1.00      0.67         1\n",
      "    275500.0       1.00      1.00      1.00         1\n",
      "    279500.0       0.00      0.00      0.00         1\n",
      "    280000.0       1.00      1.00      1.00         2\n",
      "    285000.0       0.00      0.00      0.00         1\n",
      "    288000.0       1.00      1.00      1.00         1\n",
      "    290000.0       1.00      0.67      0.80         3\n",
      "    292000.0       1.00      1.00      1.00         1\n",
      "    300000.0       1.00      0.50      0.67         2\n",
      "    323000.0       1.00      1.00      1.00         1\n",
      "    324000.0       0.00      0.00      0.00         1\n",
      "    330000.0       1.00      1.00      1.00         1\n",
      "    340000.0       0.60      1.00      0.75         3\n",
      "    345000.0       0.00      0.00      0.00         1\n",
      "    350000.0       1.00      1.00      1.00         1\n",
      "    351000.0       1.00      1.00      1.00         1\n",
      "    360000.0       0.00      0.00      0.00         1\n",
      "    365000.0       0.33      1.00      0.50         1\n",
      "    370000.0       0.60      1.00      0.75         6\n",
      "    380000.0       1.00      0.50      0.67         6\n",
      "    385000.0       0.00      0.00      0.00         1\n",
      "    390000.0       1.00      1.00      1.00         5\n",
      "    400000.0       0.67      0.67      0.67         3\n",
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      "   2275000.0       1.00      1.00      1.00         2\n",
      "   2300000.0       1.00      1.00      1.00         1\n",
      "   2310000.0       1.00      1.00      1.00         1\n",
      "   2325000.0       1.00      1.00      1.00         1\n",
      "   2330000.0       1.00      1.00      1.00         1\n",
      "   2350000.0       1.00      1.00      1.00         1\n",
      "   2360000.0       1.00      1.00      1.00         1\n",
      "   2375000.0       1.00      1.00      1.00         3\n",
      "   2400000.0       1.00      1.00      1.00         4\n",
      "   2410000.0       1.00      1.00      1.00         1\n",
      "   2425000.0       1.00      1.00      1.00         1\n",
      "   2435000.0       1.00      1.00      1.00         1\n",
      "   2450000.0       1.00      1.00      1.00         2\n",
      "   2500000.0       1.00      1.00      1.00         2\n",
      "   2504000.0       1.00      1.00      1.00         1\n",
      "   2517500.0       1.00      1.00      1.00         1\n",
      "   2520000.0       1.00      0.50      0.67         2\n",
      "   2525000.0       1.00      1.00      1.00         1\n",
      "   2550000.0       1.00      1.00      1.00         4\n",
      "   2570000.0       0.67      1.00      0.80         2\n",
      "   2575000.0       1.00      1.00      1.00         1\n",
      "   2600000.0       1.00      1.00      1.00         4\n",
      "   2608000.0       1.00      1.00      1.00         1\n",
      "   2614000.0       1.00      1.00      1.00         1\n",
      "   2615000.0       1.00      1.00      1.00         1\n",
      "   2620000.0       0.67      1.00      0.80         2\n",
      "   2625000.0       1.00      1.00      1.00         1\n",
      "   2640000.0       1.00      1.00      1.00         1\n",
      "   2650000.0       1.00      1.00      1.00         1\n",
      "   2665000.0       1.00      1.00      1.00         1\n",
      "   2700000.0       1.00      1.00      1.00         3\n",
      "   2718000.0       1.00      1.00      1.00         1\n",
      "   2730000.0       1.00      1.00      1.00         2\n",
      "   2740000.0       1.00      1.00      1.00         1\n",
      "   2750000.0       1.00      1.00      1.00         3\n",
      "   2800000.0       0.83      1.00      0.91         5\n",
      "   2801000.0       1.00      1.00      1.00         1\n",
      "   2810000.0       1.00      1.00      1.00         2\n",
      "   2820000.0       1.00      1.00      1.00         1\n",
      "   2830000.0       1.00      1.00      1.00         1\n",
      "   2840000.0       1.00      1.00      1.00         2\n",
      "   2860000.0       1.00      1.00      1.00         1\n",
      "   2900000.0       1.00      1.00      1.00         4\n",
      "   2905000.0       1.00      1.00      1.00         1\n",
      "   2910000.0       1.00      1.00      1.00         1\n",
      "   2930000.0       1.00      1.00      1.00         1\n",
      "   2950000.0       1.00      1.00      1.00         6\n",
      "   3000000.0       1.00      1.00      1.00         2\n",
      "   3020000.0       0.00      0.00      0.00         1\n",
      "   3050000.0       1.00      1.00      1.00         1\n",
      "   3105000.0       1.00      1.00      1.00         1\n",
      "   3180000.0       1.00      1.00      1.00         2\n",
      "   3207000.0       1.00      1.00      1.00         1\n",
      "   3225000.0       1.00      1.00      1.00         2\n",
      "   3230000.0       1.00      1.00      1.00         1\n",
      "   3250000.0       1.00      1.00      1.00         1\n",
      "   3270000.0       1.00      1.00      1.00         1\n",
      "   3290000.0       1.00      1.00      1.00         1\n",
      "   3295888.0       1.00      1.00      1.00         1\n",
      "   3300000.0       1.00      1.00      1.00         2\n",
      "   3320000.0       1.00      1.00      1.00         1\n",
      "   3360000.0       1.00      1.00      1.00         1\n",
      "   3400000.0       1.00      1.00      1.00         3\n",
      "   3401000.0       1.00      1.00      1.00         1\n",
      "   3510000.0       1.00      1.00      1.00         1\n",
      "   3512500.0       1.00      1.00      1.00         1\n",
      "   3550000.0       1.00      1.00      1.00         2\n",
      "   3600000.0       1.00      1.00      1.00         1\n",
      "   3625000.0       1.00      1.00      1.00         1\n",
      "   3660000.0       1.00      1.00      1.00         1\n",
      "   3695000.0       1.00      1.00      1.00         1\n",
      "   3700000.0       1.00      1.00      1.00         2\n",
      "   3755000.0       1.00      1.00      1.00         1\n",
      "   3775000.0       1.00      1.00      1.00         1\n",
      "   3800000.0       1.00      1.00      1.00         1\n",
      "   3900000.0       1.00      1.00      1.00         2\n",
      "   4000000.0       1.00      1.00      1.00         3\n",
      "   4250000.0       1.00      1.00      1.00         1\n",
      "   4350000.0       1.00      1.00      1.00         1\n",
      "   4735000.0       1.00      1.00      1.00         1\n",
      "   4760000.0       1.00      1.00      1.00         1\n",
      "   5525000.0       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           0.97      1600\n",
      "   macro avg       0.96      0.96      0.96      1600\n",
      "weighted avg       0.97      0.97      0.97      1600\n",
      "\n",
      "\n",
      "Classification Report for Test Set (Random Forest):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    131000.0       1.00      1.00      1.00         1\n",
      "    145000.0       1.00      1.00      1.00         1\n",
      "    240000.0       1.00      1.00      1.00         1\n",
      "    250000.0       0.00      0.00      0.00         1\n",
      "    252000.0       0.50      1.00      0.67         1\n",
      "    255000.0       0.00      0.00      0.00         1\n",
      "    260000.0       0.00      0.00      0.00         1\n",
      "    265000.0       1.00      1.00      1.00         1\n",
      "    270000.0       0.50      1.00      0.67         1\n",
      "    272500.0       0.50      1.00      0.67         1\n",
      "    275500.0       1.00      1.00      1.00         1\n",
      "    279500.0       0.00      0.00      0.00         1\n",
      "    280000.0       1.00      1.00      1.00         2\n",
      "    285000.0       0.00      0.00      0.00         1\n",
      "    288000.0       1.00      1.00      1.00         1\n",
      "    290000.0       1.00      0.67      0.80         3\n",
      "    292000.0       1.00      1.00      1.00         1\n",
      "    300000.0       1.00      0.50      0.67         2\n",
      "    323000.0       1.00      1.00      1.00         1\n",
      "    324000.0       0.00      0.00      0.00         1\n",
      "    330000.0       1.00      1.00      1.00         1\n",
      "    340000.0       0.60      1.00      0.75         3\n",
      "    345000.0       0.00      0.00      0.00         1\n",
      "    350000.0       1.00      1.00      1.00         1\n",
      "    351000.0       1.00      1.00      1.00         1\n",
      "    360000.0       0.00      0.00      0.00         1\n",
      "    365000.0       0.33      1.00      0.50         1\n",
      "    370000.0       0.60      1.00      0.75         6\n",
      "    380000.0       1.00      0.50      0.67         6\n",
      "    385000.0       0.00      0.00      0.00         1\n",
      "    390000.0       1.00      1.00      1.00         5\n",
      "    400000.0       0.67      0.67      0.67         3\n",
      "    400500.0       1.00      1.00      1.00         1\n",
      "    403000.0       1.00      1.00      1.00         1\n",
      "    406000.0       1.00      1.00      1.00         1\n",
      "    410000.0       1.00      1.00      1.00         1\n",
      "    411000.0       1.00      1.00      1.00         1\n",
      "    415000.0       0.17      1.00      0.29         1\n",
      "    416000.0       0.33      1.00      0.50         1\n",
      "    420000.0       1.00      1.00      1.00         3\n",
      "    421500.0       1.00      1.00      1.00         1\n",
      "    426000.0       0.33      1.00      0.50         1\n",
      "    428000.0       1.00      1.00      1.00         1\n",
      "    430000.0       1.00      1.00      1.00         1\n",
      "    432000.0       1.00      1.00      1.00         1\n",
      "    434000.0       1.00      1.00      1.00         1\n",
      "    435000.0       1.00      1.00      1.00         3\n",
      "    437000.0       1.00      1.00      1.00         1\n",
      "    440000.0       0.83      1.00      0.91         5\n",
      "    441000.0       0.00      0.00      0.00         1\n",
      "    442000.0       1.00      0.50      0.67         2\n",
      "    447000.0       1.00      1.00      1.00         1\n",
      "    450000.0       1.00      0.75      0.86         4\n",
      "    451000.0       0.00      0.00      0.00         1\n",
      "    455000.0       1.00      0.50      0.67         2\n",
      "    457000.0       1.00      1.00      1.00         1\n",
      "    460000.0       1.00      0.50      0.67         2\n",
      "    462500.0       1.00      1.00      1.00         1\n",
      "    465000.0       1.00      0.67      0.80         3\n",
      "    466000.0       1.00      1.00      1.00         1\n",
      "    467000.0       1.00      1.00      1.00         1\n",
      "    468000.0       1.00      1.00      1.00         1\n",
      "    470000.0       1.00      1.00      1.00         2\n",
      "    471000.0       1.00      1.00      1.00         1\n",
      "    473000.0       1.00      1.00      1.00         1\n",
      "    474000.0       1.00      1.00      1.00         1\n",
      "    484000.0       1.00      1.00      1.00         1\n",
      "    485000.0       1.00      1.00      1.00         2\n",
      "    486500.0       1.00      1.00      1.00         1\n",
      "    487500.0       1.00      1.00      1.00         1\n",
      "    495000.0       1.00      1.00      1.00         5\n",
      "    497000.0       1.00      1.00      1.00         1\n",
      "    497500.0       1.00      1.00      1.00         1\n",
      "    500000.0       0.86      0.75      0.80         8\n",
      "    501000.0       1.00      1.00      1.00         1\n",
      "    502000.0       1.00      1.00      1.00         2\n",
      "    502500.0       0.00      0.00      0.00         1\n",
      "    503000.0       1.00      1.00      1.00         1\n",
      "    505000.0       1.00      1.00      1.00         1\n",
      "    510000.0       1.00      0.67      0.80         3\n",
      "    512000.0       1.00      1.00      1.00         1\n",
      "    515000.0       1.00      1.00      1.00         1\n",
      "    516000.0       0.33      1.00      0.50         1\n",
      "    517500.0       1.00      1.00      1.00         1\n",
      "    520000.0       1.00      0.67      0.80         3\n",
      "    520500.0       0.00      0.00      0.00         1\n",
      "    521500.0       0.00      0.00      0.00         1\n",
      "    522500.0       1.00      1.00      1.00         1\n",
      "    523000.0       0.33      1.00      0.50         1\n",
      "    525000.0       1.00      1.00      1.00         1\n",
      "    525500.0       1.00      1.00      1.00         1\n",
      "    526000.0       0.50      1.00      0.67         1\n",
      "    530000.0       0.50      1.00      0.67         3\n",
      "    535000.0       1.00      1.00      1.00         1\n",
      "    536000.0       1.00      1.00      1.00         1\n",
      "    537000.0       1.00      1.00      1.00         1\n",
      "    537500.0       1.00      1.00      1.00         1\n",
      "    540000.0       1.00      1.00      1.00         3\n",
      "    542000.0       1.00      1.00      1.00         1\n",
      "    545000.0       1.00      1.00      1.00         2\n",
      "    546000.0       1.00      1.00      1.00         1\n",
      "    550000.0       1.00      0.75      0.86         4\n",
      "    558000.0       0.00      0.00      0.00         1\n",
      "    563000.0       1.00      1.00      1.00         1\n",
      "    565000.0       1.00      0.50      0.67         2\n",
      "    570000.0       0.67      1.00      0.80         2\n",
      "    570500.0       1.00      1.00      1.00         1\n",
      "    572000.0       1.00      1.00      1.00         1\n",
      "    575000.0       0.67      1.00      0.80         2\n",
      "    577000.0       1.00      1.00      1.00         1\n",
      "    579000.0       1.00      1.00      1.00         1\n",
      "    580000.0       1.00      1.00      1.00         3\n",
      "    585000.0       1.00      1.00      1.00         4\n",
      "    586000.0       1.00      1.00      1.00         1\n",
      "    588000.0       1.00      1.00      1.00         2\n",
      "    590000.0       0.83      0.83      0.83         6\n",
      "    592500.0       1.00      0.50      0.67         2\n",
      "    598000.0       0.60      1.00      0.75         3\n",
      "    599000.0       1.00      1.00      1.00         1\n",
      "    600000.0       1.00      1.00      1.00         4\n",
      "    601000.0       1.00      1.00      1.00         2\n",
      "    602500.0       1.00      1.00      1.00         1\n",
      "    603000.0       1.00      1.00      1.00         1\n",
      "    605000.0       1.00      1.00      1.00         6\n",
      "    605100.0       1.00      1.00      1.00         1\n",
      "    608000.0       0.00      0.00      0.00         1\n",
      "    610000.0       0.80      1.00      0.89         4\n",
      "    615000.0       1.00      1.00      1.00         1\n",
      "    616000.0       1.00      1.00      1.00         1\n",
      "    620000.0       1.00      1.00      1.00         6\n",
      "    622000.0       1.00      1.00      1.00         1\n",
      "    623000.0       1.00      1.00      1.00         1\n",
      "    625000.0       1.00      1.00      1.00         4\n",
      "    627000.0       1.00      1.00      1.00         1\n",
      "    627500.0       1.00      1.00      1.00         1\n",
      "    630000.0       1.00      1.00      1.00         1\n",
      "    632500.0       1.00      1.00      1.00         1\n",
      "    633000.0       1.00      1.00      1.00         1\n",
      "    634000.0       1.00      1.00      1.00         1\n",
      "    635000.0       1.00      1.00      1.00         4\n",
      "    636000.0       1.00      1.00      1.00         3\n",
      "    638000.0       1.00      1.00      1.00         1\n",
      "    640000.0       1.00      1.00      1.00         6\n",
      "    641000.0       1.00      1.00      1.00         2\n",
      "    645000.0       1.00      0.33      0.50         3\n",
      "    650000.0       1.00      1.00      1.00         8\n",
      "    651000.0       1.00      1.00      1.00         1\n",
      "    653000.0       1.00      1.00      1.00         1\n",
      "    655000.0       1.00      1.00      1.00         1\n",
      "    656000.0       0.67      1.00      0.80         2\n",
      "    657000.0       1.00      1.00      1.00         1\n",
      "    659500.0       1.00      1.00      1.00         1\n",
      "    660000.0       0.57      1.00      0.73         4\n",
      "    662000.0       1.00      1.00      1.00         2\n",
      "    665000.0       1.00      1.00      1.00         1\n",
      "    667000.0       1.00      1.00      1.00         1\n",
      "    670000.0       1.00      1.00      1.00         5\n",
      "    671000.0       1.00      1.00      1.00         1\n",
      "    675000.0       1.00      1.00      1.00         3\n",
      "    678000.0       1.00      1.00      1.00         2\n",
      "    680000.0       1.00      1.00      1.00         7\n",
      "    681000.0       1.00      1.00      1.00         1\n",
      "    683000.0       1.00      1.00      1.00         2\n",
      "    683500.0       1.00      1.00      1.00         1\n",
      "    685000.0       1.00      1.00      1.00         3\n",
      "    686000.0       1.00      1.00      1.00         1\n",
      "    689000.0       1.00      1.00      1.00         1\n",
      "    690000.0       1.00      0.50      0.67         2\n",
      "    690500.0       1.00      1.00      1.00         1\n",
      "    691500.0       1.00      1.00      1.00         1\n",
      "    692000.0       1.00      1.00      1.00         1\n",
      "    693000.0       1.00      1.00      1.00         1\n",
      "    695000.0       1.00      1.00      1.00         1\n",
      "    696000.0       1.00      1.00      1.00         1\n",
      "    700000.0       1.00      1.00      1.00        13\n",
      "    702000.0       1.00      1.00      1.00         2\n",
      "    704000.0       1.00      1.00      1.00         1\n",
      "    705000.0       1.00      1.00      1.00         5\n",
      "    706000.0       1.00      1.00      1.00         1\n",
      "    708000.0       1.00      1.00      1.00         1\n",
      "    710000.0       1.00      1.00      1.00         4\n",
