Logistic Regression - Wine Quality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7902961d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa47c676",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</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>white</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.001</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>white</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.994</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "0  white            7.0              0.27         0.36            20.7   \n",
       "1  white            6.3              0.30         0.34             1.6   \n",
       "\n",
       "   chlorides  free sulfur dioxide  total sulfur dioxide  density   pH  \\\n",
       "0      0.045                 45.0                 170.0    1.001  3.0   \n",
       "1      0.049                 14.0                 132.0    0.994  3.3   \n",
       "\n",
       "   sulphates  alcohol  quality  \n",
       "0       0.45      8.8        6  \n",
       "1       0.49      9.5        6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = pd.read_csv(\"C:/Users/user/Desktop/Sneha's Program/ML Datasets/winequalityN.csv\")\n",
    "wine.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd20e449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6497 entries, 0 to 6496\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   type                  6497 non-null   object \n",
      " 1   fixed acidity         6487 non-null   float64\n",
      " 2   volatile acidity      6489 non-null   float64\n",
      " 3   citric acid           6494 non-null   float64\n",
      " 4   residual sugar        6495 non-null   float64\n",
      " 5   chlorides             6495 non-null   float64\n",
      " 6   free sulfur dioxide   6497 non-null   float64\n",
      " 7   total sulfur dioxide  6497 non-null   float64\n",
      " 8   density               6497 non-null   float64\n",
      " 9   pH                    6488 non-null   float64\n",
      " 10  sulphates             6493 non-null   float64\n",
      " 11  alcohol               6497 non-null   float64\n",
      " 12  quality               6497 non-null   int64  \n",
      "dtypes: float64(11), int64(1), object(1)\n",
      "memory usage: 660.0+ KB\n"
     ]
    }
   ],
   "source": [
    "wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d5fd369",
   "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>6487.000000</td>\n",
       "      <td>6489.000000</td>\n",
       "      <td>6494.000000</td>\n",
       "      <td>6495.000000</td>\n",
       "      <td>6495.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6488.000000</td>\n",
       "      <td>6493.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.216579</td>\n",
       "      <td>0.339691</td>\n",
       "      <td>0.318722</td>\n",
       "      <td>5.444326</td>\n",
       "      <td>0.056042</td>\n",
       "      <td>30.525319</td>\n",
       "      <td>115.744574</td>\n",
       "      <td>0.994697</td>\n",
       "      <td>3.218395</td>\n",
       "      <td>0.531215</td>\n",
       "      <td>10.491801</td>\n",
       "      <td>5.818378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.296750</td>\n",
       "      <td>0.164649</td>\n",
       "      <td>0.145265</td>\n",
       "      <td>4.758125</td>\n",
       "      <td>0.035036</td>\n",
       "      <td>17.749400</td>\n",
       "      <td>56.521855</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.160748</td>\n",
       "      <td>0.148814</td>\n",
       "      <td>1.192712</td>\n",
       "      <td>0.873255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.992340</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>0.994890</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.065000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>0.996990</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>11.300000</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>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    6487.000000       6489.000000  6494.000000     6495.000000   \n",
       "mean        7.216579          0.339691     0.318722        5.444326   \n",
       "std         1.296750          0.164649     0.145265        4.758125   \n",
       "min         3.800000          0.080000     0.000000        0.600000   \n",
       "25%         6.400000          0.230000     0.250000        1.800000   \n",
       "50%         7.000000          0.290000     0.310000        3.000000   \n",
       "75%         7.700000          0.400000     0.390000        8.100000   \n",
       "max        15.900000          1.580000     1.660000       65.800000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  6495.000000          6497.000000           6497.000000  6497.000000   \n",
       "mean      0.056042            30.525319            115.744574     0.994697   \n",
