wine_quality_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "KEMAqd8_ZryK"
   },
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from warnings import filterwarnings\n",
    "\n",
    "#Model Selection and Evaluation\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "#Machine Learning Models\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#Additional Libraries\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "#Suppressing warnings\n",
    "filterwarnings(action='ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "VbNJeQsLg1K-"
   },
   "outputs": [
    {
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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>641</th>\n",
       "      <td>9.5</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.071</td>\n",
       "      <td>21.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.99920</td>\n",
       "      <td>3.46</td>\n",
       "      <td>0.63</td>\n",
       "      <td>9.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>7.6</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.11</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.079</td>\n",
       "      <td>12.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.21</td>\n",
       "      <td>0.57</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>8.8</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.99546</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.69</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>8.4</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.084</td>\n",
       "      <td>11.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99640</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>8.7</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.066</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.99640</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>11.1</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.094</td>\n",
       "      <td>16.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.99860</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1.02</td>\n",
       "      <td>10.6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.071</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.99388</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.63</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.241</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.99610</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.59</td>\n",
       "      <td>10.3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.096</td>\n",
       "      <td>26.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.00025</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.72</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>10.8</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.060</td>\n",
       "      <td>20.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.99720</td>\n",
       "      <td>3.13</td>\n",
       "      <td>0.54</td>\n",
       "      <td>9.6</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",
       "641             9.5             0.590         0.44             2.3      0.071   \n",
       "717             7.6             0.460         0.11             2.6      0.079   \n",
       "963             8.8             0.270         0.39             2.0      0.100   \n",
       "111             8.4             0.620         0.09             2.2      0.084   \n",
       "648             8.7             0.480         0.30             2.8      0.066   \n",
       "586            11.1             0.310         0.49             2.7      0.094   \n",
       "1101            7.4             0.490         0.27             2.1      0.071   \n",
       "777             6.9             0.635         0.17             2.4      0.241   \n",
       "266             8.1             0.870         0.00             3.3      0.096   \n",
       "679            10.8             0.260         0.45             3.3      0.060   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "641                  21.0                  68.0  0.99920  3.46       0.63   \n",
       "717                  12.0                  49.0  0.99680  3.21       0.57   \n",
       "963                  20.0                  27.0  0.99546  3.15       0.69   \n",
       "111                  11.0                 108.0  0.99640  3.15       0.66   \n",
       "648                  10.0                  28.0  0.99640  3.33       0.67   \n",
       "586                  16.0                  47.0  0.99860  3.12       1.02   \n",
       "1101                 14.0                  25.0  0.99388  3.35       0.63   \n",
       "777                   6.0                  18.0  0.99610  3.40       0.59   \n",
       "266                  26.0                  61.0  1.00025  3.60       0.72   \n",
       "679                  20.0                  49.0  0.99720  3.13       0.54   \n",
       "\n",
       "      alcohol  quality  \n",
       "641       9.5        5  \n",
       "717      10.0        5  \n",
       "963      11.2        6  \n",
       "111       9.8        5  \n",
       "648      11.2        7  \n",
       "586      10.6        7  \n",
       "1101     12.0        6  \n",
       "777      10.3        6  \n",
       "266       9.8        4  \n",
       "679       9.6        5  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('winequality-red.csv')\n",
