Wine-Quality .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"C://Users//hp//Desktop//Tableau data sets//winequality_red.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "      <td>5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.069</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.9964</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.065</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.9946</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.47</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.073</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0.57</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.071</td>\n",
       "      <td>17.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "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",
       "5            7.4              0.66         0.00             1.8      0.075   \n",
       "6            7.9              0.60         0.06             1.6      0.069   \n",
       "7            7.3              0.65         0.00             1.2      0.065   \n",
       "8            7.8              0.58         0.02             2.0      0.073   \n",
       "9            7.5              0.50         0.36             6.1      0.071   \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",
       "5                 13.0                  40.0   0.9978  3.51       0.56   \n",
       "6                 15.0                  59.0   0.9964  3.30       0.46   \n",
       "7                 15.0                  21.0   0.9946  3.39       0.47   \n",
       "8                  9.0                  18.0   0.9968  3.36       0.57   \n",
       "9                 17.0                 102.0   0.9978  3.35       0.80   \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",
       "5      9.4        5  \n",
       "6      9.4        5  \n",
       "7     10.0        7  \n",
       "8      9.5        7  \n",
       "9     10.5        5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
      "fixed acidity           1599 non-null float64\n",
      "volatile acidity        1599 non-null float64\n",
      "citric acid             1599 non-null float64\n",
      "residual sugar          1599 non-null float64\n",
      "chlorides               1599 non-null float64\n",
      "free sulfur dioxide     1599 non-null float64\n",
      "total sulfur dioxide    1599 non-null float64\n",
      "density                 1599 non-null float64\n",
      "pH                      1599 non-null float64\n",
      "sulphates               1599 non-null float64\n",
      "alcohol                 1599 non-null float64\n",
      "quality                 1599 non-null int64\n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
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       "      <th>quality</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1594</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1595</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1596</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1597</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1598</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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             False             False        False           False      False   \n",
       "1             False             False        False           False      False   \n",
       "2             False             False        False           False      False   \n",