      "    711000.0       1.00      1.00      1.00         1\n",
      "    712000.0       1.00      1.00      1.00         1\n",
      "    715000.0       1.00      1.00      1.00         2\n",
      "    716000.0       1.00      1.00      1.00         1\n",
      "    720000.0       1.00      1.00      1.00         2\n",
      "    722000.0       1.00      1.00      1.00         1\n",
      "    722500.0       1.00      1.00      1.00         1\n",
      "    725000.0       1.00      1.00      1.00         5\n",
      "    729000.0       1.00      1.00      1.00         1\n",
      "    730000.0       1.00      1.00      1.00         7\n",
      "    732000.0       1.00      1.00      1.00         1\n",
      "    735000.0       1.00      1.00      1.00         3\n",
      "    736000.0       1.00      1.00      1.00         1\n",
      "    737000.0       1.00      1.00      1.00         2\n",
      "    740000.0       1.00      1.00      1.00         4\n",
      "    742500.0       1.00      1.00      1.00         1\n",
      "    743000.0       1.00      1.00      1.00         1\n",
      "    745000.0       1.00      1.00      1.00         1\n",
      "    746000.0       1.00      1.00      1.00         1\n",
      "    750000.0       1.00      1.00      1.00        10\n",
      "    751000.0       1.00      1.00      1.00         1\n",
      "    752000.0       1.00      1.00      1.00         3\n",
      "    753000.0       1.00      1.00      1.00         1\n",
      "    754000.0       1.00      1.00      1.00         2\n",
      "    755000.0       1.00      1.00      1.00         1\n",
      "    756000.0       1.00      1.00      1.00         1\n",
      "    760000.0       1.00      0.80      0.89         5\n",
      "    761000.0       1.00      1.00      1.00         3\n",
      "    762000.0       1.00      1.00      1.00         1\n",
      "    763000.0       1.00      1.00      1.00         1\n",
      "    765000.0       1.00      1.00      1.00         4\n",
      "    770000.0       1.00      1.00      1.00         5\n",
      "    772000.0       1.00      1.00      1.00         2\n",
      "    772500.0       1.00      1.00      1.00         1\n",
      "    773000.0       1.00      1.00      1.00         1\n",
      "    775000.0       1.00      1.00      1.00         2\n",
      "    780000.0       1.00      1.00      1.00         5\n",
      "    781000.0       1.00      1.00      1.00         1\n",
      "    785000.0       1.00      1.00      1.00         1\n",
      "    786000.0       1.00      1.00      1.00         1\n",
      "    790000.0       1.00      1.00      1.00         7\n",
      "    791000.0       1.00      1.00      1.00         1\n",
      "    795000.0       1.00      1.00      1.00         2\n",
      "    797500.0       1.00      1.00      1.00         2\n",
      "    800000.0       1.00      1.00      1.00         7\n",
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      "   1590000.0       1.00      1.00      1.00         1\n",
      "   1600000.0       1.00      1.00      1.00        12\n",
      "   1601000.0       1.00      1.00      1.00         1\n",
      "   1602000.0       1.00      1.00      1.00         1\n",
      "   1602500.0       1.00      1.00      1.00         1\n",
      "   1615000.0       1.00      1.00      1.00         1\n",
      "   1620000.0       1.00      1.00      1.00         5\n",
      "   1622000.0       1.00      1.00      1.00         1\n",
      "   1625000.0       1.00      1.00      1.00         2\n",
      "   1630000.0       1.00      1.00      1.00         2\n",
      "   1631000.0       1.00      1.00      1.00         1\n",
      "   1635000.0       1.00      1.00      1.00         1\n",
      "   1640000.0       1.00      1.00      1.00         1\n",
      "   1646000.0       1.00      1.00      1.00         1\n",
      "   1650000.0       1.00      1.00      1.00         7\n",
      "   1655000.0       1.00      1.00      1.00         2\n",
      "   1660000.0       1.00      1.00      1.00         1\n",
      "   1661000.0       1.00      1.00      1.00         1\n",
      "   1662000.0       1.00      1.00      1.00         1\n",
      "   1665000.0       1.00      1.00      1.00         2\n",
      "   1670000.0       1.00      1.00      1.00         3\n",
      "   1675000.0       1.00      1.00      1.00         1\n",
      "   1680000.0       1.00      1.00      1.00         3\n",
      "   1690000.0       1.00      1.00      1.00         3\n",
      "   1691500.0       1.00      1.00      1.00         1\n",
      "   1698888.0       1.00      1.00      1.00         1\n",
      "   1700000.0       1.00      1.00      1.00         6\n",
      "   1701000.0       0.50      1.00      0.67         1\n",
      "   1702000.0       1.00      1.00      1.00         1\n",
      "   1705000.0       1.00      1.00      1.00         1\n",
      "   1706000.0       1.00      1.00      1.00         1\n",
      "   1710000.0       1.00      1.00      1.00         2\n",
      "   1712000.0       1.00      1.00      1.00         1\n",
      "   1715000.0       1.00      1.00      1.00         1\n",
      "   1716000.0       1.00      1.00      1.00         1\n",
      "   1717500.0       1.00      1.00      1.00         1\n",
      "   1720000.0       1.00      1.00      1.00         2\n",
      "   1725000.0       1.00      1.00      1.00         3\n",
      "   1740000.0       1.00      1.00      1.00         2\n",
      "   1742000.0       1.00      1.00      1.00         2\n",
      "   1750000.0       1.00      1.00      1.00         8\n",
      "   1755000.0       1.00      1.00      1.00         3\n",
      "   1760000.0       1.00      1.00      1.00         1\n",
      "   1761000.0       1.00      1.00      1.00         1\n",
      "   1770000.0       1.00      1.00      1.00         1\n",
      "   1774500.0       1.00      1.00      1.00         1\n",
      "   1780000.0       1.00      1.00      1.00         1\n",
      "   1782800.0       1.00      1.00      1.00         1\n",
      "   1785000.0       1.00      1.00      1.00         1\n",
      "   1790000.0       1.00      1.00      1.00         2\n",
      "   1800000.0       1.00      1.00      1.00        12\n",
      "   1801000.0       1.00      1.00      1.00         2\n",
      "   1802100.0       1.00      1.00      1.00         1\n",
      "   1802500.0       1.00      1.00      1.00         1\n",
      "   1810000.0       1.00      1.00      1.00         1\n",
      "   1815000.0       1.00      1.00      1.00         3\n",
      "   1818000.0       1.00      1.00      1.00         1\n",
      "   1825000.0       1.00      1.00      1.00         1\n",
      "   1830000.0       1.00      1.00      1.00         4\n",
      "   1836000.0       1.00      1.00      1.00         1\n",
      "   1845000.0       1.00      1.00      1.00         1\n",
      "   1850000.0       1.00      1.00      1.00         6\n",
      "   1860000.0       1.00      1.00      1.00         4\n",
      "   1865000.0       1.00      1.00      1.00         1\n",
      "   1870000.0       1.00      1.00      1.00         1\n",
      "   1875000.0       1.00      1.00      1.00         3\n",
      "   1876000.0       1.00      1.00      1.00         1\n",
      "   1880000.0       1.00      1.00      1.00         1\n",
      "   1890000.0       1.00      1.00      1.00         1\n",
      "   1900000.0       1.00      1.00      1.00        12\n",
      "   1905000.0       1.00      1.00      1.00         2\n",
      "   1910000.0       1.00      1.00      1.00         3\n",
      "   1912000.0       1.00      1.00      1.00         1\n",
      "   1915000.0       1.00      1.00      1.00         1\n",
      "   1916000.0       1.00      1.00      1.00         1\n",
      "   1918000.0       1.00      1.00      1.00         1\n",
      "   1920000.0       1.00      0.50      0.67         2\n",
      "   1939000.0       1.00      1.00      1.00         1\n",
      "   1940000.0       1.00      1.00      1.00         1\n",
      "   1950000.0       1.00      1.00      1.00         3\n",
      "   1955000.0       1.00      1.00      1.00         1\n",
      "   1960000.0       1.00      1.00      1.00         2\n",
      "   1975000.0       1.00      1.00      1.00         1\n",
      "   2000000.0       1.00      1.00      1.00        14\n",
      "   2005000.0       1.00      1.00      1.00         1\n",
      "   2015000.0       1.00      1.00      1.00         1\n",
      "   2020000.0       1.00      1.00      1.00         1\n",
      "   2030000.0       1.00      1.00      1.00         2\n",
      "   2050000.0       1.00      1.00      1.00         4\n",
      "   2068000.0       1.00      1.00      1.00         1\n",
      "   2070000.0       1.00      1.00      1.00         1\n",
      "   2090000.0       1.00      1.00      1.00         1\n",
      "   2100000.0       1.00      1.00      1.00         6\n",
      "   2105000.0       1.00      1.00      1.00         1\n",
      "   2120000.0       1.00      1.00      1.00         2\n",
      "   2125000.0       1.00      1.00      1.00         2\n",
      "   2130000.0       1.00      1.00      1.00         2\n",
      "   2140000.0       1.00      1.00      1.00         1\n",
      "   2145000.0       1.00      1.00      1.00         1\n",
      "   2150000.0       1.00      1.00      1.00         3\n",
      "   2155000.0       1.00      1.00      1.00         2\n",
      "   2170000.0       1.00      1.00      1.00         2\n",
      "   2175000.0       1.00      1.00      1.00         1\n",
      "   2177000.0       1.00      1.00      1.00         1\n",
      "   2200000.0       0.83      1.00      0.91         5\n",
      "   2205000.0       1.00      1.00      1.00         2\n",
      "   2210000.0       1.00      1.00      1.00         1\n",
      "   2215000.0       1.00      1.00      1.00         1\n",
      "   2220000.0       1.00      1.00      1.00         1\n",
      "   2228000.0       1.00      1.00      1.00         1\n",
      "   2230000.0       1.00      1.00      1.00         1\n",
      "   2250000.0       1.00      1.00      1.00         5\n",
      "   2255000.0       1.00      1.00      1.00         1\n",
      "   2266000.0       1.00      1.00      1.00         1\n",
      "   2275000.0       1.00      1.00      1.00         2\n",
      "   2300000.0       1.00      1.00      1.00         1\n",
      "   2310000.0       1.00      1.00      1.00         1\n",
      "   2325000.0       1.00      1.00      1.00         1\n",
      "   2330000.0       1.00      1.00      1.00         1\n",
      "   2350000.0       1.00      1.00      1.00         1\n",
      "   2360000.0       1.00      1.00      1.00         1\n",
      "   2375000.0       1.00      1.00      1.00         3\n",
      "   2400000.0       1.00      1.00      1.00         4\n",
      "   2410000.0       1.00      1.00      1.00         1\n",
      "   2425000.0       1.00      1.00      1.00         1\n",
      "   2435000.0       1.00      1.00      1.00         1\n",
      "   2450000.0       1.00      1.00      1.00         2\n",
      "   2500000.0       1.00      1.00      1.00         2\n",
      "   2504000.0       1.00      1.00      1.00         1\n",
      "   2517500.0       1.00      1.00      1.00         1\n",
      "   2520000.0       1.00      0.50      0.67         2\n",
      "   2525000.0       1.00      1.00      1.00         1\n",
      "   2550000.0       1.00      1.00      1.00         4\n",
      "   2570000.0       0.67      1.00      0.80         2\n",
      "   2575000.0       1.00      1.00      1.00         1\n",
      "   2600000.0       1.00      1.00      1.00         4\n",
      "   2608000.0       1.00      1.00      1.00         1\n",
      "   2614000.0       1.00      1.00      1.00         1\n",
      "   2615000.0       1.00      1.00      1.00         1\n",
      "   2620000.0       0.67      1.00      0.80         2\n",
      "   2625000.0       1.00      1.00      1.00         1\n",
      "   2640000.0       1.00      1.00      1.00         1\n",
      "   2650000.0       1.00      1.00      1.00         1\n",
      "   2665000.0       1.00      1.00      1.00         1\n",
      "   2700000.0       1.00      1.00      1.00         3\n",
      "   2718000.0       1.00      1.00      1.00         1\n",
      "   2730000.0       1.00      1.00      1.00         2\n",
      "   2740000.0       1.00      1.00      1.00         1\n",
      "   2750000.0       1.00      1.00      1.00         3\n",
      "   2800000.0       0.83      1.00      0.91         5\n",
      "   2801000.0       1.00      1.00      1.00         1\n",
      "   2810000.0       1.00      1.00      1.00         2\n",
      "   2820000.0       1.00      1.00      1.00         1\n",
      "   2830000.0       1.00      1.00      1.00         1\n",
      "   2840000.0       1.00      1.00      1.00         2\n",
      "   2860000.0       1.00      1.00      1.00         1\n",
      "   2900000.0       1.00      1.00      1.00         4\n",
      "   2905000.0       1.00      1.00      1.00         1\n",
      "   2910000.0       1.00      1.00      1.00         1\n",
      "   2930000.0       1.00      1.00      1.00         1\n",
      "   2950000.0       1.00      1.00      1.00         6\n",
      "   3000000.0       1.00      1.00      1.00         2\n",
      "   3020000.0       0.00      0.00      0.00         1\n",
      "   3050000.0       1.00      1.00      1.00         1\n",
      "   3105000.0       1.00      1.00      1.00         1\n",
      "   3180000.0       1.00      1.00      1.00         2\n",
      "   3207000.0       1.00      1.00      1.00         1\n",
      "   3225000.0       1.00      1.00      1.00         2\n",
      "   3230000.0       1.00      1.00      1.00         1\n",
      "   3250000.0       1.00      1.00      1.00         1\n",
      "   3270000.0       1.00      1.00      1.00         1\n",
      "   3290000.0       1.00      1.00      1.00         1\n",
      "   3295888.0       1.00      1.00      1.00         1\n",
      "   3300000.0       1.00      1.00      1.00         2\n",
      "   3320000.0       1.00      1.00      1.00         1\n",
      "   3360000.0       1.00      1.00      1.00         1\n",
      "   3400000.0       1.00      1.00      1.00         3\n",
      "   3401000.0       1.00      1.00      1.00         1\n",
      "   3510000.0       1.00      1.00      1.00         1\n",
      "   3512500.0       1.00      1.00      1.00         1\n",
      "   3550000.0       1.00      1.00      1.00         2\n",
      "   3600000.0       1.00      1.00      1.00         1\n",
      "   3625000.0       1.00      1.00      1.00         1\n",
      "   3660000.0       1.00      1.00      1.00         1\n",
      "   3695000.0       1.00      1.00      1.00         1\n",
      "   3700000.0       1.00      1.00      1.00         2\n",
      "   3755000.0       1.00      1.00      1.00         1\n",
      "   3775000.0       1.00      1.00      1.00         1\n",
      "   3800000.0       1.00      1.00      1.00         1\n",
      "   3900000.0       1.00      1.00      1.00         2\n",
      "   4000000.0       1.00      1.00      1.00         3\n",
      "   4250000.0       1.00      1.00      1.00         1\n",
      "   4350000.0       1.00      1.00      1.00         1\n",
      "   4735000.0       1.00      1.00      1.00         1\n",
      "   4760000.0       1.00      1.00      1.00         1\n",
      "   5525000.0       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           0.97      1600\n",
      "   macro avg       0.96      0.96      0.96      1600\n",
      "weighted avg       0.97      0.97      0.97      1600\n",
      "\n",
      "\n",
      "Accuracy Score for Training Set (Random Forest): 0.970625\n",
      "Accuracy Score for Test Set (Random Forest): 0.970625\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "# Assuming clf4 is your Random Forest pipeline already fitted\n",
    "\n",
    "# Making predictions\n",
    "y_pred_train_rf = clf4.predict(X_train_clean)\n",
    "y_pred_test_rf = clf4.predict(X_test_clean)\n",
    "\n",
    "# Printing classification reports\n",
    "print(\"Classification Report for Training Set (Random Forest):\")\n",
    "print(classification_report(y_train_clean, y_pred_train_rf))\n",
    "\n",
    "print(\"\\nClassification Report for Test Set (Random Forest):\")\n",
    "print(classification_report(y_test_clean, y_pred_test_rf))\n",
    "\n",
    "# Printing accuracy scores\n",
    "train_accuracy_rf = accuracy_score(y_train_clean, y_pred_train_rf)\n",
    "test_accuracy_rf = accuracy_score(y_test_clean, y_pred_test_rf)\n",
    "\n",
    "print(\"\\nAccuracy Score for Training Set (Random Forest):\", train_accuracy_rf)\n",
    "print(\"Accuracy Score for Test Set (Random Forest):\", test_accuracy_rf)\n",
    "\n",
    "# Optional: Printing confusion matrix\n",
    "# print(\"\\nConfusion Matrix for Training Set:\")\n",
    "# print(confusion_matrix(y_train_clean, y_pred_train_rf))\n",
    "\n",
    "# print(\"\\nConfusion Matrix for Test Set:\")\n",
    "# print(confusion_matrix(y_test_clean, y_pred_test_rf))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sama Dengan Decision Tree, random forest classifier tidak cocok untuk tugas Linear Regression karena lebih efisien untuk mendapatkan non-linear relationships. Bisa kelihatan bahwa train dan test set juga overfitting, artinya susah untuk prediksi data baru. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1603,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for Training Set (Gradient Boosting):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    131000.0       0.00      0.00      0.00         1\n",
      "    145000.0       0.00      0.00      0.00         1\n",
      "    240000.0       0.00      0.00      0.00         1\n",
      "    250000.0       0.00      0.00      0.00         1\n",
      "    252000.0       0.00      0.00      0.00         1\n",
      "    255000.0       0.00      0.00      0.00         1\n",
      "    260000.0       0.00      0.00      0.00         1\n",
      "    265000.0       0.00      0.00      0.00         1\n",
      "    270000.0       0.00      0.00      0.00         1\n",
      "    272500.0       0.00      0.00      0.00         1\n",
      "    275500.0       0.00      0.00      0.00         1\n",
      "    279500.0       0.00      0.00      0.00         1\n",
      "    280000.0       0.00      0.00      0.00         2\n",
      "    285000.0       0.00      0.00      0.00         1\n",
      "    288000.0       0.00      0.00      0.00         1\n",
