Wine Quality Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_dataset = pd.read_csv('redwine_quality.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>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  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2cece797640>"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'quality', data = wine_dataset, kind = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ceceebcf10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zSNKc9FmUBwAbJk1v7OZty78FvtBjHkmak6U9bjvTzKtpV0x+lUFR/so2lp8BnAFw8MEHz1c+SRpKn3uUG4GDJk0fCNwzdaUkhwMXAidX1fen21BVramqFVW1YtmyZb2ElaRt6bMorwWWJzk0yW7AKcAVk1dIcjBwOXBaVf1jj1kkac56O/Suqi1JzgauApYAF1XVzUnO7JavBv4M+FngI0kAtlTVir4ySdJc9PkdJVW1Flg7Zd7qSe/fBrytzwyStL28M0eSGixKSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBotSkhosSklqsCglqcGilKQGi1KSGixKSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBotSkhosSklqsCglqcGilKQGi1KSGixKSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBotSkhosSklqsCglqcGilKQGi1KSGixKSWqwKCWpodeiTHJCktuSrE9y7jTLk+RD3fLvJHlxn3kkaS56K8okS4ALgBOBw4BTkxw2ZbUTgeXdzxnAR/vKI0lz1ece5ZHA+qq6vaoeBy4FTp6yzsnAJTXwDWDfJM/pMZMkzVqfRXkAsGHS9MZu3mzXkaSRWtrjtjPNvJrDOiQ5g8GhOcAjSW7bzmzbsh9w/3xsKOe9dT42Mxvzlp33TPevpTfz92f+jgXNDfP5Z57F+Wf+9v86H1uZlXnL/qeffNqf+c9va90+i3IjcNCk6QOBe+awDlW1Blgz3wGnSrKuqlb0/Tl9WKzZF2tuWLzZF2tuGF32Pg+9rwWWJzk0yW7AKcAVU9a5AnhLd/b7KOAHVXVvj5kkadZ626Osqi1JzgauApYAF1XVzUnO7JavBtYCJwHrgceA0/vKI0lz1eehN1W1lkEZTp63etL7As7qM8Ms9X5436PFmn2x5obFm32x5oYRZc+gqyRJ2+ItjJLUYFECSXZP8q0k305yc5L3jjrTbCRZkuQfknx+1FlmI8mdSW5MckOSdaPOMxtJ9k1yWZLvJrk1yctHnaklyfO7P+utPw8ledeocw0jyTnd/5s3JflUkt0X9PM99B7ccw7sVVWPJNkV+F/AO7u7hcZekn8HrAD2qarXjjrPsJLcCayoqvm5FnEBJfk48LWqurC7qmPPqnpw1LmG1d1ivAl4WVXdNeo8M0lyAIP/Jw+rqh8m+RtgbVV9bKEyuEfJ4KRSVT3STe7a/SyKv0GSHAj8OnDhqLPsLJLsAxwD/BVAVT2+mEqyczzwf8e9JCdZCuyRZCmwJ9Ncb90ni7LTHb7eANwH/M+q+uaoMw3pg8BK4MlRB5mDAv4uyXXd3VeLxS8Am4GLu688Lkyy16hDzdIpwKdGHWIYVbUJOA+4G7iXwfXWf7eQGSzKTlU9UVUvZHB30JFJ/sWoM7UkeS1wX1VdN+osc3R0Vb2YwShSZyU5ZtSBhrQUeDHw0ap6EfAo8LRhBMdV91XB64FPjzrLMJI8i8EAOocCzwX2SvLmhcxgUU7RHUJdDZww4ijDOBp4ffdd36XAq5J8YrSRhldV93Sv9wGfZTDi1GKwEdg46ajjMgbFuVicCFxfVd8bdZAh/SvgjqraXFU/Bi4HXrGQASxKIMmyJPt27/dg8C/mu6NN1VZV/76qDqyqQxgcSn25qhb0b9q5SrJXkr23vgd+DbhptKmGU1UTwIYkz+9mHQ/cMsJIs3Uqi+Swu3M3cFSSPbsTr8cDty5kgF7vzFlEngN8vDsTuAvwN1W1qC61WYSeDXx28N89S4G/rqovjjbSrLwd+GR3GHs7i+T22yR7Aq8Gfn/UWYZVVd9MchlwPbAF+AcW+A4dLw+SpAYPvSWpwaKUpAaLUpIaLEpJarAoJanBotQOL8khSW7q3q9I8qHu/XFJFvTCZS1OXkepnUpVrQO2Dul2HPAIcM3IAmlRcI9SYy3Ju5PcluRL3TiEf5Tk6iQruuX7dbdwbt1z/FqS67ufp+0tdnuRn09yCHAmcE43NuMrk9zRDbNHkn268TJ3XbB/WI0t9yg1tpK8hMGtmS9i8N/q9cBMA4DcB7y6qn6UZDmD2/SmfbRpVd2ZZDXwSFWd133e1QyGrPvb7nM/091brJ2ce5QaZ68EPltVj1XVQzz9ccdT7Qr8ZZIbGYyMc9gsP+9Cfnor4unAxbP8fe2g3KPUuJvuHtst/PQv+cmPBDgH+B5wRLf8R7P6oKqvd4fvxwJLqmpRDNKh/rlHqXH2VeA3kuzRjTT0um7+ncBLuvdvnLT+M4F7q+pJ4DQGz5OfycPA3lPmXcLgkN29Sf2ERamxVVXXA/8DuAH4DPC1btF5wB8kuQbYb9KvfAR4a5JvAM9jMKDuTK5kUMQ3JHllN++TwLNYXMOQqWeOHqRFI8l/ZNLJl54+443AyVV1Wl+focXH7yilTpIPMxj9+6RRZ9F4cY9Skhr8jlKSGixKSWqwKCWpwaKUpAaLUpIaLEpJavj/FDGaGRmaTJYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plt.figure(figsize=(5,5))\n",
    "sns.barplot(x = 'quality', y = 'volatile acidity', data = wine_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cecefa83a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plt.figure(figsize=(10,10))\n",
    "sns.barplot(x = 'pH', y = 'residual sugar', data = wine_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cecf490af0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plt.figure(figsize=(5,5))\n",
