00_ExploratoryDataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA on White Wine Data Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook will be featuring the exploratory data analysis on the white wine dataset with visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the libraries\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Path where the dataset is saved\n",
    "file_path = r\"C:\\data\\winequality-white.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the dataset as a pandas dataframe\n",
    "wine_data = pd.read_csv(file_path, delimiter=';', header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assign column names\n",
    "wine_data.columns = ['fixed_acidity','volatile_acidity','citric_acid','residual_sugar','chlorides','free_sulfur_dioxide','total_sulfur_dioxide','density','pH','sulphates','alcohol','quality']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1.0010</td>\n",
       "      <td>3.00</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>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>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.045</td>\n",
       "      <td>30.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.9949</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.044</td>\n",
       "      <td>28.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.9938</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.45</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed_acidity  volatile_acidity  citric_acid  residual_sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "5            8.1              0.28         0.40             6.9      0.050   \n",
       "6            6.2              0.32         0.16             7.0      0.045   \n",
       "7            7.0              0.27         0.36            20.7      0.045   \n",
       "8            6.3              0.30         0.34             1.6      0.049   \n",
       "9            8.1              0.22         0.43             1.5      0.044   \n",
       "\n",
       "   free_sulfur_dioxide  total_sulfur_dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "5                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "6                 30.0                 136.0   0.9949  3.18       0.47   \n",
       "7                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "8                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "9                 28.0                 129.0   0.9938  3.22       0.45   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  \n",
       "5     10.1        6  \n",
       "6      9.6        6  \n",
       "7      8.8        6  \n",
       "8      9.5        6  \n",
       "9     11.0        6  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View dataframe\n",
    "wine_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine the column , their dtypes and the number of records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4898 entries, 0 to 4897\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed_acidity         4898 non-null   float64\n",
      " 1   volatile_acidity      4898 non-null   float64\n",
      " 2   citric_acid           4898 non-null   float64\n",
      " 3   residual_sugar        4898 non-null   float64\n",
      " 4   chlorides             4898 non-null   float64\n",
      " 5   free_sulfur_dioxide   4898 non-null   float64\n",
      " 6   total_sulfur_dioxide  4898 non-null   float64\n",
      " 7   density               4898 non-null   float64\n",
      " 8   pH                    4898 non-null   float64\n",
      " 9   sulphates             4898 non-null   float64\n",
      " 10  alcohol               4898 non-null   float64\n",
      " 11  quality               4898 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 459.3 KB\n"
     ]
    }
   ],
   "source": [
    "wine_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for duplicate records "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "937"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the total number of duplicate records\n",
    "wine_data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us take a summary of the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4898.000000\n",
       "mean        5.877909\n",
       "std         0.885639\n",
       "min         3.000000\n",
       "25%         5.000000\n",
       "50%         6.000000\n",
       "75%         6.000000\n",
       "max         9.000000\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the target variable\n",
    "wine_data.quality.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 5, 7, 8, 4, 3, 9], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the number of unique values in the target\n",
    "wine_data.quality.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can see from above the 'quality' of wine ranges from 3 to 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16, 4))\n",
    "sn.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "plt.subplot(1,2,1)\n",