       "3             False             False        False           False      False   \n",
       "4             False             False        False           False      False   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "1594          False             False        False           False      False   \n",
       "1595          False             False        False           False      False   \n",
       "1596          False             False        False           False      False   \n",
       "1597          False             False        False           False      False   \n",
       "1598          False             False        False           False      False   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density     pH  sulphates  \\\n",
       "0                   False                 False    False  False      False   \n",
       "1                   False                 False    False  False      False   \n",
       "2                   False                 False    False  False      False   \n",
       "3                   False                 False    False  False      False   \n",
       "4                   False                 False    False  False      False   \n",
       "...                   ...                   ...      ...    ...        ...   \n",
       "1594                False                 False    False  False      False   \n",
       "1595                False                 False    False  False      False   \n",
       "1596                False                 False    False  False      False   \n",
       "1597                False                 False    False  False      False   \n",
       "1598                False                 False    False  False      False   \n",
       "\n",
       "      alcohol  quality  \n",
       "0       False    False  \n",
       "1       False    False  \n",
       "2       False    False  \n",
       "3       False    False  \n",
       "4       False    False  \n",
       "...       ...      ...  \n",
       "1594    False    False  \n",
       "1595    False    False  \n",
       "1596    False    False  \n",
       "1597    False    False  \n",
       "1598    False    False  \n",
       "\n",
       "[1599 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2520x1680 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(9,6),dpi=280)\n",
    "sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### THERE IS NO MISSING VALUES IN THE GIVEN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d7990f48>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='quality', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d79edd08>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='quality', y='pH', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d7a93788>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='quality', y='alcohol', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d7a97808>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "sns.boxplot(x='quality', y='sulphates', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d7c4e148>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='quality', y='density', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d7c89408>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Svppaw8eWpPeXTDJ8TABorqCFX1RUFHiH7ffl5uaaFggAYI6ghZ+RkRHJHAAAkwUt/OHDh0v67jz8jz76SA0NDfL7/Tpy5EjEwgEAjBP2GP60adPk9Xp15MgR+Xw+XXjhhfrVr34ViWwAAAOFPUvn1KlTevHFF3XFFVdo48aN8ng8kcgFAFEv7v9eB40L8npopIUt/NjYWElSXV2d4uPj5fV6TQ8FAK1BepvzleKMU3qb862OIqkJh3QGDx6s3//+9+rVq5dGjx6tpKSkSOQCgKiXGn+eUuPPszpGQNjCv+GGG9ShQwc5HA5dd911iouLulP3AQAKcUjns88+05YtW3Tvvfdq27Zt2rp1qw4fPsw5+AAQpYLurp88eVJ/+9vfdPToUb3xxhuSJIfDofHjx0csHADAOEELPysrS1lZWaqoqNBll12mY8eOKSUlRTExUbf8DgBATTiGX1tbqxtuuEFt2rTRyZMntWjRIvXr1y8S2QAABgpb+E8//bRWr16tDh06qKqqStOmTQtZ+F6vV7Nnz9bBgwdVX1+v++67T927d1d+fr4cDod69Oih+fPn8z8FAIiwsIUfGxurDh06SJI6dOggl8sV8uv/8pe/KCUlRUuWLNHx48c1fPhw9erVSzNmzFBOTo4KCgpUWlqqm266yZgZAACaJOxudlJSkoqLi7V7924VFxerbdu2Ib/+5ptv1oMPPhj4PDY2VhUVFcrOzpYkDRw4UNu3b/+JsQEAzRV2D3/JkiVavny5fve73ykjI0OFhYUhvz4xMVHSd0syPPDAA5oxY4YWL14cWGo5MTFRtbW1YYN5PB5VVlb+4P7MzMyw39sSnWsu5xKN82vq3KTWPb9onJvE/M5o7fOTmlD4xcXFysvLC3xeVFSkmTNnhvyer7/+WlOnTtX48eN12223nXUdXLfbreTk5LDBXC5X1P4CzqU1zeX7WvPcJOYX7ew2v1BPAEEL/7XXXtP69ev173//W2VlZZKkxsZGeb3ekIVfU1Oju+66SwUFBerbt68k6dJLL1V5eblycnJUVlamPn36NGtCAICfLmjhDx06VH379tXKlSs1ZcoUSVJMTIwuuOCCkAOuWLFCJ0+e1PLly7V8+XJJ0pw5c/TYY49p6dKlysjI0JAhQwycAgCgKYIWvtPpVFpamhYtWtSsAefOnau5c+f+4P6SkpLmpwMAGIaT4QHAJih8ALAJCh8AbILCBwCboPABwCYofACwCQofAGyCwgcAm6DwAcAmKHxJ/pi4s24BoDWi8CV92+lqeZM66ttOV1sdBQBMwy6tpIa2aWpom2Z1DAAwFXv4AGATFD4A2ASFDwA2QeEDgE1Q+ABgExQ+ANgEhQ8ANkHhA4BNUPgAYBMUPgDYBIUPADZB4QOATVD4AGATFD4A2ASFDwA2YVrhf/jhh5o4caIk6auvvtK4ceM0fvx4zZ8/X42NjWZtFgAQhCmF/8ILL2ju3LnyeDySpMLCQs2YMUOrV6+W3+9XaWmpGZsFAIRgSuF36dJFy5YtC3xeUVGh7OxsSdLAgQO1fft2MzYLAAjBlEscDhkyRAcOHAh87vf75XA4JEmJiYmqra0NO4bH41FlZeUP7s/MzDQuaASday7nEo3za+rcpNY9v2icm8T8zmjt85MidE3bmJj//4+E2+1WcnJy2O9xuVxR+ws4l9Y0l+9rzXOTmF+0s9v8Qj0BROQsnUsvvVTl5eWSpLKyMmVlZUViswCA/xCRws/Ly9OyZcs0ZswYeb1eDRkyJBKbBQD8B9MO6aSlpWndunWSpPT0dJWUlJi1KQBAE/DGKwCwCQofAGyCwgcAm6DwAcAmKHwAsAkKHwBsgsIHAJug8AHAJih8ALAJCh8AbILCBwCboPABwCYofACwCQofAGyCwgcAm6DwAcAmKHwAsAkKHwBsgsIHAJug8AHAJih8ALAJCh8AbILCBwCboPABwCYofACwCQofAGyCwgcAm4iL1IYaGxu1YMECffrpp3I6nXrsscd08cUXR2rzAGB7EdvDf/vtt1VfX69XX31VM2fO1JNPPhmpTQMAFMHCf//99zVgwABJ0lVXXaWPP/44UpsGAEhy+P1+fyQ2NGfOHA0ePFjXXXedJGnQoEF6++23FRd37qNKu3btksvlikQ0AGg1PB6PrrrqqnM+FrFj+ElJSXK73YHPGxsbg5a9pKCBAQA/TsQO6VxzzTUqKyuT9N3ee8+ePSO1aQCAInhI58xZOp999pn8fr+eeOIJdevWLRKbBgAogoUPALAWb7wCAJug8AHAJih8ALCJiJ2W2VL5fD7NnTtXe/fuVWxsrAoLC9WlSxerYxnq6NGjGjFihF566aVW+UL5sGHD1KZNG0lSWlqaCgsLLU5knJUrV2rz5s3yer0aN26cRo0aZXUkw2zcuFF/+tOfJH137nhlZaW2bdum5ORki5MZw+v1Kj8/XwcPHlRMTIwWLVpk+d+f7Qv/nXfekSStXbtW5eXlKiws1HPPPWdxKuN4vV4VFBQoPj7e6iim8Hg8kqTi4mKLkxivvLxcH3zwgdasWaO6ujq99NJLVkcy1IgRIzRixAhJ0qOPPqpf//rXrabsJendd99VQ0OD1q5dq23btumpp57SsmXLLM1k+0M6N954oxYtWiRJOnTokFJTUy1OZKzFixdr7NixuvDCC62OYordu3errq5Od911lyZNmqRdu3ZZHckwW7duVc+ePTV16lRNmTJFgwYNsjqSKT766CN98cUXGjNmjNVRDJWeni6fz6fGxkadOnUq5BtNI8X6BC1AXFyc8vLytGnTJj3zzDNWxzHMxo0b1a5dOw0YMEDPP/+81XFMER8fr7vvvlujRo3Sl19+qXvuuUdvvvlmi/jj+qmOHz+uQ4cOacWKFTpw4IDuu+8+vfnmm3I4HFZHM9TKlSs1depUq2MYLiEhQQcPHtQtt9yi48ePa8WKFVZHYg//jMWLF+utt97SvHnzdPr0aavjGGLDhg3avn27Jk6cqMrKSuXl5am6utrqWIZKT0/X7bffLofDofT0dKWkpLSaOaakpKh///5yOp3KyMiQy+XSsWPHrI5lqJMnT2rPnj3q06eP1VEMt2rVKvXv319vvfWW/vznPys/Pz9wCNIqti/8119/XStXrpQknX/++XI4HIqNjbU4lTFeeeUVlZSUqLi4WJmZmVq8eLHat29vdSxDrV+/PrDUdlVVlU6dOtVq5ti7d29t2bJFfr9fVVVVqqurU0pKitWxDLVz5079/Oc/tzqGKZKTkwMnE7Rt21YNDQ3y+XyWZor+//f+RIMHD9asWbM0YcIENTQ0aPbs2azSGUVGjhypWbNmady4cXI4HHriiSdaxeEcSbr++uu1c+dOjRw5Un6/XwUFBa1mZ+SMvXv3Ki0tzeoYprjzzjs1e/ZsjR8/Xl6vVw899JASEhIszcTSCgBgE7Y/pAMAdkHhA4BNUPgAYBMUPgDYBIUPADZB4QM/0rJly7RmzRpVVlbq2WeflSRt2rRJVVVVFicDzo3CB36izMxMTZs2TZL08ssv69SpUxYnAs6tdbxDBfgR3G63Zs6cqZMnT6p79+764IMPlJKSogULFqhbt25as2aNampqNH36dBUVFenjjz+W2+