      "    290000.0       0.00      0.00      0.00         3\n",
      "    292000.0       0.00      0.00      0.00         1\n",
      "    300000.0       0.00      0.00      0.00         2\n",
      "    323000.0       0.00      0.00      0.00         1\n",
      "    324000.0       0.00      0.00      0.00         1\n",
      "    330000.0       0.00      0.00      0.00         1\n",
      "    340000.0       0.00      0.00      0.00         3\n",
      "    345000.0       0.00      0.00      0.00         1\n",
      "    350000.0       0.00      0.00      0.00         1\n",
      "    351000.0       0.00      0.00      0.00         1\n",
      "    360000.0       0.00      0.00      0.00         1\n",
      "    365000.0       0.00      0.00      0.00         1\n",
      "    370000.0       0.00      0.00      0.00         6\n",
      "    380000.0       0.00      0.00      0.00         6\n",
      "    385000.0       0.00      0.00      0.00         1\n",
      "    390000.0       0.00      0.00      0.00         5\n",
      "    400000.0       0.00      0.00      0.00         3\n",
      "    400500.0       0.00      0.00      0.00         1\n",
      "    403000.0       0.00      0.00      0.00         1\n",
      "    406000.0       0.00      0.00      0.00         1\n",
      "    410000.0       0.00      0.00      0.00         1\n",
      "    411000.0       0.00      0.00      0.00         1\n",
      "    415000.0       0.00      0.00      0.00         1\n",
      "    416000.0       0.00      0.00      0.00         1\n",
      "    420000.0       1.00      0.33      0.50         3\n",
      "    421500.0       0.00      0.00      0.00         1\n",
      "    426000.0       0.00      0.00      0.00         1\n",
      "    428000.0       0.00      0.00      0.00         1\n",
      "    430000.0       0.00      0.00      0.00         1\n",
      "    432000.0       0.00      0.00      0.00         1\n",
      "    434000.0       0.00      0.00      0.00         1\n",
      "    435000.0       0.00      0.00      0.00         3\n",
      "    437000.0       0.00      0.00      0.00         1\n",
      "    440000.0       0.00      0.00      0.00         5\n",
      "    441000.0       0.00      0.00      0.00         1\n",
      "    442000.0       0.00      0.00      0.00         2\n",
      "    447000.0       0.00      0.00      0.00         1\n",
      "    450000.0       0.00      0.00      0.00         4\n",
      "    451000.0       0.00      0.00      0.00         1\n",
      "    455000.0       0.00      0.00      0.00         2\n",
      "    457000.0       0.00      0.00      0.00         1\n",
      "    460000.0       0.00      0.00      0.00         2\n",
      "    462500.0       0.00      0.00      0.00         1\n",
      "    465000.0       0.00      0.00      0.00         3\n",
      "    466000.0       0.00      0.00      0.00         1\n",
      "    467000.0       0.00      0.00      0.00         1\n",
      "    468000.0       0.00      0.00      0.00         1\n",
      "    470000.0       0.00      0.00      0.00         2\n",
      "    471000.0       0.00      0.00      0.00         1\n",
      "    473000.0       0.00      0.00      0.00         1\n",
      "    474000.0       0.00      0.00      0.00         1\n",
      "    484000.0       0.00      0.00      0.00         1\n",
      "    485000.0       0.00      0.00      0.00         2\n",
      "    486500.0       0.00      0.00      0.00         1\n",
      "    487500.0       0.00      0.00      0.00         1\n",
      "    495000.0       0.00      0.00      0.00         5\n",
      "    497000.0       0.00      0.00      0.00         1\n",
      "    497500.0       0.00      0.00      0.00         1\n",
      "    500000.0       0.00      0.00      0.00         8\n",
      "    501000.0       0.00      0.00      0.00         1\n",
      "    502000.0       0.00      0.00      0.00         2\n",
      "    502500.0       0.00      0.00      0.00         1\n",
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      "   2950000.0       0.00      0.00      0.00         6\n",
      "   3000000.0       0.00      0.00      0.00         2\n",
      "   3020000.0       0.00      0.00      0.00         1\n",
      "   3050000.0       0.00      0.00      0.00         1\n",
      "   3105000.0       0.00      0.00      0.00         1\n",
      "   3180000.0       0.10      0.50      0.17         2\n",
      "   3207000.0       0.00      0.00      0.00         1\n",
      "   3225000.0       0.00      0.00      0.00         2\n",
      "   3230000.0       0.00      0.00      0.00         1\n",
      "   3250000.0       0.00      0.00      0.00         1\n",
      "   3270000.0       0.00      0.00      0.00         1\n",
      "   3290000.0       0.00      0.00      0.00         1\n",
      "   3295888.0       0.25      1.00      0.40         1\n",
      "   3300000.0       0.00      0.00      0.00         2\n",
      "   3320000.0       0.00      0.00      0.00         1\n",
      "   3360000.0       0.00      0.00      0.00         1\n",
      "   3400000.0       0.00      0.00      0.00         3\n",
      "   3401000.0       0.00      0.00      0.00         1\n",
      "   3510000.0       0.00      0.00      0.00         1\n",
      "   3512500.0       0.00      0.00      0.00         1\n",
      "   3550000.0       0.50      0.50      0.50         2\n",
      "   3600000.0       0.00      0.00      0.00         1\n",
      "   3625000.0       0.00      0.00      0.00         1\n",
      "   3660000.0       0.00      0.00      0.00         1\n",
      "   3695000.0       0.00      0.00      0.00         1\n",
      "   3700000.0       0.00      0.00      0.00         2\n",
      "   3755000.0       0.00      0.00      0.00         1\n",
      "   3775000.0       0.25      1.00      0.40         1\n",
      "   3800000.0       0.00      0.00      0.00         1\n",
      "   3900000.0       0.00      0.00      0.00         2\n",
      "   4000000.0       0.00      0.00      0.00         3\n",
      "   4250000.0       0.00      0.00      0.00         1\n",
      "   4350000.0       0.00      0.00      0.00         1\n",
      "   4735000.0       0.00      0.00      0.00         1\n",
      "   4760000.0       0.00      0.00      0.00         1\n",
      "   5525000.0       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.03      1600\n",
      "   macro avg       0.02      0.03      0.02      1600\n",
      "weighted avg       0.03      0.03      0.02      1600\n",
      "\n",
      "\n",
      "Classification Report for Test Set (Gradient Boosting):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    131000.0       0.00      0.00      0.00         1\n",
      "    145000.0       0.00      0.00      0.00         1\n",
      "    240000.0       0.00      0.00      0.00         1\n",
      "    250000.0       0.00      0.00      0.00         1\n",
      "    252000.0       0.00      0.00      0.00         1\n",
      "    255000.0       0.00      0.00      0.00         1\n",
      "    260000.0       0.00      0.00      0.00         1\n",
      "    265000.0       0.00      0.00      0.00         1\n",
      "    270000.0       0.00      0.00      0.00         1\n",
      "    272500.0       0.00      0.00      0.00         1\n",
      "    275500.0       0.00      0.00      0.00         1\n",
      "    279500.0       0.00      0.00      0.00         1\n",
      "    280000.0       0.00      0.00      0.00         2\n",
      "    285000.0       0.00      0.00      0.00         1\n",
      "    288000.0       0.00      0.00      0.00         1\n",
      "    290000.0       0.00      0.00      0.00         3\n",
      "    292000.0       0.00      0.00      0.00         1\n",
      "    300000.0       0.00      0.00      0.00         2\n",
      "    323000.0       0.00      0.00      0.00         1\n",
      "    324000.0       0.00      0.00      0.00         1\n",
      "    330000.0       0.00      0.00      0.00         1\n",
      "    340000.0       0.00      0.00      0.00         3\n",
      "    345000.0       0.00      0.00      0.00         1\n",
      "    350000.0       0.00      0.00      0.00         1\n",
      "    351000.0       0.00      0.00      0.00         1\n",
      "    360000.0       0.00      0.00      0.00         1\n",
      "    365000.0       0.00      0.00      0.00         1\n",
      "    370000.0       0.00      0.00      0.00         6\n",
      "    380000.0       0.00      0.00      0.00         6\n",
      "    385000.0       0.00      0.00      0.00         1\n",
      "    390000.0       0.00      0.00      0.00         5\n",
      "    400000.0       0.00      0.00      0.00         3\n",
      "    400500.0       0.00      0.00      0.00         1\n",
      "    403000.0       0.00      0.00      0.00         1\n",
      "    406000.0       0.00      0.00      0.00         1\n",
      "    410000.0       0.00      0.00      0.00         1\n",
      "    411000.0       0.00      0.00      0.00         1\n",
      "    415000.0       0.00      0.00      0.00         1\n",
      "    416000.0       0.00      0.00      0.00         1\n",
      "    420000.0       1.00      0.33      0.50         3\n",
      "    421500.0       0.00      0.00      0.00         1\n",
      "    426000.0       0.00      0.00      0.00         1\n",
      "    428000.0       0.00      0.00      0.00         1\n",
      "    430000.0       0.00      0.00      0.00         1\n",
      "    432000.0       0.00      0.00      0.00         1\n",
      "    434000.0       0.00      0.00      0.00         1\n",
      "    435000.0       0.00      0.00      0.00         3\n",
      "    437000.0       0.00      0.00      0.00         1\n",
      "    440000.0       0.00      0.00      0.00         5\n",
      "    441000.0       0.00      0.00      0.00         1\n",
      "    442000.0       0.00      0.00      0.00         2\n",
      "    447000.0       0.00      0.00      0.00         1\n",
      "    450000.0       0.00      0.00      0.00         4\n",
      "    451000.0       0.00      0.00      0.00         1\n",
      "    455000.0       0.00      0.00      0.00         2\n",
      "    457000.0       0.00      0.00      0.00         1\n",
      "    460000.0       0.00      0.00      0.00         2\n",
      "    462500.0       0.00      0.00      0.00         1\n",
      "    465000.0       0.00      0.00      0.00         3\n",
      "    466000.0       0.00      0.00      0.00         1\n",
      "    467000.0       0.00      0.00      0.00         1\n",
      "    468000.0       0.00      0.00      0.00         1\n",
      "    470000.0       0.00      0.00      0.00         2\n",
      "    471000.0       0.00      0.00      0.00         1\n",
      "    473000.0       0.00      0.00      0.00         1\n",
      "    474000.0       0.00      0.00      0.00         1\n",
      "    484000.0       0.00      0.00      0.00         1\n",
      "    485000.0       0.00      0.00      0.00         2\n",
      "    486500.0       0.00      0.00      0.00         1\n",
      "    487500.0       0.00      0.00      0.00         1\n",
      "    495000.0       0.00      0.00      0.00         5\n",
      "    497000.0       0.00      0.00      0.00         1\n",
      "    497500.0       0.00      0.00      0.00         1\n",
      "    500000.0       0.00      0.00      0.00         8\n",
      "    501000.0       0.00      0.00      0.00         1\n",
      "    502000.0       0.00      0.00      0.00         2\n",
      "    502500.0       0.00      0.00      0.00         1\n",
      "    503000.0       0.00      0.00      0.00         1\n",
      "    505000.0       0.00      0.00      0.00         1\n",
      "    510000.0       0.00      0.00      0.00         3\n",
      "    512000.0       0.00      0.00      0.00         1\n",
      "    515000.0       0.00      0.00      0.00         1\n",
      "    516000.0       0.00      0.00      0.00         1\n",
      "    517500.0       0.00      0.00      0.00         1\n",
      "    520000.0       0.00      0.00      0.00         3\n",
      "    520500.0       0.00      0.00      0.00         1\n",
      "    521500.0       0.00      0.00      0.00         1\n",
      "    522500.0       0.00      0.00      0.00         1\n",
      "    523000.0       0.00      0.00      0.00         1\n",
      "    525000.0       0.00      0.00      0.00         1\n",
      "    525500.0       0.00      0.00      0.00         1\n",
      "    526000.0       0.00      0.00      0.00         1\n",
      "    530000.0       0.00      0.00      0.00         3\n",
      "    535000.0       0.00      0.00      0.00         1\n",
      "    536000.0       0.00      0.00      0.00         1\n",
      "    537000.0       0.00      0.00      0.00         1\n",
      "    537500.0       0.00      0.00      0.00         1\n",
      "    540000.0       0.01      0.33      0.01         3\n",
      "    542000.0       0.00      0.00      0.00         1\n",
      "    545000.0       0.00      0.00      0.00         2\n",
      "    546000.0       0.00      0.00      0.00         1\n",
      "    550000.0       0.00      0.00      0.00         4\n",
      "    558000.0       0.00      0.00      0.00         1\n",
      "    563000.0       0.00      0.00      0.00         1\n",
      "    565000.0       0.00      0.00      0.00         2\n",
      "    570000.0       0.00      0.00      0.00         2\n",
      "    570500.0       0.00      0.00      0.00         1\n",
      "    572000.0       0.00      0.00      0.00         1\n",
      "    575000.0       0.00      0.00      0.00         2\n",
      "    577000.0       0.00      0.00      0.00         1\n",
      "    579000.0       0.00      0.00      0.00         1\n",
      "    580000.0       0.00      0.00      0.00         3\n",
      "    585000.0       0.00      0.00      0.00         4\n",
      "    586000.0       0.00      0.00      0.00         1\n",
      "    588000.0       0.00      0.00      0.00         2\n",
      "    590000.0       0.00      0.00      0.00         6\n",
      "    592500.0       0.00      0.00      0.00         2\n",
      "    598000.0       0.00      0.00      0.00         3\n",
      "    599000.0       0.00      0.00      0.00         1\n",
      "    600000.0       0.00      0.00      0.00         4\n",
      "    601000.0       0.00      0.00      0.00         2\n",
      "    602500.0       0.00      0.00      0.00         1\n",
      "    603000.0       0.00      0.00      0.00         1\n",
      "    605000.0       0.00      0.00      0.00         6\n",
      "    605100.0       0.00      0.00      0.00         1\n",
      "    608000.0       0.00      0.00      0.00         1\n",
      "    610000.0       0.00      0.00      0.00         4\n",
      "    615000.0       0.00      0.00      0.00         1\n",
      "    616000.0       0.00      0.00      0.00         1\n",
      "    620000.0       0.00      0.00      0.00         6\n",
      "    622000.0       0.00      0.00      0.00         1\n",
      "    623000.0       0.00      0.00      0.00         1\n",
      "    625000.0       0.00      0.00      0.00         4\n",
      "    627000.0       0.00      0.00      0.00         1\n",
      "    627500.0       0.50      1.00      0.67         1\n",
      "    630000.0       0.00      0.00      0.00         1\n",
      "    632500.0       0.00      0.00      0.00         1\n",
      "    633000.0       0.00      0.00      0.00         1\n",
      "    634000.0       0.00      0.00      0.00         1\n",
      "    635000.0       0.00      0.00      0.00         4\n",
      "    636000.0       0.00      0.00      0.00         3\n",
      "    638000.0       0.00      0.00      0.00         1\n",
      "    640000.0       0.00      0.00      0.00         6\n",
      "    641000.0       0.00      0.00      0.00         2\n",
      "    645000.0       0.00      0.00      0.00         3\n",
      "    650000.0       0.00      0.00      0.00         8\n",
      "    651000.0       0.00      0.00      0.00         1\n",
      "    653000.0       0.00      0.00      0.00         1\n",
      "    655000.0       0.00      0.00      0.00         1\n",
      "    656000.0       0.00      0.00      0.00         2\n",
      "    657000.0       0.00      0.00      0.00         1\n",
      "    659500.0       0.00      0.00      0.00         1\n",
      "    660000.0       0.00      0.00      0.00         4\n",
      "    662000.0       0.00      0.00      0.00         2\n",
      "    665000.0       0.00      0.00      0.00         1\n",
      "    667000.0       0.00      0.00      0.00         1\n",
      "    670000.0       0.00      0.00      0.00         5\n",
      "    671000.0       0.00      0.00      0.00         1\n",
      "    675000.0       0.00      0.00      0.00         3\n",
      "    678000.0       0.00      0.00      0.00         2\n",
      "    680000.0       0.00      0.00      0.00         7\n",
      "    681000.0       0.00      0.00      0.00         1\n",
      "    683000.0       0.00      0.00      0.00         2\n",
      "    683500.0       0.00      0.00      0.00         1\n",
      "    685000.0       0.00      0.00      0.00         3\n",
      "    686000.0       0.00      0.00      0.00         1\n",
      "    689000.0       0.00      0.00      0.00         1\n",
      "    690000.0       0.00      0.00      0.00         2\n",
      "    690500.0       0.00      0.00      0.00         1\n",
      "    691500.0       0.00      0.00      0.00         1\n",
      "    692000.0       0.00      0.00      0.00         1\n",
      "    693000.0       0.00      0.00      0.00         1\n",
      "    695000.0       0.00      0.00      0.00         1\n",
      "    696000.0       0.00      0.00      0.00         1\n",
      "    700000.0       0.00      0.00      0.00        13\n",
      "    702000.0       0.00      0.00      0.00         2\n",
      "    704000.0       0.00      0.00      0.00         1\n",
      "    705000.0       0.00      0.00      0.00         5\n",
      "    706000.0       0.00      0.00      0.00         1\n",
      "    708000.0       0.00      0.00      0.00         1\n",
      "    710000.0       0.00      0.00      0.00         4\n",
      "    711000.0       0.00      0.00      0.00         1\n",
      "    712000.0       0.00      0.00      0.00         1\n",
      "    715000.0       0.00      0.00      0.00         2\n",
      "    716000.0       0.00      0.00      0.00         1\n",
      "    720000.0       0.00      0.00      0.00         2\n",
      "    722000.0       0.00      0.00      0.00         1\n",
      "    722500.0       0.00      0.00      0.00         1\n",