       "std       0.035036            17.749400             56.521855     0.002999   \n",
       "min       0.009000             1.000000              6.000000     0.987110   \n",
       "25%       0.038000            17.000000             77.000000     0.992340   \n",
       "50%       0.047000            29.000000            118.000000     0.994890   \n",
       "75%       0.065000            41.000000            156.000000     0.996990   \n",
       "max       0.611000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  6488.000000  6493.000000  6497.000000  6497.000000  \n",
       "mean      3.218395     0.531215    10.491801     5.818378  \n",
       "std       0.160748     0.148814     1.192712     0.873255  \n",
       "min       2.720000     0.220000     8.000000     3.000000  \n",
       "25%       3.110000     0.430000     9.500000     5.000000  \n",
       "50%       3.210000     0.510000    10.300000     6.000000  \n",
       "75%       3.320000     0.600000    11.300000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     9.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c35ba82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['type', 'fixed acidity', 'volatile acidity', 'citric acid',\n",
       "       'residual sugar', 'chlorides', 'free sulfur dioxide',\n",
       "       'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n",
       "       'quality'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0aaad9fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "add = wine.isnull().sum()\n",
    "percent = (wine.isnull().sum()/wine.isnull().count())\n",
    "h=pd.concat([add,percent], axis =1, keys = ['Sum', 'Percentage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "57fab5d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def null_cell(df):\n",
    "    total_missing_values = df.isnull().sum()\n",
    "    missing_values_per = df.isnull().sum()/df.isnull().count()\n",
    "    \n",
    "    #crating df to list null values & percentage\n",
    "    null_values = pd.concat([total_missing_values, missing_values_per], axis =1, keys = ['Total Null', 'Percentage'])\n",
    "    #Sorting Null Values\n",
    "    null_values = null_values.sort_values('Total Null', ascending =False)\n",
    "    \n",
    "    #retuning attributes having null values\n",
    "    return null_values[null_values['Total Null'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6333b677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed acidity', 'pH', 'volatile acidity', 'sulphates', 'citric acid',\n",
       "       'residual sugar', 'chlorides'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_list =  (null_cell(wine)).index\n",
    "fill_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a61aefa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_wine =  wine.copy()\n",
    "\n",
    "for col in fill_list:\n",
    "    pp_wine.loc[:,col].fillna(pp_wine.loc[:,col].mean(), inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69343941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type                    0\n",
       "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp_wine.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b57bba75",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_4440\\923177319.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr_matrix = pp_wine.corr()\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fixed acidity</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.219918</td>\n",
       "      <td>0.323349</td>\n",
       "      <td>-0.112219</td>\n",
       "      <td>0.298282</td>\n",
       "      <td>-0.283237</td>\n",
       "      <td>-0.329409</td>\n",
       "      <td>0.458941</td>\n",
       "      <td>-0.251683</td>\n",
       "      <td>0.300225</td>\n",
       "      <td>-0.095560</td>\n",
       "      <td>-0.076967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatile acidity</th>\n",
       "      <td>0.219918</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.377759</td>\n",
       "      <td>-0.196578</td>\n",
       "      <td>0.377122</td>\n",
       "      <td>-0.353055</td>\n",
       "      <td>-0.414706</td>\n",
       "      <td>0.271107</td>\n",
       "      <td>0.260283</td>\n",
       "      <td>0.225313</td>\n",
       "      <td>-0.038215</td>\n",
       "      <td>-0.265838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citric acid</th>\n",
       "      <td>0.323349</td>\n",
       "      <td>-0.377759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.142451</td>\n",
       "      <td>0.039310</td>\n",
       "      <td>0.133426</td>\n",
       "      <td>0.195148</td>\n",
       "      <td>0.096307</td>\n",
       "      <td>-0.328354</td>\n",
       "      <td>0.057564</td>\n",
       "      <td>-0.010431</td>\n",
       "      <td>0.085699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual sugar</th>\n",
