1.Wine Quality Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fee822d2",
   "metadata": {},
   "source": [
    "# Project1- Wine Quality Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e439604d",
   "metadata": {},
   "source": [
    "### Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "82d517ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd4a473",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "641814a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(r'C:\\Users\\Lanchavi\\Downloads\\Final Project 1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4810362",
   "metadata": {},
   "source": [
    "### Exploring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "49f1e53e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c7e93670",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7adc87ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1599, 12)\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6b4cc5ba",
   "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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8050fe8a",
   "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": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "aa44a6d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of duplicate rows:  (240, 12)\n"
     ]
    }
   ],
   "source": [
    "duplicate_rows_df = dataset[dataset.duplicated()]\n",
    "print(\"No. of duplicate rows: \",duplicate_rows_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8cd93ae8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1359, 12)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.drop_duplicates()\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7375da3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.310596</td>\n",
       "      <td>0.529478</td>\n",
       "      <td>0.272333</td>\n",
       "      <td>2.523400</td>\n",
       "      <td>0.088124</td>\n",
       "      <td>15.893304</td>\n",
       "      <td>46.825975</td>\n",
       "      <td>0.996709</td>\n",
       "      <td>3.309787</td>\n",
       "      <td>0.658705</td>\n",
       "      <td>10.432315</td>\n",
       "      <td>5.623252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.736990</td>\n",
       "      <td>0.183031</td>\n",
       "      <td>0.195537</td>\n",
       "      <td>1.352314</td>\n",
       "      <td>0.049377</td>\n",
       "      <td>10.447270</td>\n",
       "      <td>33.408946</td>\n",
       "      <td>0.001869</td>\n",
       "      <td>0.155036</td>\n",
       "      <td>0.170667</td>\n",
       "      <td>1.082065</td>\n",
       "      <td>0.823578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996700</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.091000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.997820</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1359.000000       1359.000000  1359.000000     1359.000000   \n",
       "mean        8.310596          0.529478     0.272333        2.523400   \n",
       "std         1.736990          0.183031     0.195537        1.352314   \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     0.430000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1359.000000          1359.000000           1359.000000  1359.000000   \n",
       "mean      0.088124            15.893304             46.825975     0.996709   \n",
       "std       0.049377            10.447270             33.408946     0.001869   \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     0.996700   \n",
       "75%       0.091000            21.000000             63.000000     0.997820   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1359.000000  1359.000000  1359.000000  1359.000000  \n",
       "mean      3.309787     0.658705    10.432315     5.623252  \n",
       "std       0.155036     0.170667     1.082065     0.823578  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "da83a80f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "5            7.4              0.66         0.00             1.8      0.075   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "5                 13.0                  40.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "5      9.4        5  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a3ae687c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.drop(columns=['fixed acidity', 'quality'], axis=1)\n",
    "y = dataset['quality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "27fa7d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>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>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0              0.70         0.00             1.9      0.076   \n",
       "1              0.88         0.00             2.6      0.098   \n",
       "2              0.76         0.04             2.3      0.092   \n",
       "3              0.28         0.56             1.9      0.075   \n",
       "5              0.66         0.00             1.8      0.075   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "5                 13.0                  40.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  \n",
       "0      9.4  \n",
       "1      9.8  \n",
       "2      9.8  \n",
       "3      9.8  \n",
       "5      9.4  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "de210887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    5\n",
       "2    5\n",
       "3    6\n",
       "5    5\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7bc6f9b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1087, 10) (272, 10)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
    "print(x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1bce885",
   "metadata": {},
   "source": [
    "### Exploratory "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9da9cc06",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "3940e246",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b5bae615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='fixed acidity'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = 'quality', y = 'fixed acidity', data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4236e28b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='volatile acidity'>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = 'quality', y = 'volatile acidity', data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f9acda47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='citric acid'>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = 'quality', y = 'citric acid', data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6bdd6ba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='residual sugar'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = 'quality', y = 'residual sugar', data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "fdadc738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='chlorides'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = 'quality', y = 'chlorides', data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ebb489c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='free sulfur dioxide'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = 'quality', y = 'free sulfur dioxide', data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2abdfdf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='total sulfur dioxide'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = 'quality', y = 'total sulfur dioxide', data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "94c435b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='sulphates'>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = 'quality', y = 'sulphates', data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e7f31a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='alcohol'>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,5))\n",
