All dataset-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignore  the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('always')\n",
    "warnings.filterwarnings('ignore')\n",
    "# Import the librairies\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from matplotlib import style\n",
    "import missingno as msno\n",
    "# sets matplotlib to inline and displays graphs below the corressponding cell.\n",
    "%matplotlib inline  \n",
    "style.use('fivethirtyeight')\n",
    "sns.set(style='whitegrid',color_codes=True)\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydot\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import (mean_squared_error, r2_score, explained_variance_score)\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install pydot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('winequalityN.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['quality_label'] = df.quality.apply(lambda q: 'low' if q <= 5 else 'medium' if q <= 7 else 'high')\n",
    "#wines.quality_label = pd.Categorical(wines.quality_label, categories=['low', 'medium', 'high'], ordered=True)\n",
    "\n",
    "# re-shuffle records just to randomize data points\n",
    "#df = df.sample(frac=1, random_state=101).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>type</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>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0  white            7.0              0.27         0.36            20.7   \n",
       "1  white            6.3              0.30         0.34             1.6   \n",
       "2  white            8.1              0.28         0.40             6.9   \n",
       "3  white            7.2              0.23         0.32             8.5   \n",
       "4  white            7.2              0.23         0.32             8.5   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.045                 45.0                 170.0   1.0010  3.00   \n",
       "1      0.049                 14.0                 132.0   0.9940  3.30   \n",
       "2      0.050                 30.0                  97.0   0.9951  3.26   \n",
       "3      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "4      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "\n",
       "   sulphates  alcohol  quality  \n",
       "0       0.45      8.8        6  \n",
       "1       0.49      9.5        6  \n",
       "2       0.44     10.1        6  \n",
       "3       0.40      9.9        6  \n",
       "4       0.40      9.9        6  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rstr(df, pred=None): \n",
    "    obs = df.shape[0]\n",
    "    types = df.dtypes\n",
    "    counts = df.apply(lambda x: x.count())\n",
    "    uniques = df.apply(lambda x: [x.unique()])\n",
    "    nulls = df.apply(lambda x: x.isnull().sum())\n",
    "    distincts = df.apply(lambda x: x.unique().shape[0])\n",
    "    missing_ration = (df.isnull().sum()/ obs) * 100\n",
    "    skewness = df.skew()\n",
    "    kurtosis = df.kurt() \n",
    "    print('Data shape:', df.shape)\n",
    "    \n",
    "    if pred is None:\n",
    "        cols = ['types', 'counts', 'distincts', 'nulls', 'missing ration', 'uniques', 'skewness', 'kurtosis']\n",
    "        str = pd.concat([types, counts, distincts, nulls, missing_ration, uniques, skewness, kurtosis], axis = 1)\n",
    "\n",
    "    else:\n",
    "        corr = df.corr()[pred]\n",
    "        str = pd.concat([types, counts, distincts, nulls, missing_ration, uniques, skewness, kurtosis, corr], axis = 1, sort=False)\n",
    "        corr_col = 'corr '  + pred\n",
    "        cols = ['types', 'counts', 'distincts', 'nulls', 'missing_ration', 'uniques', 'skewness', 'kurtosis', corr_col ]\n",
    "    \n",
    "    str.columns = cols\n",
    "    dtypes = str.types.value_counts()\n",
    "    print('___________________________\\nData types:\\n',str.types.value_counts())\n",
    "    print('___________________________')\n",
    "    return str\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (6497, 13)\n",
      "___________________________\n",
      "Data types:\n",
      " float64    11\n",
      "int64       1\n",
      "object      1\n",
      "Name: types, dtype: int64\n",
      "___________________________\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>types</th>\n",
       "      <th>counts</th>\n",
       "      <th>distincts</th>\n",
       "      <th>nulls</th>\n",
       "      <th>missing_ration</th>\n",
       "      <th>uniques</th>\n",
       "      <th>skewness</th>\n",
       "      <th>kurtosis</th>\n",
       "      <th>corr quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fixed acidity</th>\n",
       "      <td>float64</td>\n",
       "      <td>6487</td>\n",
       "      <td>107</td>\n",
       "      <td>10</td>\n",
       "      <td>0.153917</td>\n",
       "      <td>[[7.0, 6.3, 8.1, 7.2, 6.2, 8.6, 7.9, 6.6, 8.3,...</td>\n",
       "      <td>1.722805</td>\n",
       "      <td>5.057727</td>\n",
       "      <td>-0.077031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>float64</td>\n",
       "      <td>6488</td>\n",
       "      <td>109</td>\n",
       "      <td>9</td>\n",
       "      <td>0.138525</td>\n",
       "      <td>[[3.0, 3.3, 3.26, 3.19, 3.18, 3.22, 2.99, 3.14...</td>\n",
       "      <td>0.386966</td>\n",
       "      <td>0.370068</td>\n",
       "      <td>0.019366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatile acidity</th>\n",
       "      <td>float64</td>\n",
       "      <td>6489</td>\n",
       "      <td>188</td>\n",
       "      <td>8</td>\n",
       "      <td>0.123134</td>\n",
       "      <td>[[0.27, 0.3, 0.28, 0.23, 0.32, 0.22, 0.18, 0.1...</td>\n",
       "      <td>1.495512</td>\n",
       "      <td>2.827081</td>\n",
       "      <td>-0.265953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulphates</th>\n",
       "      <td>float64</td>\n",
       "      <td>6493</td>\n",
       "      <td>112</td>\n",
       "      <td>4</td>\n",
       "      <td>0.061567</td>\n",
       "      <td>[[0.45, 0.49, 0.44, 0.4, 0.47, 0.56, 0.53, 0.6...</td>\n",
       "      <td>1.798467</td>\n",
       "      <td>8.659892</td>\n",
       "      <td>0.038729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citric acid</th>\n",
       "      <td>float64</td>\n",
       "      <td>6494</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>0.046175</td>\n",
       "      <td>[[0.36, 0.34, 0.4, 0.32, 0.16, 0.43, 0.41, 0.3...</td>\n",
       "      <td>0.473032</td>\n",
       "      <td>2.401582</td>\n",
