WinePredictor(RandomForestClassifier).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "from sklearn.model_selection import train_test_split\r\n",
    "from sklearn.metrics import accuracy_score\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import seaborn as sns\r\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "df=pd.read_csv('./SO2019/wineDataset.csv')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "df.isnull().sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "fixed acidity           0\n",
       "volatile acidity        0\n",
       "citric acid             0\n",
       "residual sugar          0\n",
       "chlorides               0\n",
       "free sulfur dioxide     0\n",
       "total sulfur dioxide    0\n",
       "density                 0\n",
       "pH                      0\n",
       "sulphates               0\n",
       "alcohol                 0\n",
       "quality                 0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# DATA ANALYSIS AND VISUALIZATION\r\n",
    "df.describe()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# NUMBER OF VALUES FOR EACH QUALITY\r\n",
    "sns.catplot(x='quality',data=df,kind='count')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x20c868fb5b0>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "df.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# VOLATILE ACCIDITY VS QUALITY\r\n",
    "plot=plt.figure(figsize=(5,5))\r\n",
    "sns.barplot(x='quality',data=df,y='volatile acidity')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='volatile acidity'>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
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   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "sns.barplot(x='quality',data=df,y='citric acid')\r\n",
    " "
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='citric acid'>"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "# FIND CORRELATION by MAKING A HEAT MAP\r\n",
    "correlation=df.corr()\r\n",
    "plt.figure(figsize=(10,10))\r\n",
    "sns.heatmap(correlation,cbar=True,fmt='.1f',annot=True,annot_kws={'size':8},cmap='Blues')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"188.134766\" xlink:href=\"#DejaVuSans-108\"/>\r\n       <use x=\"215.917969\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"243.701172\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"282.910156\" xlink:href=\"#DejaVuSans-121\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_2\">\r\n    <g id=\"ytick_1\">\r\n     <g id=\"line2d_13\">\r\n      <defs>\r\n       <path d=\"M 0 0 \r\nL -3.5 0 \r\n\" id=\"m92fe73081a\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n      </defs>\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.846875\" xlink:href=\"#m92fe73081a\" y=\"33.649219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_13\">\r\n      <!-- fixed acidity -->\r\n      <g transform=\"translate(39.892188 37.448438)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-102\"/>\r\n       <use x=\"35.205078\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use 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acidity -->\r\n      <g transform=\"translate(27.725 82.748438)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-118\"/>\r\n       <use x=\"59.179688\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"120.361328\" xlink:href=\"#DejaVuSans-108\"/>\r\n       <use x=\"148.144531\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"209.423828\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"248.632812\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"276.416016\" xlink:href=\"#DejaVuSans-108\"/>\r\n       <use x=\"304.199219\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"365.722656\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"397.509766\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"458.789062\" xlink:href=\"#DejaVuSans-99\"/>\r\n       <use x=\"513.769531\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"541.552734\" xlink:href=\"#DejaVuSans-100\"/>\r\n       <use x=\"605.029297\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"632.8125\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"672.021484\" xlink:href=\"#DejaVuSans-121\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_3\">\r\n     <g id=\"line2d_15\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.846875\" xlink:href=\"#m92fe73081a\" y=\"124.249219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_15\">\r\n      <!-- citric acid -->\r\n      <g transform=\"translate(52.33125 128.048438)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-99\"/>\r\n       <use x=\"54.980469\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"82.763672\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"121.972656\" xlink:href=\"#DejaVuSans-114\"/>\r\n       <use x=\"163.085938\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-99\"/>\r\n       <use x=\"245.849609\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"277.636719\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"338.916016\" xlink:href=\"#DejaVuSans-99\"/>\r\n       <use x=\"393.896484\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"421.679688\" xlink:href=\"#DejaVuSans-100\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_4\">\r\n     <g id=\"line2d_16\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.846875\" xlink:href=\"#m92fe73081a\" y=\"169.549219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_16\">\r\n      <!