    "sns.barplot(x = 'quality', y = 'citric acid', data = wine_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = wine_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cecf6af8e0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "sns.heatmap(correlation, cbar = True, square = True, fmt = '.1f', annot = True, annot_kws = {'size':8}, cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wine_dataset.drop('quality', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
      "0               7.4             0.700         0.00             1.9      0.076   \n",
      "1               7.8             0.880         0.00             2.6      0.098   \n",
      "2               7.8             0.760         0.04             2.3      0.092   \n",
      "3              11.2             0.280         0.56             1.9      0.075   \n",
      "4               7.4             0.700         0.00             1.9      0.076   \n",
      "...             ...               ...          ...             ...        ...   \n",
      "1594            6.2             0.600         0.08             2.0      0.090   \n",
      "1595            5.9             0.550         0.10             2.2      0.062   \n",
      "1596            6.3             0.510         0.13             2.3      0.076   \n",
      "1597            5.9             0.645         0.12             2.0      0.075   \n",
      "1598            6.0             0.310         0.47             3.6      0.067   \n",
      "\n",
      "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
      "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
      "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
      "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
      "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
      "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
      "...                   ...                   ...      ...   ...        ...   \n",
      "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
      "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
      "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
      "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
      "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
      "\n",
      "      alcohol  \n",
      "0         9.4  \n",
      "1         9.8  \n",
      "2         9.8  \n",
      "3         9.8  \n",
      "4         9.4  \n",
      "...       ...  \n",
      "1594     10.5  \n",
      "1595     11.2  \n",
      "1596     11.0  \n",
      "1597     10.2  \n",
      "1598     11.0  \n",
      "\n",
      "[1599 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = wine_dataset['quality'].apply(lambda y_value: 1 if y_value >= 7 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       0\n",
      "1       0\n",
      "2       0\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "1594    0\n",
      "1595    0\n",
      "1596    0\n",
      "1597    0\n",
      "1598    0\n",
      "Name: quality, Length: 1599, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1599,) (1279,) (320,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape, Y_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data Accuracy:  0.93125\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Data Accuracy: \", test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "The Wine Quality is Bad\n"
     ]
    }
   ],
   "source": [
    "input_data = (7.6,0.39,0.31,2.3,0.08199999999999999,23.0,71.0,0.9982,3.52,0.65,9.7)\n",
    "input_data_as_numpy_array = np.asarray(input_data)\n",
    "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
    "\n",
    "prediction = model.predict(input_data_reshaped)\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0] == 1):\n",
    "    print(\"The Wine Quality is Good\")\n",
    "\n",
    "else:\n",
    "    print(\"The Wine Quality is Bad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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