    "data.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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",
       "      <th>goodquality</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",
       "      <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",
       "      <td>0.135710</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",
       "      <td>0.342587</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",
       "      <td>0.000000</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",
       "      <td>0.000000</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",
       "      <td>0.000000</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",
       "      <td>0.000000</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",
       "      <td>1.000000</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  goodquality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023     0.135710  \n",
       "std       0.154386     0.169507     1.065668     0.807569     0.342587  \n",
       "min       2.740000     0.330000     8.400000     3.000000     0.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000     0.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000     0.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000     0.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000     1.000000  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.360000</td>\n",
       "      <td>0.884500</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>2.635000</td>\n",
       "      <td>0.122500</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>0.997464</td>\n",
       "      <td>3.398000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>9.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.779245</td>\n",
       "      <td>0.693962</td>\n",
       "      <td>0.174151</td>\n",
       "      <td>2.694340</td>\n",
       "      <td>0.090679</td>\n",
       "      <td>12.264151</td>\n",
       "      <td>36.245283</td>\n",
       "      <td>0.996542</td>\n",
       "      <td>3.381509</td>\n",
       "      <td>0.596415</td>\n",
       "      <td>10.265094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.167254</td>\n",
       "      <td>0.577041</td>\n",
       "      <td>0.243686</td>\n",
       "      <td>2.528855</td>\n",
       "      <td>0.092736</td>\n",
       "      <td>16.983847</td>\n",
       "      <td>56.513950</td>\n",
       "      <td>0.997104</td>\n",
       "      <td>3.304949</td>\n",
       "      <td>0.620969</td>\n",
       "      <td>9.899706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.347179</td>\n",
       "      <td>0.497484</td>\n",
       "      <td>0.273824</td>\n",
       "      <td>2.477194</td>\n",
       "      <td>0.084956</td>\n",
       "      <td>15.711599</td>\n",
       "      <td>40.869906</td>\n",
       "      <td>0.996615</td>\n",
       "      <td>3.318072</td>\n",
       "      <td>0.675329</td>\n",
       "      <td>10.629519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.872362</td>\n",
       "      <td>0.403920</td>\n",
       "      <td>0.375176</td>\n",
       "      <td>2.720603</td>\n",
       "      <td>0.076588</td>\n",
       "      <td>14.045226</td>\n",
       "      <td>35.020101</td>\n",
       "      <td>0.996104</td>\n",
       "      <td>3.290754</td>\n",
       "      <td>0.741256</td>\n",
       "      <td>11.465913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.566667</td>\n",
       "      <td>0.423333</td>\n",
       "      <td>0.391111</td>\n",
       "      <td>2.577778</td>\n",
       "      <td>0.068444</td>\n",
       "      <td>13.277778</td>\n",
       "      <td>33.444444</td>\n",
       "      <td>0.995212</td>\n",
       "      <td>3.267222</td>\n",
       "      <td>0.767778</td>\n",
       "      <td>12.094444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "quality                                                                 \n",
       "3             8.360000          0.884500     0.171000        2.635000   \n",
       "4             7.779245          0.693962     0.174151        2.694340   \n",
       "5             8.167254          0.577041     0.243686        2.528855   \n",
       "6             8.347179          0.497484     0.273824        2.477194   \n",
       "7             8.872362          0.403920     0.375176        2.720603   \n",
       "8             8.566667          0.423333     0.391111        2.577778   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
       "quality                                                                   \n",
       "3         0.122500            11.000000             24.900000  0.997464   \n",
       "4         0.090679            12.264151             36.245283  0.996542   \n",
       "5         0.092736            16.983847             56.513950  0.997104   \n",
       "6         0.084956            15.711599             40.869906  0.996615   \n",
       "7         0.076588            14.045226             35.020101  0.996104   \n",
       "8         0.068444            13.277778             33.444444  0.995212   \n",
       "\n",
       "               pH  sulphates    alcohol  \n",
       "quality                                  \n",
       "3        3.398000   0.570000   9.955000  \n",
       "4        3.381509   0.596415  10.265094  \n",
       "5        3.304949   0.620969   9.899706  \n",
       "6        3.318072   0.675329  10.629519  \n",