    "sn.histplot(wine_data.quality, color = 'magenta')\n",
    "plt.subplot(1,2,2)\n",
    "sn.boxplot(wine_data.quality, color = 'cyan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most of the wine are in the quality range of 4 to 7. More than 2000 instances of the wine are having the a quality of 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the IQR rule to determine the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat_values = wine_data.quality.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The outliers lie above the value 7.5 and below the value 3.5\n"
     ]
    }
   ],
   "source": [
    "IQR = stat_values['75%'] - stat_values['25%']\n",
    "higher = stat_values['75%'] + 1.5 * IQR\n",
    "lower = stat_values['25%'] - 1.5 * IQR\n",
    "print(\"The outliers lie above the value {} and below the value {}\". format(higher, lower))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us look at the upperbound and lowerbound for outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.029</td>\n",
       "      <td>29.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.98920</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.39</td>\n",
       "      <td>12.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.029</td>\n",
       "      <td>29.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.98920</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.39</td>\n",
       "      <td>12.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6.8</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.049</td>\n",
       "      <td>41.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.99300</td>\n",
       "      <td>3.47</td>\n",
       "      <td>0.48</td>\n",
       "      <td>10.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>6.7</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.31</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.046</td>\n",
       "      <td>30.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.99260</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.64</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>6.7</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.31</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.046</td>\n",
       "      <td>30.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.99260</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.64</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4765</th>\n",
       "      <td>5.2</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.038</td>\n",
       "      <td>18.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.98942</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.48</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4766</th>\n",
       "      <td>6.4</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.055</td>\n",
       "      <td>28.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.99171</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4786</th>\n",
       "      <td>4.4</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.39</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.030</td>\n",
       "      <td>31.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>0.98904</td>\n",
       "      <td>3.46</td>\n",
       "      <td>0.36</td>\n",
       "      <td>12.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4787</th>\n",
       "      <td>3.9</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.40</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.030</td>\n",
       "      <td>29.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.98900</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.36</td>\n",
       "      <td>12.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4802</th>\n",
       "      <td>5.8</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.037</td>\n",
       "      <td>24.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.98986</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0.33</td>\n",
       "      <td>12.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed_acidity  volatile_acidity  citric_acid  residual_sugar  chlorides  \\\n",
       "17              6.2             0.660         0.48             1.2      0.029   \n",
       "20              6.2             0.660         0.48             1.2      0.029   \n",
       "22              6.8             0.260         0.42             1.7      0.049   \n",
       "68              6.7             0.230         0.31             2.1      0.046   \n",
       "74              6.7             0.230         0.31             2.1      0.046   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "4765            5.2             0.300         0.34             1.5      0.038   \n",
       "4766            6.4             0.320         0.25             5.0      0.055   \n",
       "4786            4.4             0.320         0.39             4.3      0.030   \n",