       "      <td>0.085706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual sugar</th>\n",
       "      <td>float64</td>\n",
       "      <td>6495</td>\n",
       "      <td>317</td>\n",
       "      <td>2</td>\n",
       "      <td>0.030783</td>\n",
       "      <td>[[20.7, 1.6, 6.9, 8.5, 7.0, 1.5, 1.45, 4.2, 1....</td>\n",
       "      <td>1.435000</td>\n",
       "      <td>4.358134</td>\n",
       "      <td>-0.036825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorides</th>\n",
       "      <td>float64</td>\n",
       "      <td>6495</td>\n",
       "      <td>215</td>\n",
       "      <td>2</td>\n",
       "      <td>0.030783</td>\n",
       "      <td>[[0.045, 0.049, 0.05, 0.057999999999999996, 0....</td>\n",
       "      <td>5.399849</td>\n",
       "      <td>50.894874</td>\n",
       "      <td>-0.200886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <td>object</td>\n",
       "      <td>6497</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[[white, red]]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <td>float64</td>\n",
       "      <td>6497</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[[45.0, 14.0, 30.0, 47.0, 28.0, 11.0, 17.0, 16...</td>\n",
       "      <td>1.220066</td>\n",
       "      <td>7.906238</td>\n",
       "      <td>0.055463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <td>float64</td>\n",
       "      <td>6497</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[[170.0, 132.0, 97.0, 186.0, 136.0, 129.0, 63....</td>\n",
       "      <td>-0.001177</td>\n",
       "      <td>-0.371664</td>\n",
       "      <td>-0.041385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>float64</td>\n",
       "      <td>6497</td>\n",
       "      <td>998</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[[1.001, 0.9940000000000001, 0.9951, 0.9956, 0...</td>\n",
       "      <td>0.503602</td>\n",
       "      <td>6.606067</td>\n",
       "      <td>-0.305858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>float64</td>\n",
       "      <td>6497</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[[8.8, 9.5, 10.1, 9.9, 9.6, 11.0, 12.0, 9.7, 1...</td>\n",
       "      <td>0.565718</td>\n",
       "      <td>-0.531687</td>\n",
       "      <td>0.444319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>int64</td>\n",
       "      <td>6497</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[[6, 5, 7, 8, 4, 3, 9]]</td>\n",
       "      <td>0.189623</td>\n",
       "      <td>0.232322</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        types  counts  distincts  nulls  missing_ration  \\\n",
       "fixed acidity         float64    6487        107     10        0.153917   \n",
       "pH                    float64    6488        109      9        0.138525   \n",
       "volatile acidity      float64    6489        188      8        0.123134   \n",
       "sulphates             float64    6493        112      4        0.061567   \n",
       "citric acid           float64    6494         90      3        0.046175   \n",
       "residual sugar        float64    6495        317      2        0.030783   \n",
       "chlorides             float64    6495        215      2        0.030783   \n",
       "type                   object    6497          2      0        0.000000   \n",
       "free sulfur dioxide   float64    6497        135      0        0.000000   \n",
       "total sulfur dioxide  float64    6497        276      0        0.000000   \n",
       "density               float64    6497        998      0        0.000000   \n",
       "alcohol               float64    6497        111      0        0.000000   \n",
       "quality                 int64    6497          7      0        0.000000   \n",
       "\n",
       "                                                                uniques  \\\n",
       "fixed acidity         [[7.0, 6.3, 8.1, 7.2, 6.2, 8.6, 7.9, 6.6, 8.3,...   \n",
       "pH                    [[3.0, 3.3, 3.26, 3.19, 3.18, 3.22, 2.99, 3.14...   \n",
       "volatile acidity      [[0.27, 0.3, 0.28, 0.23, 0.32, 0.22, 0.18, 0.1...   \n",
       "sulphates             [[0.45, 0.49, 0.44, 0.4, 0.47, 0.56, 0.53, 0.6...   \n",
       "citric acid           [[0.36, 0.34, 0.4, 0.32, 0.16, 0.43, 0.41, 0.3...   \n",
       "residual sugar        [[20.7, 1.6, 6.9, 8.5, 7.0, 1.5, 1.45, 4.2, 1....   \n",
       "chlorides             [[0.045, 0.049, 0.05, 0.057999999999999996, 0....   \n",
       "type                                                     [[white, red]]   \n",
       "free sulfur dioxide   [[45.0, 14.0, 30.0, 47.0, 28.0, 11.0, 17.0, 16...   \n",
       "total sulfur dioxide  [[170.0, 132.0, 97.0, 186.0, 136.0, 129.0, 63....   \n",
       "density               [[1.001, 0.9940000000000001, 0.9951, 0.9956, 0...   \n",
       "alcohol               [[8.8, 9.5, 10.1, 9.9, 9.6, 11.0, 12.0, 9.7, 1...   \n",
       "quality                                         [[6, 5, 7, 8, 4, 3, 9]]   \n",
       "\n",
       "                      skewness   kurtosis  corr quality  \n",
       "fixed acidity         1.722805   5.057727     -0.077031  \n",
       "pH                    0.386966   0.370068      0.019366  \n",
       "volatile acidity      1.495512   2.827081     -0.265953  \n",
       "sulphates             1.798467   8.659892      0.038729  \n",
       "citric acid           0.473032   2.401582      0.085706  \n",
       "residual sugar        1.435000   4.358134     -0.036825  \n",
       "chlorides             5.399849  50.894874     -0.200886  \n",
       "type                       NaN        NaN           NaN  \n",
       "free sulfur dioxide   1.220066   7.906238      0.055463  \n",
       "total sulfur dioxide -0.001177  -0.371664     -0.041385  \n",
       "density               0.503602   6.606067     -0.305858  \n",
       "alcohol               0.565718  -0.531687      0.444319  \n",
       "quality               0.189623   0.232322      1.000000  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.describe(include='all')\n",
    "details = rstr(df, 'quality')\n",
    "details.sort_values(by='missing_ration', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222aa34a710>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type                    False\n",
       "fixed acidity            True\n",
       "volatile acidity         True\n",
       "citric acid              True\n",
       "residual sugar           True\n",
       "chlorides                True\n",
       "free sulfur dioxide     False\n",
       "total sulfur dioxide    False\n",
       "density                 False\n",
       "pH                       True\n",
