Practice Project 5 Wine Quality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wine Quality Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fora Downloading the Dataset (https://github.com/shubh2002/DataSets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two datasets are related to red and white variants of the Portuguese \"Vinho Verde\" wine. For more details, consult the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).\n",
    "These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Input variables (based on physicochemical tests):\n",
    "1 - fixed acidity \n",
    "2 - volatile acidity \n",
    "3 - citric acid \n",
    "4 - residual sugar \n",
    "5 - chlorides \n",
    "6 - free sulfur dioxide \n",
    "7 - total sulfur dioxide \n",
    "8 - density \n",
    "9 - pH \n",
    "10 - sulphates \n",
    "11 - alcohol \n",
    "\n",
    "Output variable (based on sensory data): \n",
    "12 - quality (score between 0 and 10) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What might be an interesting thing to do, is aside from using regression modelling, is to set an arbitrary cutoff for your dependent variable (wine quality) at e.g. 7 or higher getting classified as 'good/1' and the remainder as 'not good/0'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Wine_quality.csv',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
       "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    681\n",
       "6    638\n",
       "7    199\n",
       "4     53\n",
       "8     18\n",
       "3     10\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db37985ac8>"
      ]
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     "execution_count": 8,
     "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=df['quality'],data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db37c870b8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g5zQAGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBKG4AGIjiBoCBTFLcVfXUqvpyVf11Vb1sigwAMKKlF3dVbUnyX5I8LclDk7ygqh667BwAMKIpZtyPTvLX3f2V7r45yTuTPGuCHAAwnCmK+35Jvr7H/atmjwEAm6juXu6AVc9N8pTufvHs/ulJHt3dL9nrdWckOWN298QkX15q0M3dJ8n1U4cYgPdpft6r+Xif5ue9ms8qvk/37+7t6z2xddlJsjbD/vE97h+f5Bt7v6i7z01y7rJC3VlVtbO7d0ydY9V5n+bnvZqP92l+3qv5jPY+TbFU/j+TPKiqHlBVhyd5fpL3TZADAIaz9Bl3d99aVb+e5M+SbEnyxu7+wrJzAMCIplgqT3d/MMkHpxj7brSyy/grxvs0P+/VfLxP8/NezWeo92npJ6cBAAfOlqcAMBDFfSdV1RFV9Zmq+lxVfaGq/vXUmVZZVW2pqs9W1QemzrKqquqKqvp8VV1aVTunzrPKquqYqjq/qr5UVZdX1U9PnWnVVNWJs/+Wbv/6dlX9xtS5VlVVnTX7u/yyqnpHVR0xdabNWCq/k6qqkmzr7u9U1T2SfDLJmd39lxNHW0lV9S+S7EhydHc/Y+o8q6iqrkiyo7tX7XOkK6eq3pLkE9193uxTKUd29w1T51pVsy2mr07y97r7a1PnWTVVdb+s/R3+0O7+blW9K8kHu/vN0ybbmBn3ndRrvjO7e4/Zl99+1lFVxyf5uSTnTZ2F8VXV0Ukel+QPk6S7b1bamzolyf9R2hvamuSeVbU1yZFZZ1+RVaO4D8Bs+ffSJNcluaC7L5o604p6dZKzk9w2dZAV10k+XFUXz3YMZH0PTLI7yZtmh1/Oq6ptU4dacc9P8o6pQ6yq7r46yauSXJnkmiTf6u4PT5tqc4r7AHT397v7EVnb9e3RVfWwqTOtmqp6RpLruvviqbMM4OTufmTWrpj3a1X1uKkDraitSR6Z5PXdfVKSm5K4LPB+zA4lPDPJn0ydZVVV1bFZu8jVA5L8WJJtVXXatKk2p7jvgtky3YVJnjpxlFV0cpJnzo7fvjPJE6vqbdNGWk3d/Y3Z7XVJ3pu1K+ixr6uSXLXHCtf5WSty1ve0JJd097VTB1lhT0ry1e7e3d23JHlPkp+ZONOmFPedVFXbq+qY2ff3zNq/+C9Nm2r1dPfLu/v47j4ha8t1f97dK/+b7LJV1baqOur275P8bJLLpk21mrp7V5KvV9WJs4dOSfLFCSOtuhfEMvlmrkzymKo6cnbi8SlJLp8406Ym2TltcPdN8pbZ2ZqHJXlXd/uoEwfqR5O8d+3vjGxN8l+7+0PTRlppL0ny9tky8FeSvGjiPCupqo5M8uQk/2zqLKusuy+qqvOTXJLk1iSfzQC7qPk4GAAMxFI5AAxEcQPAQBQ3AAxEcQPAQBQ3AAxEcQMbqqoTquqy2fc7quo1s++fUFUrv1kFHGx8jhuYW3fvTHL7pUefkOQ7Sf5iskBwCDLjhoNYVb2iqr5cVf9jdq3h36qqC6tqx+z5+8y2pb19Zv2Jqrpk9rXPbHo2y/5AVZ2Q5J8nOWt2zefHVtVXZ5e6TVUdPbvO+D2W9g8LhwgzbjhIVdWjsrbd7ElZ+3/9kiQbXfTluiRP7u7vVdWDsrZd5o71XtjdV1TVG5J8p7tfNRvvwqxdxvVPZ+O+e7b/M3A3MuOGg9djk7y3u/9vd387yfs2ef09kvxBVX0+a1eUeuidHO+8/GAL0hcledOd/PPAHMy44eC23p7Gt+YHv7QfscfjZyW5NslPzZ7/3p0aqPtTs+X2xyfZ0t0ulgILYMYNB6+PJ/nFqrrn7ApkPz97/Iokj5p9/5w9Xn+vJNd0921JTk+yZZOff2OSo/Z67I+ytsRutg0LorjhINXdlyT54ySXJnl3kk/MnnpVkl+tqr9Icp89/sjrkrywqv4yyYOT3LTJEO/P2i8Gl1bVY2ePvT3JsXE5SVgYVweDQ0RV/W72OJlsQWM8J8mzuvv0RY0BhzrHuIG7RVW9NsnTkjx96ixwMDPjBoCBOMYNAANR3AAwEMUNAANR3AAwEMUNAANR3AAwkP8HCgzNJQK0NEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(y=df['fixed acidity'],x=df['quality'],data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db379b09e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(y=df['volatile acidity'],x=df['quality'],data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db37df8978>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=df['quality'],y=df['alcohol'],data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db37e73400>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=df['quality'],y=df['residual sugar'],data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1db37eed208>"