       "7        3.290754   0.741256  11.465913  \n",
       "8        3.267222   0.767778  12.094444  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('quality').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Countplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['quality'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity               AxesSubplot(0.125,0.71587;0.168478x0.16413)\n",
       "volatile acidity         AxesSubplot(0.327174,0.71587;0.168478x0.16413)\n",
       "citric acid              AxesSubplot(0.529348,0.71587;0.168478x0.16413)\n",
       "residual sugar           AxesSubplot(0.731522,0.71587;0.168478x0.16413)\n",
       "chlorides                  AxesSubplot(0.125,0.518913;0.168478x0.16413)\n",
       "free sulfur dioxide     AxesSubplot(0.327174,0.518913;0.168478x0.16413)\n",
       "total sulfur dioxide    AxesSubplot(0.529348,0.518913;0.168478x0.16413)\n",
       "density                 AxesSubplot(0.731522,0.518913;0.168478x0.16413)\n",
       "pH                         AxesSubplot(0.125,0.321957;0.168478x0.16413)\n",
       "sulphates               AxesSubplot(0.327174,0.321957;0.168478x0.16413)\n",
       "alcohol                 AxesSubplot(0.529348,0.321957;0.168478x0.16413)\n",
       "quality                 AxesSubplot(0.731522,0.321957;0.168478x0.16413)\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind ='box',subplots = True, layout =(4,4),sharex = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nMCciGvIHJ7TAgcnKdndENFc6BzAaTKEAQKIocABIFAUODNpY6QDAaDEHDgCJ4g4cABJFgQNAoihwTDq2v2y7x/YjthfbXlWGc37G9mNlOM8/2L75fOcfntn27bY/Wup1gffDX+TBZPRXkm6NiIO57W2VDDNcRHytiH226f8z3y7pMUk/H8dYqFLcgWNSsd0p6cOSttn+iu27bH8z992Pbf957vNf2H4k9/lztp+3/YLtH9j+jdz4Qtsv2+6S9CfnuF6j7X/PHfuC7fnDvvuq7Zdsv2h7XW7sYdtfON/5382cO9diSd+wvcf2VbZfGLbfNbZ3l/PfD9WFO3BMKhGxwvZCSQsi4k3bdw37ermk/7B9UNI9kj5te5akv5N0c0T02V4pqc321yVtkvRZSQckbTnHJY9IuiUiBmxfIykrqdn2rRq8e54bESdsXzb8INt1hc4fETtsb5P0WEQ8mjvuLdsfj4g9kr4k6eFR/hMBQ7gDRzIi4g1JX5O0XdI9EXFU0qclfVSDxb5H0p2S5kj6iKSDEfGLGFwr+71znHaqpE22X5L0g9y5JOlmSd+JiBO5ax/NO67Y8+f7tqQv2a6RdIekfy7yOOA9uANHaq6X9GtJV+S2LenpiFgyfCfbH5dUzEMOX5H0hqSPafCGZmDYeQsdP5aHKLZKuk/SM5J2R8Svx3AOQBJ34EiI7d+XdKukT0i61/aVknZK+gPbV+f2mWH7WkkvS7rS9lW5w5e83zklXSLpVxHxjqQ/k1STG39K0lLbM3LnvSzvuGLPf1zSxe9uRMSApH+T9C1J3yn8Xw2cGwWOJNiu1eCc89KIeF2Dc+CbNfgK2LskZW3v1WChfyRXlMslPZ77JeO5XmP8T5LutL1T0rWS+iQpIn6iwZUk3bmpmXuHHzSK8/+LpL+x/bNhZf+IBu/enxrVPwKQh0fpgQlm+15Jl0TE31c6C9LGHDgwgWz/SNJVGly9ApSEO3AASBRz4ACQKAocABJFgQNAoihwAEgUBQ4Aifo/BF7LnMYtht8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['fixed acidity'].plot(kind ='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize=(10,10),bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.078</td>\n",
       "      <td>34.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.99528</td>\n",
       "      <td>3.43</td>\n",
       "      <td>0.59</td>\n",
       "      <td>10.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>10.3</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.159</td>\n",
       "      <td>15.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.99980</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>9.1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.080</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.99652</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.57</td>\n",
       "      <td>12.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.086</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.99558</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.54</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>57.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.99341</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.44</td>\n",
       "      <td>9.5</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",
       "908             7.4              0.52         0.13             2.4      0.078   \n",
       "368            10.3              0.34         0.52             2.8      0.159   \n",
       "1190            9.1              0.40         0.57             4.6      0.080   \n",
       "1313            7.0              0.36         0.21             2.3      0.086   \n",
       "1131            5.9              0.19         0.21             1.7      0.045   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "908                  34.0                  61.0  0.99528  3.43       0.59   \n",
       "368                  15.0                  75.0  0.99980  3.18       0.64   \n",
       "1190                  6.0                  20.0  0.99652  3.28       0.57   \n",
       "1313                 20.0                  65.0  0.99558  3.40       0.54   \n",
       "1131                 57.0                 135.0  0.99341  3.32       0.44   \n",
       "\n",
       "      alcohol  quality  \n",
       "908      10.8        6  \n",
       "368       9.4        5  \n",
       "1190     12.5        6  \n",
       "1313     10.1        6  \n",
       "1131      9.5        5  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 4, 8, 3], dtype=int64)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['quality'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>goodquality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.102</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>8.2</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.081</td>\n",