      "    725000.0       0.00      0.00      0.00         5\n",
      "    729000.0       0.00      0.00      0.00         1\n",
      "    730000.0       0.00      0.00      0.00         7\n",
      "    732000.0       0.00      0.00      0.00         1\n",
      "    735000.0       0.00      0.00      0.00         3\n",
      "    736000.0       0.00      0.00      0.00         1\n",
      "    737000.0       0.00      0.00      0.00         2\n",
      "    740000.0       0.00      0.00      0.00         4\n",
      "    742500.0       0.00      0.00      0.00         1\n",
      "    743000.0       0.00      0.00      0.00         1\n",
      "    745000.0       0.00      0.00      0.00         1\n",
      "    746000.0       0.00      0.00      0.00         1\n",
      "    750000.0       0.00      0.00      0.00        10\n",
      "    751000.0       0.00      0.00      0.00         1\n",
      "    752000.0       0.00      0.00      0.00         3\n",
      "    753000.0       0.00      0.00      0.00         1\n",
      "    754000.0       0.00      0.00      0.00         2\n",
      "    755000.0       0.00      0.00      0.00         1\n",
      "    756000.0       0.00      0.00      0.00         1\n",
      "    760000.0       0.00      0.00      0.00         5\n",
      "    761000.0       0.00      0.00      0.00         3\n",
      "    762000.0       0.00      0.00      0.00         1\n",
      "    763000.0       0.00      0.00      0.00         1\n",
      "    765000.0       0.00      0.00      0.00         4\n",
      "    770000.0       0.00      0.00      0.00         5\n",
      "    772000.0       0.00      0.00      0.00         2\n",
      "    772500.0       0.00      0.00      0.00         1\n",
      "    773000.0       0.00      0.00      0.00         1\n",
      "    775000.0       0.00      0.00      0.00         2\n",
      "    780000.0       0.00      0.00      0.00         5\n",
      "    781000.0       0.00      0.00      0.00         1\n",
      "    785000.0       0.00      0.00      0.00         1\n",
      "    786000.0       0.00      0.00      0.00         1\n",
      "    790000.0       0.00      0.00      0.00         7\n",
      "    791000.0       0.00      0.00      0.00         1\n",
      "    795000.0       0.00      0.00      0.00         2\n",
      "    797500.0       0.00      0.00      0.00         2\n",
      "    800000.0       0.00      0.00      0.00         7\n",
      "    801000.0       0.00      0.00      0.00         3\n",
      "    804000.0       0.00      0.00      0.00         1\n",
      "    805000.0       0.12      0.25      0.17         8\n",
      "    806000.0       0.00      0.00      0.00         2\n",
      "    807000.0       0.00      0.00      0.00         1\n",
      "    808000.0       0.00      0.00      0.00         1\n",
      "    810000.0       0.00      0.00      0.00         5\n",
      "    812000.0       0.50      0.50      0.50         2\n",
      "    812500.0       0.00      0.00      0.00         1\n",
      "    815000.0       0.00      0.00      0.00         2\n",
      "    816000.0       0.00      0.00      0.00         1\n",
      "    817500.0       0.00      0.00      0.00         1\n",
      "    820000.0       0.14      0.12      0.13         8\n",
      "    823000.0       0.00      0.00      0.00         1\n",
      "    825000.0       0.00      0.00      0.00         8\n",
      "    826000.0       0.00      0.00      0.00         1\n",
      "    830000.0       0.00      0.00      0.00         5\n",
      "    833000.0       0.00      0.00      0.00         1\n",
      "    835000.0       0.00      0.00      0.00         4\n",
      "    836000.0       0.00      0.00      0.00         1\n",
      "    840000.0       0.25      0.33      0.29         3\n",
      "    841000.0       0.00      0.00      0.00         1\n",
      "    845000.0       0.00      0.00      0.00         2\n",
      "    845500.0       0.00      0.00      0.00         1\n",
      "    847500.0       0.00      0.00      0.00         1\n",
      "    850000.0       0.00      0.00      0.00         7\n",
      "    851000.0       0.00      0.00      0.00         2\n",
      "    855000.0       0.50      0.17      0.25         6\n",
      "    857000.0       0.00      0.00      0.00         2\n",
      "    858000.0       0.00      0.00      0.00         1\n",
      "    860000.0       0.00      0.00      0.00        10\n",
      "    865000.0       0.00      0.00      0.00         4\n",
      "    868000.0       0.00      0.00      0.00         1\n",
      "    869000.0       0.00      0.00      0.00         1\n",
      "    870000.0       0.00      0.00      0.00         4\n",
      "    871000.0       0.00      0.00      0.00         1\n",
      "    872000.0       0.00      0.00      0.00         1\n",
      "    875000.0       0.00      0.00      0.00         1\n",
      "    880000.0       0.00      0.00      0.00        11\n",
      "    881000.0       0.00      0.00      0.00         1\n",
      "    882000.0       0.00      0.00      0.00         1\n",
      "    885000.0       0.00      0.00      0.00         1\n",
      "    886000.0       0.00      0.00      0.00         1\n",
      "    890000.0       0.14      0.12      0.13         8\n",
      "    892500.0       0.00      0.00      0.00         2\n",
      "    895000.0       0.00      0.00      0.00         3\n",
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      "   1770000.0       0.00      0.00      0.00         1\n",
      "   1774500.0       0.00      0.00      0.00         1\n",
      "   1780000.0       0.00      0.00      0.00         1\n",
      "   1782800.0       0.00      0.00      0.00         1\n",
      "   1785000.0       0.00      0.00      0.00         1\n",
      "   1790000.0       0.00      0.00      0.00         2\n",
      "   1800000.0       0.00      0.00      0.00        12\n",
      "   1801000.0       0.01      0.50      0.03         2\n",
      "   1802100.0       0.00      0.00      0.00         1\n",
      "   1802500.0       0.00      0.00      0.00         1\n",
      "   1810000.0       0.00      0.00      0.00         1\n",
      "   1815000.0       0.00      0.00      0.00         3\n",
      "   1818000.0       0.00      0.00      0.00         1\n",
      "   1825000.0       0.00      0.00      0.00         1\n",
      "   1830000.0       0.00      0.00      0.00         4\n",
      "   1836000.0       0.00      0.00      0.00         1\n",
      "   1845000.0       0.00      0.00      0.00         1\n",
      "   1850000.0       0.00      0.00      0.00         6\n",
      "   1860000.0       0.00      0.00      0.00         4\n",
      "   1865000.0       0.00      0.00      0.00         1\n",
      "   1870000.0       0.00      0.00      0.00         1\n",
      "   1875000.0       0.00      0.00      0.00         3\n",
      "   1876000.0       0.00      0.00      0.00         1\n",
      "   1880000.0       0.00      0.00      0.00         1\n",
      "   1890000.0       0.00      0.00      0.00         1\n",
      "   1900000.0       0.00      0.00      0.00        12\n",
      "   1905000.0       0.00      0.00      0.00         2\n",
      "   1910000.0       0.00      0.00      0.00         3\n",
      "   1912000.0       0.00      0.00      0.00         1\n",
      "   1915000.0       0.00      0.00      0.00         1\n",
      "   1916000.0       0.00      0.00      0.00         1\n",
      "   1918000.0       0.00      0.00      0.00         1\n",
      "   1920000.0       0.00      0.00      0.00         2\n",
      "   1939000.0       0.00      0.00      0.00         1\n",
      "   1940000.0       0.00      0.00      0.00         1\n",
      "   1950000.0       0.14      0.33      0.20         3\n",
      "   1955000.0       0.00      0.00      0.00         1\n",
      "   1960000.0       0.00      0.00      0.00         2\n",
      "   1975000.0       0.00      0.00      0.00         1\n",
      "   2000000.0       0.00      0.00      0.00        14\n",
      "   2005000.0       0.00      0.00      0.00         1\n",
      "   2015000.0       0.00      0.00      0.00         1\n",
      "   2020000.0       0.00      0.00      0.00         1\n",
      "   2030000.0       0.00      0.00      0.00         2\n",
      "   2050000.0       0.00      0.00      0.00         4\n",
      "   2068000.0       0.00      0.00      0.00         1\n",
      "   2070000.0       0.00      0.00      0.00         1\n",
      "   2090000.0       0.00      0.00      0.00         1\n",
      "   2100000.0       0.00      0.00      0.00         6\n",
      "   2105000.0       0.00      0.00      0.00         1\n",
      "   2120000.0       0.00      0.00      0.00         2\n",
      "   2125000.0       0.00      0.00      0.00         2\n",
      "   2130000.0       0.00      0.00      0.00         2\n",
      "   2140000.0       0.00      0.00      0.00         1\n",
      "   2145000.0       0.00      0.00      0.00         1\n",
      "   2150000.0       0.00      0.00      0.00         3\n",
      "   2155000.0       0.00      0.00      0.00         2\n",
      "   2170000.0       0.00      0.00      0.00         2\n",
      "   2175000.0       0.00      0.00      0.00         1\n",
      "   2177000.0       0.00      0.00      0.00         1\n",
      "   2200000.0       0.11      0.20      0.14         5\n",
      "   2205000.0       0.00      0.00      0.00         2\n",
      "   2210000.0       0.00      0.00      0.00         1\n",
      "   2215000.0       0.00      0.00      0.00         1\n",
      "   2220000.0       0.00      0.00      0.00         1\n",
      "   2228000.0       0.00      0.00      0.00         1\n",
      "   2230000.0       0.00      0.00      0.00         1\n",
      "   2250000.0       0.00      0.00      0.00         5\n",
      "   2255000.0       0.00      0.00      0.00         1\n",
      "   2266000.0       0.00      0.00      0.00         1\n",
      "   2275000.0       0.00      0.00      0.00         2\n",
      "   2300000.0       0.00      0.00      0.00         1\n",
      "   2310000.0       0.00      0.00      0.00         1\n",
      "   2325000.0       0.00      0.00      0.00         1\n",
      "   2330000.0       0.00      0.00      0.00         1\n",
      "   2350000.0       0.00      0.00      0.00         1\n",
      "   2360000.0       1.00      1.00      1.00         1\n",
      "   2375000.0       0.00      0.00      0.00         3\n",
      "   2400000.0       0.12      0.25      0.17         4\n",
      "   2410000.0       0.00      0.00      0.00         1\n",
      "   2425000.0       0.00      0.00      0.00         1\n",
      "   2435000.0       0.00      0.00      0.00         1\n",
      "   2450000.0       0.00      0.00      0.00         2\n",
      "   2500000.0       0.00      0.00      0.00         2\n",
      "   2504000.0       0.00      0.00      0.00         1\n",
      "   2517500.0       0.00      0.00      0.00         1\n",
      "   2520000.0       0.00      0.00      0.00         2\n",
      "   2525000.0       0.00      0.00      0.00         1\n",
      "   2550000.0       0.00      0.00      0.00         4\n",
      "   2570000.0       0.00      0.00      0.00         2\n",
      "   2575000.0       0.00      0.00      0.00         1\n",
      "   2600000.0       0.00      0.00      0.00         4\n",
      "   2608000.0       0.00      0.00      0.00         1\n",
      "   2614000.0       0.00      0.00      0.00         1\n",
      "   2615000.0       0.00      0.00      0.00         1\n",
      "   2620000.0       0.00      0.00      0.00         2\n",
      "   2625000.0       0.00      0.00      0.00         1\n",
      "   2640000.0       0.00      0.00      0.00         1\n",
      "   2650000.0       0.00      0.00      0.00         1\n",
      "   2665000.0       0.00      0.00      0.00         1\n",
      "   2700000.0       0.02      0.33      0.03         3\n",
      "   2718000.0       0.00      0.00      0.00         1\n",
      "   2730000.0       0.00      0.00      0.00         2\n",
      "   2740000.0       0.00      0.00      0.00         1\n",
      "   2750000.0       0.00      0.00      0.00         3\n",
      "   2800000.0       0.00      0.00      0.00         5\n",
      "   2801000.0       0.00      0.00      0.00         1\n",
      "   2810000.0       0.00      0.00      0.00         2\n",
      "   2820000.0       0.00      0.00      0.00         1\n",
      "   2830000.0       0.00      0.00      0.00         1\n",
      "   2840000.0       0.00      0.00      0.00         2\n",
      "   2860000.0       0.00      0.00      0.00         1\n",
      "   2900000.0       0.11      0.25      0.15         4\n",
      "   2905000.0       0.00      0.00      0.00         1\n",
      "   2910000.0       0.00      0.00      0.00         1\n",
      "   2930000.0       0.00      0.00      0.00         1\n",
      "   2950000.0       0.00      0.00      0.00         6\n",
      "   3000000.0       0.00      0.00      0.00         2\n",
      "   3020000.0       0.00      0.00      0.00         1\n",
      "   3050000.0       0.00      0.00      0.00         1\n",
      "   3105000.0       0.00      0.00      0.00         1\n",
      "   3180000.0       0.10      0.50      0.17         2\n",
      "   3207000.0       0.00      0.00      0.00         1\n",
      "   3225000.0       0.00      0.00      0.00         2\n",
      "   3230000.0       0.00      0.00      0.00         1\n",
      "   3250000.0       0.00      0.00      0.00         1\n",
      "   3270000.0       0.00      0.00      0.00         1\n",
      "   3290000.0       0.00      0.00      0.00         1\n",
      "   3295888.0       0.25      1.00      0.40         1\n",
      "   3300000.0       0.00      0.00      0.00         2\n",
      "   3320000.0       0.00      0.00      0.00         1\n",
      "   3360000.0       0.00      0.00      0.00         1\n",
      "   3400000.0       0.00      0.00      0.00         3\n",
      "   3401000.0       0.00      0.00      0.00         1\n",
      "   3510000.0       0.00      0.00      0.00         1\n",
      "   3512500.0       0.00      0.00      0.00         1\n",
      "   3550000.0       0.50      0.50      0.50         2\n",
      "   3600000.0       0.00      0.00      0.00         1\n",
      "   3625000.0       0.00      0.00      0.00         1\n",
      "   3660000.0       0.00      0.00      0.00         1\n",
      "   3695000.0       0.00      0.00      0.00         1\n",
      "   3700000.0       0.00      0.00      0.00         2\n",
      "   3755000.0       0.00      0.00      0.00         1\n",
      "   3775000.0       0.25      1.00      0.40         1\n",
      "   3800000.0       0.00      0.00      0.00         1\n",
      "   3900000.0       0.00      0.00      0.00         2\n",
      "   4000000.0       0.00      0.00      0.00         3\n",
      "   4250000.0       0.00      0.00      0.00         1\n",
      "   4350000.0       0.00      0.00      0.00         1\n",
      "   4735000.0       0.00      0.00      0.00         1\n",
      "   4760000.0       0.00      0.00      0.00         1\n",
      "   5525000.0       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.03      1600\n",
      "   macro avg       0.02      0.03      0.02      1600\n",
      "weighted avg       0.03      0.03      0.02      1600\n",
      "\n",
      "\n",
      "Accuracy Score for Training Set (Gradient Boosting): 0.031875\n",
      "Accuracy Score for Test Set (Gradient Boosting): 0.031875\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "#Boosting\n",
    "\n",
    "# Assuming clf5 is your Gradient Boosting pipeline already fitted\n",
    "\n",
    "# Making predictions\n",
    "y_pred_train_gb = clf5.predict(X_train_clean)\n",
    "y_pred_test_gb = clf5.predict(X_test_clean)\n",
    "\n",
    "# Printing classification reports\n",
    "print(\"Classification Report for Training Set (Gradient Boosting):\")\n",
    "print(classification_report(y_train_clean, y_pred_train_gb))\n",
    "\n",
    "print(\"\\nClassification Report for Test Set (Gradient Boosting):\")\n",
    "print(classification_report(y_test_clean, y_pred_test_gb))\n",
    "\n",
    "# Printing accuracy scores\n",
    "train_accuracy_gb = accuracy_score(y_train_clean, y_pred_train_gb)\n",
    "test_accuracy_gb = accuracy_score(y_test_clean, y_pred_test_gb)\n",
    "\n",
    "print(\"\\nAccuracy Score for Training Set (Gradient Boosting):\", train_accuracy_gb)\n",
    "print(\"Accuracy Score for Test Set (Gradient Boosting):\", test_accuracy_gb)\n",
    "\n",
    "# Optional: Printing confusion matrix\n",
    "# print(\"\\nConfusion Matrix for Training Set:\")\n",
    "# print(confusion_matrix(y_train_clean, y_pred_train_gb))\n",
    "\n",
    "# print(\"\\nConfusion Matrix for Test Set:\")\n",
    "# print(confusion_matrix(y_test_clean, y_pred_test_gb))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gradient boosting digunakan untuk bermacam regresi yang biasanya kompleks dan punya data non-linear. Digunakan untuk sequential decision trees yang memperbbaikan model sebelumnya. Classifier tidak cocok untuk test tersebut karena underfitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1604,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.031875\n"
     ]
    }
   ],
   "source": [
    "r2_scorez = clf5.score(X_test, y_test)\n",
    "print(r2_scorez)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1605,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE): 640708.6272646561\n"
     ]
    }
   ],
   "source": [
    "#Boosting\n",
    "\n",
    "y_pred1 = clf5.predict(X_train_clean)\n",
    "mse = mean_squared_error(y_train_clean, y_pred1)\n",
    "rmse = mse ** 0.5\n",
    "\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sekarang coba prediksi X_train terhadap RMSE untuk melihat data yang bisa akan lebih akurat dengan sequential learning. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1606,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = clf1.predict(X_train_clean)\n",
    "y_test_pred = clf1.predict(X_test_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sekarang mau coba prediksi linear model dengan error dan r2 scores. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1608,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Evaluation Metrics:\n",
      "\n",
      "Mean Absolute Error (MAE)\n",
      "   - Train Set: 333710.9326041857\n",
      "   - Test Set: 333710.9326041857\n",
      "\n",
      "\n",
      "Mean Squared Error (MSE)\n",
      "   - Train Set: 226574060193.32565\n",
      "   - Test Set: 226574060193.32565\n",
      "\n",
      "\n",