      ]
     },
     "execution_count": 13,
     "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.boxplot(x=df['quality'],y=df['fixed acidity'],data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity              AxesSubplot(0.125,0.125;0.0545775x0.755)\n",
       "volatile acidity        AxesSubplot(0.190493,0.125;0.0545775x0.755)\n",
       "citric acid             AxesSubplot(0.255986,0.125;0.0545775x0.755)\n",
       "residual sugar          AxesSubplot(0.321479,0.125;0.0545775x0.755)\n",
       "chlorides               AxesSubplot(0.386972,0.125;0.0545775x0.755)\n",
       "free sulfur dioxide     AxesSubplot(0.452465,0.125;0.0545775x0.755)\n",
       "total sulfur dioxide    AxesSubplot(0.517958,0.125;0.0545775x0.755)\n",
       "density                 AxesSubplot(0.583451,0.125;0.0545775x0.755)\n",
       "pH                      AxesSubplot(0.648944,0.125;0.0545775x0.755)\n",
       "sulphates               AxesSubplot(0.714437,0.125;0.0545775x0.755)\n",
       "alcohol                  AxesSubplot(0.77993,0.125;0.0545775x0.755)\n",
       "quality                 AxesSubplot(0.845423,0.125;0.0545775x0.755)\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df.plot(kind='box',subplots=True,figsize=(20,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 4, 8, 3], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['quality'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "review=[]\n",
    "for i in df['quality']:\n",
    "    if i==3 or i==4:\n",
    "        review.append('1')\n",
    "    elif i==5 or i==6:\n",
    "        review.append('2')\n",
    "    else:\n",
    "        review.append('3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review']=review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>review</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>2</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",
       "      <td>2</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 review  \n",
       "0      9.4        5      2  \n",
       "1      9.8        5      2  \n",
       "2      9.8        5      2  \n",
       "3      9.8        6      2  \n",
       "4      9.4        5      2  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>review</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>2</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>2</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>2</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>2</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>2</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 review  \n",
       "0      9.4      2  \n",
       "1      9.8      2  \n",
       "2      9.8      2  \n",
       "3      9.8      2  \n",
       "4      9.4      2  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop('quality',axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 11)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,df.columns!='review']\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaling=StandardScaler()\n",
    "x=scaling.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 11)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,df.columns=='review']\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model1=LogisticRegression()\n",
    "model1=model1.fit(x_train,y_train)\n",
    "y_pred=model1.predict(x_test)\n",
    "acc1=accuracy_score(y_test,y_pred)\n",
    "acc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8725"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model2=SVC()\n",
    "model2=model2.fit(x_train,y_train)\n",
    "y_pred=model2.predict(x_test)\n",
    "acc2=accuracy_score(y_test,y_pred)\n",
    "acc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8125"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model3=DecisionTreeClassifier(criterion='gini')\n",
    "model3=model3.fit(x_train,y_train)\n",
    "y_pred=model3.predict(x_test)\n",
    "acc3=accuracy_score(y_test,y_pred)\n",
    "acc3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7625"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model4=GaussianNB()\n",