       "      <td>-0.112219</td>\n",
       "      <td>-0.196578</td>\n",
       "      <td>0.142451</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.128887</td>\n",
       "      <td>0.403242</td>\n",
       "      <td>0.495704</td>\n",
       "      <td>0.552487</td>\n",
       "      <td>-0.266717</td>\n",
       "      <td>-0.185716</td>\n",
       "      <td>-0.359656</td>\n",
       "      <td>-0.036819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorides</th>\n",
       "      <td>0.298282</td>\n",
       "      <td>0.377122</td>\n",
       "      <td>0.039310</td>\n",
       "      <td>-0.128887</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.194978</td>\n",
       "      <td>-0.279547</td>\n",
       "      <td>0.362580</td>\n",
       "      <td>0.044754</td>\n",
       "      <td>0.395073</td>\n",
       "      <td>-0.256844</td>\n",
       "      <td>-0.200818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <td>-0.283237</td>\n",
       "      <td>-0.353055</td>\n",
       "      <td>0.133426</td>\n",
       "      <td>0.403242</td>\n",
       "      <td>-0.194978</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.720934</td>\n",
       "      <td>0.025717</td>\n",
       "      <td>-0.145116</td>\n",
       "      <td>-0.188471</td>\n",
       "      <td>-0.179838</td>\n",
       "      <td>0.055463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <td>-0.329409</td>\n",
       "      <td>-0.414706</td>\n",
       "      <td>0.195148</td>\n",
       "      <td>0.495704</td>\n",
       "      <td>-0.279547</td>\n",
       "      <td>0.720934</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032395</td>\n",
       "      <td>-0.237523</td>\n",
       "      <td>-0.275291</td>\n",
       "      <td>-0.265740</td>\n",
       "      <td>-0.041385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>0.458941</td>\n",
       "      <td>0.271107</td>\n",
       "      <td>0.096307</td>\n",
       "      <td>0.552487</td>\n",
       "      <td>0.362580</td>\n",
       "      <td>0.025717</td>\n",
       "      <td>0.032395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011913</td>\n",
       "      <td>0.259432</td>\n",
       "      <td>-0.686745</td>\n",
       "      <td>-0.305858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>-0.251683</td>\n",
       "      <td>0.260283</td>\n",
       "      <td>-0.328354</td>\n",
       "      <td>-0.266717</td>\n",
       "      <td>0.044754</td>\n",
       "      <td>-0.145116</td>\n",
       "      <td>-0.237523</td>\n",
       "      <td>0.011913</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.191075</td>\n",
       "      <td>0.120939</td>\n",
       "      <td>0.019356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulphates</th>\n",
       "      <td>0.300225</td>\n",
       "      <td>0.225313</td>\n",
       "      <td>0.057564</td>\n",
       "      <td>-0.185716</td>\n",
       "      <td>0.395073</td>\n",
       "      <td>-0.188471</td>\n",
       "      <td>-0.275291</td>\n",
       "      <td>0.259432</td>\n",
       "      <td>0.191075</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003261</td>\n",
       "      <td>0.038713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>-0.095560</td>\n",
       "      <td>-0.038215</td>\n",
       "      <td>-0.010431</td>\n",
       "      <td>-0.359656</td>\n",
       "      <td>-0.256844</td>\n",
       "      <td>-0.179838</td>\n",
       "      <td>-0.265740</td>\n",
       "      <td>-0.686745</td>\n",
       "      <td>0.120939</td>\n",
       "      <td>-0.003261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.444319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>-0.076967</td>\n",
       "      <td>-0.265838</td>\n",
       "      <td>0.085699</td>\n",
       "      <td>-0.036819</td>\n",
       "      <td>-0.200818</td>\n",
       "      <td>0.055463</td>\n",
       "      <td>-0.041385</td>\n",
       "      <td>-0.305858</td>\n",
       "      <td>0.019356</td>\n",
       "      <td>0.038713</td>\n",
       "      <td>0.444319</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      fixed acidity  volatile acidity  citric acid  \\\n",
       "fixed acidity              1.000000          0.219918     0.323349   \n",
       "volatile acidity           0.219918          1.000000    -0.377759   \n",
       "citric acid                0.323349         -0.377759     1.000000   \n",
       "residual sugar            -0.112219         -0.196578     0.142451   \n",
       "chlorides                  0.298282          0.377122     0.039310   \n",
       "free sulfur dioxide       -0.283237         -0.353055     0.133426   \n",
       "total sulfur dioxide      -0.329409         -0.414706     0.195148   \n",
       "density                    0.458941          0.271107     0.096307   \n",
       "pH                        -0.251683          0.260283    -0.328354   \n",