      "Root Mean Squared Error (RMSE)\n",
      "   - Train Set: 475997.9623835859\n",
      "   - Test Set: 475997.9623835859\n",
      "\n",
      "\n",
      "R^2 Score\n",
      "   - Train Set: 0.5355749742504881\n",
      "   - Test Set: 0.5355749742504881\n"
     ]
    }
   ],
   "source": [
    "# Evaluate using metrics MAE, MSE, and Rsquared\n",
    "mae_train = mean_absolute_error(y_train, y_train_pred)\n",
    "mae_test = mean_absolute_error(y_test, y_test_pred)\n",
    "\n",
    "mse_train = mean_squared_error(y_train, y_train_pred)\n",
    "mse_test = mean_squared_error(y_test, y_test_pred)\n",
    "\n",
    "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n",
    "rmse_test = mean_squared_error(y_test, y_test_pred, squared=False)\n",
    "\n",
    "r2_train = r2_score(y_train, y_train_pred)\n",
    "r2_test = r2_score(y_test, y_test_pred)\n",
    "\n",
    "# Print the evaluation report\n",
    "print(\"Model Evaluation Metrics:\\n\")\n",
    "print(f\"Mean Absolute Error (MAE)\\n   - Train Set: {mae_train}\\n   - Test Set: {mae_test}\\n\")\n",
    "print()\n",
    "print(f\"Mean Squared Error (MSE)\\n   - Train Set: {mse_train}\\n   - Test Set: {mse_test}\\n\")\n",
    "print()\n",
    "print(f\"Root Mean Squared Error (RMSE)\\n   - Train Set: {rmse_train}\\n   - Test Set: {rmse_test}\\n\")\n",
    "print()\n",
    "print(f\"R^2 Score\\n   - Train Set: {r2_train}\\n   - Test Set: {r2_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Terlihat diatas ada beberapa tipe error seperti MAE, MSE, dan RMSE. Ketiga error harus serendah-rendah mungkin untuk model lebih performa tinggi. \n",
    "\n",
    "R2 atau Coefficient of determination adalah pengujian regression model terhadap data. Lebih dari 53% data di model adalah \"good fit\", artinya model bisa jelaskan lebih dari setengah variansi di dataset tersebut.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation and Parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1610,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Model (clf1)\n",
      "Mean Absolute Error (MAE) - All - Cross Validation  :  [355890.69093339 317326.30879805 317049.33766558 355154.06355726\n",
      " 337193.86047283]\n",
      "MAE - Mean - Cross Validation :  336522.8522854203\n",
      "MAE - Std - Cross Validation  :  17148.822191910975\n",
      "--------------------------------------------------\n",
      "Mean Squared Error (MSE) - All - Cross Validation  :  [2.91025085e+11 1.93836330e+11 1.86931673e+11 2.65624965e+11\n",
      " 2.15653687e+11]\n",
      "MSE - Mean - Cross Validation :  230614348037.1316\n",
      "MSE - Std - Cross Validation  :  40889657686.052345\n",
      "--------------------------------------------------\n",
      "Root Mean Squared Error (RMSE) - All - Cross Validation  :  [539467.40865652 440268.47528432 432355.95647107 515388.16949119\n",
      " 464385.27808966]\n",
      "RMSE - Mean - Cross Validation :  478373.05759855063\n",
      "RMSE - Std - Cross Validation  :  42113.72461496857\n",
      "--------------------------------------------------\n",
      "R^2 Score - All - Cross Validation  :  [0.48423008 0.54473855 0.55368422 0.53728281 0.52447312]\n",
      "R^2 Score - Mean - Cross Validation :  0.528881754372353\n",
      "R^2 Score - Std - Cross Validation  :  0.024291504969467994\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# Example cross-validation for Linear Regression (clf1)\n",
    "cv_scores_mae = cross_val_score(clf1, X_train, y_train, cv=5, scoring=\"neg_mean_absolute_error\")\n",
    "cv_scores_mse = cross_val_score(clf1, X_train, y_train, cv=5, scoring=\"neg_mean_squared_error\")\n",
    "cv_scores_rmse = cross_val_score(clf1, X_train, y_train, cv=5, scoring=\"neg_root_mean_squared_error\")\n",
    "cv_scores_r2 = cross_val_score(clf1, X_train, y_train, cv=5, scoring=\"r2\")\n",
    "\n",
    "# Convert negative scores to positive for MAE, MSE, RMSE\n",
    "cv_scores_mae = -cv_scores_mae\n",
    "cv_scores_mse = -cv_scores_mse\n",
    "cv_scores_rmse = -cv_scores_rmse\n",
    "\n",
    "# Print cross-validation results\n",
    "print(\"Linear Regression Model (clf1)\")\n",
    "print('Mean Absolute Error (MAE) - All - Cross Validation  : ', cv_scores_mae)\n",
    "print('MAE - Mean - Cross Validation : ', cv_scores_mae.mean())\n",
    "print('MAE - Std - Cross Validation  : ', cv_scores_mae.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('Mean Squared Error (MSE) - All - Cross Validation  : ', cv_scores_mse)\n",
    "print('MSE - Mean - Cross Validation : ', cv_scores_mse.mean())\n",
    "print('MSE - Std - Cross Validation  : ', cv_scores_mse.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('Root Mean Squared Error (RMSE) - All - Cross Validation  : ', cv_scores_rmse)\n",
    "print('RMSE - Mean - Cross Validation : ', cv_scores_rmse.mean())\n",
    "print('RMSE - Std - Cross Validation  : ', cv_scores_rmse.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('R^2 Score - All - Cross Validation  : ', cv_scores_r2)\n",
    "print('R^2 Score - Mean - Cross Validation : ', cv_scores_r2.mean())\n",
    "print('R^2 Score - Std - Cross Validation  : ', cv_scores_r2.std())\n",
    "print('-'*50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sekarang akan gunakan cross variance untuk menguji beberapa \"fit\" model yang digunakan bisa dipakai lagi untuk independent dataset. Biar bisa evaluasi performa model, data akan dibagi jadi subsets, dan akan di-train. \n",
    "\n",
    "Terlihat jawaban dari R2 score tidak terlalu beda dari yang diatas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1611,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine Model (clf2)\n",
      "Accuracy - All - Cross Validation  :  [0.0125   0.01875  0.0125   0.00625  0.009375]\n",
      "Accuracy - Mean - Cross Validation :  0.011875\n",
      "Accuracy - Std - Cross Validation  :  0.00414578098794425\n",
      "--------------------------------------------------\n",
      "Precision - All - Cross Validation  :  [nan nan nan nan nan]\n",
      "Precision - Mean - Cross Validation :  nan\n",
      "Precision - Std - Cross Validation  :  nan\n",
      "--------------------------------------------------\n",
      "Recall - All - Cross Validation  :  [nan nan nan nan nan]\n",
      "Recall - Mean - Cross Validation :  nan\n",
      "Recall - Std - Cross Validation  :  nan\n",
      "--------------------------------------------------\n",
      "F1 Score - All - Cross Validation  :  [nan nan nan nan nan]\n",
      "F1 Score - Mean - Cross Validation :  nan\n",
      "F1 Score - Std - Cross Validation  :  nan\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "# Example cross-validation for SVM (clf2)\n",
    "cv_scores_accuracy = cross_val_score(clf2, X_train, y_train, cv=5, scoring=\"accuracy\")\n",
    "cv_scores_precision = cross_val_score(clf2, X_train, y_train, cv=5, scoring=\"precision\")\n",
    "cv_scores_recall = cross_val_score(clf2, X_train, y_train, cv=5, scoring=\"recall\")\n",
    "cv_scores_f1 = cross_val_score(clf2, X_train, y_train, cv=5, scoring=\"f1\")\n",
    "\n",
    "# Print cross-validation results\n",
    "print(\"Support Vector Machine Model (clf2)\")\n",
    "print('Accuracy - All - Cross Validation  : ', cv_scores_accuracy)\n",
    "print('Accuracy - Mean - Cross Validation : ', cv_scores_accuracy.mean())\n",
    "print('Accuracy - Std - Cross Validation  : ', cv_scores_accuracy.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('Precision - All - Cross Validation  : ', cv_scores_precision)\n",
    "print('Precision - Mean - Cross Validation : ', cv_scores_precision.mean())\n",
    "print('Precision - Std - Cross Validation  : ', cv_scores_precision.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('Recall - All - Cross Validation  : ', cv_scores_recall)\n",
    "print('Recall - Mean - Cross Validation : ', cv_scores_recall.mean())\n",
    "print('Recall - Std - Cross Validation  : ', cv_scores_recall.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', cv_scores_f1)\n",
    "print('F1 Score - Mean - Cross Validation : ', cv_scores_f1.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', cv_scores_f1.std())\n",
    "print('-'*50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disini kita akan uji SVM (Linear). Akurasi rendah karena test tidak cocok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1612,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Model (clf3)\n",
      "Accuracy - All - Cross Validation  :  [0.       0.       0.0125   0.015625 0.0125  ]\n",
      "Accuracy - Mean - Cross Validation :  0.008125\n",
      "Accuracy - Std - Cross Validation  :  0.006731456008918131\n",
      "--------------------------------------------------\n",
      "Precision - All - Cross Validation  :  [nan nan nan nan nan]\n",
      "Precision - Mean - Cross Validation :  nan\n",
      "Precision - Std - Cross Validation  :  nan\n",
      "--------------------------------------------------\n",
      "Recall - All - Cross Validation  :  [nan nan nan nan nan]\n",
      "Recall - Mean - Cross Validation :  nan\n",
      "Recall - Std - Cross Validation  :  nan\n",
      "--------------------------------------------------\n",
      "F1 Score - All - Cross Validation  :  [nan nan nan nan nan]\n",
      "F1 Score - Mean - Cross Validation :  nan\n",
      "F1 Score - Std - Cross Validation  :  nan\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "# Example cross-validation for Decision Tree (clf3)\n",
    "cv_scores_accuracy = cross_val_score(clf3, X_train, y_train, cv=5, scoring=\"accuracy\")\n",
    "cv_scores_precision = cross_val_score(clf3, X_train, y_train, cv=5, scoring=\"precision\")\n",
    "cv_scores_recall = cross_val_score(clf3, X_train, y_train, cv=5, scoring=\"recall\")\n",
    "cv_scores_f1 = cross_val_score(clf3, X_train, y_train, cv=5, scoring=\"f1\")\n",
    "\n",
    "# Print cross-validation results\n",
    "print(\"Decision Tree Model (clf3)\")\n",
    "print('Accuracy - All - Cross Validation  : ', cv_scores_accuracy)\n",
    "print('Accuracy - Mean - Cross Validation : ', cv_scores_accuracy.mean())\n",
    "print('Accuracy - Std - Cross Validation  : ', cv_scores_accuracy.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('Precision - All - Cross Validation  : ', cv_scores_precision)\n",
    "print('Precision - Mean - Cross Validation : ', cv_scores_precision.mean())\n",
    "print('Precision - Std - Cross Validation  : ', cv_scores_precision.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('Recall - All - Cross Validation  : ', cv_scores_recall)\n",
    "print('Recall - Mean - Cross Validation : ', cv_scores_recall.mean())\n",
    "print('Recall - Std - Cross Validation  : ', cv_scores_recall.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', cv_scores_f1)\n",
    "print('F1 Score - Mean - Cross Validation : ', cv_scores_f1.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', cv_scores_f1.std())\n",
    "print('-'*50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disini kita akan uji Decision Tree. Akurasi rendah karena test tidak cocok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1613,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Model (clf4)\n",
      "Accuracy - All - Cross Validation  :  [0.003125 0.0125   0.0125   0.0125   0.0125  ]\n",
      "Accuracy - Mean - Cross Validation :  0.010625\n",
      "Accuracy - Std - Cross Validation  :  0.0037500000000000007\n",
      "--------------------------------------------------\n",
      "Precision - All - Cross Validation  :  [nan nan nan nan nan]\n",
      "Precision - Mean - Cross Validation :  nan\n",
      "Precision - Std - Cross Validation  :  nan\n",
      "--------------------------------------------------\n",
      "Recall - All - Cross Validation  :  [nan nan nan nan nan]\n",
      "Recall - Mean - Cross Validation :  nan\n",
      "Recall - Std - Cross Validation  :  nan\n",
      "--------------------------------------------------\n",
      "F1 Score - All - Cross Validation  :  [nan nan nan nan nan]\n",
      "F1 Score - Mean - Cross Validation :  nan\n",
      "F1 Score - Std - Cross Validation  :  nan\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "# Example cross-validation for Random Forest (clf4)\n",
    "cv_scores_accuracy = cross_val_score(clf4, X_train, y_train, cv=5, scoring=\"accuracy\")\n",
    "cv_scores_precision = cross_val_score(clf4, X_train, y_train, cv=5, scoring=\"precision\")\n",
    "cv_scores_recall = cross_val_score(clf4, X_train, y_train, cv=5, scoring=\"recall\")\n",
    "cv_scores_f1 = cross_val_score(clf4, X_train, y_train, cv=5, scoring=\"f1\")\n",
    "\n",
    "# Print cross-validation results\n",
    "print(\"Random Forest Model (clf4)\")\n",
    "print('Accuracy - All - Cross Validation  : ', cv_scores_accuracy)\n",
    "print('Accuracy - Mean - Cross Validation : ', cv_scores_accuracy.mean())\n",
    "print('Accuracy - Std - Cross Validation  : ', cv_scores_accuracy.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('Precision - All - Cross Validation  : ', cv_scores_precision)\n",
    "print('Precision - Mean - Cross Validation : ', cv_scores_precision.mean())\n",
    "print('Precision - Std - Cross Validation  : ', cv_scores_precision.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('Recall - All - Cross Validation  : ', cv_scores_recall)\n",
    "print('Recall - Mean - Cross Validation : ', cv_scores_recall.mean())\n",
    "print('Recall - Std - Cross Validation  : ', cv_scores_recall.std())\n",
    "print('-'*50)\n",
    "\n",
    "print('F1 Score - All - Cross Validation  : ', cv_scores_f1)\n",
    "print('F1 Score - Mean - Cross Validation : ', cv_scores_f1.mean())\n",
    "print('F1 Score - Std - Cross Validation  : ', cv_scores_f1.std())\n",
    "print('-'*50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disini kita akan uji Random Forest. Akurasi rendah karena test tidak cocok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1654,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #INI PROSES SANGAT LAMA\n",
    "\n",
    "# from sklearn.model_selection import cross_val_score\n",
    "# from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "# # Example cross-validation for Gradient Boosting (clf5)\n",
    "# cv_scores_r2 = cross_val_score(clf5, X_train, y_train, cv=5, scoring=\"accuracy\")\n",
    "# #cv_scores_precision = cross_val_score(clf5, X_train, y_train, cv=5, scoring=\"precision\")\n",
    "# #cv_scores_recall = cross_val_score(clf5, X_train, y_train, cv=5, scoring=\"recall\")\n",
    "# #cv_scores_f1 = cross_val_score(clf5, X_train, y_train, cv=5, scoring=\"f1\")\n",
    "\n",
    "# # Print cross-validation results\n",
    "# print(\"Gradient Boosting Model (clf5)\")\n",
    "# print('Accuracy - All - Cross Validation  : ', cv_scores_r2)\n",
    "# # print('Accuracy - Mean - Cross Validation : ', cv_scores_r2.mean())\n",
    "# # print('Accuracy - Std - Cross Validation  : ', cv_scores_r2.std())\n",
    "# # print('-'*50)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disini kita akan uji Gradient Boosting. Akurasi rendah karena test tidak cocok."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1622,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Define the Pipeline\n",
    "\n",
    "# pipe = Pipeline([('scaler', MinMaxScaler()), ('logreg', LinearRegression())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1623,
   "metadata": {},
   "outputs": [],
   "source": [
    "def performance_check(clf, X, y):\n",
    "    y_pred = clf.predict(X)\n",
    "    mae = mean_absolute_error(y, y_pred)\n",
    "    mse = mean_squared_error(y, y_pred)\n",
    "    rmse = mean_squared_error(y, y_pred, squared=False)  # RMSE is the square root of MSE\n",
    "    r2 = r2_score(y, y_pred)\n",
    "\n",
    "    return {\n",
    "        'MAE': mae,\n",
    "        'MSE': mse,\n",
    "        'RMSE': rmse,\n",
    "        'R²': r2\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sini kita buat performance check untuk errors and R2 score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1624,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
      "Best Parameters: {'regressor__fit_intercept': True, 'preprocessor__num__imputer__strategy': 'mean'}\n",
      "Best Negative Mean Squared Error: -230614348037.1316\n",
      "Root Mean Squared Error (RMSE) on Test Set: 475997.9623835859\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Define the pipeline including preprocessing and model for Linear Regression\n",
    "clf1 = Pipeline([\n",
    "    (\"preprocessor\", preprocessor),\n",
    "    (\"regressor\", LinearRegression())\n",
    "])\n",
    "\n",
    "# Define parameter distributions for RandomizedSearchCV\n",
    "param_dist = {\n",
    "    'regressor__fit_intercept': [True, False],\n",
    "    'preprocessor__num__imputer__strategy': ['mean', 'median'],\n",
    "    # Add more parameters and their distributions as needed\n",
    "}\n",
    "\n",
    "# Initialize RandomizedSearchCV\n",
    "random_search = RandomizedSearchCV(clf1, param_distributions=param_dist, n_iter=10, cv=5, scoring='neg_mean_squared_error', verbose=1, random_state=42)\n",
    "\n",
    "# Fit the randomized search\n",
    "random_search.fit(X_train_clean, y_train)\n",
    "\n",
    "# Print best parameters and best score\n",
    "print(\"Best Parameters:\", random_search.best_params_)\n",
    "print(\"Best Negative Mean Squared Error:\", random_search.best_score_)\n",
    "\n",
    "# Get the best estimator\n",
    "best_clf1 = random_search.best_estimator_\n",
    "\n",
    "# Optionally, evaluate performance on a test set if available\n",
    "y_pred = best_clf1.predict(X_test_clean)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "print(f\"Root Mean Squared Error (RMSE) on Test Set: {rmse}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita telah set hyperparameter untuk Random Search terhadap model kita. Kita akan gunakan CLF1 \"linear regression\" untuk optimisasi model tersebut jadi tidak overfitting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1625,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-39 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-39 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-39 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-39 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-39 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-39 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-39 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-39 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-39 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-39 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-39 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-39 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-39 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-39 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-39 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-39 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-39 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-39 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-39 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-39\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                                                                Winsorizer(fold=3,\n",