       "sulphates                True\n",
       "alcohol                 False\n",
       "quality                 False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sub2['income'] = sub2['income'].fillna((sub2['income'].mean()))\n",
    "df['fixed acidity'] = df['fixed acidity'].fillna((df['fixed acidity'].mean()))\n",
    "df['volatile acidity'] = df['volatile acidity'].fillna((df['volatile acidity'].mean()))\n",
    "df['citric acid'] = df['citric acid'].fillna((df['citric acid'].mean()))\n",
    "df['residual sugar'] = df['residual sugar'].fillna((df['residual sugar'].mean()))\n",
    "df['chlorides'] = df['chlorides'].fillna((df['chlorides'].mean()))\n",
    "df['pH'] = df['pH'].fillna((df['pH'].mean()))\n",
    "df['sulphates'] = df['sulphates'].fillna((df['sulphates'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type                    False\n",
       "fixed acidity           False\n",
       "volatile acidity        False\n",
       "citric acid             False\n",
       "residual sugar          False\n",
       "chlorides               False\n",
       "free sulfur dioxide     False\n",
       "total sulfur dioxide    False\n",
       "density                 False\n",
       "pH                      False\n",
       "sulphates               False\n",
       "alcohol                 False\n",
       "quality                 False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()\n",
    "#df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>type</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>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0  white            7.0              0.27         0.36            20.7   \n",
       "1  white            6.3              0.30         0.34             1.6   \n",
       "2  white            8.1              0.28         0.40             6.9   \n",
       "3  white            7.2              0.23         0.32             8.5   \n",
       "4  white            7.2              0.23         0.32             8.5   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.045                 45.0                 170.0   1.0010  3.00   \n",
       "1      0.049                 14.0                 132.0   0.9940  3.30   \n",
       "2      0.050                 30.0                  97.0   0.9951  3.26   \n",
       "3      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "4      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "\n",
       "   sulphates  alcohol  quality  \n",
       "0       0.45      8.8        6  \n",
       "1       0.49      9.5        6  \n",
       "2       0.44     10.1        6  \n",
       "3       0.40      9.9        6  \n",
       "4       0.40      9.9        6  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>type</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>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6497</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>white</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.216579</td>\n",
       "      <td>0.339691</td>\n",
       "      <td>0.318722</td>\n",
       "      <td>5.444326</td>\n",
       "      <td>0.056042</td>\n",
       "      <td>30.525319</td>\n",
       "      <td>115.744574</td>\n",
       "      <td>0.994697</td>\n",
       "      <td>3.218395</td>\n",
       "      <td>0.531215</td>\n",
       "      <td>10.491801</td>\n",
       "      <td>5.818378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.295751</td>\n",
       "      <td>0.164548</td>\n",
       "      <td>0.145231</td>\n",
       "      <td>4.757392</td>\n",
       "      <td>0.035031</td>\n",
       "      <td>17.749400</td>\n",
       "      <td>56.521855</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.160637</td>\n",
       "      <td>0.148768</td>\n",
       "      <td>1.192712</td>\n",
       "      <td>0.873255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.992340</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>0.994890</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.065000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>0.996990</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    6497    6497.000000       6497.000000  6497.000000     6497.000000   \n",
       "unique      2            NaN               NaN          NaN             NaN   \n",
       "top     white            NaN               NaN          NaN             NaN   \n",
       "freq     4898            NaN               NaN          NaN             NaN   \n",
       "mean      NaN       7.216579          0.339691     0.318722        5.444326   \n",
       "std       NaN       1.295751          0.164548     0.145231        4.757392   \n",
       "min       NaN       3.800000          0.080000     0.000000        0.600000   \n",
       "25%       NaN       6.400000          0.230000     0.250000        1.800000   \n",
       "50%       NaN       7.000000          0.290000     0.310000        3.000000   \n",
       "75%       NaN       7.700000          0.400000     0.390000        8.100000   \n",
       "max       NaN      15.900000          1.580000     1.660000       65.800000   \n",
       "\n",
       "          chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count   6497.000000          6497.000000           6497.000000  6497.000000   \n",
       "unique          NaN                  NaN                   NaN          NaN   \n",
       "top             NaN                  NaN                   NaN          NaN   \n",
       "freq            NaN                  NaN                   NaN          NaN   \n",
       "mean       0.056042            30.525319            115.744574     0.994697   \n",
       "std        0.035031            17.749400             56.521855     0.002999   \n",
       "min        0.009000             1.000000              6.000000     0.987110   \n",
       "25%        0.038000            17.000000             77.000000     0.992340   \n",
       "50%        0.047000            29.000000            118.000000     0.994890   \n",
       "75%        0.065000            41.000000            156.000000     0.996990   \n",
       "max        0.611000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                 pH    sulphates      alcohol      quality  \n",
       "count   6497.000000  6497.000000  6497.000000  6497.000000  \n",
       "unique          NaN          NaN          NaN          NaN  \n",
       "top             NaN          NaN          NaN          NaN  \n",
       "freq            NaN          NaN          NaN          NaN  \n",