       "      <td>27.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.99603</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.54</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>7.1</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.079</td>\n",
       "      <td>24.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.99514</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.61</td>\n",
       "      <td>10.9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>7.9</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.084</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.99590</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.55</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>6.8</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.123</td>\n",
       "      <td>15.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.99538</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.63</td>\n",
       "      <td>11.3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "534            10.0              0.48         0.24             2.7      0.102   \n",
       "1004            8.2              0.43         0.29             1.6      0.081   \n",
       "1250            7.1              0.60         0.01             2.3      0.079   \n",
       "134             7.9              1.04         0.05             2.2      0.084   \n",
       "1258            6.8              0.64         0.00             2.7      0.123   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "534                  13.0                  32.0  1.00000  3.28       0.56   \n",
       "1004                 27.0                  45.0  0.99603  3.25       0.54   \n",
       "1250                 24.0                  37.0  0.99514  3.40       0.61   \n",
       "134                  13.0                  29.0  0.99590  3.22       0.55   \n",
       "1258                 15.0                  33.0  0.99538  3.44       0.63   \n",
       "\n",
       "      alcohol  quality  goodquality  \n",
       "534      10.0        6            0  \n",
       "1004     10.3        5            0  \n",
       "1250     10.9        6            0  \n",
       "134       9.9        6            0  \n",
       "1258     11.3        6            0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If wine quality is 7 or above then will consider as good quality wine\n",
    "data['goodquality'] = [1 if x >= 7 else 0 for x in data['quality']]\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1382\n",
       "1     217\n",
       "Name: goodquality, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['goodquality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate depedent and indepedent variables\n",
    "X = data.drop(['quality','goodquality'], axis = 1)\n",
    "Y = data['goodquality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99512</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.076</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.99574</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.075</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99547</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.067</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99549</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1599 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      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]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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: goodquality, Length: 1599, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.07592279 0.09689288 0.09627918 0.0757045  0.06957828 0.06658784\n",
      " 0.07916358 0.08740363 0.06747822 0.11393879 0.17105031]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "classifiern = ExtraTreesClassifier()\n",
    "classifiern.fit(X,Y)\n",
    "score = classifiern.feature_importances_\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "zHG5VW-vg4VN"
   },
   "outputs": [],
   "source": [
    "X = data.drop('quality', axis=1)\n",
    "y = data['quality']\n",
    "# Train and Test splitting of data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_res=pd.DataFrame(columns=['Model', 'Score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "PHTcNDxBg7IP",
    "outputId": "e1245770-1e77-4778-a574-baae8139487d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "V6LcrefNhAYw"
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QSGQ3thzhEG1",
    "outputId": "84e131d6-4105-403f-8462-fc8d94c1d84c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.41123487175042056\n",
      "R-squared: 0.35138853325052266\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "KUvv8lZXhGA_",
    "outputId": "7b4d4147-b595-4501-d55f-6934ee87f1ee"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel(\"Actual Wine Quality\")\n",
    "plt.ylabel(\"Predicted Wine Quality\")\n",
    "plt.title(\"Actual vs. Predicted Wine Quality\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "DjTEkQYDhXUj",
    "outputId": "704a450c-713a-4fd3-a727-2836d390f40b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Citric Acid\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['citric acid'].mean().plot(kind='bar', color='#FEA3AA')\n",
    "plt.title('Quality vs. Citric Acid')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Citric Acid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "FdtWrSXLi8vI",
