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P1M2_Ryan_Trisnadi_inf.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/miniconda3/lib/python3.12/site-packages/threadpoolctl.py:1214: RuntimeWarning: \n",
+      "Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at\n",
+      "the same time. Both libraries are known to be incompatible and this\n",
+      "can cause random crashes or deadlocks on Linux when loaded in the\n",
+      "same Python program.\n",
+      "Using threadpoolctl may cause crashes or deadlocks. For more\n",
+      "information and possible workarounds, please see\n",
+      "    https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md\n",
+      "\n",
+      "  warnings.warn(msg, RuntimeWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import pickle\n",
+    "\n",
+    "# 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": 2,
+   "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": [
+    "import pandas as pd\n",
+    "\n",
+    "# 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": 3,
+   "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_dummy_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": 5,
+   "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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1101277.02454045, 1665725.95948649, 1297970.1974852 ,\n",
+       "       1639455.71625785, 1297855.42299958])"
+      ]
+     },
+     "execution_count": 6,
+     "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."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}