-- residual sugar -->\r\n      <g transform=\"translate(29.915625 173.348438)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-114\"/>\r\n       <use x=\"38.863281\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"100.386719\" xlink:href=\"#DejaVuSans-115\"/>\r\n       <use x=\"152.486328\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"180.269531\" xlink:href=\"#DejaVuSans-100\"/>\r\n       <use x=\"243.746094\" xlink:href=\"#DejaVuSans-117\"/>\r\n       <use x=\"307.125\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"368.404297\" xlink:href=\"#DejaVuSans-108\"/>\r\n       <use x=\"396.1875\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"427.974609\" xlink:href=\"#DejaVuSans-115\"/>\r\n       <use x=\"480.074219\" xlink:href=\"#DejaVuSans-117\"/>\r\n       <use x=\"543.453125\" xlink:href=\"#DejaVuSans-103\"/>\r\n       <use x=\"606.929688\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"668.208984\" xlink:href=\"#DejaVuSans-114\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_5\">\r\n     <g id=\"line2d_17\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.846875\" xlink:href=\"#m92fe73081a\" y=\"214.849219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_17\">\r\n      <!-- chlorides -->\r\n      <g transform=\"translate(55.514063 218.648438)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-99\"/>\r\n       <use x=\"54.980469\" xlink:href=\"#DejaVuSans-104\"/>\r\n       <use x=\"118.359375\" xlink:href=\"#DejaVuSans-108\"/>\r\n       <use x=\"146.142578\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"207.324219\" xlink:href=\"#DejaVuSans-114\"/>\r\n       <use x=\"248.4375\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"276.220703\" xlink:href=\"#DejaVuSans-100\"/>\r\n       <use x=\"339.697266\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"401.220703\" xlink:href=\"#DejaVuSans-115\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_6\">\r\n     <g id=\"line2d_18\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.846875\" xlink:href=\"#m92fe73081a\" y=\"260.149219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_18\">\r\n      <!-- free sulfur dioxide -->\r\n      <g transform=\"translate(10.353125 263.948438)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-102\"/>\r\n       <use x=\"35.205078\" xlink:href=\"#DejaVuSans-114\"/>\r\n       <use x=\"74.068359\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"135.591797\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"197.115234\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"228.902344\" xlink:href=\"#DejaVuSans-115\"/>\r\n       <use x=\"281.001953\" xlink:href=\"#DejaVuSans-117\"/>\r\n       <use x=\"344.380859\" xlink:href=\"#DejaVuSans-108\"/>\r\n       <use x=\"372.164062\" xlink:href=\"#DejaVuSans-102\"/>\r\n       <use x=\"407.369141\" xlink:href=\"#DejaVuSans-117\"/>\r\n       <use x=\"470.748047\" xlink:href=\"#DejaVuSans-114\"/>\r\n       <use x=\"511.861328\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"543.648438\" xlink:href=\"#DejaVuSans-100\"/>\r\n       <use x=\"607.125\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"634.908203\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"692.964844\" xlink:href=\"#DejaVuSans-120\"/>\r\n       <use x=\"752.144531\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"779.927734\" xlink:href=\"#DejaVuSans-100\"/>\r\n       <use x=\"843.404297\" xlink:href=\"#DejaVuSans-101\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_7\">\r\n     <g id=\"line2d_19\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.846875\" xlink:href=\"#m92fe73081a\" y=\"305.449219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_19\">\r\n      <!-- total sulfur dioxide -->\r\n      <g transform=\"translate(7.2 309.248438)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"39.208984\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"100.390625\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"139.599609\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"200.878906\" xlink:href=\"#DejaVuSans-108\"/>\r\n       <use x=\"228.662109\" xlink:href=\"#DejaVuSans-32\"/>\r\n       <use x=\"260.449219\" xlink:href=\"#DejaVuSans-115\"/>\r\n       <use x=\"312.548828\" xlink:href=\"#DejaVuSans-117\"/>\r\n       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y=\"350.749219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_20\">\r\n      <!-- density -->\r\n      <g transform=\"translate(64.18125 354.548438)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-100\"/>\r\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"125\" xlink:href=\"#DejaVuSans-110\"/>\r\n       <use x=\"188.378906\" xlink:href=\"#DejaVuSans-115\"/>\r\n       <use x=\"240.478516\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"268.261719\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"307.470703\" xlink:href=\"#DejaVuSans-121\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_9\">\r\n     <g id=\"line2d_21\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.846875\" xlink:href=\"#m92fe73081a\" y=\"396.049219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_21\">\r\n      <!