       "sulphates                  0.300225          0.225313     0.057564   \n",
       "alcohol                   -0.095560         -0.038215    -0.010431   \n",
       "quality                   -0.076967         -0.265838     0.085699   \n",
       "\n",
       "                      residual sugar  chlorides  free sulfur dioxide  \\\n",
       "fixed acidity              -0.112219   0.298282            -0.283237   \n",
       "volatile acidity           -0.196578   0.377122            -0.353055   \n",
       "citric acid                 0.142451   0.039310             0.133426   \n",
       "residual sugar              1.000000  -0.128887             0.403242   \n",
       "chlorides                  -0.128887   1.000000            -0.194978   \n",
       "free sulfur dioxide         0.403242  -0.194978             1.000000   \n",
       "total sulfur dioxide        0.495704  -0.279547             0.720934   \n",
       "density                     0.552487   0.362580             0.025717   \n",
       "pH                         -0.266717   0.044754            -0.145116   \n",
       "sulphates                  -0.185716   0.395073            -0.188471   \n",
       "alcohol                    -0.359656  -0.256844            -0.179838   \n",
       "quality                    -0.036819  -0.200818             0.055463   \n",
       "\n",
       "                      total sulfur dioxide   density        pH  sulphates  \\\n",
       "fixed acidity                    -0.329409  0.458941 -0.251683   0.300225   \n",
       "volatile acidity                 -0.414706  0.271107  0.260283   0.225313   \n",
       "citric acid                       0.195148  0.096307 -0.328354   0.057564   \n",
       "residual sugar                    0.495704  0.552487 -0.266717  -0.185716   \n",
       "chlorides                        -0.279547  0.362580  0.044754   0.395073   \n",
       "free sulfur dioxide               0.720934  0.025717 -0.145116  -0.188471   \n",
       "total sulfur dioxide              1.000000  0.032395 -0.237523  -0.275291   \n",
       "density                           0.032395  1.000000  0.011913   0.259432   \n",
       "pH                               -0.237523  0.011913  1.000000   0.191075   \n",
       "sulphates                        -0.275291  0.259432  0.191075   1.000000   \n",
       "alcohol                          -0.265740 -0.686745  0.120939  -0.003261   \n",
       "quality                          -0.041385 -0.305858  0.019356   0.038713   \n",
       "\n",
       "                       alcohol   quality  \n",
       "fixed acidity        -0.095560 -0.076967  \n",
       "volatile acidity     -0.038215 -0.265838  \n",
       "citric acid          -0.010431  0.085699  \n",
       "residual sugar       -0.359656 -0.036819  \n",
       "chlorides            -0.256844 -0.200818  \n",
       "free sulfur dioxide  -0.179838  0.055463  \n",
       "total sulfur dioxide -0.265740 -0.041385  \n",
       "density              -0.686745 -0.305858  \n",
       "pH                    0.120939  0.019356  \n",
       "sulphates            -0.003261  0.038713  \n",
       "alcohol               1.000000  0.444319  \n",
       "quality               0.444319  1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = pp_wine.corr()\n",
    "corr_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10e5f4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['quality', 'alcohol', 'density', 'volatile acidity', 'chlorides',\n",
       "       'citric acid', 'fixed acidity', 'free sulfur dioxide',\n",
       "       'total sulfur dioxide', 'sulphates', 'residual sugar', 'pH'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_list =corr_matrix.quality.abs().sort_values(ascending = False).index[0:]\n",
    "corr_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "12a92f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d1e1212",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "089f5a5c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49958658",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dfcc836",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d2a36e1e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_bins' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[15], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize \u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m7\u001b[39m))\n\u001b[1;32m----> 2\u001b[0m sns\u001b[38;5;241m.\u001b[39mscatterplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mquality_range\u001b[39m\u001b[38;5;124m'\u001b[39m, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124malcohol\u001b[39m\u001b[38;5;124m'\u001b[39m, hue \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m, data \u001b[38;5;241m=\u001b[39m \u001b[43mdf_bins\u001b[49m);\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mQuality\u001b[39m\u001b[38;5;124m'\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15\u001b[39m)\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAlcohol\u001b[39m\u001b[38;5;124m'\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df_bins' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x700 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(8,7))\n",