    "model4=model4.fit(x_train,y_train)\n",
    "y_pred=model4.predict(x_test)\n",
    "acc4=accuracy_score(y_test,y_pred)\n",
    "acc4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.885"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model5=RandomForestClassifier(n_estimators=1000)\n",
    "model5=model5.fit(x_train,y_train)\n",
    "y_pred=model5.predict(x_test)\n",
    "acc5=accuracy_score(y_test,y_pred)\n",
    "acc5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.795"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model6=AdaBoostClassifier()\n",
    "model6=model6.fit(x_train,y_train)\n",
    "y_pred=model6.predict(x_test)\n",
    "acc6=accuracy_score(y_pred,y_test)\n",
    "acc6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8525"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model7=GradientBoostingClassifier()\n",
    "model7=model7.fit(x_train,y_train)\n",
    "y_pred=model7.predict(x_test)\n",
    "acc7=accuracy_score(y_test,y_pred)\n",
    "acc7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8825"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "model8=XGBClassifier(n_estimators=1000)\n",
    "model8=model8.fit(x_train,y_train)\n",
    "y_pred=model8.predict(x_test)\n",
    "acc8=accuracy_score(y_test,y_pred)\n",
    "acc8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8425"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model9=KNeighborsClassifier()\n",
    "model9=model9.fit(x_train,y_train)\n",
    "y_pred=model9.predict(x_test)\n",
    "acc9=accuracy_score(y_test,y_pred)\n",
    "acc9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "CV=[]\n",
    "for i in [model1,model2,model3,model4,model5,model6,model7,model8,model9]:\n",
    "    score=cross_val_score(i,x,y,cv=20,scoring='accuracy')\n",
    "    score=score.mean()\n",
    "    CV.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>MODELS</th>\n",
       "      <th>SCORE</th>\n",
       "      <th>CROSS VALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RANDOM FOREST</td>\n",
       "      <td>0.8850</td>\n",
       "      <td>0.844973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XG BOOST</td>\n",
       "      <td>0.8825</td>\n",
       "      <td>0.824365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SUPPORT VECTOR MACHINE</td>\n",
       "      <td>0.8725</td>\n",
       "      <td>0.842497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LOGISTIC REGRESSION</td>\n",
       "      <td>0.8600</td>\n",
       "      <td>0.838730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GRADIENT BOOSTING</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.824309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>K NEIGHBORS CLASSIFIER</td>\n",
       "      <td>0.8425</td>\n",
       "      <td>0.813034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DECISION TREE CLASSIFIER</td>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.769181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ADA BOOST</td>\n",
       "      <td>0.7950</td>\n",
       "      <td>0.748678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NAIVE BAYES</td>\n",
       "      <td>0.7625</td>\n",
       "      <td>0.774298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     MODELS   SCORE  CROSS VALID\n",
       "4             RANDOM FOREST  0.8850     0.844973\n",
       "7                  XG BOOST  0.8825     0.824365\n",
       "1    SUPPORT VECTOR MACHINE  0.8725     0.842497\n",
       "0       LOGISTIC REGRESSION  0.8600     0.838730\n",
       "6         GRADIENT BOOSTING  0.8525     0.824309\n",
       "8    K NEIGHBORS CLASSIFIER  0.8425     0.813034\n",
       "2  DECISION TREE CLASSIFIER  0.8125     0.769181\n",
       "5                 ADA BOOST  0.7950     0.748678\n",
       "3               NAIVE BAYES  0.7625     0.774298"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models=pd.DataFrame({'MODELS':['LOGISTIC REGRESSION','SUPPORT VECTOR MACHINE','DECISION TREE CLASSIFIER','NAIVE BAYES','RANDOM FOREST'\n",
    "                              ,'ADA BOOST','GRADIENT BOOSTING','XG BOOST','K NEIGHBORS CLASSIFIER'],\n",
    "                               'SCORE':[acc1,acc2,acc3,acc4,acc5,acc6,acc7,acc8,acc9],'CROSS VALID':CV})\n",
    "models.sort_values(by='SCORE',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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