       "mean       3.218395     0.531215    10.491801     5.818378  \n",
       "std        0.160637     0.148768     1.192712     0.873255  \n",
       "min        2.720000     0.220000     8.000000     3.000000  \n",
       "25%        3.110000     0.430000     9.500000     5.000000  \n",
       "50%        3.210000     0.510000    10.300000     6.000000  \n",
       "75%        3.320000     0.600000    11.300000     6.000000  \n",
       "max        4.010000     2.000000    14.900000     9.000000  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#g = sns.pairplot(df,palette=\"husl\",diag_kind=\"kde\",hue='type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222aa444940>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(df.corr(),annot=True,vmin=-1,cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualising the frequency distribution\n",
    "def distribution(data =df): \n",
    "   #Alcohol level also goes higher as te quality of wine increases\n",
    "   plt.figure(figsize=(12,10))\n",
    "   sns.barplot(x=df.type, y=df.alcohol,data=df, hue=df.quality,palette='spring')\n",
    "   plt.xlabel('Quality',fontsize=16)\n",
    "   plt.ylabel('Alcohol',fontsize=16) \n",
    "   \n",
    "   plt.figure(figsize=(12,10))\n",
    "   sns.barplot(x=df.type, y=df['fixed acidity'],data=df, hue=df.quality,palette='spring')\n",
    "   plt.xlabel('Quality',fontsize=16)\n",
    "   plt.ylabel('fixed acidity',fontsize=16)\n",
    "    \n",
    "   #Sulphates level goes higher with the quality of wine\n",
    "   plt.figure(figsize=(12,10))\n",
    "   sns.barplot(x=df.type, y=df.sulphates,data=df, hue=df.quality,palette='spring')\n",
    "   plt.xlabel('Quality',fontsize=16)\n",
    "   plt.ylabel('Sulphates',fontsize=16) \n",
    "   \n",
    "   plt.figure(figsize=(12,10))\n",
    "   sns.barplot(x=df.type, y=df.pH,data=df, hue=df.quality,palette='spring')\n",
    "   plt.xlabel('Quality',fontsize=16)\n",
    "   plt.ylabel('pH',fontsize=16) \n",
    "    \n",
    "   plt.figure(figsize=(12,10))\n",
    "   sns.barplot(x=df.type, y=df.density,data=df, hue=df.quality,palette='spring')\n",
    "   plt.xlabel('Quality',fontsize=16)\n",
    "   plt.ylabel('Density',fontsize=16) \n",
    "   \n",
    "   plt.figure(figsize=(12,10))\n",
    "   sns.barplot(x=df.type, y=df['volatile acidity'],data=df, hue=df.quality,palette='spring')\n",
    "   plt.xlabel('Quality',fontsize=16)\n",
    "   plt.ylabel('volatile acidity',fontsize=16) \n",
    "   \n",
    "   #fig = plt.figure(figsize = (10,6))\n",
    "   #sns.barplot(x = 'quality', y = 'volatile acidity', data = df)\n",
    "    \n",
    "   #fig = plt.figure(figsize = (10,6))\n",
    "   #sns.barplot(x = 'quality', y = 'citric acid', data = df)\n",
    "    \n",
    "   #fig = plt.figure(figsize = (10,6))\n",
    "   #sns.barplot(x = 'quality', y = 'pH', data = df)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x1296 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using a histogram to see the distribution of the variables.\n",
    "fig,axes=plt.subplots(6,6)\n",
    "columns=['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
    "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
    "       'pH', 'sulphates', 'alcohol', 'quality']\n",
    "for i in range (6):\n",
    "    for j in range (6):\n",
    "        axes[i,j].hist(x=columns[i+j],data=df,edgecolor='#000000',linewidth=2,color='#ff4125')\n",
    "        axes[i,j].set_title('Variation of '+columns[i+j])\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(18,18)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>type</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>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0  white            7.0              0.27         0.36            20.7   \n",
       "1  white            6.3              0.30         0.34             1.6   \n",
       "2  white            8.1              0.28         0.40             6.9   \n",
       "3  white            7.2              0.23         0.32             8.5   \n",
       "4  white            7.2              0.23         0.32             8.5   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.045                 45.0                 170.0   1.0010  3.00   \n",
       "1      0.049                 14.0                 132.0   0.9940  3.30   \n",
       "2      0.050                 30.0                  97.0   0.9951  3.26   \n",
       "3      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "4      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "\n",
       "   sulphates  alcohol  quality  \n",
       "0       0.45      8.8        6  \n",
       "1       0.49      9.5        6  \n",
       "2       0.44     10.1        6  \n",
       "3       0.40      9.9        6  \n",
       "4       0.40      9.9        6  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,1:12].values\n",
    "y = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6497,)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of feature names\n",
    "feat_labels = ['fixed acidity','volatile acidity','citric acid','residual sugar','chlorides', 'free sulfur dioxide','total sulfur dioxide',\n",
    "              'density','pH', '\tsulphates', '\talcohol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (5197, 11)\n",
      "Training Labels Shape: (5197,)\n",
      "Testing Features Shape: (1300, 11)\n",
      "Testing Labels Shape: (1300,)\n"
     ]
    }
   ],
   "source": [
    "# Splitting the dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "print('Training Features Shape:', X_train.shape)\n",
    "print('Training Labels Shape:', y_train.shape)\n",
    "print('Testing Features Shape:', X_test.shape)\n",
    "print('Testing Labels Shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_estimators': [500], 'max_features': ['auto', 'sqrt', 'log2']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_dict={'n_estimators':[500],'max_features':['auto','sqrt','log2']}\n",
    "clf_rf=GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1),param_grid=params_dict,scoring='accuracy',cv=10)\n",
    "clf_rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the traning set\n",
    "rf_pred = clf_rf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.34 degrees.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the absolute errors\n",
    "errors = abs(rf_pred - y_test)\n",
    "# Print out the mean absolute error (mae)\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: [ 0.         14.28571429  0.         ... 16.66666667  0.\n",
      "  0.        ]\n",
      "Accuracy: 94.09 %.\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean absolute percentage error (MAPE)\n",