       "                                                                                                           missing_values=&#x27;ignore&#x27;,\n",
       "                                                                                                           tail=&#x27;both&#x27;,\n",
       "                                                                                                           variables=[&#x27;Distance&#x27;,\n",
       "                                                                                                                      &#x27;YearBuilt&#x27;,\n",
       "                                                                                                                      &#x27;Propertycount&#x27;])),\n",
       "                                                                                               (&#x27;outlier_iqr&#x27;,\n",
       "                                                                                                Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                                                           fold=1.5,\n",
       "                                                                                                           tail=&#x27;both&#x27;,\n",
       "                                                                                                           variables=[&#x27;Rooms&#x27;,\n",
       "                                                                                                                      &#x27;Bathroom&#x27;,\n",
       "                                                                                                                      &#x27;Car...\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               [&#x27;Rooms&#x27;,\n",
       "                                                                                &#x27;Distance&#x27;,\n",
       "                                                                                &#x27;Bathroom&#x27;,\n",
       "                                                                                &#x27;Car&#x27;,\n",
       "                                                                                &#x27;Landsize&#x27;,\n",
       "                                                                                &#x27;BuildingArea&#x27;,\n",
       "                                                                                &#x27;YearBuilt&#x27;,\n",
       "                                                                                &#x27;Propertycount&#x27;]),\n",
       "                                                                              (&#x27;cat&#x27;,\n",
       "                                                                               &#x27;passthrough&#x27;,\n",
       "                                                                               [])])),\n",
       "                                             (&#x27;regressor&#x27;,\n",
       "                                              LinearRegression())]),\n",
       "                   param_distributions={&#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
       "                                                                                 &#x27;median&#x27;],\n",
       "                                        &#x27;regressor__fit_intercept&#x27;: [True,\n",
       "                                                                     False]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-284\" type=\"checkbox\" ><label for=\"sk-estimator-id-284\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                                                                Winsorizer(fold=3,\n",
       "                                                                                                           missing_values=&#x27;ignore&#x27;,\n",
       "                                                                                                           tail=&#x27;both&#x27;,\n",
       "                                                                                                           variables=[&#x27;Distance&#x27;,\n",
       "                                                                                                                      &#x27;YearBuilt&#x27;,\n",
       "                                                                                                                      &#x27;Propertycount&#x27;])),\n",
       "                                                                                               (&#x27;outlier_iqr&#x27;,\n",
       "                                                                                                Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                                                           fold=1.5,\n",
       "                                                                                                           tail=&#x27;both&#x27;,\n",
       "                                                                                                           variables=[&#x27;Rooms&#x27;,\n",
       "                                                                                                                      &#x27;Bathroom&#x27;,\n",
       "                                                                                                                      &#x27;Car...\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               [&#x27;Rooms&#x27;,\n",
       "                                                                                &#x27;Distance&#x27;,\n",
       "                                                                                &#x27;Bathroom&#x27;,\n",
       "                                                                                &#x27;Car&#x27;,\n",
       "                                                                                &#x27;Landsize&#x27;,\n",
       "                                                                                &#x27;BuildingArea&#x27;,\n",
       "                                                                                &#x27;YearBuilt&#x27;,\n",
       "                                                                                &#x27;Propertycount&#x27;]),\n",
       "                                                                              (&#x27;cat&#x27;,\n",
       "                                                                               &#x27;passthrough&#x27;,\n",
       "                                                                               [])])),\n",
       "                                             (&#x27;regressor&#x27;,\n",
       "                                              LinearRegression())]),\n",
       "                   param_distributions={&#x27;preprocessor__num__imputer__strategy&#x27;: [&#x27;mean&#x27;,\n",
       "                                                                                 &#x27;median&#x27;],\n",
       "                                        &#x27;regressor__fit_intercept&#x27;: [True,\n",
       "                                                                     False]},\n",
       "                   random_state=42, scoring=&#x27;neg_mean_squared_error&#x27;,\n",
       "                   verbose=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-285\" type=\"checkbox\" ><label for=\"sk-estimator-id-285\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                                   Winsorizer(fold=3,\n",
       "                                                                              missing_values=&#x27;ignore&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Distance&#x27;,\n",
       "                                                                                         &#x27;YearBuilt&#x27;,\n",
       "                                                                                         &#x27;Propertycount&#x27;])),\n",
       "                                                                  (&#x27;outlier_iqr&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              fold=1.5,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Rooms&#x27;,\n",
       "                                                                                         &#x27;Bathroom&#x27;,\n",
       "                                                                                         &#x27;Car&#x27;,\n",
       "                                                                                         &#x27;Landsize&#x27;,\n",
       "                                                                                         &#x27;BuildingArea&#x27;])),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Rooms&#x27;, &#x27;Distance&#x27;,\n",
       "                                                   &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                                   &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;,\n",
       "                                                   &#x27;YearBuilt&#x27;,\n",
       "                                                   &#x27;Propertycount&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])),\n",
       "                (&#x27;regressor&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-286\" type=\"checkbox\" ><label for=\"sk-estimator-id-286\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                  Winsorizer(fold=3,\n",
       "                                                             missing_values=&#x27;ignore&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Distance&#x27;,\n",
       "                                                                        &#x27;YearBuilt&#x27;,\n",
       "                                                                        &#x27;Propertycount&#x27;])),\n",
       "                                                 (&#x27;outlier_iqr&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             fold=1.5,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Rooms&#x27;,\n",
       "                                                                        &#x27;Bathroom&#x27;,\n",
       "                                                                        &#x27;Car&#x27;,\n",
       "                                                                        &#x27;Landsize&#x27;,\n",
       "                                                                        &#x27;BuildingArea&#x27;])),\n",
       "                                                 (&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Rooms&#x27;, &#x27;Distance&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                  &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;, &#x27;YearBuilt&#x27;,\n",
       "                                  &#x27;Propertycount&#x27;]),\n",
       "                                (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-287\" type=\"checkbox\" ><label for=\"sk-estimator-id-287\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Rooms&#x27;, &#x27;Distance&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;, &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;, &#x27;YearBuilt&#x27;, &#x27;Propertycount&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-288\" type=\"checkbox\" ><label for=\"sk-estimator-id-288\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Winsorizer</label><div class=\"sk-toggleable__content fitted\"><pre>Winsorizer(fold=3, missing_values=&#x27;ignore&#x27;, tail=&#x27;both&#x27;,\n",
       "           variables=[&#x27;Distance&#x27;, &#x27;YearBuilt&#x27;, &#x27;Propertycount&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-289\" type=\"checkbox\" ><label for=\"sk-estimator-id-289\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Winsorizer</label><div class=\"sk-toggleable__content fitted\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, fold=1.5, tail=&#x27;both&#x27;,\n",
       "           variables=[&#x27;Rooms&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;, &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-290\" type=\"checkbox\" ><label for=\"sk-estimator-id-290\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-291\" type=\"checkbox\" ><label for=\"sk-estimator-id-291\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-292\" type=\"checkbox\" ><label for=\"sk-estimator-id-292\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-293\" type=\"checkbox\" ><label for=\"sk-estimator-id-293\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-294\" type=\"checkbox\" ><label for=\"sk-estimator-id-294\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('preprocessor',\n",
       "                                              ColumnTransformer(transformers=[('num',\n",
       "                                                                               Pipeline(steps=[('outlier_gaussian',\n",
       "                                                                                                Winsorizer(fold=3,\n",
       "                                                                                                           missing_values='ignore',\n",
       "                                                                                                           tail='both',\n",
       "                                                                                                           variables=['Distance',\n",
       "                                                                                                                      'YearBuilt',\n",
       "                                                                                                                      'Propertycount'])),\n",
       "                                                                                               ('outlier_iqr',\n",
       "                                                                                                Winsorizer(capping_method='iqr',\n",
       "                                                                                                           fold=1.5,\n",
       "                                                                                                           tail='both',\n",
       "                                                                                                           variables=['Rooms',\n",
       "                                                                                                                      'Bathroom',\n",
       "                                                                                                                      'Car...\n",
       "                                                                                                StandardScaler())]),\n",
       "                                                                               ['Rooms',\n",
       "                                                                                'Distance',\n",
       "                                                                                'Bathroom',\n",
       "                                                                                'Car',\n",
       "                                                                                'Landsize',\n",
       "                                                                                'BuildingArea',\n",
       "                                                                                'YearBuilt',\n",
       "                                                                                'Propertycount']),\n",
       "                                                                              ('cat',\n",
       "                                                                               'passthrough',\n",
       "                                                                               [])])),\n",
       "                                             ('regressor',\n",
       "                                              LinearRegression())]),\n",
       "                   param_distributions={'preprocessor__num__imputer__strategy': ['mean',\n",
       "                                                                                 'median'],\n",
       "                                        'regressor__fit_intercept': [True,\n",
       "                                                                     False]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=1)"
      ]
     },
     "execution_count": 1625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(X_train_clean, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "masukan data X_train dan y_train ke random_search dengan fungsi fit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'regressor__fit_intercept': True,\n",
       " 'preprocessor__num__imputer__strategy': 'mean'}"
      ]
     },
     "execution_count": 1626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "masukan dictionary kedalam random search. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1627,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-40 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-40 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-40 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-40 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-40 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-40 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-40 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-40 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-40 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-40 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-40 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-40 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-40 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-40 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-40 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-40 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-40 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-40 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-40 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-40\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                                   Winsorizer(fold=3,\n",