1Wt27dzlqCuby8XGvXrtXQoUMDS1icWdsnLy9PPp9Pw4YN04YNG+R0Oi2cMeyOPXzY1urVq3XJJZdo9erVGjZsmNxu9zm/7tSpU0pOTtYf//hHrV27Vrt27TrnYZtBgwYFlrC49dZbVVpaKp/Ppy1btignJ4eyh+XYw4dtHThwQAMGDJAkXXPNNT8o5DNvQj+zaFlubq4SEhJ0+vRpeb3ekGMnJSXp2muv1datW7Vx40bdf//95kwCaAb28GFbl1xyif71r39Jkj799FPV19fL6XQGVtv85JNPJEllZWX6+uuvtXTpUuXm5urbb79VsBVJHA5H4LHRo0frtdde09GjR9WrV68IzAgIjcKHbY0aNUo1NTWaMGGC/vCHP0iSJk2apIULF+ruu+8OrGx4xRVXaP/+/Ro9erQeeOABde7cWUeOHDnnmFdffbUeeeQRnThxQldeeaW++uor3XbbbRGbExAKi6cB+u5Sibfccos2b95s2JiNjY0aN26cXnzxRSUlJRk2LvBjsYcPmGD//v0aPny4hg4dStmjxWAPHwBsgj18ALAJCh8AbILCBwCboPABwCYofACwif8Fg5rwfsKyl0gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='quality', y='total sulfur dioxide', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d7cc1808>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['total sulfur dioxide'], bins=30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d7e18d88>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['density'], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d7bff108>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['free sulfur dioxide'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d7fa9e08>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,8))\n",
    "sns.boxplot(data=train, y='chlorides', x='quality')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d806ad08>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['residual sugar'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d8100748>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,8))\n",
    "sns.boxplot(data=train, y='residual sugar', x='quality')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d81f1c88>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wN3YcLcCxi1Vd/p5E9CuHR+7vvvsudu7cCU9Pz6u2ZWZmYu3atYiLi+uScK6kttGMs5fqMDUutMvf68aBgThZWIv1e87jjiEhLe7L2hEHzpXhTEkdZgwPQ4i3uZNSOqZSyrFwTH/886c8fHK0AIIgYHy0a6+CSSQVDo/cIyIisGHDhla3ZWZmYtOmTZg/fz7eeeedTg/nSo5frIYgAKMjA7r8veQyGZJGh8NDIceSfx5DvanjFzfpimvx7elLGBHuJ8oVsCqlHCk3RWJQby3+fawQ/0kv6vYMRD2RwyP3KVOmoKCgoNVt06ZNw4IFC6DVavHYY49h3759mDhxot3XMxqNEAQBOp2uY4lF8nV6JeQywLPhEiweniguKXNq/5ggFYpLip3a58kJ4Xj+qxw8svkgnhkX3K5xcpNS2/w+lfUWbDtVgyBvBW7uo0TJpRJYzOZWc3QknzP73DZADbPJiHVfn0VB8SWkjPRv97j/lX+ntvJfS7bLKgJkqCvJc2ofZzQ2Nrrd9/2V3Dm/O2cHmvJrNBqn9unwCVVBELBo0SL4+DTN6pgwYQJOnz7tsNzVajUAIDY2tqNvLYq8gz8hJtQXo4YPRcb5fISFOney09PLy+l9xkcHQS+osO7rsxg+IBTLbo92WIgFVfUICxVQaTDhi/RsqJQK/PaWqOZ1cJpOSF6doyP5nN1ncWgYjuRU4F8nitGo8MbLs4e1a8nky38noO3815oNAAKDAhHu33XLL+t0Orf7vr+SO+d35+wAOvSDqcPz3PV6PaZPnw6DwQBBEHD48GHJjr1brDYcv1iN0f07PuOjo5ZMiELy6H5Yv/cC1n19FoLgeKZIhd6Id7/PhtFiw30397/mBc46i0IuwzNTr8Mf7ojGp8cLMfPNg8gq04sdi0iSnD5y37VrF+rr65GcnIxly5Zh4cKFUKlUuOmmmzBhwoSuyCi6MyV1qDdZMSqy+8tdLpfh5dnDIJfLsHF/FqobzFh5V2yby/Km5Vbi3e+zYbEJWHzLAPTpdfWJcDHJZDI8Nmkwhvb1w5Op6Zix4Qc8e2cMFtwY2SmzkCoNJpwpqcU3mSXIrTCgrtECb7USgd4qRAR6ISHCv9VbBhJJTbvKPTw8HNu2bQMAzJgxo/nxmTNnYubMmV2TzIVk/HKlZXy/7i93oKng/zIzDj4aJTYdyMa+M6X447RYTLyuN7zVSgiCgOxyAzbuy8InxwoQ6K3CfTdHItTPuTG67jTxut747xPj8NS2DDz3n0x8fCQff7wrFmMHBTo1B99qE5BbYcDZkjqcLalDmd4IAOjto4aXSoGwXp4wGC3IKtPjeH41dutKcUtUIMYODurwYmlE7oAXMbVDZlEtfDVK9AsQ7yhYLpdh5V2xmDI0FH/89CQe+/g45DJgYLAWFfqmO0Mp5TKkjInAoN4+bnF0GubniY8euBFfnirBX77Q4d73D2Nwby3m3xCB8dHBGBjkDfn/HM0LgoBLtY3Iq6jHhTI9zl+qg9Fig0Iuw4Agb9wwIAAxoT64bUgIjl+sbrFfbkU99p8txdenL+FCmR733BjZodskErkDlns7ZBbWYGgfP5e4KfSoSH/sevwW/HC+HOn51cgsqkFCRC8kRPhj7KAgyGRw+iYVYpLJZLhrWBgmxfTGzowifPRTHl74/DSApptyRwR4QamQoabBjLoGC6rrjTDbKgAAvholhvX1Q0yoD6J6a6FWtl3UMllT+Q8IGoBjeVX49/ECvHMgC4tucp1