Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wine Quality Prediction Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "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",
       "    </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",
       "      <td>5</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",
       "      <td>5</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",
       "      <td>5</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",
       "      <td>6</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",
       "      <td>5</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",
       "      <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",
       "      <td>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",
       "      <td>6</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",
       "      <td>6</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",
       "      <td>5</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",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1599 rows × 12 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  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  \n",
       "...       ...      ...  \n",
       "1594     10.5        5  \n",
       "1595     11.2        6  \n",
       "1596     11.0        6  \n",
       "1597     10.2        5  \n",
       "1598     11.0        6  \n",
       "\n",
       "[1599 rows x 12 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = pd.read_csv(\"winequality-red.csv\")\n",
    "wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, alcohol, quality]\n",
       "Index: []"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine[wine.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity           0.124052\n",
       "volatile acidity       -0.390558\n",
       "citric acid             0.226373\n",
       "residual sugar          0.013732\n",
       "chlorides              -0.128907\n",
       "free sulfur dioxide    -0.050656\n",
       "total sulfur dioxide   -0.185100\n",
       "density                -0.174919\n",
       "pH                     -0.057731\n",
       "sulphates               0.251397\n",
       "alcohol                 0.476166\n",
       "quality                 1.000000\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.corr()[\"quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "sb.heatmap(wine.corr(), annot = True, cmap = 'YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 4, 8, 3], dtype=int64)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine[\"quality\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>1218</th>\n",
       "      <td>8.2</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.082</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.99600</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.69</td>\n",
       "      <td>10.6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.077</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.99555</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.69</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.095</td>\n",
       "      <td>19.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99460</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.57</td>\n",
       "      <td>11.7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.059</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.56</td>\n",
       "      <td>11.3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>9.2</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.081</td>\n",
       "      <td>15.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.23</td>\n",
       "      <td>0.59</td>\n",
       "      <td>9.5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.080</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.99550</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>8.6</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.074</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.99790</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>12.3</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.089</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.00020</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.44</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>7.9</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.084</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.99619</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.68</td>\n",
       "      <td>10.9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>11.6</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.53</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.105</td>\n",
       "      <td>33.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.00100</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.95</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5</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",
       "1218            8.2             0.400         0.31             1.9      0.082   \n",
       "1503            6.2             0.440         0.39             2.5      0.077   \n",
       "571             6.2             0.360         0.24             2.2      0.095   \n",
       "685             8.1             0.780         0.23             2.6      0.059   \n",
       "905             9.2             0.580         0.20             3.0      0.081   \n",
       "25              6.3             0.390         0.16             1.4      0.080   \n",
       "654             8.6             0.470         0.47             2.4      0.074   \n",
       "581            12.3             0.500         0.49             2.2      0.089   \n",
       "1282            7.9             0.765         0.00             2.0      0.084   \n",
       "289            11.6             0.420         0.53             3.3      0.105   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "1218                  8.0                  24.0  0.99600  3.24       0.69   \n",
       "1503                  6.0                  14.0  0.99555  3.51       0.69   \n",
       "571                  19.0                  42.0  0.99460  3.57       0.57   \n",
       "685                   5.0                  15.0  0.99700  3.37       0.56   \n",
       "905                  15.0                 115.0  0.99800  3.23       0.59   \n",
       "25                   11.0                  23.0  0.99550  3.34       0.56   \n",
       "654                   7.0                  29.0  0.99790  3.08       0.46   \n",
       "581                   5.0                  14.0  1.00020  3.19       0.44   \n",
       "1282                  9.0                  22.0  0.99619  3.33       0.68   \n",
       "289                  33.0                  98.0  1.00100  3.20       0.95   \n",
       "\n",
       "      alcohol  quality  goodquality  \n",
       "1218     10.6        6            0  \n",
       "1503     11.0        6            0  \n",
       "571      11.7        6            0  \n",
       "685      11.3        5            0  \n",
       "905       9.5        5            0  \n",
       "25        9.3        5            0  \n",
       "654       9.5        5            0  \n",
       "581       9.6        5            0  \n",
       "1282     10.9        6            0  \n",
       "289       9.2        5            0  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine[\"goodquality\"] = [1 if x >= 7 else 0 for x in wine[\"quality\"]]\n",
    "wine.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "goodquality\n",
       "0    1382\n",
       "1     217\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine[\"goodquality\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = wine.drop([\"goodquality\", \"quality\"], axis = 1)\n",
    "y = wine[\"goodquality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>383</th>\n",
       "      <td>8.3</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.080</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.99740</td>\n",
       "      <td>3.21</td>\n",
       "      <td>0.80</td>\n",
       "      <td>9.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>7.6</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.072</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.99650</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1.08</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.074</td>\n",
       "      <td>16.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.99842</td>\n",
       "      <td>3.53</td>\n",
       "      <td>0.72</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>10.8</td>\n",
