practice-red-wine quality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Red Wine Quality Prediction\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99512</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.076</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.99574</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.075</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99547</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.067</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99549</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1599 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.4             0.700         0.00             1.9      0.076   \n",
       "1               7.8             0.880         0.00             2.6      0.098   \n",
       "2               7.8             0.760         0.04             2.3      0.092   \n",
       "3              11.2             0.280         0.56             1.9      0.075   \n",
       "4               7.4             0.700         0.00             1.9      0.076   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "1594            6.2             0.600         0.08             2.0      0.090   \n",
       "1595            5.9             0.550         0.10             2.2      0.062   \n",
       "1596            6.3             0.510         0.13             2.3      0.076   \n",
       "1597            5.9             0.645         0.12             2.0      0.075   \n",
       "1598            6.0             0.310         0.47             3.6      0.067   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
       "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
       "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
       "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
       "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
       "\n",
       "      alcohol  quality  \n",
       "0         9.4        5  \n",
       "1         9.8        5  \n",
       "2         9.8        5  \n",
       "3         9.8        6  \n",
       "4         9.4        5  \n",
       "...       ...      ...  \n",
       "1594     10.5        5  \n",
       "1595     11.2        6  \n",
       "1596     11.0        6  \n",
       "1597     10.2        5  \n",