       "4787            3.9             0.225         0.40             4.2      0.030   \n",
       "4802            5.8             0.280         0.34             2.2      0.037   \n",
       "\n",
       "      free_sulfur_dioxide  total_sulfur_dioxide  density    pH  sulphates  \\\n",
       "17                   29.0                  75.0  0.98920  3.33       0.39   \n",
       "20                   29.0                  75.0  0.98920  3.33       0.39   \n",
       "22                   41.0                 122.0  0.99300  3.47       0.48   \n",
       "68                   30.0                  96.0  0.99260  3.33       0.64   \n",
       "74                   30.0                  96.0  0.99260  3.33       0.64   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "4765                 18.0                  96.0  0.98942  3.56       0.48   \n",
       "4766                 28.0                 138.0  0.99171  3.27       0.50   \n",
       "4786                 31.0                 127.0  0.98904  3.46       0.36   \n",
       "4787                 29.0                 118.0  0.98900  3.57       0.36   \n",
       "4802                 24.0                 125.0  0.98986  3.36       0.33   \n",
       "\n",
       "      alcohol  quality  \n",
       "17       12.8        8  \n",
       "20       12.8        8  \n",
       "22       10.5        8  \n",
       "68       10.7        8  \n",
       "74       10.7        8  \n",
       "...       ...      ...  \n",
       "4765     13.0        8  \n",
       "4766     12.4        8  \n",
       "4786     12.8        8  \n",
       "4787     12.8        8  \n",
       "4802     12.8        8  \n",
       "\n",
       "[180 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data[wine_data['quality']> 7.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>8.5</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.21</td>\n",
       "      <td>16.20</td>\n",
       "      <td>0.074</td>\n",
       "      <td>41.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.50</td>\n",
       "      <td>9.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>5.8</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.44</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.029</td>\n",
       "      <td>5.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.99130</td>\n",
       "      <td>3.53</td>\n",
       "      <td>0.43</td>\n",
       "      <td>11.7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>9.1</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.066</td>\n",
       "      <td>34.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.23</td>\n",
       "      <td>0.38</td>\n",
       "      <td>8.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>7.1</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.038</td>\n",
       "      <td>16.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.99370</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.40</td>\n",
       "      <td>11.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>6.9</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>0.022</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.99150</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.37</td>\n",
       "      <td>12.6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>10.3</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.037</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.99390</td>\n",
       "      <td>2.89</td>\n",
       "      <td>0.28</td>\n",
       "      <td>9.6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>7.9</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.46</td>\n",
       "      <td>10.60</td>\n",
       "      <td>0.244</td>\n",
       "      <td>33.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>0.99830</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.74</td>\n",
       "      <td>9.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>8.3</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.033</td>\n",
       "      <td>18.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.99110</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.32</td>\n",
       "      <td>12.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>8.6</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.35</td>\n",
       "      <td>15.55</td>\n",
       "      <td>0.057</td>\n",
       "      <td>35.5</td>\n",
       "      <td>366.5</td>\n",
       "      <td>1.00010</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.63</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1484</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.24</td>\n",
       "      <td>4.60</td>\n",
       "      <td>0.053</td>\n",
       "      <td>8.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.99580</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.50</td>\n",
       "      <td>9.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>6.7</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.041</td>\n",