    "outputId": "75882a79-f597-499a-f32c-9d7e49162d8e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Sulphates\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['sulphates'].mean().plot(kind='bar', color='#F2BB66')\n",
    "plt.title('Quality vs. Sulphates')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Sulphates')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "6sjVB1ayi9a7",
    "outputId": "a39f5cd7-df7a-41c5-b95d-ef1f50fb243a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c13JdETEEerkX4+Ix448CH2iroIgYHt0+cu4K23tLeoDmk6UenwXY9vTWq4uIgerWgzgcwPbGk1RLRAyZbscgHr/WQdKFk1BLRAyZbgGhjuHt2y4kIoZPt4BwZTgi1hLdjkHsJul+mp7EBmUYcpAyYq1RDQjbUyazkIgYPvnqvYioSkBERFUCIiKqEhARUZWAiIiqBEREVCUgIqIqARERVQmIiKhKQEREVQIiIqoSEBFRlYCIiKoERERUJSAioioBERFVCYiIqEpARERVAiIiqhIQEVGVgIiIqgRERFQlICKiKgEREVUJiIioai0gJG0j6ZuSFku6QdLxbbUVEe3o9t2cT9ajwLttXy1pY2CRpK/ZvrHFNiOij1rrQdheZvvqMvwAsBh4elvtRUT/TcoxCEmzgecC35+M9iKiP1oPCEkbARcC77B9/zjzj5G0UNLCkZGRtsuJiAloNSAkrUsTDufZvmi8ZWzPtz3H9pxZs2a1WU5ETFCbZzEEnA4stv2PbbUTEe1pswexF/AmYD9J15THAS22FxF91tppTttXAGpr/RHRvlxJGRFVCYiIqEpARERVAiIiqhIQEVGVgIiIqgRERFQlICKiKgEREVUJiIioSkBERFUCIiKqEhARUZWAiIiqBEREVCUgIqIqARERVQmIiKhKQEREVQIiIqoSEBFRlYCIiKoERERUJSAioioBERFVCYiIqEpARERVAiIiqhIQEVGVgIiIqgRERFQlICKiKgEREVUJiIioSkBERFUCIiKqEhARUZWAiIiqVgNC0v6Sfizpp5JOaLOtiOi/1gJC0hTgn4FXADsDh0naua32IqL/2uxB7AH81PYS278FPge8usX2IqLPZLudFUuvA/a3fXQZfxPwQtvHjlnuGOCYMroT8OM+ND8TWN6H9fTTMNYEw1lXaupNP2va1vassROn9mnl49E4056QRrbnA/P72rC00Pacfq7zyRrGmmA460pNvZmMmtrcxbgN2KZjfGvg9hbbi4g+azMgfgDsKGk7SesBbwD+u8X2IqLPWtvFsP2opGOBy4ApwBm2b2irvTH6usvSJ8NYEwxnXampN63X1NpByohY/eVKyoioSkBERFUCIiKq1oiAkLSHpBeU4Z0lvUvSAYOuq5OkswddQydJe5ft9PIB1/FCSdPL8AaSTpJ0iaSPStpkQDUdJ2mblS85eSStJ2mepLll/I2STpP0Nknrttbu6n6QUtL7ae73mAp8DXghsACYC1xm+0MDqGns6VwBfwx8A8D2QQOo6Srbe5ThtwBvA/4LeDlwie2PTHZNpZYbgN3KWa/5wEPABcBLy/SDB1DTfcCDwM+A/wTOtz0y2XWMqek8mv/xacC9wEbARTTbSbaPbKVh26v1A7iO5jTqNOB+YHqZvgHwowHVdDVwLrAvsE/5uawM7zOgmn7YMfwDYFYZ3hC4boB/v8Wd223MvGsGta1oetcvB04HRoBLgSOBjQdU04/Kz6nAncCUMq42/8/XhF2MR23/zvZDwM9s3w9g+9fAYwOqaQ6wCDgRuM/2AuDXtr9l+1sDqmkdSZtKeirNO84IgO0HgUcHVBPA9ZKOKsPXSpoDIOlZwCMDqsm2H7N9ue0/B7YCPg3sDywZUE3rlAsON6Z5Mxzd/VofaG0Xo817MSbLbyVNKwHx/NGJZf91IAFh+zHgk5LOLz/vZPDbehOa0BJgSVvYvkPSRox/38xkORo4VdLf0tx4dKWkpcDSMm8QVtgeth+huQr4vyVtMJiSOB24iaa3fCJwvqQlwIto7pRuxZpwDGJ9278ZZ/pMYEvb1w2grLG1vBLYy/bfDLqWsSRNAza3ffOA69gY2J4mSG+zfecAa3mW7f8bVPs1krYCsH27pBk0x9lutX1Va22u7gEREe1ZE45BRERLEhARUZWACAAkbS3pi5J+ImlJuQhn/VVc14KOsxFfkTSjPP6yv1VH2xIQgSTRXHRzse0dgR1priP52JNdt+0DbN8LzAASEKuZBEQA7Ac8bPtMANu/A94JzJN0rKTTRheU9CVJ+5bhf5G0UNINkk4ab8WSbilnlD4CPFPSNZI+LukcSa/uWO48SZN+hWl0N+hz8zEcdqG5RuJxtu+XdAvd/0dOtH13+YqDr0t6ju0fVZY9AdjV9u4AkvahCaEvlmtW9qS5UjGGSHoQAeXiqcr0bg6VdDXNpcm70Hz/SU/KFaU7SHoacBhwoe1BXtEZ40hABMANNJeHP67cYbk5cBcr/p88pczfDngP8FLbzwG+PDpvAs4BDgeOAs5cpcqjVQmIAPg6ME3SPHj8W9E+AZwG3AzsLmmdcgv0HuU502nueLxP0uY0d9R28wDNfQSdPgO8A8CT93mlMQEJiMDN5bSvBV4n6Sc0vYbH3Nwq/12akLgOOJnmTlVsX0uza3EDcEZZrlsbdwHflXS9pI+XaXcCi0nvYWjlUut4Akl70nwOwsG2F61s+SfRzjSa4Hme7fvaaidWXXoQ8QS2/9f2ti2Hw1yauxM/lXAYXulBRERVehARUZWAiIiqBEREVCUgIqIqARERVQmIiKj6f+ys+49j51dWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Fixed Acidity\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['fixed acidity'].mean().plot(kind='bar', color='lightgreen')\n",
    "plt.title('Quality vs. Fixed Acidity')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Fixed Acidity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "6B99cdDFkFR7",
    "outputId": "caa0c4d0-17d0-42a9-ccbe-4bc603982e6e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. pH\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['pH'].mean().plot(kind='bar', color='#A74AC7')\n",
    "plt.title('Quality vs. pH')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('pH')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "iDftrA6WkHSN",
    "outputId": "2aa731aa-2367-4b89-948a-772c68b074d8"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Alcohol\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['alcohol'].mean().plot(kind='bar', color='skyblue')\n",
    "plt.title('Quality vs. Alcohol')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Alcohol')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "6GqqALuFkHsI",
    "outputId": "4b592539-1721-4e0f-b4d0-9648d99011d4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5yNY3V3jdXsDKhu5VwFubprUXcDTwLuAtFaZp1hm+sn1b7baRfEbS08CUxu0jwKPAdytMu9X1XZt3J/8j8OmIeKnthKTZkhZJWrR27doKszazgdTumq1fzNtHzouIEfmxc0TsFhGfqTDtVcC4hu6xwOqmcaYC8yX9GvggcJGkP2lRy7yImBoRU0ePHl1h1mY2kKoc2foZSbsCk4AdG/r/qJeX3g1MkjQR+C/SPXKOa5r2xE3PJV0O3BIRN1Yt3swGh16DRNLJwCd4+cLPBwF3kLZr9CgiNkg6jbQ3ZghwaUQsk3RKHj63rHQzGyyqbGz9BGlD6J0RcaikfYFzqkw8IhaQTvJr7NcyQCLixCrTNLPBp8rZv89FxHMAknaIiF8Ab6y3LDPrJlVaJKskjSRdXvEHkn7HqzeamtlWrMrG1qPz07Ml/RDYBfh+rVWZWVdpdxmB17TovTT/HQ48XktFZtZ12rVIFpMOIOvpwLLX1VKRmXWddiftTexpmJlZoyobW5F0JPA/c+fCiLilvpLMrNtUOfv3XNKxJD/Pj09I+mLdhZlZ96jSIjkCOCBfBBpJVwD3AFXOtzGzrUDVm4iPbHi+Sw11mFkXq9Ii+SJwTz6GRKRtJW6NmNlm7Y4juQC4OiKukbSQdL6NSNcPeWSA6jOzLtCuRfIgcL6kPYBrgWsiYsmAVGVmXaXdhY2+EhH/AziYdBTrZZLul/Q5SfsMWIVmNuj1urE1Ih6OiC9FxB+RLkx0NHB/7ZWZWdeochzJdpL+WNJVwL8AvwSOqb0yM+sa7Ta2vgeYCbwPuIt0O4nZEbF+gGozsy7RbmPrmcDVwF9HhM/0NbMetTtp79CBLMTMulfVI1vNzHrkIDGzYg4SMyvmIDGzYg4SMyvmIDGzYg4SMyvmIDGzYg4SMyvmIDGzYg4SMyvmIDGzYg4SMyvmIDGzYg4SMyvmIDGzYg4SMyvmIDGzYg4SMytWa5BImi7pAUnLJc1pMfx4Sffmx08k7V9nPWZWj9qCRNIQ4EJgBjAZmClpctNoDwEHR8QU4PPAvLrqMbP61NkimQYsj4gVEfEC6b44RzWOEBE/iYjf5c47gbE11mNmNakzSPYCVjZ0r8r9evJR0p38zKzLtLtBVim16BctR5QOJQXJO3oYPhuYDTB+/Pj+qs/M+kmdLZJVwLiG7rHA6uaRJE0BvgEcFRGPtZpQRMyLiKkRMXX06NG1FGtmfVdnkNwNTJI0UdL2wLHATY0jSBoP3AD