       "1598     11.0        6  \n",
       "\n",
       "[1599 rows x 12 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('winequality-red.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    681\n",
       "6    638\n",
       "7    199\n",
       "4     53\n",
       "8     18\n",
       "3     10\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity           0\n",
       "volatile acidity        0\n",
       "citric acid             0\n",
       "residual sugar          0\n",
       "chlorides               0\n",
       "free sulfur dioxide     0\n",
       "total sulfur dioxide    0\n",
       "density                 0\n",
       "pH                      0\n",
       "sulphates               0\n",
       "alcohol                 0\n",
       "quality                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checkihng for missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis and Visualisation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Statical Visualization of dataset\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2159536f0d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#number of values for each quality\n",
    "sns.catplot(x='quality', data=data, kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='volatile acidity'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Volatile Acidity Vs Quality\n",
    "plot=plt.figure(figsize=(5,5))\n",
    "sns.barplot(x='quality',y='volatile acidity', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='citric acid'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUoAAAE/CAYAAAAgxop3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAT/klEQVR4nO3dfbRldX3f8fdn7jCLh4iQzKyO5SFDkqmW1WLAGzQhitGSolUnGrtCGjSPi5IlpmrplDZdpurKH1J1ZSUlmRBKAtWGGoyuiR1LHqwPlZrOQIgKkXSKCHfwhkFFHcTKON/+cfaQ4+XO/Z175+57zpl5v9a665z9cPf5MMBn9j57799OVSFJOrJ14w4gSZPOopSkBotSkhosSklqsCglqcGilKSGXosyyaVJ7k2yN8k1iyx/YZKvJLmr+3lzn3kkaSXW97XhJDPAdcAlwBywO8nOqrpnwaofr6qX9ZVDko5Wb0UJXAjsrar7AJLcAmwDFhblsmzcuLG2bNly9Okkacgdd9zxSFVtWmxZn0V5BvDg0PQc8NxF1vvBJH8JPARcXVV3L7XRLVu2sGfPntVLKUlAks8faVmfRZlF5i28X/JO4Lur6kCSlwIfALY+ZUPJFcAVAGefffYqx5SkpfV5MmcOOGto+kwGe41PqqqvVtWB7v0u4IQkGxduqKqur6rZqprdtGnRPWNJ6k2fRbkb2JrknCQbgMuAncMrJNmcJN37C7s8X+wxkyQtW2+H3lV1MMlVwG3ADHBjVd2d5Mpu+Q7g1cAvJjkIPA5cVg5nJGnCZNp6aXZ2tjyZI2m1JbmjqmYXW+adOZLUYFFKUoNFKUkNFqUkNViUktTQ5505krRqtm/fzvz8PJs3b+baa69d08+2KCVNhfn5efbt2zeWz/bQW5IaLEpJarAoJanBopSkBotSkhosSklqsCglqcGilKQGi1KSGixKSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBotSkhosSklqsCglqcGilKQGi1KSGixKSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBotSkhosSklqsCglqcGilKQGi1KSGixKSWqwKCWpwaKUpAaLUpIaLEpJaui1KJNcmuTeJHuTXLPEej+Q5FtJXt1nHklaifV9bTjJDHAdcAkwB+xOsrOq7llkvbcDt/WVRdLA9u3bmZ+fZ/PmzVx77bXjjjM1eitK4EJgb1XdB5DkFmAbcM+C9V4PvA/4gR6zSALm5+fZt2/fuGNMnT4Pvc8AHhyanuvmPSnJGcArgR095pCko9JnUWaRebVg+teAf11V31pyQ8kVSfYk2bN///7VyidJI+nz0HsOOGto+kzgoQXrzAK3JAHYCLw0ycGq+sDwSlV1PXA9wOzs7MKylaRe9VmUu4GtSc4B9gGXAf9seIWqOufw+yS/B3xwYUlK0rj1VpRVdTDJVQzOZs8AN1bV3Umu7Jb7vaSkqdDnHiVVtQvYtWDeogVZVT/TZxZJWqlei1KSfvXy1bmP5EsPf2XwOv+FVdnmL7/71pHX9RZGSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBq+jlKbEf/yXf3TU23j0kceefF2N7V31zpcf9TamgXuUktRgUUpSg0UpSQ0WpSQ1WJSS1GBRSlKDRSlJDRalJDVYlJLUYFFKUoNFKUkNFqUkNViUktRgUUpSg8OsSceRUzac+m2vGo1FKR1HLvreV407wlTy0FuSGixKSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBotSkhosSklq8BbGzvbt25mfn2fz5s1ce+21444jaYJYlJ35+Xn27ds37hiSJpCH3pLUYFFKUoNFKUkNFqUkNViUktRgUUpSg0UpSQ0WpSQ19FqUSS5Ncm+SvUmuWWT5tiSfSnJXkj1JfrjPPJK0Er3dmZNkBrgOuASYA3Yn2VlV9wyt9mfAzqqqJOcB7wWe1VcmSVqJPvcoLwT2VtV9VfVN4BZg2/AKVXWgqqqbPAUoJGnC9FmUZwAPDk3PdfO+TZJXJvks8N+An+sxjyStSJ9FmUXmPWWPsareX1XPAn4MeNuiG0qu6L7D3LN///7VTSlJDX0W5Rxw1tD0mcBDR1q5qj4GfG+SjYssu76qZqtqdtOmTaufVJKW0GdR7ga2JjknyQbgMmDn8ApJvi9JuvcXABuAL/aYSdKUOnFmHSfNrOPEmbW/qrG3s95VdTDJVcBtwAxwY1XdneTKbvkO4MeB1yZ5Angc+ImhkzuS9KTzv+tpY/vsIxZlkk+zxFnoqjqvtfGq2gXsWjBvx9D7twNvHympJI3JUnuUL+teX9e9/ufu9aeAr/eWSJoCPjrk+HLEoqyqzwMkuaiqLhpadE2STwBv7TucNKl8dMjxZZRvRU8ZvrUwyQ8xuDhcko4Lo5zM+XngxiRP76YfxQvDJR1HmkVZVXcAz05yKpCq+kr/sSRpcix11vvyqnp3kjctmA9AVb2r52ySNBGW2qM8/D3k+C5ekqQJsNRZ79/uXt+ydnEkafI0z3onuSnJaUPTpye5sddUkjRBRjnrfV5VPXp4oqq+nOT8/iJJ/fnoCy5ele08vn4GEh6fm1uVbV78sY+uQir1ZZTrKNclOf3wRJLvpMd7xCVp0oxSeO8Ebk9yazf9T4Ff7S+SJE2WUa6jvDnJHcCPMBiM91ULnnsjSce0kQ6hu+HR9gMnAiQ5u6oe6DWZJE2IUc56vyLJ/wE+B3wUuB/4UM+5JGlijHIy523A84C/rqpzgBcDn+g1lSRNkFGK8omq+iKDs9/rqup/AN/fbyxJmhyjfEf5aJLvAD4GvCfJw8DBfmPpeDDNg9+e1j2x5DSfXHJcGKUotzF4ns0bGYxu/nQctFerYJoHv738W4fGHUFraJTLgx7r3h4Cbuo3jiRNnrV/7qMkTRmLUpIaRrmO8pQk64am1yU5ud9YkjQ5Rtmj/DNguBhPBv60nziSNHlGKcoTq+rA4YnuvXuUko4bo1we9FiSC6rqToAkz2FwuZCOYxf9xkXtlRo2PLqBdazjwUcfXJXtfeL13jCmfoxSlG8A/iDJQ930M4Cf6C2RJE2YUa6j3J3kWcAzGQyz9tmqeqL3ZJI0IZZ6XO2LqurDSV61YNHWJFTVH/acTZImwlJ7lBcDHwZevsiyAixKSceFpR5X+yvd9ZMfqqr3rmEmLdM0Dy4hTYMlLw+qqkPAVWuURSt0eHCJ+fn5cUeRjkmjXEf5J0muTnJWku88/NN7MkmaEKNcHvRz3evrhuYV8D2rH0eSJs8oRfn3q+obwzOSnNhTHh1H6uTiEIeokx38VpNtlKK8HbhghHnSsjxxkZfjajosdR3lZuAM4KQk5zO42BzgVLzXW9JxZKk9yn8M/AxwJvCuoflfA/5tj5kkaaIsdR3lTcBNSX68qt63hpkkaaIsdeh9eVW9G9iS5E0Ll1fVuxb5NUk65ix16H1K9/odaxFEkibVUofev929vmXt4kjS5BnlmTk3JTltaPr0JDf2mkqSJsgotzCeV1WPHp6oqi8D5/eWSJImzChFuS7J6Ycnuvu8R7lQXZKOCaMU5TuB25O8LclbGdyVM9JYXkkuTXJvkr1Jrllk+U8l+VT3c3uSZy8vviT1b5RHQdycZA/wIgZ357yqqu5p/V6SGeA64BJgDtidZOeC3/0ccHFVfTnJS4Drgeeu4J9Dknoz0iF0V27NclzgQmBvVd0HkOQWYNvwdqrq9qH1P8ngLiBJmiijHHqv1BnAg0PTc928I/l54EM95pGkFenzpEwWmbfoeFpJfoRBUf7wEZZfAVwBcPbZZ69WPkkaSZ97lHPAWUPTZwIPLVwpyXnADcC2qvriYhuqquuraraqZjdt2tRLWEk6kj6LcjeDR9uek2QDcBmwc3iFJGczeJrja6rqr3vMIkkr1tuhd1UdTHIVcBswA9xYVXcnubJbvgN4M/BdwG8mAThYVbN9ZZKklej1wvGq2gXsWjBvx9D7XwB+oc8MknS0+jz0lqRjgkUpSQ0WpSQ1WJSS1GBRSlKDRSlJDRalJDVYlJLUYFFKUoOPdBijB976D1dlOwe/NHg6x8EvfX5Vtnn2mz999KGkY8gxUZTP+Vc3H/U2nvbI15gBHnjka6uyvTv+w2uPehuSJoOH3pLUYFFKUoNFKUkNFqUkNViUktRgUUpSg0UpSQ0WpSQ1WJSS1GBRSlKDRSlJDRalJDVYlJLUYFFKUoNFKUkNFqUkNViUktRgUUpSg0UpSQ0WpSQ1WJSS1GBRSlKDRSlJDRalJDVYlJLUYFFKUsP6cQfQ0dt44iHgYPcqabVZlMeAq897dNwRpGOah96S1GBRSlKDRSlJDRalJDVYlJLUYFFKUkOvRZnk0iT3Jtmb5JpFlj8ryf9K8v+SXN1nFklaqd6uo0wyA1wHXALMAbuT7Kyqe4ZW+xLwS8CP9ZVDko5Wn3uUFwJ7q+q+qvomcAuwbXiFqnq4qnYDT/SYQ5KOSp9FeQbw4ND0XDdPkqZKn0WZRebVijaUXJFkT5I9+/fvP8pYkrQ8fRblHHDW0PSZwEMr2VBVXV9Vs1U1u2nTplUJJ0mj6rModwNbk5yTZANwGbCzx8+TpF70dta7qg4muQq4DZgBbqyqu5Nc2S3fkWQzsAc4FTiU5A3AuVX11b5ySdJy9TrMWlXtAnYtmLdj6P08g0NySZpY3pkjSQ0WpSQ1WJSS1GBRSlKDRSlJDRalJDVYlJLUYFFKUoNFKUkNFqUkNViUktRgUUpSg0UpSQ0WpSQ1WJSS1GBRSlKDRSlJDRalJDVYlJLUYFFKUoNFKUkNFqUkNViUktRgUUpSg0UpSQ0WpSQ1WJSS1GBRSlKDRSlJDRalJDVYlJLUYFFKUoNFKUkNFqUkNViUktRgUUpSg0UpSQ3rxx1gUhzacMq3vUrSYRZl57GtPzruCJImlIfektRgUUpSg0UpSQ0WpSQ1WJSS1GBRSlJDr0WZ5NIk9ybZm+SaRZYnya93yz+V5II+80jSSvRWlElmgOuAlwDnAj+Z5NwFq70E2Nr9XAH8Vl95JGml+tyjvBDYW1X3VdU3gVuAbQvW2QbcXAOfBE5L8oweM0nSsvVZlGcADw5Nz3XzlruOJI1Vn7cwZpF5tYJ1SHIFg0NzgANJ7j3KbEeyEXhkNTaUd/z0amxmOVYtO7+y2L+W3qzen/kvrWluWM0/80znn/nr37UaW1mWVcv+797zlD/z7z7Sun0W5Rxw1tD0mcBDK1iHqroeuH61Ay6UZE9Vzfb9OX2Y1uzTmhumN/u05obxZe/z0Hs3sDXJOUk2AJcBOxessxN4