       "      <td>118.5</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.55</td>\n",
       "      <td>0.63</td>\n",
       "      <td>9.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>7.1</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.047</td>\n",
       "      <td>146.5</td>\n",
       "      <td>307.5</td>\n",
       "      <td>0.99240</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.37</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>11.8</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.38</td>\n",
       "      <td>11.10</td>\n",
       "      <td>0.034</td>\n",
       "      <td>15.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>0.99970</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.55</td>\n",
       "      <td>9.7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>7.6</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.034</td>\n",
       "      <td>5.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.99256</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.54</td>\n",
       "      <td>10.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3087</th>\n",
       "      <td>6.1</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.34</td>\n",
       "      <td>9.50</td>\n",
       "      <td>0.041</td>\n",
       "      <td>38.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>0.99500</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3265</th>\n",
       "      <td>4.2</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.23</td>\n",
       "      <td>5.10</td>\n",
       "      <td>0.041</td>\n",
       "      <td>64.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.99688</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.44</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>9.4</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.29</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.037</td>\n",
       "      <td>124.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>0.99395</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.38</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3409</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.051</td>\n",
       "      <td>24.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99160</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.43</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3810</th>\n",
       "      <td>6.8</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.34</td>\n",
       "      <td>15.10</td>\n",
       "      <td>0.060</td>\n",
       "      <td>42.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.99705</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.52</td>\n",
       "      <td>10.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4745</th>\n",
       "      <td>6.1</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.047</td>\n",
       "      <td>289.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>0.99314</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.64</td>\n",
       "      <td>10.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed_acidity  volatile_acidity  citric_acid  residual_sugar  chlorides  \\\n",
       "251             8.5             0.260         0.21           16.20      0.074   \n",
       "253             5.8             0.240         0.44            3.50      0.029   \n",
       "294             9.1             0.590         0.38            1.60      0.066   \n",
       "445             7.1             0.320         0.32           11.00      0.038   \n",
       "740             6.9             0.390         0.40            4.60      0.022   \n",
       "873            10.3             0.170         0.47            1.40      0.037   \n",
       "1034            7.9             0.640         0.46           10.60      0.244   \n",
       "1229            8.3             0.330         0.42            1.15      0.033   \n",
       "1417            8.6             0.550         0.35           15.55      0.057   \n",
       "1484            7.5             0.320         0.24            4.60      0.053   \n",
       "1688            6.7             0.250         0.26            1.55      0.041   \n",
       "1931            7.1             0.490         0.22            2.00      0.047   \n",
       "2050           11.8             0.230         0.38           11.10      0.034   \n",
       "2373            7.6             0.480         0.37            1.20      0.034   \n",
       "3087            6.1             0.200         0.34            9.50      0.041   \n",
       "3265            4.2             0.215         0.23            5.10      0.041   \n",
       "3307            9.4             0.240         0.29            8.50      0.037   \n",
       "3409            6.2             0.230         0.35            0.70      0.051   \n",
       "3810            6.8             0.260         0.34           15.10      0.060   \n",