    "mape = 100 * (errors / y_test)\n",
    "print('Error:', mape)\n",
    "# Calculate and display accuracy\n",
    "accuracy = 100 - np.mean(mape)\n",
    "print('Accuracy:', round(accuracy, 2), '%.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222bf7c8c50>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,rf_pred),annot = True,fmt = 'd',cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_set , y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "\n",
    "Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in range(9)]).T\n",
    "# Xpred now has a grid for x1 and x2 and average value (0) for x3 through x13\n",
    "pred = clf_rf.predict(Xpred).reshape(X1.shape)   # is a matrix of 0's and 1's !\n",
    "plt.contourf(X1, X2, pred,\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('yellow', 'green'))(i), label = j)\n",
    "plt.title('Random Forest (Training set)')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Estimated quality')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set =  X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "\n",
    "Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in range(9)]).T\n",
    "# Xpred now has a grid for x1 and x2 and average value (0) for x3 through x11\n",
    "pred = clf_rf.predict(Xpred).reshape(X1.shape)   # is a matrix of 0's and 1's !\n",
    "plt.contourf(X1, X2, pred,\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('yellow', 'green'))(i), label = j)\n",
    "plt.title('Random Forest (Test set)')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Estimated quality')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixed acidity          -0.076967\n",
      "volatile acidity       -0.265838\n",
      "citric acid             0.085699\n",
      "residual sugar         -0.036819\n",
      "chlorides              -0.200818\n",
      "free sulfur dioxide     0.055463\n",
      "total sulfur dioxide   -0.041385\n",
      "density                -0.305858\n",
      "pH                      0.019356\n",
      "sulphates               0.038713\n",
      "alcohol                 0.444319\n",
      "Name: quality, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Visualize the correlations of each variable about the variable \" Quality\"\n",
    "correlations = df.corr()['quality'].drop('quality')\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = correlations.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rf_pred - y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4023076923076923"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean squared error\n",
    "mean_squared_error(y_test, rf_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report: \n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          3       0.00      0.00      0.00         2\n",
      "          4       1.00      0.13      0.23        46\n",
      "          5       0.71      0.76      0.74       420\n",
      "          6       0.67      0.79      0.73       579\n",
      "          7       0.74      0.51      0.60       221\n",
      "          8       0.89      0.25      0.39        32\n",
      "\n",
      "avg / total       0.71      0.70      0.68      1300\n",
      "\n",
      "\n",
      "Accuracy Score:  0.6953846153846154\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222a9a85240>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##getting the classification report\n",
    "print (\"\")\n",
    "print (\"Classification Report: \")\n",
    "print (classification_report(y_test, rf_pred))\n",
    "\n",
    "print (\"\")\n",
    "print (\"Accuracy Score: \", accuracy_score(y_test, rf_pred))\n",
    "\n",
    "##getting the confusion matrix..\n",
    "sns.heatmap(confusion_matrix(y_test,rf_pred),annot = True,fmt = 'd',cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_pred_op = clf_rf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is:  0.6953846153846154\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222a97670b8>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ac_2 = accuracy_score(y_test,clf_rf.predict(X_test))\n",
    "print('Accuracy is: ',ac_2)\n",
    "cm_2 = confusion_matrix(y_test,clf_rf.predict(X_test))\n",
    "sns.heatmap(cm_2,annot=True,fmt=\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score list: [8.15104734e+00 3.19979199e+01 3.04637928e+00 1.97985078e+02\n",
      " 4.48248870e+00 7.43496666e+02 1.18285070e+03 4.74222651e-03\n",
      " 1.23197458e-01 1.32331363e+00 1.54036384e+02]\n"
     ]
    }
   ],
   "source": [
    "select_feature = SelectKBest(chi2, k=9).fit(X_train,y_train)\n",
    "print('Score list:', select_feature.scores_)\n",
    "#print('Feature list:', X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_2 = select_feature.transform(X_train)\n",
    "x_test_2 = select_feature.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=20, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_estimators': [1000], 'max_features': ['auto', 'sqrt', 'log2']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_dict={'n_estimators':[1000],'max_features':['auto','sqrt','log2']}\n",
    "clf_rf_2=GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1  ),param_grid=params_dict,scoring='accuracy',cv=20)\n",
    "clf_rf_2.fit(x_train_2, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicts = clf_rf_2.predict(x_test_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.35 degrees.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the absolute errors\n",
    "errors = abs(predicts - y_test)\n",
    "# Print out the mean absolute error (mae)\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.82 %.\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean absolute percentage error (MAPE)\n",
    "mape = 100 * (errors / y_test)\n",
    "# Calculate and display accuracy\n",
    "accuracy = 100 - np.mean(mape)\n",
    "print('Accuracy:', round(accuracy, 2), '%.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_set , y_set = x_train_2, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "\n",
    "Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in range(7)]).T\n",
    "# Xpred now has a grid for x1 and x2 and average value (0) for x3 through x13\n",
    "pred = clf_rf_2.predict(Xpred).reshape(X1.shape)   # is a matrix of 0's and 1's !\n",
    "plt.contourf(X1, X2, pred,\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('yellow', 'green'))(i), label = j)\n",
    "plt.title('RF with important variables (Training set)')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Estimated quality')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set =  x_test_2, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "\n",
