{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "redwine_dir = r'./data/winequality-red.csv'\n",
    "whitewine_dir = r'./data/winequality-white.csv'\n",
    "df_redwine = pd.read_csv(redwine_dir, sep=';')\n",
    "df_whitewine= pd.read_csv(whitewine_dir, sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# O = redwine; 1= whitewine\n",
    "df_redwine['wine_type'] = 0\n",
    "df_whitewine['wine_type']= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_whitewine.columns == df_redwine.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_whitewine.columns == df_redwine.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>wine_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  wine_type  \n",
       "0      9.4          0  \n",
       "1      9.8          0  \n",
       "2      9.8          0  \n",
       "3      9.8          0  \n",
       "4      9.4          0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df = pd.concat([df_redwine,\n",
    "                      df_whitewine],\n",
    "                      axis = 0)\n",
    "merge_df.drop('quality', axis = 1, inplace= True)\n",
    "merge_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6497, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wine_type\n",
       "1    4898\n",
       "0    1599\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df['wine_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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KpZLvf//7+eEPf5jPfvazY/HyAIBTrFL2AAAAAACYeL7zne/koosuOuLxd3/3d7nnnnvyF3/xF7ngggvy0EMP5a677jrqY6dMmZI//dM/zUc/+tFceumlmTZtWh5++OHh82vXrs3Xv/71fOMb38iKFStyySWX5K/+6q8yf/78sXyJAMApVNRqtVrZgwAAAAAAAGDyMsMKAAAAAACAUglWAAAAAAAAlEqwAgAAAAAAoFSCFQAAAAAAAKUSrAAAAAAAACiVYAUAAAAAAECpBCsAAAAAAABKJVgBAAAAAABQKsEKAAAAAACAUglWAAAAAAAAlEqwAgAAAAAAoFSCFQAAAAAAAKX6//QLwgVMOKOrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_count = merge_df['wine_type'].value_counts()\n",
    "fig,axes = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "sns.set_theme(style='darkgrid', palette='pastel')\n",
    "color = sns.color_palette(palette='pastel')\n",
    "explode = [0.02]*len(label_count)\n",
    "\n",
    "axes[0].pie(label_count.values, labels=label_count.index, autopct='%1.1f%%', colors=color, explode=explode)\n",
    "axes[0].set_title('Percentage Label',fontsize=16)\n",
    "\n",
    "sns.barplot(x=label_count.index, y=label_count.values, ax=axes[1])\n",
    "axes[1].set_title('Count Label',fontsize=16)\n",
    "axes[1].set_xlabel('Label')\n",
    "axes[1].set_ylabel('Count')\n",
    "\n",
    "fig.suptitle('Visual Label Count',fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>wine_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4893</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4894</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4895</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4896</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6497 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.4              0.70         0.00             1.9      0.076   \n",
       "1               7.8              0.88         0.00             2.6      0.098   \n",
       "2               7.8              0.76         0.04             2.3      0.092   \n",
       "3              11.2              0.28         0.56             1.9      0.075   \n",
       "4               7.4              0.70         0.00             1.9      0.076   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "4893            6.2              0.21         0.29             1.6      0.039   \n",
       "4894            6.6              0.32         0.36             8.0      0.047   \n",
       "4895            6.5              0.24         0.19             1.2      0.041   \n",
       "4896            5.5              0.29         0.30             1.1      0.022   \n",
       "4897            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  wine_type  \n",
       "0         9.4          0  \n",
       "1         9.8          0  \n",
       "2         9.8          0  \n",
       "3         9.8          0  \n",
       "4         9.4          0  \n",
       "...       ...        ...  \n",
       "4893     11.2          1  \n",
       "4894      9.6          1  \n",
       "4895      9.4          1  \n",
       "4896     12.8          1  \n",
       "4897     11.8          1  \n",
       "\n",
       "[6497 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split x and y\n",
    "x = merge_df.drop('wine_type', axis = 1)\n",
    "y = merge_df['wine_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN  0.994 0.002\n",
      "LR   0.994 0.002\n",