    "sns.barplot(x = 'quality', y = 'alcohol', data = dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe92dd45",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b69e0d5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(dataset.corr(), cbar=True, square=True, fmt='.1f', annot=True, annot_kws = {'size':10}, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ecdaf558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    577\n",
       "6    535\n",
       "7    167\n",
       "4     53\n",
       "8     17\n",
       "3     10\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "36449532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='count'>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=dataset['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3c5da0b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat(x):\n",
    "    if x < 6.5:\n",
    "        return 'poor'\n",
    "    else:\n",
    "        return 'good'\n",
    "dataset['quality']=dataset['quality'].apply(lambda x: cat(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dd1afad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "poor    1175\n",
      "good     184\n",
      "Name: quality, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset['quality'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "f0e4f626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='count'>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(dataset['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "da3df7bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_quality = LabelEncoder()\n",
    "dataset['quality'] = label_quality.fit_transform(dataset['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "149eacc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    1175\n",
      "0     184\n",
      "Name: quality, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataset['quality'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a0ec8a",
   "metadata": {},
   "source": [
    "### Training and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "721ccb69",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.drop(['quality'], axis=1)\n",
    "y = dataset['quality']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e019ce",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e53e0b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "x = sc.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae93dc23",
   "metadata": {},
   "source": [
    "### Splitting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0786a2c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1087, 11) (272, 11)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
    "print(x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "96fcd289",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_predict(model1):\n",
    "    model = model1\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred=model.predict(x_test)\n",
    "    print(\"Accuracy score: \",(accuracy_score(y_test, y_pred)*100))\n",
    "    print(\"\\nConfusion Matirx\")\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "    print(\"\\nClassification report\")\n",
    "    print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a435d57",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "90e0a69c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "be7e4f25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  89.33823529411765\n",
      "\n",
      "Confusion Matirx\n",
      "[[ 13  21]\n",
      " [  8 230]]\n",
      "\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.38      0.47        34\n",
      "           1       0.92      0.97      0.94       238\n",
      "\n",
      "    accuracy                           0.89       272\n",
      "   macro avg       0.77      0.67      0.71       272\n",
      "weighted avg       0.88      0.89      0.88       272\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit_predict(LogisticRegression())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b1a0f55",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "22a6c891",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "0539f737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  90.07352941176471\n",
      "\n",
      "Confusion Matirx\n",
      "[[ 17  17]\n",
      " [ 10 228]]\n",
      "\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.50      0.56        34\n",
      "           1       0.93      0.96      0.94       238\n",
      "\n",
      "    accuracy                           0.90       272\n",
      "   macro avg       0.78      0.73      0.75       272\n",
      "weighted avg       0.89      0.90      0.90       272\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit_predict(KNeighborsClassifier(n_neighbors=7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44878170",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "30744f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "38561e0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  90.80882352941177\n",
      "\n",
      "Confusion Matirx\n",
      "[[ 20  14]\n",
      " [ 11 227]]\n",
      "\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.59      0.62        34\n",
      "           1       0.94      0.95      0.95       238\n",
      "\n",
      "    accuracy                           0.91       272\n",
      "   macro avg       0.79      0.77      0.78       272\n",
      "weighted avg       0.90      0.91      0.91       272\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit_predict(DecisionTreeClassifier(max_depth=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a65eda",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "84a92eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "db92cf1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "8e2512d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  91.54411764705883\n",
      "\n",
      "Confusion Matirx\n",
      "[[ 14  20]\n",
      " [  3 235]]\n",
      "\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.41      0.55        34\n",
      "           1       0.92      0.99      0.95       238\n",
      "\n",
      "    accuracy                           0.92       272\n",
      "   macro avg       0.87      0.70      0.75       272\n",
      "weighted avg       0.91      0.92      0.90       272\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit_predict(RandomForestClassifier(n_estimators=200, random_state=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfeacd13",
   "metadata": {},
   "source": [
    "### Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "2bf82ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "56db97da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  91.91176470588235\n",
      "\n",
      "Confusion Matirx\n",
      "[[ 15  19]\n",
      " [  3 235]]\n",
      "\n",
      "Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.44      0.58        34\n",
      "           1       0.93      0.99      0.96       238\n",
      "\n",
      "    accuracy                           0.92       272\n",
      "   macro avg       0.88      0.71      0.77       272\n",
      "weighted avg       0.91      0.92      0.91       272\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit_predict(SVC())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a5b8eb",
   "metadata": {},
   "source": [
    "### Test case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d937ffa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Quality of wine is good.\n"
     ]
    }
   ],
   "source": [
    "model = SVC()                                          \n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "test_case_list = [7.4, 0.36, 0.3, 1.8, 0.074, 17, 24, 0.99419, 3.24, 0.7, 13.4]\n",
    "\n",
    "test_case_df = pd.DataFrame(test_case_list)               \n",
    "test_case = test_case_df.T                                \n",
    "test_case_scaled = sc.transform(test_case)                \n",
    "test_case_prediction = model.predict(test_case_scaled)    \n",
    "if(test_case_prediction == 0):                            \n",
    "    print('The Quality of wine is good.')\n",
    "else:\n",
    "    print('The Quality of wine is not good.')"
   ]
  }
 ],
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