_Wine quality project (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d367b369",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "133fc8c9",
   "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": "markdown",
   "id": "c4a6cb31",
   "metadata": {},
   "source": [
    "# Importing & Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "649f43d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset = pd.read_csv('winequality-red.csv')\n",
    "wine_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edfd444d",
   "metadata": {},
   "source": [
    "# Dataset Info:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee0057b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "wine_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff27d6d8",
   "metadata": {},
   "source": [
    "# Dataset Shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9fbbbc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7110abaa",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30667571",
   "metadata": {},
   "source": [
    "Checking the Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f35543e",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3804b390",
   "metadata": {},
   "source": [
    "# Data Analysis and Visulaization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48867b2",
   "metadata": {},
   "source": [
    "statistical measures of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8618153b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dfa3366",
   "metadata": {},
   "source": [
    "number of values for each quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6306325e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x204db68c4c0>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.catplot(x='quality', data = wine_dataset, kind = 'count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1353e43",
   "metadata": {},
   "source": [
    "volatile acidity vs Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0801e77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='volatile acidity'>"
      ]
     },
     "execution_count": 8,
     "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 = 'volatile acidity', data = wine_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a450af4",
   "metadata": {},
   "source": [
    "citric acid vs Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e513f232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='citric acid'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HTJK0aL3tUVbVoSRXADcBa4Frq+r2JJd3y3cCu4HnAvuAzwI/1lceSVqqPg+9qardDMpweN7OofcFvKzPDIvU++F9j6Y1+7TmhunNPq25YUzZM+gqSdLReAujJDVYlECSE5L8c5J/TXJ7kteMO9NiJFmb5F+SvGPcWRYjyd1JPpTktiR7x51nMZKcnOTGJB9J8m9J/ue4M7UkeXL3Z33k59NJXjHuXKNI8sru/80PJ/njJCes6Od76D245xzYUFUPJXkc8A/Az3R3C028JK8CtgInVdXzxp1nVEnuBrZW1fJci7iCklwHvK+qrumu6jixqg6OOdbIuluM9wPfUlUfG3eehSQ5lcH/k2dX1SNJ3grsrqo/WKkM7lEyOKlUVQ91k4/rfqbib5AkpwHfA1wz7iyrRZKTgGcCvwdQVZ+fppLsPBv4j0kvySHrgMcnWQecyDzXW/fJoux0h6+3AfcDf1NV/zTmSKP6NWA7cHjMOZaigL9Ockt399W0+GrgAPD73Vce1yTZMO5Qi3QJ8MfjDjGKqtoPvAG4B/g4g+ut/3olM1iUnar6QlV9E4O7g85P8g1jjtSU5HnA/VV1y7izLNEFVXUeg1GkXpbkmeMONKJ1wHnAb1fVucDDwGOGEZxU3VcFLwD+dNxZRpHkFAYD6JwJfBWwIcmlK5nBopyjO4R6N3DxeJOM5ALgBd13fTcAz0ry5vFGGl1V3de93g+8ncGIU9NgBpgZOuq4kUFxTovnALdW1X+OO8iIvhP4aFUdqKpHgT8DvnUlA1iUQJJNSU7u3j+ewb+Yj4w11Aiq6uer6rSq2sLgUOpdVbWif9MuVZINSZ5w5D3wXcCHx5tqNFU1C9yb5MndrGcDd4wx0mL9IFNy2N25B3h6khO7E6/PBv5tJQP0emfOFHkScF13JnAN8NaqmqpLbabQVwJvH/x3zzrgj6rqr8YbaVFeDrylO4y9iym5/TbJicBFwE+NO8uoquqfktwI3AocAv6FFb5Dx8uDJKnBQ29JarAoJanBopSkBotSkhosSklqsCh13EuyJcmHu/dbk/x69/7bk6zohcuaTl5HqVWlqvYCR4Z0+3bgIeDmsQXSVHCPUhMtyS8kuTPJ33bjEL46ybuTbO2Wb+xu4Tyy5/i+JLd2P4/ZW+z2It+RZAtwOfDKbmzGZyT5aDfMHklO6sbLfNzK/dNqUrlHqYmV5GkMbs08l8F/q7cCCw0Acj9wUVV9LslZDG7Tm/fRplV1d5KdwENV9Ybu897NYMi6P+8+923dvcVa5dyj1CR7BvD2qvpsVX2axz7ueK7HAb+b5EMMRsY5e5Gfdw3/fSvijwG/v8jf13HKPUpNuvnusT3Ef/8lP/xIgFcC/wk8tVv+uUV9UNX7u8P3C4G1VTUVg3Sof+5RapK9F3hhksd3Iw09v5t/N/C07v2Lh9b/cuDjVXUYeAmD58kv5DPAE+bMu57BIbt7k/oii1ITq6puBf4EuA14G/C+btEbgP+V5GZg49Cv/BbwI0k+AHwdgwF1F/IXDIr4tiTP6Oa9BTiF6RqGTD1z9CBNjST/l6GTLz19xouBbVX1kr4+Q9PH7yilTpLfYDD693PHnUWTxT1KSWrwO0pJarAoJanBopSkBotSkhosSklqsCglqeG/AOrkzLHdfSWlAAAAAElFTkSuQmCC\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": "markdown",
   "id": "8d51dafd",
   "metadata": {},
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d71a79f0",
   "metadata": {},
   "source": [
    "1.Positive Correlation\n",
    "2.Negative Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67fdcd59",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = wine_dataset.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f21e36",
   "metadata": {},
   "source": [
    "constructing a heatmap to understand the correlation between the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b648a43e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "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": "markdown",
   "id": "2e376277",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "901c3577",
   "metadata": {},
   "source": [
    "separate the data and Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf3ff9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wine_dataset.drop('quality',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2718989a",
   "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": "markdown",
   "id": "0858db1b",
   "metadata": {},
   "source": [
    "# Label Binarizaton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e11bfdc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = wine_dataset['quality'].apply(lambda y_value: 1 if y_value>=7 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "af5c4ae5",
   "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": "markdown",
   "id": "04089e32",
   "metadata": {},
   "source": [
    "# Train & Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c925882",
   "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": 17,
   "id": "1ed9efa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1599,) (1279,) (320,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape, Y_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd6c2ae1",
   "metadata": {},
   "source": [
    "# Model Training:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fed9c77e",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "92501638",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a445d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7508b9d",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a03b3ba",
   "metadata": {},
   "source": [
    "# Accuracy Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78e4608b",
   "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": 21,
   "id": "f06c6842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.934375\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c438f4ae",
   "metadata": {},
   "source": [
    "# Building a Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "64352e56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "Bad Quality Wine\n"
     ]
    }
   ],
   "source": [
    "input_data = (7.5,0.5,0.36,6.1,0.071,17.0,102.0,0.9978,3.35,0.8,10.5)\n",
    "\n",
    "# changing the input data to a numpy array\n",
    "input_data_as_numpy_array = np.asarray(input_data)\n",
    "\n",
    "# reshape the data as we are predicting the label for only one instance\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('Good Quality Wine')\n",
    "else:\n",
    "  print('Bad Quality Wine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb968590",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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