       "4745            6.1             0.260         0.25            2.90      0.047   \n",
       "\n",
       "      free_sulfur_dioxide  total_sulfur_dioxide  density    pH  sulphates  \\\n",
       "251                  41.0                 197.0  0.99800  3.02       0.50   \n",
       "253                   5.0                 109.0  0.99130  3.53       0.43   \n",
       "294                  34.0                 182.0  0.99680  3.23       0.38   \n",
       "445                  16.0                  66.0  0.99370  3.24       0.40   \n",
       "740                   5.0                  19.0  0.99150  3.31       0.37   \n",
       "873                   5.0                  33.0  0.99390  2.89       0.28   \n",
       "1034                 33.0                 227.0  0.99830  2.87       0.74   \n",
       "1229                 18.0                  96.0  0.99110  3.20       0.32   \n",
       "1417                 35.5                 366.5  1.00010  3.04       0.63   \n",
       "1484                  8.0                 134.0  0.99580  3.14       0.50   \n",
       "1688                118.5                 216.0  0.99490  3.55       0.63   \n",
       "1931                146.5                 307.5  0.99240  3.24       0.37   \n",
       "2050                 15.0                 123.0  0.99970  2.93       0.55   \n",
       "2373                  5.0                  57.0  0.99256  3.05       0.54   \n",
       "3087                 38.0                 201.0  0.99500  3.14       0.44   \n",
       "3265                 64.0                 157.0  0.99688  3.42       0.44   \n",
       "3307                124.0                 208.0  0.99395  2.90       0.38   \n",
       "3409                 24.0                 111.0  0.99160  3.37       0.43   \n",
       "3810                 42.0                 162.0  0.99705  3.24       0.52   \n",
       "4745                289.0                 440.0  0.99314  3.44       0.64   \n",
       "\n",
       "      alcohol  quality  \n",
       "251       9.8        3  \n",
       "253      11.7        3  \n",
       "294       8.5        3  \n",
       "445      11.5        3  \n",
       "740      12.6        3  \n",
       "873       9.6        3  \n",
       "1034      9.1        3  \n",
       "1229     12.4        3  \n",
       "1417     11.0        3  \n",
       "1484      9.1        3  \n",
       "1688      9.4        3  \n",
       "1931     11.0        3  \n",
       "2050      9.7        3  \n",
       "2373     10.4        3  \n",
       "3087     10.1        3  \n",
       "3265      8.0        3  \n",
       "3307     11.0        3  \n",
       "3409     11.0        3  \n",
       "3810     10.5        3  \n",
       "4745     10.5        3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_data[wine_data['quality']< 3.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the attributes and their relationship with the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def box_plt(col):\n",
    "    x = wine_data['quality'].tolist()\n",
    "    y = wine_data[col].tolist()\n",
    "    plt.figure(figsize = (6, 6))\n",
    "    plt.ylabel(col)\n",
    "    plt.xlabel('quality')\n",
    "    sn.boxplot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('fixed_acidity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute fixed_acidity has a weak association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('volatile_acidity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute volatile_acidity has some association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('citric_acid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute citric_acid has somewhat positive association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('residual_sugar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute residual_sugar has a weak association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('chlorides')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute chlorides has a negative association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('free_sulfur_dioxide')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute free_sulfur_dioxide has a weak association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAFxCAYAAAB+/K8+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAnRklEQVR4nO3de3hU9YH/8c9khuGSBCegIj8RQrAUtUZQCutyyVCpbGu7KoOBhAfXSrePrMrDRblYCGGx0rSQWtm60gLyGAoxOqzt2n3sAibc3IelbBG52TVIJIABQqa5EJKZyfz+gGQJQpiQmXNmOO/XPzDJ5MwngeST7znf8/3aQqFQSAAAy0owOwAAwFwUAQBYHEUAABZHEQCAxVEEAGBxFAEAWJzD7ADtFQwGFQwGzY4BAHHF6XRe9X1xWQSVlZVmxwCAuNK7d++rvo9TQwBgcRQBAFgcRQAAFkcRAIDFUQQAYHEUAQBYHEUAABZHEQCAxVEEl6iqqlJubq58Pp/ZUQDAMBTBJbxerw4fPiyv12t2FAAwDEVwUVVVlUpKShQKhVRSUsKoAIBlUAQXeb1eNW/f3NTUxKgAgGVQBBft2LFDgUBAkhQIBLR9+3aTEwGAMSiCi0aOHCmH48JirA6HQ6NGjTI5EQAYgyK4yOPxyGazSZISEhLk8XhMTgQAxqAILkpJSZHb7ZbNZpPb7ZbL5TI7EgAYIu42pokmj8ej8vJyRgMALMUWap4qEycaGxvZoQwA2okdygAAV0URAIDFUQQAYHEUAQBYHEUAABZHEQCAxVEEAGBxFAEAWBxFAAAWRxEAgMVRBABgcRQBAFgcRQAAFkcRAIDFUQQAYHEUAQBYHEUAABZHEQCAxVEEAGBxUSuCyspKZWRkqLS0VGVlZcrKylJ2drYWLVqkpqYmSVJRUZHGjx+vzMxMFRcXRysKAKANUSkCv9+vnJwcdenSRZK0dOlSzZgxQ+vXr1coFNKWLVt0+vRpFRQUqLCwUKtXr1Z+fr4aGxujEQcA0IaoFEFeXp4mTZqkW2+9VZJ04MABDRs2TJI0evRoffTRR9q3b5+GDBkip9Op5ORk9e3bV4cPH45GHABAGxyRPuDGjRvVo0cPjRo1Sr/+9a8lSaFQSDabTZKUmJiompoa1dbWKjk5ueXjEhMTVVtbe83j2+129ezZM9KxAcCyIl4EXq9XNptN//Vf/6VDhw5p7ty5Onv2bMv76+rq1L17dyUlJamurq7V2y8thqsJBoOqrKyMdGwAuKH17t37qu+L+Kmh3/72t1q3bp0KCgp01113KS8vT6NHj9auXbskSdu2bdPQoUOVnp6uPXv2qKGhQTU1NSotLdXAgQMjHQcAcA0RHxFcydy5c7Vw4ULl5+crLS1N48aNk91u15QpU5Sdna1QKKSZM2eqc+fORsQBAFzCFgqFQmaHaI/GxkZODQFAOxl6aggAEF8oAgCwOIoAACyOIgAAi6MIAMDiKAIAsDiKAAAsjiIAAIujCADA4igCALA4igAALI4iAACLowgAwOIoAgCwOIrgElVVVcrNzZXP5zM7CgAYhiK4hNfr1eHDh+X1es2OAgCGoQguqqqqUklJiUKhkEpKShgVALAMiuAir9er5s3ampqaGBUAsAyK4KIdO3YoEAhIkgKBgLZv325yIgAwBkVw0ciRI+VwOCRJDodDo0aNMjkRABiDIrjI4/HIZrNJkhISEuTxeExOBADGoAguSklJkdvtls1mk9vtlsvlMjsSABjCYXaAWOLxeFReXs5oAICl2ELNU2XiRGNjoyorK82OAQBxpXfv3ld9H6eGAMDiKAIAsDiKAAAsjiIAAIujCADA4igCALA4igAxg/0gAHNQBIgZ7AcBmIMiQExgPwjAPBQBYgL7QQDmoQgQE9gPAjAPRYCYwH4QgHkoAsQE9oMAzEMRICawHwRgHvYjQMxgPwjAHOxHAAAWwH4EAICroggAwOIoAgCwOIoAACyOIgAAi6MIAMDiKAIAsDiKAAAsjiIAAIujCADA4igCALA4igAxg83rAXNQBIgZbF4PmIMiQExg83rAPBQBYgKb1wPmoQgQE9i8HjAPRYCYwOb1gHkoAsQENq8HzGOJPYu3bt2qkpKSaz6v+QJlOBunu91uZWRkdCwYWqSkpOjBBx/Utm3b9OCDD7J5PWAgRgSX8Pl8zFYBYDlsXn+JxYsXS5IWLVoUlePj6qqqqvT888/L7/fL6XRqxYoVjAqACDJ88/pgMKj58+dr0qRJmjx5sr744guVlZUpKytL2dnZWrRokZqamiRJRUVFGj9+vDIzM1VcXByNOIgDTB8FzBOVawTNP9ALCwu1a9cuLV26VKFQSDNmzNDw4cOVk5OjLVu2aPDgwSooKJDX61VDQ4Oys7M1YsQIOZ3OaMRCDLvS9NGpU6eanAqwhqiMCMaOHaslS5ZIkk6cOKGbb75ZBw4c0LBhwyRJo0eP1kcffaR9+/ZpyJAhcjqdSk5OVt++fXX48OFoREKMY/ooYJ6ozRpyOByaO3euNm3apNdee03FxcUt0wMTExNVU1Oj2tpaJScnt3xMYmKiamtr2zyu3W5Xz549o5ZZUtSOj6v74Q9/qK1bt0q6MH30hz/8oXr06GFyqvBVVlbq5Zdf1sKFC+MqNyBFefpoXl6eXnjhBWVmZqqhoaHl7XV1derevbuSkpJUV1fX6u2XFsOVBIPBqF0sbj41Ea3jo20ZGRnavHmz3G63QqFQXP07rFq1Svv379eqVas4pYWYZPjF4vfee08rV66UJHXt2lU2m03f+MY3tGvXLknStm3bNHToUKWnp2vPnj1qaGhQTU2NSktLNXDgwGhEQhzweDwaNGhQ3N1MxoJ5iHdRGRE8/PDDmj9/viZPnqxAIKCXXnpJAwYM0MKFC5Wfn6+0tDSNGzdOdrtdU6ZMUXZ2tkKhkGbOnKnOnTtHIxLiQEpKinJzc82O0W5XmvHEqADxhPsILsF9BLgeTz31lOrr61sed+3aVWvXrjUvEHAFhp8aAqyEGU+IdxQB0EEsmId4RxEAHZSSkiK32y2bzSa3283SGIg7FAFiRjxvXh+vM54AiSJADInnzeubZzwxGkA8oggQE5iLD5gnrCKoqKjQCy+8oKlTp6qoqEgff/xxtHPBYlh9FDBPWEWwcOFCeTweNTY2aujQofrJT34S7VywGDavB8wTVhE0NDTowQcflM1mU1paGnf/IuKYiw+YJ6wicDqd2r59u5qamrR37172C0DEMRcfME9YRbBkyRJt3LhRVVVVWrNmTVyuB4PYFu9z8eN56isQ1qJzt912m37xi19EOwsszuPxqLy8PC5HA5dOfWXBOcSbNotg5MiRkiS/36/6+nr17t1bFRUV6tGjhz788ENDAsI64nX10cunvno8nrgb0cDa2iyCHTt2SJJeeOEFzZ49u6UIli5dakg4XLB161aVlJRc83nNpyWu9UPI7XYrIyOj48EgiWWoEf/CukZQXl7esoRpr169dPLkyaiGwvXx+XycozYBU18R78K6RjBgwAC9+OKLSk9P1969e/XAAw9EOxcukZGREdZv8OynYI6RI0equLhYgUCAqa+IS2HPGnrkkUd0/vx5PfLII5ozZ060cwFxg6mviHdtFkFxcbEk6Z133lFFRYW6d++u06dP6+233zYkHBAP4n3qK9DmqaHm882nT582IgsQt+J56ivQZhE8/vjjkqR777231TnqdevWRTcVEGfideorIIV5jeDNN9/UL37xC1VWVuqZZ57RgQMHop0LAGCQsGYNvfnmm5o7d67cbrfmzZunyZMnRzsXbiDcBwHEtrBGBK+++qrKysqUl5ent99+W++//360c8GCuA8CMEdYIwK/36/169fLbrdrxIgRWrBggb73ve9FOxtuEFa4D2Lfvn165ZVXtGDBAn3jG98wOw7QLmGNCObMmaMdO3Zo1apV2r17t1asWBHtXEBcyc/PVygU0rJly8yOArRbWEWwfPlyvfvuu3I4HHrvvfeUl5cX7VxA3Ni3b5/q6+slSfX19dq/f7/JiYD2CevU0O7du1VYWChJ+od/+AdlZmZGNRQQT/Lz81s9XrZsmdauXWtOGOA6hDUiCAQCampqkiSFQqGW2+kBqGU0cLXHQKwLa0Tw3e9+V1lZWbrvvvu0b98+ffe73412LgCAQcIqgqefflojR47UkSNHNGHCBA0cODDauYCYca37IBwOR8sy1M2Pm2dAXQn3QSDWtFkE77zzjp544gktX7685XTQwYMHJUmzZs2KfjogDgSDwTYfA7GuzSK47bbbJElpaWmGhAFi0bXug1i1apU2bdrU8vjb3/42O5QhrrRZBF26dNHu3bvVp08fo/IAccfj8bQUQadOnViBFHGnzSLYsGGDJOmLL76Q3+/Xvffeq4MHDyoxMVEFBQWGBARiXUpKilJSUlRVVaUxY8awHwHiTptF0Dw/+kc/+pFef/11ORwOBYNB/ehHPzIkHBAvbr75ZjU0NDAaQFwK6z6CSzemCQaDOnv2bNQCAfGoU6dOSk1NZTSAuBTW9NEJEybokUce0cCBA/XZZ59p+vTp0c4FADBIWEUwefJkPfroozpy5Ij69OmjHj16SJI2b96ssWPHRjUgACC6wjo1JElJSUlKT09vKQFJeuutt6ISCgBgnLCL4EpCoVCkcgAATNKhImDxOeDGsGHDBk2cOFFFRUVmR4EJOlQEAG4M7733niTJ6/WaGwSm4NQQYHHNN442Y1RgPWEVwdKlS6/49h/84AcRDQPAeM2jgWaMCqwnrCIoLS1VdXX1V97+rW99K+KBAADGCus+gtLSUg0fPlw9evRouUC8Y8eOqAYDABgjrCIoLi6Odg4AJnnsscdanR5ivSTrCasI5s+f/5W3Xe26AYD4kpWV1aoIMjMzzQsDU4S9Z7F0YZbQwYMHderUqaiGAmCs5lEBowFrCqsIRo0a1fL30aNH6+mnn45aIADGy8rKUlZWltkxYJKwiuDSC8OnT5/WmTNnohYIgPGOHj2q3NxcLV68WP369TM7DgwWVhH84Q9/aPm70+nUK6+8ErVAAIy3fPly1dfXa9myZVqxYoXZcWCwNotg6tSpWr16tW6//XY999xzRmUCYKCjR4+2XPc7deqUysrKGBVYTJtFUFdXp+nTp2vPnj36/PPPW71v+fLlUQ0GwBiXfy8zKrCeNovgN7/5jT799FN98cUXmjhxolGZABjo8lmAsTYrcOvWrSopKWnzOT6fT5LC2irU7XYrIyOj48FuIG0WwZkzZ9SzZ0/97Gc/U6dOnYzKBADt0p4iwFe1WQQ5OTmy2WxfWWXUZrPFzO5ka9euVVlZWUSOdfToUUnS4sWLI3K8fv366amnnorIsYDrda3fqC//HrfZbG1+Dxj9G3VGRsY1X68576JFi4yIdMNpswgKCgqMynHdysrKdODTz9TUrce1n3wNtqYLo55Pjp3t8LESznX8GIARLv9Fj+XlrSes6aPf+ta3Wu1Glpyc/JWla83U1K2Hzt/9PbNjtNLl4PtmRwAkXfs36lWrVmnTpk0tjx9++GFNnTrViGiIEWEVwQcffCDpwm8K+/fvb3kMIP55PJ6WInA4HCwzYUFh7UfgdDrldDrVuXNnPfDAAzp48GC0cwEwSEpKilJSUiRdGP1zwdV6whoRLF++vOXU0KlTp5SQcPX+8Pv9eumll3T8+HE1NjZq2rRpuvPOOzVv3jzZbDZ97Wtf06JFi5SQkKCioiIVFhbK4XBo2rRpGjNmTGQ+KwDtcvPNN6uhoYHRgEWFVQRpaWktfx80aFCrRegu9/vf/14ul0s///nPVVVVpccff1yDBg3