zEkSdieXugMVqw5mSOqSMiRQ7SjMPhRwTY3pjYkzvq7a1Z90VV6TxUCBpdD8kje6HrDI9juZW4djFKlyqbURVvQkWq4AQXzX6aGUY1CcIkYFeCPRWdegHbkKkP3w9PfDR4Tx8eCgXSyYM4n1gSXJY7g5klRlgtNgwtG/n3JyDHIsK1iIqWIuk65umJRZU1f/P2jLXfu5jUG8tFtwQgb//mIv/pBdizqjwa35NIlfCwxUHThXWAACG9uHa5FIzOMQHk2J643h+NdJyufYNSQvL3YHMolpoPOQYGNT5a7iT+CbG9Mbg3lrsOlGEwuoGseMQdRqWuwOZRTWICfWF0g1mn5Dz5DIZZieEQy6T4W97L4gdh6jTsLHssNkEnC6qxdA+HG+XMj9PD9x6XTC+P1+OH867z0wjIntY7nbkV9WjzmjhvUB7gLGDgtCnlwZ/3pUJs9Umdhyia8ZytyOzqBYAeOTeA3go5Fg6aTDOl+qx9ed8seMQXTOWux2nCmugkMsQ3QX3MyXXM3ZQIEZF+uPt/VkwWXj0Tu6N5W5HZlEtBvfW8hL1HqJpUbNBKKxuwGfHC8WOQ3RNWO5tEAQBmUU1nN/ew9waHYy4vr7YuP8CLBx7JzfGcm9DaZ0R5XoTx9t7GJlMhscmDkZuRT2+OOncnZyIXAnLvQ2ZRU1XpnKmTM9zx5AQRIdo8db+rHbdHIXIFbHc25BZ2DRTJjaMJ1N7GrlchgfGDcSZkjocyqoQOw5Rh7Dc23CqqAb9A73go/EQOwqJ4Dcj+iDQW4XNP+aKHYWoQ1jubcgsqsVQDsn0WBoPBRbcGIHduku4WOGeyyhTz8Zyb0VNvRkFVQ08mdrD3TsmEgqZDB8eyhU7CpHTWO6tuHwyldMge7YQXw3uGhaGbT/nQ2+0iB2HyCks91Zw2QG67L6x/VFntPCiJnI7LPdWZBbVINRXgyCtWuwoJLL4fr0Q19cXWw7lcVokuRWWeytOcZlf+oVMJsPCMf1x9lIdfubdmsiN8B6q/6PBZEV2mR53DQsTOwosVpvTN7w2mq1dlKbnmjGiD9Z8cRr/OJSLGwYEiB2HqF3aVe4ZGRl49dVXsWXLlhaP7927F2+++SaUSiUSExORlJTUJSG7k66kFjbBNcbbG8w2HM+qdGqf+IheXZSm5/JUKTB3dD98+GMuSusa0dtHI3YkIocclvu7776LnTt3wtPTs8XjZrMZL7/8Mnbs2AFPT0/Mnz8fEydORHBwcJeF7Q6ZzTfEFr/cpchdfxu5d0wk3v8hB1uP5GPp5MFixyFyyGG5R0REYMOGDXjmmWdaPJ6VlYWIiAj4+TVNFxw1ahTS0tJw55132n09o9EIQRCg0+muIXbXOXi6DD5qOWqLc6ErkbX6HAvUKC4pc+p1Y4JUKC5xbiGqrtjHYja3ur278lUGqbDvVJ5T+0yMi2x+n7byd0a2mhA1KsrbXm5gZF8tPjyYg1vC1VDIf/3e0CgE2IwGh6/f2Njost/37eHO+d05O9CUX6Nx7jdGh+U+ZcoUFBQUXPW4Xq+Hj8+v6654e3tDr9c7fEO1umkGSmxsrDM5u03h7goM7+ePIUOGtPmcjPP5CAt1bkze08vLJfYpLiludbur5HO0T1v5O+N95GpPZF4ytrl9WL9ApBfmYUdmTYsF5cZHByHcP8Lh6+t0Opf9vm8Pd87vztkBdOgHU4dny2i1WhgMvx6tGAyGFmXvjsxWG86W1PHiJWrVdaE+6OXpgcM5XEyMXF+Hyz0qKgp5eXmorq6GyWRCWloa4uPjOzNbtzt/SQ+T1cbxdmqVXCbDDQMCkFVmQGldo9hxiOxyutx37dqF1NRUeHh4YMWKFVi8eDHmzZuHxMREhISEdEXGbsNlB8iR0f0DoJDJcDjHuVlMRN2tXVMhw8PDsW3bNgDAjBkzmh+fNGkSJk2a1DXJRJBZVAtPDwUGBHmLHYVclFatRFxfXxzLq8KUIaFQKXkdILkmfmdeIbOoBkP6+LaYCUH0v8YMDITRYkN6frXYUYjaxHL/hc0m4DSXHaB2iAjwQpifBj9lV3C9GXJZLPdf5FXWw2CystzJIZlMhhsHBKKkthEXK3kjD3JNLPdfnCrkyVRqv5H9ekGtlOOnbE6LJNfEcv/FqaIaeChkiA5x77n61D1USjkSIv1xqrAWlQaT2HGIrsJy/8XJghrEhPpy9gO1240DAmAVBHzKG3mQC2KToelk6snCGgwP55AMtV9vHw1iQn3wydEC1Jt4Gz5yLSx3NJ1MrWu0sNzJaROig1HbaMHWI/liRyFqgeUO4ERB03zlYX25Fjo5JzLQGyP7+eG977NhstjEjkPUjOWOpvF2tVKOwSFasaOQG7rnxkgU1TRiZ0aR2FGImrHcAZwoaLoy1UPBLwc5b8zAAMSG+WLjvguwWHn0Tq6hx7eZ1SbgVFENRoRzSIY6RiaTYdltg5FdbsC2tKvvfUAkhh5f7tlletSbrBjWlydTqeNuHxKCUZH++L/d59BgEv+2gEQ9vtxPFDRdmcqZMnQtZDIZlk+NQWmdEZt/zBE7DhHL/WRhDbxUCgwM5slUujY3DAjA5JjeeGt/Fq9aJdH1+HLPKKhGXF8/LvNLnWL5nTFoMFnx0n/d92bMJA09utyNFisyi2oxgkMy1EmiQ3zw0PiB2HG0AD9eKBc7DvVgPbrcTxXWwGSxYVRkgNhRSEKWTh6MyEAvrPz0JBrNPLlK4ujR5Z6WWwUAGN3fX+QkJCUaDwVemjUMuRX1eO2bs2LHoR6qZ5d7XhUGBHkjSKsWOwpJzNhBQbjnxgi8+30OvsksETsO9UA9ttwFQcDRvCqMiuRRO3WN56YPwfBwPzy1PQO55Qax41AP02PLPbvcgEqDCaNZ7tRFNB4KvLkgAXKZDL/751HoeXETdaMeW+5Hm8fbeTKVuk6/AC9smB+PrDI9Vu8ugcHIdd+pezgsd5vNhtWrVyM5ORkpKSnIy8trsX3NmjWYPXs2UlJSkJKSgrq6ui4L25l+zq2Ev5cHooK9xY5CEjc+Ohjr58XjbLkRiz/8mcsTULdQOnrC7t27YTKZkJqaivT0dLzyyit46623mrdnZmbivffeQ0CAex0BXx5vl8l48RJ1vTuHheGpW4Lx6g9lmLfpEN5dOBq9fTVixyIJc1juR48exbhx4wAAI0eOxKlTp5q32Ww25OXlYfXq1SgvL8ecOXMwZ86crkvbSSr0RmSXGzB3dD+xo5AEWKw2FFTVO3zeLdFh8A8KwQufn8aMv/2A9xddjzguWEddxGG56/V6aLW/rruiUChgsVigVCpRX1+Pe++9F7/97W9htVqxcOFCxMXFISYmps3XMxqNEAQBOp14l2cfzGuauRAsq+1QDgvUKC4pc2qfmCAVikuKRd/HYja3ut1V8jnap638YmUDgMogFfadynP4PIvZDKWHB2bG+mJ3lgEz3/wBC+MDMHuIeyx/0djYKOq/22