8WUT8ssZazKxGta3aRMQGSacBtwJDgEsjYpmkU/LwucDngN2AiyQBbIiIqXXVZGb1qHMbCRGxAFjQ1G9uw/OTgZPrrMHM6ucjW82smIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIo5SMysmIPEzIrVGiSSpkt6QNJySXNaDJekr+bh90o6sM56zKwetQWJpCHAhcAMYDIwU9LkptFmAJPyYzZwcV31mFl96myRTAOWR8SKiHgBmA8c1TTOUcC3IrkTGClpjxprMrMabFvjtPcCVjZ0rwLeWmGcvYA1jSNJmk1qsQA8I+mBfqpxFLCu/Sjz+2lWlXVpTTDAdQ3GmqBrl1+lmvbuaUCdQaIW/aIP4xAR84B5/VHUK2YuLYqIqf093RKuqZrBWBMMzroGoqY6V21WAeMauscCq/swjpkNcnUGyd3AJEkTJW0PHAvc1DTOTcCsvPfmIODJiFjTPCEzG9xqW7WJiA2STgNuBYYAl0bEMkmn5OFzgQXAEcBy4FngpLrq6UG/ry71A9dUzWCsCQZnXbXXpIhXbZIwM9siPrLVzIo5SMysmIPEzIptVUEiaZqkt+TnkyWdIemITte1iaRvdbqGZpLekT+nwztYw1sljcjPh0o6R9LNkr4kaZcO1fRXksb1PubAkbS9pFmS3p27j5N0gaRTJW1X67y3lo2tkv4v6dyebYEfkI6yXQi8G7g1Iv5mgOtp3hUu4FDg3wEi4siBrGdzEdJdETEtP/8YcCrwz8DhwM0RcW4HaloG7J/3BM4j7eH7DnBY7v+BDtT0JLAe+BVwDXBdRKwd6DqaarqK9P89DHgCGA7cQPqcFBEn1DbziNgqHsBS0m7oYcBTwIjcfyhwbwfq+SnwbeAQ4OD8d01+fnAHP6d7Gp7fDYzOz3cClnaopvsbP7emYUs69TmRWvSHA98E1gLfB04Adu5QTffmv9sCjwJDcrfq/h/fmlZtNkTESxHxLPCriHgKICJ+D2zsQD1TgcXAZ0kH4i0Efh8Rt0fE7R2oZ5NtJO0qaTfSr9hagIhYD2zoUE33Sdp0jNHPJE0FkLQP8GKHaoqI2BgRt0XER4E9gYuA6cCKDtW0TT74c2fSD+am1b4dgFpXbeo812aweUHSsBwkb97UM69jD3iQRMRG4B8kXZf/PsrgWB67kAJOQEjaPSIekTSc1udGDYSTga9IOot08tkdklaSTvg8uUM1veKziIgXSUdq3yRpaGdK4pvAL0gt788C10laARxEzWcKbk3bSHaIiOdb9B8F7BERSztQVmMd7wPeHhFndrKOnkgaBoyJiIc6WMPOwOtIgbsqIh7tYC37RMQvOzX/nkjaEyAiVksaSdoG+JuIuKvW+W4tQWJm9dmatpGYWU0cJGZWzEFilUkaK+m7kh6UtCIf7LRDH6e1sGHvywJJI/PjL/q3ahsIDhKrRJJIBzfdGBGbLtg9FPi70mlHxBER8QQwEnCQdCEHiVX1LuC5iLgMICJeAk4nXZjqNEkXbBpR0i2SDsnPL5a0SNIySee0mrCkX+e9Z+cCr5e0RNJ5kq6UdFTDeFdJ6sgRv9beYDhuwbrDfqTjSzaLiKck/Zr2/0efjYjH8+1J/k3SlIi4t4dx5wBviogDACQdTAqr7+bjfd5GOnLUBhm3SKwq0eLC3PR+kNqHJP2UdEj5fqR7HFWSj/B9g6TXAjOB6yOiU0fXWhsOEqtqGemw/s3yGbljgMd45f/Sjnn4ROCvgcMiYgrwvU3DtsCVwPGky3Be1qfKrXYOEqvq34BhkmbB5jspng9cADwEHCBpm3xq/bT8mhGkM2SflDSGdPZ1O0+TzhNpdDnwSYCIWFb+NqwODhKrJNIh0EcDH5T0IKkVsjHS5Rf+kxQmS4G/J53ZTET8jLRKswy4NI/Xbh6PAf8p6T5J5+V+jwL349bIoOZD5K1PJL2NdB2OD0TE4t7GL5jPMFJAHRgRT9Y1HyvjFon1SUT8JCL2rjlE3k06m/VrDpHBzS0SMyvmFomZFXOQmFkxB4mZFXOQmFkxB4mZFXOQmFmx/w8ElgfztPxotQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Volatile Acidity\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['volatile acidity'].mean().plot(kind='bar', color='#FFDB58')\n",
    "plt.title('Quality vs. Volatile Acidity')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Volatile Acidity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "MLgldi_cmDBG",
    "outputId": "da7c8ce9-2961-4b51-9dc2-81934e260163"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Residual Sugar\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['residual sugar'].mean().plot(kind='bar', color='#915F6D')\n",
    "plt.title('Quality vs. Residual Sugar')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Residual Sugar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "W27Suq9dmFzx",
    "outputId": "c2ed1cf6-3008-41f4-870b-9372b872ff6b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Chlorides\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['chlorides'].mean().plot(kind='bar', color='#B0BF1A')\n",
    "plt.title('Quality vs. Chlorides')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Chlorides')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "ETDw_FrUmIBu",
    "outputId": "552131dd-e7c8-4cdb-be97-076430abc4db"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Free Sulfur Dioxide\n",
    "plt.figure(figsize=(4,4))\n",
    "data.groupby('quality')['free sulfur dioxide'].mean().plot(kind='bar', color='#4C787E')\n",
    "plt.title('Quality vs. Free Sulfur Dioxide')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Free Sulfur Dioxide')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "7Ynad-mqmKCW",
    "outputId": "463a5da7-0a9c-47be-d07c-5ce9a1add740"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Total Sulfur Dioxide\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['total sulfur dioxide'].mean().plot(kind='bar', color='#B3446C')\n",
    "plt.title('Quality vs. Total Sulfur Dioxide')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Total Sulfur Dioxide')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "id": "z7JJp8ihmLko",
    "outputId": "9582b887-a2f1-4268-ab40-69088424c6b5"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for Quality vs. Density\n",
    "plt.figure(figsize=(4, 4))\n",
    "data.groupby('quality')['density'].mean().plot(kind='bar', color='#4E5180')\n",
    "plt.title('Quality vs. Density')\n",
    "plt.xlabel('Quality')\n",
    "plt.ylabel('Density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.677083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model     Score\n",
       "0  LogisticRegression  0.677083"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "# accuracy_score(Y_test,Y_pred)\n",
    "model_res.loc[len(model_res)] = ['LogisticRegression', accuracy_score(y_test,y_pred)]\n",
    "model_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.677083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Model     Score\n",
       "0    LogisticRegression  0.677083\n",
       "1  KNeighborsClassifier  0.466667"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "model_res.loc[len(model_res)] = ['KNeighborsClassifier', accuracy_score(y_test,y_pred)]\n",
    "model_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.7208333333333333\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.677083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.720833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model     Score\n",