      "NB   0.976 0.007\n",
      "GBC  0.994 0.002\n",
      "SVC  0.996 0.002\n",
      "RFC  0.996 0.002\n",
      "DT   0.987 0.003\n",
      "XGB  0.995 0.003\n"
     ]
    }
   ],
   "source": [
    "encoded = LabelEncoder()\n",
    "sc = StandardScaler()\n",
    "\n",
    "x_sc = sc.fit_transform(x_train)\n",
    "\n",
    "df_sc = pd.DataFrame(x_sc, columns=x.columns)\n",
    "\n",
    "models = []\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('GBC', GradientBoostingClassifier()))\n",
    "models.append(('SVC', SVC()))\n",
    "models.append(('RFC', RandomForestClassifier()))\n",
    "models.append(('DT', DecisionTreeClassifier()))\n",
    "models.append(('XGB', XGBClassifier()))\n",
    "\n",
    "result = []\n",
    "names = []\n",
    "\n",
    "for name, model in models:\n",
    "    cvs = cross_val_score(model, x_sc, y_train, cv=10)\n",
    "    result.append(cvs)\n",
    "    names.append(name)\n",
    "    print('{:4} {:.3f} {:.3f}'.format(name, cvs.mean(), cvs.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(result)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'learning_rate': 0.2, 'max_depth': 3, 'n_estimators': 300}\n",
      "Best Score: 0.9969212260309469\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],  \n",
    "    'learning_rate': [0.01, 0.1, 0.2], \n",
    "    'max_depth': [3, 5, 7], }\n",
    "\n",
    "xgb = XGBClassifier()\n",
    "\n",
    "grid_search = GridSearchCV(estimator=xgb, param_grid=param_grid, cv=5, n_jobs=-1)\n",
    "grid_search.fit(x_sc, y_train)\n",
    "\n",
    "print(\"Best Hyperparameters:\", grid_search.best_params_)\n",
    "print(\"Best Score:\", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross val score:[1.   1.   1.   1.   0.99 1.   1.   1.   1.   0.99]\n",
      "Average 99.67 %\n"
     ]
    }
   ],
   "source": [
    "model = grid_search.best_estimator_\n",
    "cvs = cross_val_score(model,x_sc,y_train,cv=10)\n",
    "print('Cross val score:{}'.format(cvs.round(2)))\n",
    "print('Average {:.2f} %'.format(cvs.mean()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'n_estimators': 300, 'max_depth': 3, 'learning_rate': 0.16}\n",
      "Best Score: 0.9965364255571185\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from xgboost import XGBClassifier\n",
    "import numpy as np\n",
    "\n",
    "param_dist = {\n",
    "    'n_estimators': np.arange(100, 1001, 100),  \n",
    "    'learning_rate': np.linspace(0.01, 0.2, 20),  \n",
    "    'max_depth': [3, 5, 7, 9],  }\n",
    "\n",
    "xgb = XGBClassifier()\n",
    "\n",
    "random_search = RandomizedSearchCV(estimator=xgb, param_distributions=param_dist, n_iter=10, cv=5, n_jobs=-1)\n",
    "random_search.fit(x_sc, y_train)\n",
    "\n",
    "print(\"Best Hyperparameters:\", random_search.best_params_)\n",
    "print(\"Best Score:\", random_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross val score:[1.   1.   1.   1.   0.99 1.   1.   1.   1.   0.99]\n",
      "Average 99.71 %\n"
     ]
    }
   ],
   "source": [
    "model_rs = random_search.best_estimator_\n",
    "cvs = cross_val_score(model_rs,x_sc,y_train,cv=10)\n",
    "print('Cross val score:{}'.format(cvs.round(2)))\n",
    "print('Average {:.2f} %'.format(cvs.mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.16, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=300, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.16, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=300, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.16, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=300, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Train: 0.99\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98       321\n",
      "           1       0.99      1.00      0.99       979\n",
      "\n",
      "    accuracy                           0.99      1300\n",
      "   macro avg       0.99      0.99      0.99      1300\n",
      "weighted avg       0.99      0.99      0.99      1300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score,classification_report\n",
    "\n",
    "pipe1 = Pipeline([\n",
    "    ('scaler',StandardScaler()),\n",
    "    ('xgb',XGBClassifier(n_estimators= 500, max_depth=5, learning_rate=0.13))\n",
    "])\n",
    "\n",
    "pipe1.fit(x_train,y_train)\n",
    "y_pred = pipe1.predict(x_test)\n",
    "\n",
    "print('Accuracy Train: {:.2f}'.format(accuracy_score(y_test,y_pred)))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_wine_binary.pkl']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(model, 'model_wine_binary.pkl')"
   ]
  }
 ],
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