       "                                                                              missing_values=&#x27;ignore&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Distance&#x27;,\n",
       "                                                                                         &#x27;YearBuilt&#x27;,\n",
       "                                                                                         &#x27;Propertycount&#x27;])),\n",
       "                                                                  (&#x27;outlier_iqr&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              fold=1.5,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Rooms&#x27;,\n",
       "                                                                                         &#x27;Bathroom&#x27;,\n",
       "                                                                                         &#x27;Car&#x27;,\n",
       "                                                                                         &#x27;Landsize&#x27;,\n",
       "                                                                                         &#x27;BuildingArea&#x27;])),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Rooms&#x27;, &#x27;Distance&#x27;,\n",
       "                                                   &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                                   &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;,\n",
       "                                                   &#x27;YearBuilt&#x27;,\n",
       "                                                   &#x27;Propertycount&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])),\n",
       "                (&#x27;regressor&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-295\" type=\"checkbox\" ><label for=\"sk-estimator-id-295\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                                   Winsorizer(fold=3,\n",
       "                                                                              missing_values=&#x27;ignore&#x27;,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Distance&#x27;,\n",
       "                                                                                         &#x27;YearBuilt&#x27;,\n",
       "                                                                                         &#x27;Propertycount&#x27;])),\n",
       "                                                                  (&#x27;outlier_iqr&#x27;,\n",
       "                                                                   Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                                              fold=1.5,\n",
       "                                                                              tail=&#x27;both&#x27;,\n",
       "                                                                              variables=[&#x27;Rooms&#x27;,\n",
       "                                                                                         &#x27;Bathroom&#x27;,\n",
       "                                                                                         &#x27;Car&#x27;,\n",
       "                                                                                         &#x27;Landsize&#x27;,\n",
       "                                                                                         &#x27;BuildingArea&#x27;])),\n",
       "                                                                  (&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;Rooms&#x27;, &#x27;Distance&#x27;,\n",
       "                                                   &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                                   &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;,\n",
       "                                                   &#x27;YearBuilt&#x27;,\n",
       "                                                   &#x27;Propertycount&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])),\n",
       "                (&#x27;regressor&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-296\" type=\"checkbox\" ><label for=\"sk-estimator-id-296\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                  Winsorizer(fold=3,\n",
       "                                                             missing_values=&#x27;ignore&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Distance&#x27;,\n",
       "                                                                        &#x27;YearBuilt&#x27;,\n",
       "                                                                        &#x27;Propertycount&#x27;])),\n",
       "                                                 (&#x27;outlier_iqr&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             fold=1.5,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Rooms&#x27;,\n",
       "                                                                        &#x27;Bathroom&#x27;,\n",
       "                                                                        &#x27;Car&#x27;,\n",
       "                                                                        &#x27;Landsize&#x27;,\n",
       "                                                                        &#x27;BuildingArea&#x27;])),\n",
       "                                                 (&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Rooms&#x27;, &#x27;Distance&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                  &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;, &#x27;YearBuilt&#x27;,\n",
       "                                  &#x27;Propertycount&#x27;]),\n",
       "                                (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-297\" type=\"checkbox\" ><label for=\"sk-estimator-id-297\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Rooms&#x27;, &#x27;Distance&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;, &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;, &#x27;YearBuilt&#x27;, &#x27;Propertycount&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-298\" type=\"checkbox\" ><label for=\"sk-estimator-id-298\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Winsorizer</label><div class=\"sk-toggleable__content fitted\"><pre>Winsorizer(fold=3, missing_values=&#x27;ignore&#x27;, tail=&#x27;both&#x27;,\n",
       "           variables=[&#x27;Distance&#x27;, &#x27;YearBuilt&#x27;, &#x27;Propertycount&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-299\" type=\"checkbox\" ><label for=\"sk-estimator-id-299\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Winsorizer</label><div class=\"sk-toggleable__content fitted\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, fold=1.5, tail=&#x27;both&#x27;,\n",
       "           variables=[&#x27;Rooms&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;, &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-300\" type=\"checkbox\" ><label for=\"sk-estimator-id-300\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-301\" type=\"checkbox\" ><label for=\"sk-estimator-id-301\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-302\" type=\"checkbox\" ><label for=\"sk-estimator-id-302\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-303\" type=\"checkbox\" ><label for=\"sk-estimator-id-303\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-304\" type=\"checkbox\" ><label for=\"sk-estimator-id-304\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('outlier_gaussian',\n",
       "                                                                   Winsorizer(fold=3,\n",
       "                                                                              missing_values='ignore',\n",
       "                                                                              tail='both',\n",
       "                                                                              variables=['Distance',\n",
       "                                                                                         'YearBuilt',\n",
       "                                                                                         'Propertycount'])),\n",
       "                                                                  ('outlier_iqr',\n",
       "                                                                   Winsorizer(capping_method='iqr',\n",
       "                                                                              fold=1.5,\n",
       "                                                                              tail='both',\n",
       "                                                                              variables=['Rooms',\n",
       "                                                                                         'Bathroom',\n",
       "                                                                                         'Car',\n",
       "                                                                                         'Landsize',\n",
       "                                                                                         'BuildingArea'])),\n",
       "                                                                  ('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['Rooms', 'Distance',\n",
       "                                                   'Bathroom', 'Car',\n",
       "                                                   'Landsize', 'BuildingArea',\n",
       "                                                   'YearBuilt',\n",
       "                                                   'Propertycount']),\n",
       "                                                 ('cat', 'passthrough', [])])),\n",
       "                ('regressor', LinearRegression())])"
      ]
     },
     "execution_count": 1627,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf1 = random_search.best_estimator_\n",
    "best_clf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Masukan best estimator tuning kedalam random search. Terus masukan ke variable baru. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1628,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_regressor = best_clf1.named_steps['regressor']\n",
    "best_preprocessor = best_clf1.named_steps['preprocessor']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita mau tarik data dari dari random search dari input regressor dan preprocessor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1629,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-41 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-41 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-41 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-41 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-41 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-41 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-41 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-41 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-41 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-41 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-41 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-41 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-41 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-41 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-41 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-41 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-41 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-41 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-41 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-41\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-305\" type=\"checkbox\" checked><label for=\"sk-estimator-id-305\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 1629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-42 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-42 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-42 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-42 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-42 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-42 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-42 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-42 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-42 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-42 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-42 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-42 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-42 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-42 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-42 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-42 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-42 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-42 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-42 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-42\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                  Winsorizer(fold=3,\n",
       "                                                             missing_values=&#x27;ignore&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Distance&#x27;,\n",
       "                                                                        &#x27;YearBuilt&#x27;,\n",
       "                                                                        &#x27;Propertycount&#x27;])),\n",
       "                                                 (&#x27;outlier_iqr&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             fold=1.5,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Rooms&#x27;,\n",
       "                                                                        &#x27;Bathroom&#x27;,\n",
       "                                                                        &#x27;Car&#x27;,\n",
       "                                                                        &#x27;Landsize&#x27;,\n",
       "                                                                        &#x27;BuildingArea&#x27;])),\n",
       "                                                 (&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Rooms&#x27;, &#x27;Distance&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                  &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;, &#x27;YearBuilt&#x27;,\n",
       "                                  &#x27;Propertycount&#x27;]),\n",
       "                                (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-306\" type=\"checkbox\" ><label for=\"sk-estimator-id-306\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;outlier_gaussian&#x27;,\n",
       "                                                  Winsorizer(fold=3,\n",
       "                                                             missing_values=&#x27;ignore&#x27;,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Distance&#x27;,\n",
       "                                                                        &#x27;YearBuilt&#x27;,\n",
       "                                                                        &#x27;Propertycount&#x27;])),\n",
       "                                                 (&#x27;outlier_iqr&#x27;,\n",
       "                                                  Winsorizer(capping_method=&#x27;iqr&#x27;,\n",
       "                                                             fold=1.5,\n",
       "                                                             tail=&#x27;both&#x27;,\n",
       "                                                             variables=[&#x27;Rooms&#x27;,\n",
       "                                                                        &#x27;Bathroom&#x27;,\n",
       "                                                                        &#x27;Car&#x27;,\n",
       "                                                                        &#x27;Landsize&#x27;,\n",
       "                                                                        &#x27;BuildingArea&#x27;])),\n",
       "                                                 (&#x27;imputer&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;Rooms&#x27;, &#x27;Distance&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;,\n",
       "                                  &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;, &#x27;YearBuilt&#x27;,\n",
       "                                  &#x27;Propertycount&#x27;]),\n",
       "                                (&#x27;cat&#x27;, &#x27;passthrough&#x27;, [])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-307\" type=\"checkbox\" ><label for=\"sk-estimator-id-307\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Rooms&#x27;, &#x27;Distance&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;, &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;, &#x27;YearBuilt&#x27;, &#x27;Propertycount&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-308\" type=\"checkbox\" ><label for=\"sk-estimator-id-308\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Winsorizer</label><div class=\"sk-toggleable__content fitted\"><pre>Winsorizer(fold=3, missing_values=&#x27;ignore&#x27;, tail=&#x27;both&#x27;,\n",
       "           variables=[&#x27;Distance&#x27;, &#x27;YearBuilt&#x27;, &#x27;Propertycount&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-309\" type=\"checkbox\" ><label for=\"sk-estimator-id-309\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">Winsorizer</label><div class=\"sk-toggleable__content fitted\"><pre>Winsorizer(capping_method=&#x27;iqr&#x27;, fold=1.5, tail=&#x27;both&#x27;,\n",
       "           variables=[&#x27;Rooms&#x27;, &#x27;Bathroom&#x27;, &#x27;Car&#x27;, &#x27;Landsize&#x27;, &#x27;BuildingArea&#x27;])</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-310\" type=\"checkbox\" ><label for=\"sk-estimator-id-310\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-311\" type=\"checkbox\" ><label for=\"sk-estimator-id-311\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-312\" type=\"checkbox\" ><label for=\"sk-estimator-id-312\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-313\" type=\"checkbox\" ><label for=\"sk-estimator-id-313\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('outlier_gaussian',\n",
       "                                                  Winsorizer(fold=3,\n",
       "                                                             missing_values='ignore',\n",
       "                                                             tail='both',\n",
       "                                                             variables=['Distance',\n",