       "0      LogisticRegression  0.677083\n",
       "1    KNeighborsClassifier  0.466667\n",
       "2  DecisionTreeClassifier  0.720833"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model = DecisionTreeClassifier(criterion='entropy',random_state=7)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Accuracy Score:\",accuracy_score(y_test,y_pred))\n",
    "model_res.loc[len(model_res)] = ['DecisionTreeClassifier', accuracy_score(y_test,y_pred)]\n",
    "model_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.6833333333333333\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.677083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.720833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.683333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model     Score\n",
       "0      LogisticRegression  0.677083\n",
       "1    KNeighborsClassifier  0.466667\n",
       "2  DecisionTreeClassifier  0.720833\n",
       "3              GaussianNB  0.683333"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model3 = GaussianNB()\n",
    "model3.fit(X_train,y_train)\n",
    "y_pred = model3.predict(X_test)\n",
    "\n",
    "print(\"Accuracy Score:\",accuracy_score(y_test,y_pred))\n",
    "model_res.loc[len(model_res)] = ['GaussianNB', accuracy_score(y_test,y_pred)]\n",
    "model_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "r-Ie1M0Y--FJ"
   },
   "outputs": [],
   "source": [
    "# Define the bins and group names for binary classification\n",
    "bins = (2, 6.5, 8)\n",
    "group_names = ['bad', 'good']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "KhwtG_aY_9aH"
   },
   "outputs": [],
   "source": [
    "# Create a new column 'quality_label' based on the 'quality' column\n",
    "data['quality_label'] = pd.cut(data['quality'], bins=bins, labels=group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "ATq9bGYkACpR"
   },
   "outputs": [],
   "source": [
    "# Use LabelEncoder to map 'bad' to 0 and 'good' to 1\n",
    "label_quality = LabelEncoder()\n",
    "data['quality_label'] = label_quality.fit_transform(data['quality_label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "8a2rHkuQAc4P"
   },
   "outputs": [],
   "source": [
    "# Separate the dataset into feature variables (X) and the response variable (y)\n",
    "X = data.drop(['quality', 'quality_label'], axis=1)\n",
    "y = data['quality_label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "GfsOByBJApdF"
   },
   "outputs": [],
   "source": [
    "# Applying Standard scaling to get optimized results\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "IyBfJl5bKSz8"
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200, random_state=42)\n",
    "rfc.fit(X_train, y_train)\n",
    "pred_rfc = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DjjbD_9sKw7M",
    "outputId": "5a5bc8e0-b828-47b8-9733-c678d92984a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.00      0.00      0.00        17\n",
      "           5       0.73      0.77      0.75       195\n",
      "           6       0.63      0.70      0.66       200\n",
      "           7       0.57      0.48      0.52        61\n",
      "           8       0.50      0.17      0.25         6\n",
      "\n",
      "    accuracy                           0.67       480\n",
      "   macro avg       0.41      0.35      0.36       480\n",
      "weighted avg       0.64      0.67      0.65       480\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Random Forest Classifier Classification Report:\")\n",
    "report_rfc = classification_report(y_test, pred_rfc)\n",
    "print(report_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kumrACH1KxAK",
    "outputId": "9fbba537-97e9-4444-8d75-e24145e0de6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier Confusion Matrix:\n",
      "[[  0   0   1   0   0   0]\n",
      " [  0   0  10   7   0   0]\n",
      " [  0   0 150  43   2   0]\n",
      " [  0   0  44 140  16   0]\n",
      " [  0   0   0  31  29   1]\n",
      " [  0   0   0   1   4   1]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Random Forest Classifier Confusion Matrix:\")\n",
    "confusion_rfc = confusion_matrix(y_test, pred_rfc)\n",
    "print(confusion_rfc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Stochastic Gradient Descent Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "CD3l2uE4KxEO"
   },
   "outputs": [],
   "source": [
    "sgd = SGDClassifier(penalty=None, random_state=42)\n",
    "sgd.fit(X_train, y_train)\n",
    "pred_sgd = sgd.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iURxSnebKxHT",
    "outputId": "0d8a107f-63d2-498f-866f-5b888631cc9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stochastic Gradient Descent Classifier Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.00      0.00      0.00        17\n",
      "           5       0.65      0.69      0.67       195\n",
      "           6       0.48      0.60      0.54       200\n",
      "           7       0.40      0.07      0.11        61\n",
      "           8       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.54       480\n",
      "   macro avg       0.26      0.23      0.22       480\n",
      "weighted avg       0.52      0.54      0.51       480\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Stochastic Gradient Descent Classifier Classification Report:\")\n",