       "      <td>0.26</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>9.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>9.1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.065</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.99774</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.070</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.99630</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.66</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.093</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.99960</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.63</td>\n",
       "      <td>10.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.100</td>\n",
       "      <td>50.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.99544</td>\n",
       "      <td>3.59</td>\n",
       "      <td>0.68</td>\n",
       "      <td>11.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.050</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.99348</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.54</td>\n",
       "      <td>11.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>12.2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.066</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1.18</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "383             8.3              0.26         0.42             2.0      0.080   \n",
       "161             7.6              0.68         0.02             1.3      0.072   \n",
       "1515            6.9              0.84         0.21             4.1      0.074   \n",
       "679            10.8              0.26         0.45             3.3      0.060   \n",
       "980             9.1              0.50         0.30             1.9      0.065   \n",
       "97              7.0              0.50         0.25             2.0      0.070   \n",
       "407            12.0              0.39         0.66             3.0      0.093   \n",
       "1154            6.6              0.58         0.00             2.2      0.100   \n",
       "1492            6.2              0.65         0.06             1.6      0.050   \n",
       "340            12.2              0.34         0.50             2.4      0.066   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "383                  11.0                  27.0  0.99740  3.21       0.80   \n",
       "161                   9.0                  20.0  0.99650  3.17       1.08   \n",
       "1515                 16.0                  65.0  0.99842  3.53       0.72   \n",
       "679                  20.0                  49.0  0.99720  3.13       0.54   \n",
       "980                   8.0                  17.0  0.99774  3.32       0.71   \n",
       "97                    3.0                  22.0  0.99630  3.25       0.63   \n",
       "407                  12.0                  30.0  0.99960  3.18       0.63   \n",
       "1154                 50.0                  63.0  0.99544  3.59       0.68   \n",
       "1492                  6.0                  18.0  0.99348  3.57       0.54   \n",
       "340                  10.0                  21.0  1.00000  3.12       1.18   \n",
       "\n",
       "      alcohol  \n",
       "383      9.40  \n",
       "161      9.20  \n",
       "1515     9.25  \n",
       "679      9.60  \n",
       "980     10.50  \n",
       "97       9.20  \n",
       "407     10.80  \n",
       "1154    11.40  \n",
       "1492    11.95  \n",
       "340      9.20  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84      0\n",
       "391     0\n",
       "1379    0\n",
       "579     0\n",
       "1241    0\n",
       "518     0\n",
       "1044    0\n",
       "969     0\n",
       "194     0\n",
       "1483    0\n",
       "Name: goodquality, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.07720289, 0.09948383, 0.09301314, 0.07254346, 0.06739277,\n",
       "       0.0685788 , 0.08493462, 0.08543919, 0.06904988, 0.10970322,\n",
       "       0.1726582 ])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "classifier = ExtraTreesClassifier()\n",
    "classifier.fit(x,y)\n",
    "score = classifier.feature_importances_\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_res = pd.DataFrame(columns=['Model', 'Score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\arpan\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "y_test_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>Logistic Regression</td>\n",
       "      <td>90.3125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model    Score\n",
       "0  Logistic Regression  90.3125"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "\n",
    "model_res.loc[len(model_res)] = [\"Logistic Regression\", accuracy_score(y_test, y_test_pred)*100]\n",
    "model_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K NEIGHBORS CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Logistic Regression</td>\n",
       "      <td>90.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model    Score\n",
       "0     Logistic Regression  90.3125\n",
       "1  K Neighbors Classifier  84.6875"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model = KNeighborsClassifier()\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "y_test_pred = model.predict(x_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "model_res.loc[len(model_res)] = [\"K Neighbors Classifier\", accuracy_score(y_test, y_test_pred)*100]\n",
    "model_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SUPPRT VECTOR CLASSIFICATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Logistic Regression</td>\n",
       "      <td>90.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Support Vector Classification</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Model    Score\n",
       "0            Logistic Regression  90.3125\n",
       "1         K Neighbors Classifier  84.6875\n",
       "2  Support Vector Classification  84.6875"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC()\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "model_res.loc[len(model_res)] = [\"Support Vector Classification\", accuracy_score(y_test, y_test_pred)*100]\n",
    "model_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RANDOM FOREST CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>Logistic Regression</td>\n",
       "      <td>90.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Support Vector Classification</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Model    Score\n",
       "0            Logistic Regression  90.3125\n",
       "1         K Neighbors Classifier  84.6875\n",
       "2  Support Vector Classification  84.6875\n",
       "3       Random Forest Classifier  84.6875"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier()\n",
    "\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "model_res.loc[len(model_res)] = [\"Random Forest Classifier\", accuracy_score(y_test, y_test_pred)*100]\n",
    "model_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DECISION TREE CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Logistic Regression</td>\n",
       "      <td>90.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Support Vector Classification</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>89.0625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Model    Score\n",
       "0            Logistic Regression  90.3125\n",
       "1         K Neighbors Classifier  84.6875\n",
       "2  Support Vector Classification  84.6875\n",
       "3       Random Forest Classifier  84.6875\n",
       "4       Decision Tree Classifier  89.0625"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "model_res.loc[len(model_res)] = [\"Decision Tree Classifier\", accuracy_score(y_test, y_pred) * 100]\n",
    "model_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>90.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>89.0625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K Neighbors Classifier</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Support Vector Classification</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>84.6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Model    Score\n",
       "0            Logistic Regression  90.3125\n",
       "4       Decision Tree Classifier  89.0625\n",
       "1         K Neighbors Classifier  84.6875\n",
       "2  Support Vector Classification  84.6875\n",
       "3       Random Forest Classifier  84.6875"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_res = model_res.sort_values(by = \"Score\", ascending = False)\n",
    "model_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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