    "Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in range(7)]).T\n",
    "# Xpred now has a grid for x1 and x2 and average value (0) for x3 through x11\n",
    "pred = clf_rf_2.predict(Xpred).reshape(X1.shape)   # is a matrix of 0's and 1's !\n",
    "plt.contourf(X1, X2, pred,\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('yellow', 'green'))(i), label = j)\n",
    "plt.title('RF with important variables (Test set)')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Estimated quality')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is:  0.6815384615384615\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222a826f438>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ac_2 = accuracy_score(y_test,clf_rf_2.predict(x_test_2))\n",
    "print('Accuracy is: ',ac_2)\n",
    "cm_2 = confusion_matrix(y_test,clf_rf_2.predict(x_test_2))\n",
    "sns.heatmap(cm_2,annot=True,fmt=\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################################# SVM ##############################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>type</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>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0  white            7.0              0.27         0.36            20.7   \n",
       "1  white            6.3              0.30         0.34             1.6   \n",
       "2  white            8.1              0.28         0.40             6.9   \n",
       "3  white            7.2              0.23         0.32             8.5   \n",
       "4  white            7.2              0.23         0.32             8.5   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.045                 45.0                 170.0   1.0010  3.00   \n",
       "1      0.049                 14.0                 132.0   0.9940  3.30   \n",
       "2      0.050                 30.0                  97.0   0.9951  3.26   \n",
       "3      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "4      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "\n",
       "   sulphates  alcohol  quality  \n",
       "0       0.45      8.8        6  \n",
       "1       0.49      9.5        6  \n",
       "2       0.44     10.1        6  \n",
       "3       0.40      9.9        6  \n",
       "4       0.40      9.9        6  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df)):\n",
    "    if df.iloc[i,12]>=7:\n",
    "        df.iloc[i,12]='good'\n",
    "    else:\n",
    "        df.iloc[i,12]='bad'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</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>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>white</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.045</td>\n",
       "      <td>30.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.9949</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.6</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>white</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>white</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.044</td>\n",
       "      <td>28.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.9938</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.45</td>\n",
       "      <td>11.0</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0  white            7.0              0.27         0.36            20.7   \n",
       "1  white            6.3              0.30         0.34             1.6   \n",
       "2  white            8.1              0.28         0.40             6.9   \n",
       "3  white            7.2              0.23         0.32             8.5   \n",
       "4  white            7.2              0.23         0.32             8.5   \n",
       "5  white            8.1              0.28         0.40             6.9   \n",
       "6  white            6.2              0.32         0.16             7.0   \n",
       "7  white            7.0              0.27         0.36            20.7   \n",
       "8  white            6.3              0.30         0.34             1.6   \n",
       "9  white            8.1              0.22         0.43             1.5   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.045                 45.0                 170.0   1.0010  3.00   \n",
       "1      0.049                 14.0                 132.0   0.9940  3.30   \n",
       "2      0.050                 30.0                  97.0   0.9951  3.26   \n",
       "3      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "4      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "5      0.050                 30.0                  97.0   0.9951  3.26   \n",
       "6      0.045                 30.0                 136.0   0.9949  3.18   \n",
       "7      0.045                 45.0                 170.0   1.0010  3.00   \n",
       "8      0.049                 14.0                 132.0   0.9940  3.30   \n",
       "9      0.044                 28.0                 129.0   0.9938  3.22   \n",
       "\n",
       "   sulphates  alcohol quality  \n",
       "0       0.45      8.8     bad  \n",
       "1       0.49      9.5     bad  \n",
       "2       0.44     10.1     bad  \n",
       "3       0.40      9.9     bad  \n",
       "4       0.40      9.9     bad  \n",
       "5       0.44     10.1     bad  \n",
       "6       0.47      9.6     bad  \n",
       "7       0.45      8.8     bad  \n",
       "8       0.49      9.5     bad  \n",
       "9       0.45     11.0     bad  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .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>type</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>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>white</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>white</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.045</td>\n",
       "      <td>30.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.9949</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>white</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>white</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>white</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.044</td>\n",
       "      <td>28.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.9938</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.45</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0  white            7.0              0.27         0.36            20.7   \n",
       "1  white            6.3              0.30         0.34             1.6   \n",