vhztmBpvwajXO/6Tksd61WC4Ph12lcNpsNSmXTbp6enli4cCE8PT0BAGPGjMGZM2fslrta3TSnPDY21qmgnekfupPQqpW4+5YRHbpBR8b5fISFhjm1j6eXl0vsU1xS3Op2V8nnaJ+28ouVzZn9LmcPA3DPBF+8uTcLHxwtwYlyAS/PHobBIT5Ov3d30ul0ov67vRbunB1Ah34wOWy2hIQEHDhwAACQnp6O6Ojo5m25ublYsGABrFYrzGYzjh07hqFDhzodojsJgoAD58pwc1Qg77xEovFRK/HHaTF4bnoszl6qw9Q3vsfKf5/AuUu1KKiqb/VPTT1XmqT2c3jkfvvtt+PgwYOYN28eBEHASy+9hM2bNyMiIgKTJ0/GjBkzkJSUBA8PD9x9990YPHhwd+TusKwyAwqrG7Dk1iixo1AP1mC24fjFanh6KPH4pMH4OrMEHx/JxxcnS3DXsDDE9fG96mT/+Ogg+HmpREpM7sZhucvlcrzwwgstHouK+rUYH3zwQTz44IOdn6yLHDjXNFY+ITpY5CRETbRqJRITwjE60h87M4rwryMXMbi3FjNG9OHSGNRhPW5c4sD5MgwM8ka/AC+xoxC1EBnojUduHYTpw8NwsbIeb+w5j29Ol8Bk4U23yXk9qtwbzVb8lF2B8TxqJxelkMtwc1QQnrw9GsP6+mH/2TK8seccdMW1YkcjN9Ojyv3n3Eo0mm0YHx0kdhQiu3w0Hkga3Q8PjBsAD4UcW37Kw8pPT6KaJ1WpnXpUuR84VwaVQo4xAwPFjkLULgODtHh80mBMHRqKHy9U4K43vkdabqXYscgN9JhyFwQBX54qwY0DA+ClcngemchlKOQyjI8Oxtv3JsBDKUfypp/w5r4LsNkEsaORC+sx5SO3QiMAAA86SURBVH7sYjUKqhpw98i+Ykch6pCYMF98/vgtuGtYGNZ9fRYLPziC0rpGsWORi+ox5f6f9EKolXJMGRoidhSiDvPReGD9vJF4ZfYwpOVVYsaGH3A0r0rsWOSCekS5m602fHGiGLfFhsBH4yF2HKJrIpPJMO+GCHz6yFiolQrM23QI//wpD4LAYRr6VY8o94MXylFhMOE3I/uIHYWo08SG+WLXY7dg7KAgrPrsFJ7ZcQKNZq4VT016RLnvTC+Cr0aJW6/j/HaSFj8vD3yw6HosnTQI248WIOmdQyisbhA7FrkAyZe7wWjB15lN63WolQqx4xB1OrlchifvuA7vLhyNnDIDZmz4AT9eKBc7FolM8uX+ryMXYTBZkXw9b8xB7u3yTUHa+hMb5oN3UkbBV6PEve8fxl+/OoNqg1Hs2CQSSU/4NlqsePf7bIwZGID4CH+x4xBdkwazDcezHF/AtOim/vjkWAE27s/CmZI6bJgfD2+1pP+pUyskfeT+2fFCXKo14pFbB4kdhajbqD0UmH9DBKYODcX+s6WYtfEgzpRwbZqeRrLlbrUJePu7bMT19cW4wVxLhnoWmazpqtbXkkag0mDCbzYcxHvfZ/Oq1h5EsuX+xcli5JQbsGTCoKtuekDUU1zfPwBf/X48xkcHY80XOszb9BMulOrFjkXdQJLlXl1vwoufn0ZsmC+mxoWKHYdIVEFaNd5dOArr5gzH2Ut1uOuN7/H6t+fQYOKceCmTZLm/sOs0qgwmvDp3uFvcVZ6oq8lkMswd3Q+7n5yAKXGheGPPeUx+bT/+k17IK1slSnKn0PfoLuHfxwuxdNIgDO3jJ3YcIlFdnj55peVTr8MdQ3pj/Z4LeGJrOt7cdwH33zIAY6MCIZPJ4KNW8l6tEiCpcj9/qQ5P7ziBmFAfPDbJtW/UTdQd7E2fXHRzf6TnV2PvmVKs+OQk+vTSYNzgYCy5dSDLXQIkU+5ZZXrMf/cwFHIZNt6TAJVSkiNORJ1GLpMhIcIfI8J74fjFKhw4X47Un/Ox70wpUsZEIun6fgjx1YgdkzpIEuV+7GIVlvzzKAAB/3pwDAYGa8WOROQ2FHIZRvcPQEKkP86W1OF0cQ1e+/Yc/m/PeYwbHITfjOiD24dwqWx349bl3mCy4rVvzuL9gzkI89XgHw/ciEG9fcSOReSW5DIZYsN88fCEgbBYBaSm5WNnehGe3JYBD4UMMUFqTC3xwLjBQRjax4+TFVycw3K32Wx4/vnncfbsWahUKqxZswaRkZHN27dt24atW7dCqVRiyZIlmDhxYpcGFgQBWWV6pP6cjx1HC1BVb8aCGyPw7J0xXKudqJP0D/LG8qkxePqO63A8vwrfni7Ftyfzse7rs1j39Vn08vLA6Eh/xIb5IjbMFzGhPugf6A05C99lOCz33bt3w2QyITU1Fenp6XjllVfw1ltvAQDKysqwZcsWfPLJJzAajViwYAHGjh0LlarzT8YUVTfgL//V4eecSpTWGaGUy3DH0BD8duwAXN8/oNPfj6in+t8ZNiG+Gtw7JgJ3DvKG3NMHP+dV4eecSuiKa7HvTBmsv0yl1HjI0c/fC6F+GoT6ahDqp0FvXw18NUr4aJTwVimh1SjhpVJCKZdBIZdd8V855PKmISKb0HSFuc0mwCr8+l+r7dc/NkGA1QbYhKaPBeHyx/jl818+tjX9N7e4ARXKctgEATIZIIOs6b+/fCyXNU0XlcmApp9PVzyGpt9qLl8LKW9+3i+vgSv3lf3yedPHQNPfpabBDLPVhoQI/277Aeiw3I8ePYpx48YBAEaOHIlTp041bztx4gTi4+OhUqmgUqkQERGBM2fOYPjw4Z0etLC6AVmletwcFYhR/QMwZWgIevvwZA9RZ2trhk1xSTHCQsOgUSowbnAwxg0OhtlqQ2mtESW1DVDIZahpMKOk1ojzl8pRWtcI11rtoFjsAPjw/hswIbp77ivhsNz1ej202l9PUCoUClgsFiiVSuj1evj4/DrG7e3tDb3e/qXNRqMRarUaOp3OqaBaAK9PufxFqUdFQQ4qnHqFzqMCEO/r5E7VBpfYJ97XF4ChW96rK/ZpM79Y2ZzYr0V2N/l6X6nNr70/AKh/+UQFwNvJN+xBrOXQ6Tq21r7R6NzyzQ7LXavVwmD49X+ozWaDUqlsdZvBYGhR9q0ZOXKkUwGJiMh5DieDJyQk4MCBAwCA9PR0REdHN28bPnw4jh49CqPRiLq6OmRlZbXYTkRE4pAJDhaWuDxb5ty5cxAEAS+99BIOHDiAiIgITJ48Gdu2bUNqaioEQcDDDz+MKVOmdFd2IiJqg8NyJyIi98Nr9ImIJIjlTkQkQSx3IiIJ6pZyb2xsxOOPP44FCxbgwQcfRGXl1RdIrF27FsnJyUhMTMS2bdu6I5ZdNpsNq1evRnJyMlJSUpCXl9di+7Zt2zB79mwkJSVh3759IqVsm6P8f//73zF37lzMnTsXf/vb30RK2TpH2S8/54EHHsC//vUvERLa5yj/d999h6SkJCQlJeH55593qZtlOMr+/vvvY/bs2UhMTMS3334rUkr7MjIykJKSctXje