-- pH -->\r\n      <g transform=\"translate(86.978125 399.848437)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-112\"/>\r\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-72\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_10\">\r\n     <g id=\"line2d_22\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.846875\" xlink:href=\"#m92fe73081a\" y=\"441.349219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_22\">\r\n      <!-- sulphates -->\r\n      <g transform=\"translate(52.425 445.148438)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-115\"/>\r\n       <use x=\"52.099609\" xlink:href=\"#DejaVuSans-117\"/>\r\n       <use x=\"115.478516\" xlink:href=\"#DejaVuSans-108\"/>\r\n       <use x=\"143.261719\" xlink:href=\"#DejaVuSans-112\"/>\r\n       <use x=\"206.738281\" xlink:href=\"#DejaVuSans-104\"/>\r\n       <use x=\"270.117188\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"331.396484\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"370.605469\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"432.128906\" xlink:href=\"#DejaVuSans-115\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_11\">\r\n     <g id=\"line2d_23\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.846875\" xlink:href=\"#m92fe73081a\" y=\"486.649219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_23\">\r\n      <!-- alcohol -->\r\n      <g transform=\"translate(65.089063 490.448437)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"61.279297\" xlink:href=\"#DejaVuSans-108\"/>\r\n       <use x=\"89.0625\" xlink:href=\"#DejaVuSans-99\"/>\r\n       <use x=\"144.042969\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"205.224609\" xlink:href=\"#DejaVuSans-104\"/>\r\n       <use x=\"268.603516\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"329.785156\" xlink:href=\"#DejaVuSans-108\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_12\">\r\n     <g id=\"line2d_24\">\r\n      <g>\r\n       <use 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  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "# DATA PREPROCESSING\r\n",
    "#   SEPARATE THE DATA AND LABEL\r\n",
    "X=df.drop('quality',axis=1)\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "# LABEL BINARIZATION\r\n",
    "Y=df['quality'].apply(lambda x: 1 if x>=7 else 0)\r\n",
    "Y.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    1382\n",
       "1     217\n",
       "Name: quality, dtype: int64"
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     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "metadata": {}
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  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "# SPLITTING THE DATA\r\n",
    "XTrain, XTest, YTrain, YTest= train_test_split(X, Y, test_size=0.2, random_state=3)\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "YTrain.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(1279,)"
      ]
     },
     "metadata": {},
     "execution_count": 27
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   ],
   "metadata": {}
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  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "# MODEL TRAINING \r\n",
    "# RANDOM FOREST CLASSIFIER\r\n",
    "model=RandomForestClassifier()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "model.fit(XTrain, YTrain)\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "metadata": {}
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  {
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   "execution_count": 20,
   "source": [
    "# EVALUATION\r\n",
    "#  accuracy score of TRAINING DATA\r\n",
    "XTrainPred=model.predict(XTrain)\r\n",
    "trainDataAcc=accuracy_score(XTrainPred, YTrain)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "trainDataAcc"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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     },
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   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "# ACCURACY SCORE ON TEST DATASET\r\n",
    "XTestPred=model.predict(XTest)\r\n",
    "testDataAcc=accuracy_score(XTestPred,YTest)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "testDataAcc"
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   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.93125"
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     "metadata": {},
     "execution_count": 23
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   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "# BUILDING A PREDICTIVE SYSTEM\r\n",
    "inputData=(7.3,0.65,0.0,1.2,0.065,15.0,21.0,0.9946,3.39,0.47,10.0)\r\n",
    "inputNP=np.array(inputData)\r\n",
    "# RESHAPE THE DATA\r\n",
    "inputReshape=inputNP.reshape(1,-1)\r\n",
    "prediction=model.predict(inputReshape)\r\n",
    "prediction"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "metadata": {}
  }
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