       "2  white            8.1              0.28         0.40             6.9   \n",
       "3  white            7.2              0.23         0.32             8.5   \n",
       "4  white            7.2              0.23         0.32             8.5   \n",
       "5  white            8.1              0.28         0.40             6.9   \n",
       "6  white            6.2              0.32         0.16             7.0   \n",
       "7  white            7.0              0.27         0.36            20.7   \n",
       "8  white            6.3              0.30         0.34             1.6   \n",
       "9  white            8.1              0.22         0.43             1.5   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
       "0      0.045                 45.0                 170.0   1.0010  3.00   \n",
       "1      0.049                 14.0                 132.0   0.9940  3.30   \n",
       "2      0.050                 30.0                  97.0   0.9951  3.26   \n",
       "3      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "4      0.058                 47.0                 186.0   0.9956  3.19   \n",
       "5      0.050                 30.0                  97.0   0.9951  3.26   \n",
       "6      0.045                 30.0                 136.0   0.9949  3.18   \n",
       "7      0.045                 45.0                 170.0   1.0010  3.00   \n",
       "8      0.049                 14.0                 132.0   0.9940  3.30   \n",
       "9      0.044                 28.0                 129.0   0.9938  3.22   \n",
       "\n",
       "   sulphates  alcohol  quality  \n",
       "0       0.45      8.8        0  \n",
       "1       0.49      9.5        0  \n",
       "2       0.44     10.1        0  \n",
       "3       0.40      9.9        0  \n",
       "4       0.40      9.9        0  \n",
       "5       0.44     10.1        0  \n",
       "6       0.47      9.6        0  \n",
       "7       0.45      8.8        0  \n",
       "8       0.49      9.5        0  \n",
       "9       0.45     11.0        0  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelencdoer=LabelEncoder()\n",
    "df['quality']=labelencdoer.fit_transform(df['quality'])\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,1:12].values\n",
    "y = df.iloc[:, 12].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling the Xvalue\n",
    "sc=StandardScaler()\n",
    "X=sc.fit_transform(X[:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (4547, 11)\n",
      "Training Labels Shape: (4547,)\n",
      "Testing Features Shape: (1950, 11)\n",
      "Testing Labels Shape: (1950,)\n"
     ]
    }
   ],
   "source": [
    "#Divide dataset into Training data and validation data\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "#                                                    y,\n",
    "#                                                    test_size=0.35,\n",
    "#                                                    shuffle = True\n",
    "#                                                    )\n",
    "\n",
    "#ransom_state = 42\n",
    "#Splitting into train and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state=42)\n",
    "print('Training Features Shape:', X_train.shape)\n",
    "print('Training Labels Shape:', y_train.shape)\n",
    "print('Testing Features Shape:', X_test.shape)\n",
    "print('Testing Labels Shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_estimators': [500], 'max_features': ['auto', 'sqrt', 'log2']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_dict={'n_estimators':[500],'max_features':['auto','sqrt','log2']}\n",
    "rfr=GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1),param_grid=params_dict,scoring='accuracy',cv=10)\n",
    "rfr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs = rfr.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.91\n"
     ]
    }
   ],
   "source": [
    "probs = probs[:, 1]\n",
    "auc = roc_auc_score(y_test, probs)\n",
    "print('AUC: %.2f' % auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_svm = rfr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08665510769230769"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report: \n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.97      0.93      1571\n",
      "          1       0.80      0.54      0.64       379\n",
      "\n",
      "avg / total       0.88      0.88      0.87      1950\n",
      "\n",
      "\n",
      "Accuracy Score:  0.884102564102564\n"
     ]
    }
   ],
   "source": [
    "print (\"\")\n",
    "print (\"Classification Report: \")\n",
    "print (classification_report(y_test, predict_svm))\n",
    "\n",
    "print (\"\")\n",
    "print (\"Accuracy Score: \", accuracy_score(y_test, predict_svm))\n",
    "#print(classification_report(y_test, pred_svc2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, predict_svm)\n",
    "# Plotting the ROC\n",
    "def plot_roc_curve(fpr, tpr):\n",
    "    plt.plot(fpr, tpr, color='orange', label='ROC %0.2f' % auc )\n",
    "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_set , y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "\n",
    "Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in range(9)]).T\n",
    "# Xpred now has a grid for x1 and x2 and average value (0) for x3 through x13\n",
    "pred = rfr.predict(Xpred).reshape(X1.shape)   # is a matrix of 0's and 1's !\n",
    "plt.contourf(X1, X2, pred,\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'yellow'))(i), label = j)\n",
    "plt.title('RF with important features (Training set)')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Estimated quality')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set =  X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "\n",
    "Xpred = np.array([X1.ravel(), X2.ravel()] + [np.repeat(0, X1.ravel().size) for _ in range(9)]).T\n",
    "# Xpred now has a grid for x1 and x2 and average value (0) for x3 through x11\n",
    "pred = rfr.predict(Xpred).reshape(X1.shape)   # is a matrix of 0's and 1's !\n",
    "plt.contourf(X1, X2, pred,\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('yellow', 'green'))(i), label = j)\n",
    "plt.title('RF with important features (Test set)')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Estimated quality')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  88.41025641025641 %\n"
     ]
    }
   ],
   "source": [
    "#Final Prediction\n",
    "y_pred=np.matrix.round(rfr.predict(X_test))\n",
    "acc=accuracy_score(y_test, y_pred)\n",