    "report_sgd = classification_report(y_test, pred_sgd)\n",
    "print(report_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NlHCCIgqKxL4",
    "outputId": "dde720b6-352e-4cbd-e1c8-d07e49a221dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stochastic Gradient Descent Classifier Confusion Matrix:\n",
      "[[  0   0   0   1   0   0]\n",
      " [  2   0   3  12   0   0]\n",
      " [  0   3 135  57   0   0]\n",
      " [  1   6  66 121   6   0]\n",
      " [  0   1   3  53   4   0]\n",
      " [  0   0   0   6   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Stochastic Gradient Descent Classifier Confusion Matrix:\")\n",
    "confusion_sgd = confusion_matrix(y_test, pred_sgd)\n",
    "print(confusion_sgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "GUddqGYWKYF-"
   },
   "outputs": [],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)\n",
    "pred_svc = svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SSjmqLJhKYMQ",
    "outputId": "7da3869d-cd2b-4cec-dd71-59c1f7bedc61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Classifier Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.00      0.00      0.00        17\n",
      "           5       0.64      0.78      0.71       195\n",
      "           6       0.57      0.60      0.59       200\n",
      "           7       0.59      0.28      0.38        61\n",
      "           8       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.61       480\n",
      "   macro avg       0.30      0.28      0.28       480\n",
      "weighted avg       0.57      0.61      0.58       480\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Support Vector Classifier Classification Report:\")\n",
    "report_svc = classification_report(y_test, pred_svc)\n",
    "print(report_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vNXQrWpnKYRk",
    "outputId": "daf60f66-5938-40e8-da39-36486bc4cfb5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Classifier Confusion Matrix:\n",
      "[[  0   0   1   0   0   0]\n",
      " [  0   0  13   4   0   0]\n",
      " [  0   0 153  41   1   0]\n",
      " [  0   0  71 121   8   0]\n",
      " [  0   0   0  44  17   0]\n",
      " [  0   0   0   3   3   0]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Support Vector Classifier Confusion Matrix:\")\n",
    "confusion_svc = confusion_matrix(y_test, pred_svc)\n",
    "print(confusion_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "id": "nfWFqFykKYVl",
    "outputId": "cc556232-2803-4ac8-cac3-39441552236d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
       "                         'gamma': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],\n",
       "                         'kernel': ['linear', 'rbf']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid Search CV for SVC\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",
    "grid_svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QoyrJAztKYZK",
    "outputId": "6550b2b8-b9e1-49f1-9a2b-d5eb2af0e2f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for Support Vector Classifier:\n",
      "{'C': 1.3, 'gamma': 0.8, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameters for Support Vector Classifier:\")\n",
    "print(grid_svc.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "G5bzlKm2KYb6"
   },
   "outputs": [],
   "source": [
    "svc2 = SVC(C=1.2, gamma=0.9, kernel='rbf')\n",
    "svc2.fit(X_train, y_train)\n",
    "pred_svc2 = svc2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WQrwoUAxKYep",
    "outputId": "03d3b466-2982-4f54-eb4f-ebb71cf4b522"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved Support Vector Classifier Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.00      0.00      0.00        17\n",
      "           5       0.68      0.73      0.71       195\n",
      "           6       0.60      0.69      0.64       200\n",
      "           7       0.64      0.41      0.50        61\n",
      "           8       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.64       480\n",
      "   macro avg       0.32      0.31      0.31       480\n",
      "weighted avg       0.61      0.64      0.62       480\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Improved Support Vector Classifier Classification Report:\")\n",
    "report_svc2 = classification_report(y_test, pred_svc2)\n",
    "print(report_svc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6Wj7s_3-KYj2",
    "outputId": "b54dd8d7-107e-4795-c005-937127603e26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier Cross Validation Score:\n",
      "0.685505148005148\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation Score for Random Forest\n",
    "rfc_eval = cross_val_score(estimator=rfc, X=X_train, y=y_train, cv=10)\n",
    "print(\"Random Forest Classifier Cross Validation Score:\")\n",
    "print(rfc_eval.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "P7o5VUdcL5-N"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Wine Quality: [5.66251128]\n"
     ]
    }
   ],
   "source": [
    "# Assuming you have a new wine sample with feature values\n",
    "new_sample = [[11.2,0.28,0.56,1.9,0.075,17,60,0.998,3.16,0.58,9.8]]\n",
    "\n",
    "# Make predictions using your trained model\n",
    "predicted_quality = model.predict(new_sample)\n",
    "\n",
    "print(\"Predicted Wine Quality:\", predicted_quality)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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