SjBkzNHz4cOXk5GjLli0aPHiwCgoK5PV61dDQoOzsbI0YMUJOp7PjnxWAdunUqZNSU1MZDVhUWEUwbtw4VVdXy263q6ioSPfdd5+Sk5Ov+Ny/+7u/07hx41oe2+12HThwQMOGDZN0YfXSnTt3KiEhQUOGDGk57dS3b18dPnxY6enpEfi0AADhCqsIXnjhBY0fP17/+Z//qTvvvFM5OTlavXr1FZ+bmJgoSaqtrdX06dM1Y8YM5eXltZxaSkxMVE1NjWpra1uVSWJiompra6+ZxW63q2fPnv/3CTjC+hRM4XA4WmU14vUkGfqakRTP+eM5u0R+qwvrp2h1dbUeeughvfXWW/rZz36m7du3t/n8kydP6tlnn1V2dra+//3v6+c//3nL++rq6tS9e3clJSWprq6u1duvNsq4VDAYVGVlZcvjQCAQzqdgikAg0CqrEa8nydDXjKR4zh/P2SXyW0Hv3r2v+r6wZg35/X6tWbNG99xzjz777LNWP8Avd+bMGT399NN68cUXNWHCBEnS3XffrV27dkmStm3bpqFDhyo9PV179uxRQ0ODampqVFpaqoEDB7bn8wIAREBYI4K5c+dq8+bNmjZtmv793/9dubm5V33uG2+8oerqar3++ut6/fXXJUk//vGP9fLLLys/P19paWkaN26c7Ha7pkyZouzsbIVCIc2cOVOdO3eOyCcFAAhfWEVw//336/7775ckTZ48ueXtzz77rH71q1+1eu6CBQu0YMGCrxxj3bp1X3lbZmYmG2UDgMnCOjV0NdXV1ZHKAQAwSYeK4NL1hwAA8alDRQAAiH8UAQBYXIeK4KabbopUDgCASdqcNXTpYnOXmzVrFhtcA8ANoM0iuHSxOeBK2CoUiH9tFsHjjz8u6cLt25988okCgYBCoZBOnTplSDjEvrKyMh343wOSKwIHs1/448DpAx0/lq/jhwCsIqwbyp577jn5/X6dOnVKwWBQt956q773vdjaGhImcklN7iazU7SSUMI8CCBcYX231NbWavXq1UpPT9fGjRvV0NAQ7VwAAIOEVQR2+4Uxe319vbp06SK/3x/VUAAA44RVBA8//LB+9atfadCgQcrMzFRSUlK0cwEADBLWNYKHHnpIvXr1ks1mU0ZGRkxvBgMAaJ82f6L/5S9/UUVFhZYtW6YXX3xR0oWNYfLz8/W73/3OkIA3OqZfAjBbm0VQXV2t//iP/1BlZaX+8Ic/SLqw0Fx2drYh4aygrKxMR/+yX32Tgh0+1k26cPNf04mPO3ysL2rtHT4GgPjQZhEMHTpUQ4cO1YEDB3TPPffo7NmzcrlcSkhgal4k9U0KasHQa+/XbKSX/8R1IMAqwjrZX1NTo4ceekjJycmqrq7WkiVLNGLEiGhnAwAYIKwi+OUvf6n169erV69eqqio0HPPPUcRAMANIqwisNvt6tWrlySpV69e7C2MG0IsX6iXuFgP44RVBElJSSooKNA3v/lN7d69Wy6XK8qxgOgrKyvTkYMHdVsEjtXt4p/nDh6MwNGkLyNyFCA8YRXBvffeq5MnT+rVV19VWlqaevToEe1cgCFukzRVsbfl6mqFzI4AC2mzCN555x29++67Ki0t1YABAyRJu3fvViAQMCRcOHw+nxLOVarLwffNjtJKwrlK+XzMrgIQ+9osgkcffVQPPvigVq5cqWeeeUaSlJCQoJ49exoSDgAQfW0WgdPpVJ8+fbRkyRKj8rSby+XSsZomnb87tpbF7nLwfa6lAIgLnLsAAIujCADA4igCALA41pNGh/h8PskXg1tD+iRfJ5/ZKYC4QBEAMEUs39lttbu6KQJ0iMvl0nH/8ZjcvJ5ZW7GtrKxMfzl0SMnOCPwYCl74/3ey9H87fKiaxti5T8ooFAEA0yQ7HRrWK8XsGK38d0WV2REMF2MndgEARmNEAMQpzrEjUigCIE6VlZXpfz8tlavbrR0+lr2piyTp9LGaDh/Ld+5Uh48BY1EEQBxzdbtVYwZNMjtGK8WHC82OgHbiGgEAWBwjAgCIkq1bt6qkpOSaz/P5fJIU1pRnt9utjIyMjgW7DCMCADCZz+drKQMzMCIAgCjJyMgI67f35tlaixYtinakK2JEAAAWx4gAluXz+VSp2Nwf+KSkniaeKoC1MCIAAItjRADLcrlccp44oamymR3lK1YrpG4smgeDMCIAAIujCADA4igCALA4rhEAwHW4kVZ/pQiAOOXz+eQ7Vxlzi7z5zp1SJ1/w2s/z+VTTGIi5jWBqGgPqGsbU3bKyMn3+l1LdkXxbh1+zu7pKkgIn6zp8rGM1X7b7YygCdJwvQpvXn7/4Z5eOH0o+SbdE4DhAG+5Ivk2zh/3A7BitLP/vN9v9MRQBOqRfv34RO1bz8Dj1ltSOH+yWyGaLRS6XS/4ae0wuQ+1yJV/zeS6XS/WVp2Nyq8pwFn/z+Xyqqqm8rh+80XSs5kuldO3Zro+hCNAhkdyFyuz1VgCroghMduG3Crte/lOS2VFaKauxK4UlDoCrcrlcSqrvFJOnhhyuxHZ9DNNHAcDiGBGYzOVyqfu5Mi0YWmt2lFZe/lOSEljiAGjTsZov27xGUN1Qq782RvZ7+yZnkrp3vvoZhGM1X6p/7wHtOiZFAADXIZzJCAk+v2y++oi+boKrc5unfvr3HtDuiRIUAQBch0hOlDAb1wgAwOIYEcDSvlRkNqZpPgscqblfX0pKi9CxgGu5IYog4dxZdTn4foePY/NfOJcX6tS1w8dKOHdWUo8OHwfRE8kbzk5dvBnu1tTUiBwvTTf+DXGIHXFfBFG5s/WO2yNwtB58I8c4boYDLohaEXz88cdatmyZCgoKVFZWpnnz5slms+lrX/uaFi1apISEBBUVFamwsFAOh0PTpk3TmDFj2v06fDMDQMdEpQh+85vf6Pe//726dr1wimXp0qWaMWOGhg8frpycHG3ZskWDBw9WQUGBvF6vGhoalJ2drREjRsjpdEYjEoAYFKnVRxuCTZKkzvaOz3+paQyod4ePEl+iUgR9+/bVihUrNGfOHEnSgQMHNGzYMEnS6NGjtXPnTiUkJGjIkCFyOp1yOp3q27evDh8+rPT09GhEAm5IvnOnIrIM9Xn/heWPu3Rq39IEV+I7d0q36NqLzkXjtG7vCFyj6S3rXZ+JShGMGzdO5eXlLY9DoZBstgsbhCcmJqqmpka1tbVKTv6//yyJiYmqrb32HXh2u109e7ZvZb1wORwXvhzROv7VXrPRsFdrH4fDYfjXQjL26x8pZmQfNGhQy+t2VGnpWUlS7/59Onys3krRnXfeec2vxezZszv8Ws1mzZolScrPz4/YMa3EkIvFCQn/N1yrq6tT9+7dlZSUpLq6ulZvv7QYriYYDKqysjIqOQOBgCRF7fhtvWYsCgQCpnwtjHzNSDEj+8SJEyN2rObrYz/+8Y8jdkz+78SW3r2vfsLLkBvK7r77bu3atUuStG3bNg0dOlTp6enas2ePGhoaVFNTo9LSUg0cONCIOACASxgyIpg7d64WLlyo/Px8paWlady4cbLb7ZoyZYqys7MVCoU0c+ZMde7c2Yg4AIBLRK0I+vTpo6KiIklS//79tW7duq88JzMzU5mZmdGKAAAIA2sNAYDFUQQAYHEUAQCYrKqqSrm5ufKZtD0sRQAAJvN6vTp8+LC8Xq8pr08RAICJqqqqVFJSolAopJKSElNGBXG/+uiN4Itau17+U8dXsv9r44W7t29ydnx9/S9q7Urt8FEAXIvX61UodOF7tqmpSV6vV1OnTjU0A0VgskiuafLXi+utpPy/1A4fK1XWW28FMMOOHTta7owOBALavn07RWA1LKMNWNvIkSNVXFysQCAgh8OhUaNGGZ6BawQAYCKPx9OyKGdCQoI8Ho/hGSgCADBRSkqK3G63bDab3G63XC6X4Rk4NQQAJvN4PCovLzdlNCBRBABgupSUFOXm5pr2+pwaAgCLowgAwOIoAgCwOIoAACyOIgAAi6MIAMDiKAIAsDiKAAAsjiIAAIujCADA4igCALA41hoCENO2bt2qkpKSNp9z9OKmTM17crTF7XYrIyMjAsluHBQBgLhnxtLNNxKKAEBMy8jI4Df4KOMaAQBYHEUAABZHEQCAxVEEAGBxFAEAWBxFAAAWRxEAgMVRBABgcRQBAFgcRQAAFkcRAIDFUQQA5Pf7dfToUfl8PrOjwAQsOgfc4MJZxvnIkSMKBoOaN2+eevfu3eZzWcb5xsOIALA4v9+vYDAoSfL5fAoEAiYngtEYESBm+P1+HT9+XD6fj/XlI+hayzivWrVKn3/+uQKBgOx2u1JTUzV16lQDE8JsjAgQM06dOqVz585p/fr1ZkexlB07drSMAgKBgLZv325yIhiNIkBMqKqq0l//+ldJ0vbt27loaaCRI0fK4bhwcsDhcGjUqFEmJ4LRODWEqAvnYuXx48db/t7U1KQ5c+bo9ttvv+JzuVgZWR6Pp+XfJyEhQR6Px9xAMBwjAsSE5tHA1R4jelJSUuR2u2Wz2eR2u7k+Y0GMCBB14ew5m52d3TJzRZLsdrsWLVoU7Wi4yOPxqLy8nNGARTEiQEy47777Wj0ePHiwOUEsKiUlRbm5uYwGLIoRAWLCyZMnWz0+ceKESUm+KpxrHEePHpUkLV68+JrH4xoHYg0jAsSEy4vg8sexrlu3bqqvr9f58+fNjgK0GyMCxITExETV1dW1ehwrwrnGMXv2bIVCITU2Nmrp0qUGJQMiwxJFEM7QXmJ4b6bLlzWIp2UOjh49qvLycklSeXm5ysrK1K9fP5NTAeHj1NAlXC4XF8tMMnr06FaP46lkV6xY0erxa6+9ZlIS4PpYYkQQztAe5vJ4PCouLlYgEFCnTp3iahpj82jgao+BWMeIADEhJSVFY8aMkc1m05gxY+JqZNanT582HwOxjiJAzBg7dqy6dOmisWPHmh2lXZ5//vlWj6dPn25SEuD6UASIGZs3b9b58+e1efNms6O0S2pqassooE+fPlwoRtyxhUKhkNkh2qOxsVGVlZVmxzBUe2c9paamtvm8WJzxVFVVpeeff15+v19Op1MrVqyIq9NDR48eVW5urhYvXkwRICa1tfMcI4IbSDzPevJ6vWr+naSpqUler9fkRO2TmpqqtWvXUgKIS4wIEBOeeuop1dfXtzzu2rWr1q5da14g4AbDiAAxj81RAPNQBIgJHo9HNptNEpujAEYzvQiampqUk5OjiRMnasqUKSorKzM7EkzA5iiAeUwvgs2bN6uxsVFvv/22Zs+erZ/+9KdmR4JJPB6PBg0axGgAMJjpS0zs2bOn5Xzw4MGDtX//fpMTwSzNm6MAMJbpRVBbW6ukpKSWx3a7XYFAoOXC4eXsdrt69uxpVDwAuOGZXgRJSUmt1qFvamq6aglIUjAYZPooALRTTE8fvf/++7Vt2zZJ0t69ezVw4ECTEwGAtZg+Ivj2t7+tnTt3atKkSQqFQnrllVfMjgQAlsKdxQBgATF9aggAYC6KAAAsjiIAAIujCADA4uLuYjEAILIYEQCAxVEEAGBxFAEAWBxFAAAWRxEAgMVRBABgcaYvOhcrgsGgFixYoM8//1x2u11Lly5V3759zY7VLpWVlRo/frzWrFmjAQMGmB2nXR577DElJydLkvr06aOlS5eanKh9Vq5cqQ8//FB+v19ZWVl64oknzI4Uto0bN+rf/u3fJEkNDQ06dOiQdu7cqe7du5ucLDx+v1/z5s3T8ePHlZCQoCVLlsTV///GxkbNnz9fx44dU1JSknJycpSammpoBorgouLiYklSYWGhdu3apaVLl+pf//VfTU4VPr/fr5ycHHXp0sXsKO3W0NAgSSooKDA5yfXZtWuX/vznP2vDhg2qr6/XmjVrzI7ULuPHj9f48eMlSYsXL5bH44mbEpCkrVu3KhAIqLCwUDt37tSrr76qFStWmB0rbEVFRerWrZuKiop05MgRLVmyRKtXrzY0A6eGLho7dqyWLFkiSTpx4oRuvvlmkxO1T15eniZNmqRbb73V7CjtdvjwYdXX1+vpp5/Wk08+qb1795odqV127NihgQMH6tlnn9Uzzzwjt9ttdqTr8sknn+izzz7TxIkTzY7SLv3791cwGFRTU5Nqa2vb3NgqFn322WcaPXq0JCktLU2lpaWGZ4ivr1iUORwOzZ07V5s2bdJrr71mdpywbdy4UT169NCoUaP061//2uw47dalSxdNnTpVTzzxhI4ePap//Md/1AcffBA339BVVVU6ceKE3njjDZWXl2vatGn64IMPZLPZzI7WLitXrtSzzz5rdox269atm44fP67vfOc7qqqq0htvvGF2pHa56667VFxcrLFjx+rjjz9WRUWFgsGg7Ha7YRkYEVwmLy9Pf/zjH7Vw4UKdO3fO7Dhh8Xq9+uijjzRlyhQdOnRIc+fO1enTp82OFbb+/fvr7//+72Wz2dS/f3+5XK64yu9yuTRy5Eg5nU6lpaWpc+fOOnv2rNmx2qW6ulpHjhzR3/zN35gdpd3Wrl2rkSNH6o9//KN+97vfad68eS2nG+OBx+NRUlKSnnzySRUXF+uee+4xtAQkiqDFe++9p5UrV0qSunbtKpvNZvg/xvX67W9/q3Xr1qmgoEB33XWX8vLydMstt5gdK2zvvvuufvrTn0qSKioqVFtbG1f5H3jgAW3fvl2hUEgVFRWqr6+Xy+UyO1a77N69W3/7t39rdozr0r1795aJBjfddJMCgYCCwaDJqcL3ySef6IEHHlBBQYHGjh2rO+64w/AM8TH2NsDDDz+s+fPna/LkyQoEAnrppZfUuXNns2NZwoQJEzR//nxlZWXJZrPplVdeiZvTQpI0ZswY7d69WxMmTFAoFFJOTk7c/BLR7PPPP1efPn3MjnFdnnrqKb300kvKzs6W3+/XzJkz1a1bN7Njha1fv3765S9/qTVr1ig5OVk/+clPDM/A6qMAYHGcGgIAi6MIAMDiKAIAsDiKAAAsjiIAAIujCIAIW7FihTZs2KBDhw7pX/7lXyRJmzZtUkVFhcnJgCujCIAoueuuu/Tcc89Jkt566y3V1taanAi4svi5awcwSF1dnWbPnq3q6mrdeeed+vOf/yyXy6Xc3FwNGDBAGzZs0JkzZ/T8889r+fLl2r9/v+rq6jRgwIBWy2fv2rVLhYWFevTRR1uW/mheT2nu3LkKBoN67LHH5PV65XQ6TfyMYXWMCIDLrF+/Xl//+te1fv16PfbYY6qrq7vi82pra9W9e3e9+eabKiws1N69e694+sftdrcs/fHII49oy5YtCgaD2r59u4YPH04JwHSMCIDLlJeXa9SoUZKk+++//ys/qJtvxm9eXG7WrFnq1q2bzp07J7/f3+axk5KS9M1vflM7duzQxo0b9U//9E/R+SSAdmBEAFzm61//uv7nf/5HkvTpp5+qsbFRTqezZUXUgwcPSpK2bdumkydPKj8/X7NmzdL58+d1tRVbbDZby/syMzP1zjvvqLKyUoMGDTLgMwLaRhEAl3niiSd05swZTZ48WatWrZIkPfnkk/rnf/5nTZ06tWVly/T0dB07dkyZmZmaPn267rjjDp06deqKxxwyZIjmzJkjn8+n++67T2VlZfr+979v2OcEtIVF54A2NDQ06Dvf+Y4+/PDDiB2zqalJWVlZWr16tZKSkiJ2XOB6MSIADHTs2DE9/vjjevTRRykBxAxGBABgcYwIAMDiKAIAsDiKAAAsjiIAAIujCADA4igCALC4/w8aPD8a52e/CgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('total_sulfur_dioxide')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute total_sulfur_dioxide has somewhat negative association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('density')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute density has a negative association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('pH')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute pH has a positive association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('sulphates')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute sulphates has a weak association with quality of wine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_plt('alcohol')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot we can infer that the attribute alcohol has a positive association with quality of wine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the correlation matrix\n",
    "\n",
    "Let us try and examine the correlation between variables and quality of the wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a correlation matrix\n",
    "corrMatrix = wine_data.corr()\n",
    "\n",
    "\n",
    "#visualize as correlation matrix heatmap\n",
    "fig= plt.figure(figsize=(10,10))\n",
    "sn.heatmap(corrMatrix, annot=True, cmap= 'viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of the Correlation Matrix\n",
    "\n",
    "From the correlation values we can infer that:\n",
    "1. The variable alcohol has moderate positive correlation to the wine quality. \n",
    "2. The variable density has moderate negative correlation to the wine quality.\n",
    "3. There is a high positive correletion between density and residual sugar.\n",
    "4. Total sulphur dioxide and free sulphur dioxide have a positive correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now since we have gained the understanding about the attributes and the target variable through the EDA, we can move ahead and try various Machine Learning Algorithms to design a system to predict the quality of wine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The continuation will be found in the notebook - 01_ModelTesting.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}