       "                                                                        'YearBuilt',\n",
       "                                                                        'Propertycount'])),\n",
       "                                                 ('outlier_iqr',\n",
       "                                                  Winsorizer(capping_method='iqr',\n",
       "                                                             fold=1.5,\n",
       "                                                             tail='both',\n",
       "                                                             variables=['Rooms',\n",
       "                                                                        'Bathroom',\n",
       "                                                                        'Car',\n",
       "                                                                        'Landsize',\n",
       "                                                                        'BuildingArea'])),\n",
       "                                                 ('imputer', SimpleImputer()),\n",
       "                                                 ('scaler', StandardScaler())]),\n",
       "                                 ['Rooms', 'Distance', 'Bathroom', 'Car',\n",
       "                                  'Landsize', 'BuildingArea', 'YearBuilt',\n",
       "                                  'Propertycount']),\n",
       "                                ('cat', 'passthrough', [])])"
      ]
     },
     "execution_count": 1630,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_preprocessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1631,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient :  [ 132170.09243312 -129259.49333926  200375.86912614   36395.34308928\n",
      "  195031.94791959  106469.09015283  -90136.58042171   12972.88208969]\n",
      "Intercept   :  1247863.1099999999\n"
     ]
    }
   ],
   "source": [
    "# Get result from a particular step such as coefficients and intercept from Logistic Regression\n",
    "\n",
    "print('Coefficient : ', best_clf1.named_steps['regressor'].coef_)\n",
    "print('Intercept   : ', best_clf1.named_steps['regressor'].intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Numerical (Coefficients):\n",
    "    'Rooms','Distance','Bathroom','Car',\n",
    "    'Landsize','BuildingArea','YearBuilt','Propertycount'\n",
    "\n",
    "Target:\n",
    "    'Price'\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sekarang sudah dapat Coefficient dan Intercept dari hyperparameter. Bisa dibilang ada variasi sedikit dari yang linear regresi awal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1632,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 - Train :  {'MAE': 333710.9326041857, 'MSE': 226574060193.32565, 'RMSE': 475997.9623835859, 'R²': 0.5355749742504881}\n",
      "R2 - Test  :  {'MAE': 333710.9326041857, 'MSE': 226574060193.32565, 'RMSE': 475997.9623835859, 'R²': 0.5355749742504881}\n"
     ]
    }
   ],
   "source": [
    "# Check the Performance\n",
    "\n",
    "print('R2 - Train : ', performance_check(best_clf1, X_train_clean, y_train))\n",
    "print('R2 - Test  : ', performance_check(best_clf1, X_test_clean, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita gunakan performance check yang dari awal untuk membandingkan errors yang akan didapatkan dan R2. Dari observasi, error dan R2 tidak terlalu memiliki variansi besar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1633,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_train_clean, y_pred, color='blue', label='Actual vs. Predicted')\n",
    "plt.plot([y_train_clean.min(), y_train_clean.max()], [y_train_clean.min(), y_train_clean.max()], color='red', linestyle='--', linewidth=2, label='Ideal Fit')\n",
    "plt.title('Actual vs. Predicted')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita mau lihat sekali lagi linear relationship antar actual dan predicted values. kelihatan ada korelasi positif yang parallel dengan garis trend line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1637,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Rooms', 'Distance', 'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'YearBuilt', 'Propertycount']\n"
     ]
    }
   ],
   "source": [
    "# Assuming X_train_columns is a DataFrame\n",
    "column_list = X_train_columns.tolist()\n",
    "print(column_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "namakan kolom baru untuk disimpan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1638,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model saving\n",
    "\n",
    "with open('list_num_cols.txt', 'w') as file_1:\n",
    "  json.dump(column_list, file_1)\n",
    "\n",
    "with open('model.pkl', 'wb') as file_2:\n",
    "  pickle.dump(lr, file_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan save data-data tersebut didalam Json files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Inference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1639,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the saved list of numerical columns\n",
    "with open('list_num_cols.txt', 'r') as file_1:\n",
    "    combined_columns = json.load(file_1)\n",
    "\n",
    "# Load the saved model\n",
    "with open('model.pkl', 'rb') as file_2:\n",
    "    lr = pickle.load(file_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan coba buka yang kita tadi save untuk dipake untuk inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1640,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Dummy Data:\n",
      "       Suburb  Rooms      Price  Distance  Bathroom  Car  Landsize  \\\n",
      "0  Abbotsford      2  1035000.0       2.5       1.0  0.0     156.0   \n",
      "1  Abbotsford      3  1465000.0       2.5       2.0  0.0     134.0   \n",
      "2  Abbotsford      4  1600000.0       2.5       1.0  2.0     120.0   \n",
      "3  Abbotsford      3  1876000.0       2.5       2.0  0.0     245.0   \n",
      "4  Abbotsford      2  1636000.0       2.5       1.0  2.0     256.0   \n",
      "\n",
      "   BuildingArea  YearBuilt  Propertycount  \n",
      "0          79.0     1900.0         4019.0  \n",
      "1         150.0     1900.0         4019.0  \n",
      "2         142.0     2014.0         4019.0  \n",
      "3         210.0     1910.0         4019.0  \n",
      "4         107.0     1890.0         4019.0  \n"
     ]
    }
   ],
   "source": [
    "# Assuming df_data_dummy is your DataFrame with the data\n",
    "df_data_dummy = pd.DataFrame({\n",
    "\n",
    "    \"Suburb\": [\"Abbotsford\", \"Abbotsford\", \"Abbotsford\", \"Abbotsford\", \"Abbotsford\"],\n",
    "    \"Rooms\": [2, 3, 4, 3, 2],\n",
    "    \"Price\": [1035000.0, 1465000.0, 1600000.0, 1876000.0, 1636000.0],\n",
    "    \"Distance\": [2.5, 2.5, 2.5, 2.5, 2.5],\n",
    "    \"Bathroom\": [1.0, 2.0, 1.0, 2.0, 1.0],\n",
    "    \"Car\": [0.0, 0.0, 2.0, 0.0, 2.0],\n",
    "    \"Landsize\": [156.0, 134.0, 120.0, 245.0, 256.0],\n",
    "    \"BuildingArea\": [79.0, 150.0, 142.0, 210.0, 107.0],\n",
    "    \"YearBuilt\": [1900.0, 1900.0, 2014.0, 1910.0, 1890.0],\n",
    "    \"Propertycount\": [4019.0, 4019.0, 4019.0, 4019.0, 4019.0]\n",
    "\n",
    "})\n",
    "\n",
    "df_dummy_data = pd.DataFrame(df_data_dummy)\n",
    "print(\"Original Dummy Data:\")\n",
    "print(df_dummy_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan membuat dataset \"dummy\" baru dan masukan ke dataframe dinamakan \"df_dummy_data\". Kita mau uji nanti dengan linear regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1655,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>3</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>4</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>3</td>\n",
       "      <td>1876000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>1636000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb  Rooms      Price  Distance  Bathroom  Car  Landsize  \\\n",
       "0  Abbotsford      2  1035000.0       2.5       1.0  0.0     156.0   \n",
       "1  Abbotsford      3  1465000.0       2.5       2.0  0.0     134.0   \n",
       "2  Abbotsford      4  1600000.0       2.5       1.0  2.0     120.0   \n",
       "3  Abbotsford      3  1876000.0       2.5       2.0  0.0     245.0   \n",
       "4  Abbotsford      2  1636000.0       2.5       1.0  2.0     256.0   \n",
       "\n",
       "   BuildingArea  YearBuilt  Propertycount  \n",
       "0          79.0     1900.0         4019.0  \n",
       "1         150.0     1900.0         4019.0  \n",
       "2         142.0     2014.0         4019.0  \n",
       "3         210.0     1910.0         4019.0  \n",
       "4         107.0     1890.0         4019.0  "
      ]
     },
     "execution_count": 1655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1656,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rooms', 'Distance', 'Bathroom', 'Car', 'Landsize', 'BuildingArea',\n",
       "       'YearBuilt', 'Propertycount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1656,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1643,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dummy_data_new = df_dummy_data[combined_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "masukan kolom ke data dummy. Berikutnya namakan variable baru."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1644,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lr.predict(df_dummy_data_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Akan membuat prediksi dengan linear regression di test. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1645,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1101277.02454045, 1665725.95948649, 1297970.1974852 ,\n",
       "       1639455.71625785, 1297855.42299958])"
      ]
     },
     "execution_count": 1645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keluarlah prediksi harga rumah di beberapa bulan kedepan. Harganya semua diatas AU$1 Juta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1646,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the predictions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(predictions, marker='o', linestyle='-', color='b', label='Predictions')\n",
    "plt.title('Predicted House Prices in Melbourne')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price in AU$')\n",
    "plt.legend()\n",
    "plt.xticks(range(len(predictions)), ['05/2017', '07/2017', '09/2017', '11/2017', '01/2018'])  # Replace with actual labels if available\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bisa kelihatan jika dimasukan ke timeseries chart dengan time-frame yang sama di EDA, maka bisa lihat trend naik-turun terhadap harga rumah di Melbourne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1647,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1400456.8641539158"
      ]
     },
     "execution_count": 1647,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Harga rata-rata 10 bulan kedepan. (2 bulan x 5 interval = 10 bulan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1648,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1665725.9594864864"
      ]
     },
     "execution_count": 1648,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "harga rumah tertinggi 10 bulan kedepan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1649,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1101277.0245404532"
      ]
     },
     "execution_count": 1649,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "harga rumah terendah 10 bulan kedepan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1650,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Overall: 1101277.02\n"
     ]
    }
   ],
   "source": [
    "# Predict the score\n",
    "predicted_score = lr.predict(df_dummy_data_new)\n",
    "\n",
    "# Show result\n",
    "print(f\"Predicted Overall: {predicted_score[0]:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dengan output prediksi dari Linear Regression, kita bisa bilang bahwa harga rumah di Melbourne 1 tahun kemudian akan naik turun antara AU$ 1.7 Juta setingginya, dan AU$ 1.1 Juta serendahnya. Ini bisa dilihat di trend EDA jika harga rumah ada puncaknya oleh karena musim atau \"season\" yang orang atau perusahaan beli rumah dengan rombongan di kota Melbourne. \n",
    "\n",
    "Teori sebelumnya adalah harga rumah akan selalu naik, tetapi setelah di train dan test dengan linear regression, bisa dilihat bahwa setiap bulan kedepan pasti ada bulan yang harga akan anjlok kebawah. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Pada kesimpulan, harga rumah di Melbourne susah diprediksi karena harus mengikuti trend \"Consumer sentiment\" terhadap business climate di negara itu. Bisakah negara memiliki perekonomian yang baik untuk mendorong harga rumah di kota tersebut untuk naik terus, dan pembeli individu atau perusahaan akan sangat optimis terhadap investasi di rumah lama atau baru. Dengan analisa itu, kita bisa lihat lebih dalam terhadap puncak dan jurang harga rumah karena faktor-faktor lain seperti jarak jauh dari CBD, jumlah kamar, luas lahan, dan beberapa mobil bisa muat di garasi memiliki dampak yang sangat efektif terhadap harga rumah rata-rata. \n",
    "\n",
    "Bisa saja harga rumah bisa di-segmentasikan jadi setiap perumahan atau lingkungan memiliki \"pasar pembeli\", jadi daerah tersebut bisa lebih dianalisa terhadap isu tingkat sosial, fasilitas, persekolahan, dan faktor lain yang bisa berdampak terhadap persepsi harga rumah. Oleh karena sentiment publik sangat efektif dengan setiap lingkungan atau kota, maka prediksi harus memiliki parameter yang tertentu karena setiap \"market\" punya pola pembeli individu/konsumer. \n",
    "\n",
    "Sebagai saran penutup, terlihat bahwa harga rumah rata-rata di melbourne akan tidak naik terlalu drastis karena setiap bulan akan turun juga dengan drastis. Dari serendahnya investor akan mendapatkan harga termurah itu AU$ 1.1 Juta, dan kalau mau jual paling tinggi tidak lebih dari AU $1.7 Juta. \n",
    "\n",
    "Jika ada individu atau perusahaan yang mau beli rumah di Melbourne, sarannya bukan untuk di-investasi karena harga tidak bisa naik terus, jadi lebih baik untuk dijadikan tempat tinggal saja (homeowner, not investor). \n",
    "\n",
    "### Pertanyaan (objective):\n",
    "-Dengan Linear Regression, bisakah kita prediksi harga rumah bisa naik terus?\n",
    "\n",
    "    Kita bisa prediksi rumah dengan cara train dan test model dengan Pipeline. Dari grafik time-series harga rumah akan naik dan turun dalam satu tahun kedepan.\n",
    "\n",
    "-Kolom Numerikal apa saja yang memiliki dampak paling besar terhadap harga rumah?\n",
    "\n",
    "    Bathroom dan Landsize adalah faktor yang mempunyai coefficient positif terhadap harga rumah.\n",
    "\n",
    "-Apakah ada dampak signifikan antara harga rumah yang dekat CBD (Central Business District) dengan yang jauh? \n",
    "\n",
    "    Distance tidak memiliki dampak signifikan terhadap harga rumah, malah coefficient cenderung negatif. Ini artinya jika rumah lebih jauh, maka harga rumah lebih mahal. "
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    "### Conceptual Problems\n",
    "### Jawab pertanyaan berikut:\n",
    "\n",
    "Jelaskan latar belakang adanya bagging dan cara kerja bagging !\n",
    "\n",
    "    Bagging adalah teknik yang digunakan untuk mengurangi variansi dalam model prediktif dengan cara menggabungkan hasil dari beberapa model yang sama jenisnya, yang dibangun dari sampel data yang diambil secara acak dengan penggantian. Latar belakangnya adalah untuk meningkatkan akurasi dan konsistensi model, terutama pada model yang cenderung overfitting terhadap data training.\n",
    "\n",
    "    Bootstrap Sampling: Membuat beberapa dataset acak dari dataset pelatihan dengan melakukan sampling dengan penggantian.\n",
    "\n",
    "    Model Training: Membangun model pada setiap dataset bootstrap yang dihasilkan.\n",
    "    \n",
    "    Aggregating: Menggabungkan hasil prediksi dari semua model yang dibangun, misalnya dengan menggunakan rata-rata atau voting.\n",
    "\n",
    "Jelaskan perbedaan cara kerja algoritma Random Forest dengan algoritma boosting yang Anda pilih!\n",
    "\n",
    "    Random Forest:\n",
    "\n",
    "    Cara Kerja: Random Forest juga merupakan jenis ensemble yang menggunakan teknik bagging dengan pohon keputusan.\n",
    "    Perbedaan Utama: Setiap pohon keputusan dalam Random Forest dilatih secara independen, dan hasil dari semua pohon digunakan untuk menghasilkan prediksi akhir dengan cara voting.\n",
    "    \n",
    "    Gradient Boosting:\n",
    "\n",
    "    Cara Kerja: Gradient Boosting adalah jenis ensemble yang membangun model secara bertahap, di mana setiap model berikutnya berfokus untuk memperbaiki kesalahan prediksi model sebelumnya.\n",
    "    Perbedaan Utama: Gradient Boosting tidak menggunakan bagging tetapi menggunakan boosting, di mana setiap model ditambahkan secara berurutan untuk mengoreksi kesalahan prediksi yang sebelumnya terjadi.\n",
    "\n",
    "Jelaskan apa yang dimaksud dengan Cross Validation !\n",
    "\n",
    "    Definisi: Cross Validation adalah teknik untuk mengevaluasi kinerja model machine learning dengan cara membagi dataset menjadi subset yang lebih kecil, kemudian melakukan pelatihan dan pengujian model pada subset-subset ini secara bergantian.\n",
    "\n",
    "    Tujuan: Mengukur seberapa baik model akan berkinerja pada data yang tidak terlihat dengan menghindari overfitting atau underfitting.\n",
    "    \n",
    "    Cara Kerja: Dalam validasi silang K-Fold, data dibagi menjadi K subset yang sama ukurannya. Model dilatih pada K-1 subset dan diuji pada subset yang tersisa, dilakukan K kali. Hasil akhir evaluasi adalah rata-rata dari semua pengujian."
   ]
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    "### Business Recommendation\n",
    "\n",
    "Setelah analisis, ada beberapa saran yang bisa dikasih terhadap pembeli rumah dan perusahaan:\n",
    "\n",
    "1. Jangan tunggu untuk beli rumah - Oleh karena trend harga rumah sangat susah diprediksi bulan ke bulan, tidak ada gunakan untuk pembeli nunggu sampai harga murah. Harga rumah adalah refleksi dari sentimen pasar properti. \n",
    "2. Beli share-room atau duplex daripada single house - Pembeli tidak harus beli rumah yang besar dulu di dekat CBD Melbourne. Pembeli harus coba investasi di properti yang lebih jauh dan sederhana untuk naik \"property ladder\". \n",
    "3. Lihat dikota lain untuk investasi - Melbourne adalah kota mahal dan sudah saturasi. Harga rumah tidak akan naik lagi dengan banyak dan harga bisa juga turun drastis. Pembeli rumah harus hati-hati untuk tidak mengambil resiko beli properti tidak mengetahui harga pasaran di setiap lingkungan atau daerah.  "
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