/fuRWJiIpKTk12iY9rSVv7PP/8cc+fOxbx587B69WrYbDb7LyR0gw8++EBYv369IAiC8Pnnnwsvvvhii+2HDh0SHnnkEUEQBMFoNAq33XabUF1d3R3R2vT1118Ly5cvFwRBEI4fPy787ne/a95WWloqTJ8+XTAajUJtbW3zx67EXv6LFy8Ks2bNEiwWi2C1WoXk5GRBp9OJFfUq9rJf9tprrwlz5swRPv744+6O55C9/HV1dcK0adOEiooKQRAEYdOmTc0fuwJ72WtqaoQJEyYIRqNRqK6uFm699VaxYrZp06ZNwvTp04W5c+e2eNxkMjX3itFoFGbPni2UlpaKlLJtbeVvaGgQJk+eLNTX1wuCIAjLli0Tdu/ebfe1uuXI/colDMaPH49Dhw612B4fH4+XXnqp+XOr1dp8oZRY2rvsgo+PT/OyC67EXv7Q0FC89957UCgUkMvlsFgsUKvVbb1Ut7OXHQC++uoryGQyjB8/Xox4DtnLf/z4cURHR2Pt2rVYsGABgoKCEBDgOmsj2cvu6emJPn36oKGhAQ0NDS554/mIiAhs2LDhqsezsrIQEREBPz8/qFQqjBo1CmlpaSIktK+t/CqVClu3boWnpycAtOvfbKc36Pbt2/Hhhx+2eCwwMLD5ylVvb2/U1dW12K5Wq6FWq2E2m7FixQokJyfD21vcS5g7e9mF7mYvv4eHBwICAiAIAv76179iyJAhGDBggIhpW7KX/dy5c/j888+xfv16vPnmmyKmbJu9/FVVVTh8+DA+++wzeHl54Z577sHIkSNd5utvLzsAhIWFYdq0abBarXj44YfFitmmKVOmoKCg4KrH3eHfLNB2frlcjqCgIADAli1bUF9fj7Fjx9p9rU4v98vjuFd67LHHmpcpMBgM8PW9euGKmpoaLF26FDfccINLfNN09rIL3c1efqBpnYqVK1fC29sbf/rTn8SI2CZ72T/77DNcunQJixYtQmFhITw8PNC3b1+XOoq3l79Xr14YNmwYgoOb1kkaPXo0dDqdy5S7vewHDhxAaWkp9uzZAwBYvHgxEhISumShwM7mDv9mHbHZbFi3bh1ycnKwYcMGh785dcuwTEJCAr777jsATd8go0aNarG9sbER9913HxITE/Hoo492RySH3H3ZBXv5BUHAI488guuuuw4vvPACFAqFWDFbZS/7M888g+3bt2PLli2YNWsW7rvvPpcqdsB+/ri4OJw7dw6VlZWwWCzIyMjAoEGDxIp6FXvZ/fz8oNFooFKpoFar4ePjg9raWrGiOiUqKgp5eXmorq6GyWRCWloa4uPjxY7llNWrV8NoNGLjxo3NwzP2dMvA9vz587F8+XLMnz8fHh4eeO211wAAf/3rXzF16lQcO3YM+fn52L59O7Zv3w4AeOmll9CvX7/uiNeq22+/HQcPHsS8efOal13YvHlz87ILKSkpWLBgAQRBwLJly1xqzBqwn99ms+HIkSMwmUz4/vvvAQBPPvmky3yzO/rauzpH+Z966ik88MADAICpU6e61IGBo+w//vgjkpKSIJfLkZCQ4HBoQGy7du1CfX09kpOTsWLFCixevBiCICAxMREhIa5/68DL+ePi4rBjxw6MHj0aixYtAgAsXLgQt99+e5v7cvkBIiIJ4kVMREQSxHInIpIgljsRkQSx3ImIJIjlTkQkQSx3kowDBw4gNTUVAJCamgqz2XzVc5YtWwaTydRp76nT6VpdeG3ZsmU4fPhwp70PkbM4FZIkadKkSfjyyy9Fu/5g2bJlmDdvHm688UZR3p9I3NW5iDqosbERzz77LIqKimA2m/Hcc88hJycH2dnZiIyMRFlZGZYtW4ZFixbh1VdfhYeHB5KSkrB+/Xp8+eWXKC4uxqpVq2A2m6HRaPD666+3WMDrq6++wkcffdT8+RtvvIFevXphzZo1OHHiBMxmMx5//HH4+Phg69ateP311/HRRx9h+/btCA4ORkVFhRhfFqJmHJYht7R161b07dsXqampeOWVV5CRkdG8be7cuQgODsbrr78OoGkdnY8//hgzZ85sfs7atWvx0EMPITU1FcnJyTh9+nSL18/NzcWmTZuwZcsWDBgwAD/88AP27NmDqqoq7NixA++99x5OnjzZ/Py6ujr84x//wLZt27Bx48ZWh4SIuhOP3MktZWdnN68pEx0djejoaPz73/9u9bmtLcqVk5PTvNzCXXfdddX2wMBALF++HN7e3sjOzsbIkSORk5ODkSNHAgCCg4NbjKtnZ2dj0KBBUKlUAOAWi2mRtPHIndxSVFRU85Fzfn4+nnrqqRbbZTJZ851q5PKrv82v3H/nzp3YsmVL87a6ujqsX78er7/+OtasWQO1Wg1BEDBw4MDmferq6rB48eLmffr164cLFy6gsbERVqsVOp2uc//CRE7ikTu5pXnz5mHlypW49957YbVasXLlSpw/f755++jRo/HQQw+1ucroM888g9WrV+Ott96CRqPBunXrmrdptVokJCRg1qxZ8PLygq+vL0pLSzF79mwcOnQI8+fPh9VqbfHaAQEBeOKJJzBv3jwEBAS0a9U+oq7E2TJERBLEYRkiIgliuRMRSRDLnYhIgljuREQSxHInIpIgljsRkQSx3ImIJOj/AXZFqylHyimVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['citric acid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d841ec48>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=train, x='quality', y='citric acid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d84f7d08>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.distplot(train['volatile acidity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d8720948>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=train, x='quality', y='volatile acidity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d87f0248>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['fixed acidity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e6d88b7888>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='quality', y='fixed acidity', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binaryclassifier(data):\n",