    "sns.scatterplot(x='quality_range', y = 'alcohol', hue = 'type', data = df_bins);\n",
    "plt.xlabel('Quality', size=15)\n",
    "plt.ylabel('Alcohol', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14ec653e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_bins' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[17], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m f,(ax1,ax2) \u001b[38;5;241m=\u001b[39mplt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m14\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m      2\u001b[0m f\u001b[38;5;241m.\u001b[39msuptitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWine Types by Quality & Acidity\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m sns\u001b[38;5;241m.\u001b[39mviolinplot(x \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mquality_range\u001b[39m\u001b[38;5;124m'\u001b[39m, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvolatile_acidity\u001b[39m\u001b[38;5;124m'\u001b[39m, hue \u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m type\u001b[39m\u001b[38;5;124m'\u001b[39m, data \u001b[38;5;241m=\u001b[39m\u001b[43mdf_bins\u001b[49m, split \u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, inner \u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mquart\u001b[39m\u001b[38;5;124m'\u001b[39m, linewidth \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.3\u001b[39m, palette \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwhite\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwhite\u001b[39m\u001b[38;5;124m'\u001b[39m}, ax\u001b[38;5;241m=\u001b[39max1)\n\u001b[0;32m      5\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWine Quality Class \u001b[39m\u001b[38;5;124m'\u001b[39m,size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m15\u001b[39m, alpha \u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m)\n\u001b[0;32m      6\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWine Acidity Class \u001b[39m\u001b[38;5;124m'\u001b[39m,size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m15\u001b[39m, alpha \u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df_bins' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,(ax1,ax2) =plt.subplots(1,2, figsize=(14, 8))\n",
    "f.suptitle('Wine Types by Quality & Acidity', fontsize=14)\n",
    "\n",
    "sns.violinplot(x = 'quality_range', y = 'volatile_acidity', hue =' type', data =df_bins, split =True, inner ='quart', linewidth = 1.3, palette = {'red':'red','white':'white'}, ax=ax1)\n",
    "ax1.set_xlabel('Wine Quality Class ',size = 15, alpha =0.8)\n",
    "ax1.set_ylabel('Wine Acidity Class ',size = 15, alpha =0.8)\n",
    "\n",
    "sns.violinplot(x='quality_range', y='alcohol', hue = 'type', data= df_bins, split = True, inner ='quart', linewidth = 1.3, palette = {'red':'darkred','white':'white'}, ax=ax2)\n",
    "ax2.set_xlabel('Wine Quality Class ',size = 15, alpha =0.8)\n",
    "ax2.set_ylabel('Wine Acidity Class ',size = 15, alpha =0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6423b190",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_bins' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m----> 3\u001b[0m low_quality \u001b[38;5;241m=\u001b[39m \u001b[43mdf_bins\u001b[49m[df_bins[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mquality_range\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchlorides\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      4\u001b[0m high_quality \u001b[38;5;241m=\u001b[39m df_bins[df_bins[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mquality_range\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchlorides\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      6\u001b[0m ax \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mkdeplot(data\u001b[38;5;241m=\u001b[39mlow_quality, label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlow_quality\u001b[39m\u001b[38;5;124m'\u001b[39m, shades \u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, color \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df_bins' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "low_quality = df_bins[df_bins['quality_range']==0]['chlorides']\n",
    "high_quality = df_bins[df_bins['quality_range']==1]['chlorides']\n",
    "\n",
    "ax = sns.kdeplot(data=low_quality, label = 'low_quality', shades =True, color = None)\n",
    "ax = sns.kdeplot(data=high_quality, label = 'high_quality', shades =True, color = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1ef2226",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}