    "print(\"accuracy: \",acc*100,'%' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222aa593a58>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,predict_svm),annot = True,fmt = 'd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4], 'kernel': ['linear', 'rbf'], 'gamma': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "#Finding best parameters for our SVC model\n",
    "param = {\n",
    "    'C': [0.1,0.8,0.9,1,1.1,1.2,1.3,1.4],\n",
    "    'kernel':['linear', 'rbf'],\n",
    "    'gamma' :[0.1,0.8,0.9,1,1.1,1.2,1.3,1.4]\n",
    "}\n",
    "grid_svc = GridSearchCV(svc, param_grid=param, scoring='accuracy', cv=10)\n",
    "#Fitting the model\n",
    "grid_svc.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.4, 'gamma': 1.4, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_svc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report: \n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.96      0.92      1571\n",
      "          1       0.76      0.52      0.62       379\n",
      "\n",
      "avg / total       0.87      0.87      0.86      1950\n",
      "\n",
      "\n",
      "Accuracy Score:  0.8743589743589744\n"
     ]
    }
   ],
   "source": [
    "#Let's run our SVC again with the best parameters.\n",
    "svc2 = SVC(C = 3, gamma = 1.4, kernel= 'rbf')\n",
    "svc2.fit(X_train, y_train)\n",
    "pred_svc2 = svc2.predict(X_test)\n",
    "print (\"\")\n",
    "print (\"Classification Report: \")\n",
    "print (classification_report(y_test, pred_svc2))\n",
    "\n",
    "print (\"\")\n",
    "print (\"Accuracy Score: \", accuracy_score(y_test, pred_svc2))\n",
    "#print(classification_report(y_test, pred_svc2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222a8680be0>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,pred_svc2),annot = True,fmt = 'd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.74\n"
     ]
    }
   ],
   "source": [
    "auc = roc_auc_score(y_test, pred_svc2)\n",
    "print('AUC: %.2f' % auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, pred_svc2)\n",
    "# Plotting the ROC\n",
    "def plot_roc_curve(fpr, tpr):\n",
    "    plt.plot(fpr, tpr, color='orange', label='ROC %0.2f' % auc )\n",
    "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 120 candidates, totalling 360 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 360 out of 360 | elapsed: 11.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 8, 'max_features': 'auto', 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(random_state = 42)\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 500,700,1000],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [4,5,6,7,8],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "\n",
    "grid_rf = GridSearchCV(estimator = rf,\n",
    "                        param_grid = param_grid,\n",
    "                       scoring = 'recall',\n",
    "                       cv = 3,\n",
    "                       verbose = 1)\n",
    "grid_rf.fit(X_train, y_train)\n",
    "\n",
    "print(grid_rf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report: \n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.97      0.93      1571\n",
      "          1       0.81      0.54      0.65       379\n",
      "\n",
      "avg / total       0.88      0.89      0.88      1950\n",
      "\n",
      "\n",
      "Accuracy Score:  0.8856410256410256\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222a893a198>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Fitting randomforest..\n",
    "rf_op = RandomForestClassifier(n_estimators = 800,max_depth = 60,criterion = 'gini',max_features = 'auto').fit(X_train, y_train)\n",
    "pred_rf_op = rf_op.predict(X_test)\n",
    "\n",
    "\n",
    "##getting the classification report\n",
    "print (\"\")\n",
    "print (\"Classification Report: \")\n",
    "print (classification_report(y_test, pred_rf_op))\n",
    "\n",
    "print (\"\")\n",
    "print (\"Accuracy Score: \", accuracy_score(y_test, pred_rf_op))\n",
    "\n",
    "##getting the confusion matrix..\n",
    "sns.heatmap(confusion_matrix(y_test,pred_rf_op),annot = True,fmt = 'd',cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Features selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=10000, random_state=0, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10000, n_jobs=-1,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the classifier\n",
    "clf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('fixed acidity', 0.06790755416162683)\n",
      "('volatile acidity', 0.08968266597399177)\n",
      "('citric acid', 0.07700706569891591)\n",
      "('residual sugar', 0.08144972593435375)\n",
      "('chlorides', 0.08516122903015372)\n",
      "('free sulfur dioxide', 0.07503802068942901)\n",
      "('total sulfur dioxide', 0.08237635205258537)\n",
      "('density', 0.11843411546866173)\n",
      "('pH', 0.0807953605380357)\n",
      "('\\tsulphates', 0.08255075490220601)\n",
      "('\\talcohol', 0.15959715555003995)\n"
     ]
    }
   ],
   "source": [
    "# Print the name and gini importance of each feature\n",
    "for feature in zip(feat_labels, clf.feature_importances_):\n",
    "    print(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectFromModel(estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10000, n_jobs=-1,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False),\n",
       "        norm_order=1, prefit=False, threshold=0.82)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a selector object that will use the random forest classifier to identify\n",
    "# features that have an importance of more than 0.15\n",
    "sfm = SelectFromModel(clf, threshold=0.82)\n",
    "\n",
    "# Train the selector\n",
    "sfm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the names of the most important features\n",
    "for feature_list_index in sfm.get_support(indices=True):\n",
    "\n",
    "    print(feat_labels[feature_list_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the data to create a new dataset containing only the most important features\n",
    "# Note: We have to apply the transform to both the training X and test X data.\n",
    "X_important_train = sfm.transform(X_train)\n",
    "X_important_test = sfm.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(4547, 0), dtype=float64)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_important_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries for model parameter search and hyperparameter tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "#from bayes_opt import BayesianOptimization\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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