    "    if data > 6:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['good'] = train['quality'].apply(binaryclassifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>good</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>0</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  good  \n",
       "0      9.4        5     0  \n",
       "1      9.8        5     0  \n",
       "2      9.8        5     0  \n",
       "3      9.8        6     0  \n",
       "4      9.4        5     0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('quality', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>good</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>0</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  good  \n",
       "0      9.4     0  \n",
       "1      9.8     0  \n",
       "2      9.8     0  \n",
       "3      9.8     0  \n",
       "4      9.4     0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ALGORITHM IMPLEMENTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('good', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['good']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.33)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = dtree.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.90      0.92       467\n",
      "           1       0.44      0.61      0.51        61\n",
      "\n",
      "    accuracy                           0.86       528\n",
      "   macro avg       0.69      0.75      0.71       528\n",
      "weighted avg       0.89      0.86      0.87       528\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[419  48]\n",
      " [ 24  37]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []\n",
    "\n",
    "for num_trees in range(1,41):\n",
    "    clf = RandomForestClassifier(n_estimators = num_trees)\n",
    "    scores.append(cross_val_score(clf, X, y, cv=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86335404 0.83850932 0.74375    0.8        0.83125    0.8125\n",
      " 0.725      0.80503145 0.81761006 0.8427673 ]\n"
     ]
    }
   ],
   "source": [
    "print(scores[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86956522 0.8757764  0.84375    0.8375     0.8875     0.86875\n",
      " 0.85       0.83647799 0.87421384 0.87421384]\n"
     ]
    }
   ],
   "source": [
    "print(scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(data=scores)\n",
    "plt.xlabel('Number of trees')\n",
    "plt.ylabel('Classification score')\n",
    "plt.title('Classification score as a function of the number of trees')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=16,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[451  16]\n",
      " [ 36  25]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(confusion_matrix(y_test, rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95       467\n",
      "           1       0.61      0.41      0.49        61\n",
      "\n",
      "    accuracy                           0.90       528\n",
      "   macro avg       0.77      0.69      0.72       528\n",
      "weighted avg       0.89      0.90      0.89       528\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9549718574108818"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RANDOM_FOREST=dtree.score(X,y)\n",
    "RANDOM_FOREST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The accuracy of model is about 95 percet."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}