bXf2+3nAV6rqCz1mkqRl622PsqoOJrkKuA2YAW6sqruTXNkt3wHsAl4K7AW+DvxsX3kkaaV6HWatqnYxKMPheTuG3hfwuj4zLFPvh/c9mtbs05obpjf7tOaGMWXPoKskSUfiLYyS1GBRAklOTPK/k/xlkruTvGXcmZYjyUySv0jywXFnWY4k9yf5dJK7kuwZd57lSHJakluTfDbJXyX5wXFnaknyzO7P+vDPV5O8Ydy5RpHkjd3/m59J8vtJTlzTz/fQe3DPOXBKVR1IcgLwP4F/0d0tNPGSvAmYBU6tqpeNO8+oktwPzFbV6lyLuIaS3AR8vKpu6K7qOLmqHh1zrJF1txjvA55bVZ8fd56lJDmDwf+T51bV40neC+yqqt9bqwzuUTI4qVRVB7rJE7qfqfgbJMmZwD8Bbhh3luNFklOBFwD/CaCqvjlNJdl5MfB/J70kh6wHTkqyHjiZRa637pNF2ekOX+8CHgb+pKr+fMyRRvVrwHbg0JhzrEQBf5zkju7uq2nxPcB+4He7rzxuSDJtj++8DPj9cYcYRVXtA94BPAB8gcH11n+8lhksyk5Vfauqvp/B3UEXJvkHY47UlORlwMNVdce4s6zQRVV1AYNRpF6X5AXjDjSi9cAFwG9V1fnAY8BThhGcVN1XBa8A/mDcWUaR5HQGA+icA/xd4JQkl69lBotyge4Q6iPApeNNMpKLgFd03/XdArwoybvHG2l0VfVQ9/ow8H4GI05Ngzlgbuio41YGxTktXgLcWVV/M+4gI/pHwOeqan9VPQH8IfBDaxnAogSSbEpyWvf+JAb/Yj471lAjqKp/U1VnVtUWBodSH66qNf2bdqWSnJLkaYffAz8KfGa8qUZTVfPAg0me2c16MXDPGCMt108yJYfdnQeA5yU5uTvx+mLgr9YyQK935kyRZwA3dWcC1wHvraqputRmCv0d4P2D/+5ZD/yXqvrv4420LK8H3tMdxt7HlNx+m+Rk4BLgn487y6iq6s+T3ArcCRwE/oI1vkPHy4MkqcFDb0lqsCglqcGilKQGi1KSGixKSWqwKHXMS7IlyWe697NJfr17/8Ika3rhsqaT11HquFJVe4DDQ7q9EDgA3D62QJoK7lFqoiX55ST3JvnTbhzCq5N8JMlst3xjdwvn4T3Hjye5s/t5yt5itxf5wSRbgCuBN3ZjMz4/yee6YfZIcmo3XuYJa/dPq0nlHqUmVpLnMLg183wG/63eCSw1AMjDwCVV9Y0kWxncprfoo02r6v4kO4ADVfWO7vM+wmDIug90n/u+7t5iHefco9Qkez7w/qr6elV9lac+7nihE4DfSfJpBiPjnLvMz7uBv70V8WeB313m7+sY5R6lJt1i99ge5G//kh9+JMAbgb8Bnt0t/8ayPqjqE93h+8XATFVNxSAd6p97lJpkHwNemeSkbqShl3fz7wee071/9dD6Twe+UFWHgNcweJ78Ur4GPG3BvJsZHLK7N6knWZSaWFV1J/BfgbuA9wEf7xa9A/jFJLcDG4d+5TeBn07ySeDvMRhQdyl/xKCI70ry/G7ee4DTma5hyNQzRw/S1Ejy7xk6+dLTZ7wa2FZVr+nrMzR9/I5S6iT5DQajf7903Fk0WdyjlKQGv6OUpAaLUpIaLEpJarAoJanBopSkBotSkhr+P5i1JPu2whZFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# citric acid Vs Quality\n",
    "plot=plt.figure(figsize=(5,5))\n",
    "sns.barplot(x='quality',y='citric acid', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation\n",
    "constructing a heatmap to correlation between columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation=data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m/+Wy78zw40iSJC2ZlbGBGKCW4LiblvBcewPrAttV1dbAtcBqMzh2xjmr6vCq2r6qtl/9wcvl3hlJkgTNT+fZfPXUythAPBV4XpK/A5iia/dGYM0x60ddBqyfZIf2XGsmGe2WXxu4rqr+luTxwEYzyVFVfwKuT7JTu2rvGeSRJEla7la6MYhVdXGS9wCnJ7kNOI/mruVBRwKHJfkLsOM057o1yV7Ax5KsTjP+cFfgzwO7HQN8I8l8YAFNo3KmOfYDPpfkZuCkxf+0kiRJy95K10AEqKqjgKNG1h0y8P4rwFcGNm88su++A+/PBh41zbV+xxSNzBnkOAfYamDzIUiSpM54k0pjZexiliRJ0lJYKSuIkiRJS8IKYsMKoiRJkoZYQZQkSWpZQWxYQZQkSdIQK4iSJEkTLCACVhAlSZI0wgaiJElSK8msvmaQ5ylJfpLkiiQHT7HPLkkWJLk4yenL4utgF7MkSVIPJVkF+E9gN+Aq4Owkx1fVJQP73BP4BPCUqvpVkvssi2vbQJQkSWr17C7mRwBXVNXPAZIcC+wBXDKwzwuB/6mqXwFU1XXL4sJ2MUuSJPXTBsCvB5avatcN2hy4V5LTkpyTZJ9lcWEriJIkSa3ZriAm2R/Yf2DV4VV1+MTmMYfUyPJdgO2AJwKrAz9M8qOq+unS5LKBKEmS1JG2MXj4FJuvAh4wsHx/4Oox+/yuqm4CbkpyBrAVsFQNRLuYJUmSWj27i/lsYLMkmyS5K/B84PiRfb4OPDbJXZLcHXgkcOnSfh2sIEqSJPVQVS1M8lrgJGAV4HNVdXGSV7bbD6uqS5OcCFwA3A58pqouWtpr20CUJEnqqao6AThhZN1hI8uHAocuy+vaQJQkSZrQq1luumMDcQWz755bdR1hki9dcFXXESZ5xSGv7TrCJJ865ONdR5jk7R98Q9cRxjrzFzd0HWGS228fvXGwe6vM699Psu/+7NquI0yy6xbrdB1hrJtvvb3rCJPc7wFrdh1BPWEDUZIkqdWzibI7413MkiRJGmIFUZIkqWUFsWEFUZIkSUOsIEqSJLUsIDasIEqSJGmIFURJkqSWYxAbVhAlSZI0xAqiJElSywJiwwqiJEmShlhBlCRJajkGsWEFUZIkSUOsIEqSJLUsIDasIEqSJGmIDURJkiQNsYtZkiSpNW+efcxgBVGSJEkjrCBKkiS1vEmlYQVRkiRJQ1bYBmKSzyTZYsz6fZN8fCnO++elSyZJklZUSWb11Ve96GJO8xVKVd0+02Oq6h+XY6ROJVmlqm7rOockSZqbOqsgJtk4yaVJPgGcCzwgyZuTnJ3kgiTvave7R5JvJTk/yUVJ9mrXn5Zk+/b9fkl+muR04DED1zgyyXMGlv/c/rlGklOTnJvkwiR7LCLrVBmuTLJO+377JKe179dNckp7/k8l+eXAfl9Lck6Si5PsP5gtybuT/C+w41J/gSVJ0mJLZvfVV113MT8I+HxVbdO+3wx4BLA1sF2SnYGnAFdX1VZV9TDgxMETJFkPeBdNw3A3YFK38xi3AM+qqm2BxwMfyvR13mkzjPFO4Lvt+b8KbDiw7aVVtR2wPXBAkr9r198DuKiqHllVPxj5jPsnmZ9k/vknfmkGH0+SJGnJdd1A/GVV/ah9/6T2dR5NRfHBNA3GC4Fdk7w/yWOr6vqRczwSOK2qfltVtwIzaUEF+LckFwDfATYA7jvN/ovKMGon4FiAqjoR+OPAtgOSnA/8CHhA+xkBbgO+Mu5kVXV4VW1fVdtv9ZS9FnFpSZK0pByD2Oh6DOJNA+8DvLeqPjW6U5LtgKcC701yclW9e2SXmuL8C2kbwW2F8K7t+r2BdYHtqupvSa4EVpsqZFX9dIoMd5x/5Pixf+NJdgF2BXasqpvbLumJ425x3KEkSeqDriuIg04CXppkDYAkGyS5T5L1gZur6gvAB4FtR477X2CXJH+XZFXguQPbrgS2a9/vAazavl8buK5tHD4e2Gi6YNNkGDz/swcO+QHwvPbYJwH3GrjuH9vG4YOBR013XUmSNLusIDa6riDeoapOTvIQ4IftF+zPwIuAvwcOTXI78DfgVSPHXZPkEOCHwDU03dOrtJs/DXw9yY+BU7mzYnkM8I0k84EFwGWLiPfwKTK8C/hskv9H01BlYP0X25tZTm9z3UgzdvGVbdf2T2i6mSVJknqlswZiVV0JPGxk3UeAj4zs+jOa6uLo8bsMvD8COGLMPtcyXKX7p3b975jiTuGqWmPMupOmyPB9YPMxp7keeHJVLUyyI/D4qvpru233mV5XkiTNrh4X9WZVbyqIK5kNgf9OMg+4FXh5x3kkSZJmzAbiclBVlwPbdJ1DkiQtnj6PC5xNfbpJRZIkST1gA1GSJElD7GKWJElq2cPcsIIoSZKkIVYQJUmSWt6k0rCCKEmSpCFWECVJkloWEBtWECVJkjTECqIkSVLLMYgNK4iSJEkaYgVRkiSpZQGxYQVRkiRJQ6wgSpIktRyD2LCCKEmSpCFWEFcwq87r3282199yW9cRJvnzLQu7jjDJ2z/4hq4jTPKvB/1H1xHGev/H3tR1hEn+/Nfbu46wQnjgWmt0HWGSz/zg0q4jjPXPT9686wiT/PGWW7uO0DkLiA0riJIkSRpiBVGSJKnlGMSGFURJkiQNsYEoSZKkIXYxS5IktexhblhBlCRJ0hAriJIkSS1vUmlYQZQkSdIQK4iSJEktC4gNK4iSJEk9leQpSX6S5IokB0+z3w5JbkvynGVxXSuIkiRJrT6NQUyyCvCfwG7AVcDZSY6vqkvG7Pd+4KRldW0riJIkSf30COCKqvp5Vd0KHAvsMWa/1wFfAa5bVhe2gShJktRKMtuv/ZPMH3jtPxBnA+DXA8tXtesG824APAs4bFl+HexiliRJ6khVHQ4cPsXmcf3dNbL8H8Bbq+q2Zdk9bgNRkiSp1aMhiNBUDB8wsHx/4OqRfbYHjm0bh+sAT02ysKq+tjQXtoEoSZLUT2cDmyXZBPg/4PnACwd3qKpNJt4nORL45tI2DsEGoiRJ0h36dBdzVS1M8lqau5NXAT5XVRcneWW7fZmOOxxkA1GSJKmnquoE4ISRdWMbhlW177K6rg1ESZKkVo8KiJ2ak9PcJDly3EzjSTZOctFinmv9JMdNse20JNsvaU5JkqQuWEFcCknuUlVXA8vksTaSJEl9MCcqiEn2SXJBkvOTHN2u3jnJWUl+PkU1cbUkRyS5MMl5SR7frt83yZeTfAM4ebDqmGT1JMe21/oSsPrA+Z6U5IdJzm2PX6Nd/74kl7THfHC5fzEkSdKUZnui7L5a6RuISR4KvA14QlVtBby+3bQesBPwNOB9Yw59DUBVPRx4AXBUktXabTsCL6mqJ4wc8yrg5qraEngPsF2bYR3g7cCuVbUtMB94Y5J708x+/tD2mH+d4jPcMcv6ed/+0mJ/DSRJkhbHXOhifgJwXFX9DqCq/tC22L9WVbcDlyS575jjdgI+1h5zWZJfApu3206pqj+MOWZn4KPtMRckuaBd/yhgC+DM9tp3BX4I3ADcAnwmybeAb477AIOzrL/t2z8dnUFdkiQtIz0u6s2qudBADJMfSwPw15F9xh03lZum2TbuWqFpVL5g0obkEcATaSa/fC1Ng1aSJKkzK30XM3Aq8LwkfwfQduvOxBnA3u0xmwMbAj9ZjGMeBmzZrv8R8Jgkf99uu3uSzdtxiGu3cxy9Adh6htkkSdJyMC+Z1VdfrfQVxHbG8fcApye5DThvhod+AjgsyYXAQmDfqvrrIgaUfhI4ou1aXgD8uM3w2yT7Al9Mcrd237cDNwJfb8c2BjhwsT6cJEnScrDSNxABquoo4Khptq/R/nkl8LD2/S3AvmP2PRI4cmB58Ji/0HQVj7vGd4Edxmx6xAw+giRJmgU9LurNqrnQxSxJkqTFMCcqiJIkSTPR57kJZ5MVREmSJA2xgihJktSaZwERsIIoSZKkEVYQJUmSWo5BbFhBlCRJ0hAriJIkSS0LiA0riJIkSRpiA1GSJElD7GKWJElqBfuYwQqiJEmSRlhBlCRJajlRdsMKoiRJkoZYQZQkSWo5UXbDCqIkSZKGWEFcwTzq/mt3HWGSX93wl64jTLLhpv37Op35ixu6jjDJ+z/2pq4jjPXW132o6wiTrLXd47qOMMm7XrZD1xEmOfkXv+s6wiS7PvQ+XUcY69c33tx1hEkWVnUdoXMWEBtWECVJkjTECqIkSVJrniVEwAqiJEmSRlhBlCRJallAbFhBlCRJ0hAriJIkSS3nQWxYQZQkSdIQK4iSJEktC4gNK4iSJEkaYgNRkiRJQ+xiliRJajlRdsMKoiRJkoZYQZQkSWpZP2xYQZQkSdIQK4iSJEktJ8puWEGUJEnSECuIkiRJrXkWEAEriJIkSRphBVGSJKnlGMTGYlUQkxyQ5NIkxyyvQDPMcUiSg9r3D06yIMl5STZdRue/Msk67fuzlvAcr0yyz5j1Gye5aGkzSpIkLS+LW0F8NbB7Vf1icGWSu1TVwmUXa7E8E/h6Vb1zpgcsTt6qevSShKqqw5bkOEmS1B0LiI0ZVxCTHAY8EDg+yYFtFe/wJCcDn0+ybpKvJDm7fT2mPe4eST7XrjsvyR5jzr1ekjPaSuBFSR7brv/zwD7PSXLkyHFPBd4A/GOS741W55IclOSQ9v1pSf4tyenA60fO83dJTm7zfYqBeTInMqRxaJvvwiR7tes/muQd7fsnt59j3kiVc7sk5yf5IfCagXOv0p7z7CQXJHnFTP8+JEmSlpcZNxCr6pXA1cDjq+rD7ertgD2q6oXAR4APV9UOwLOBz7T7vA34brv+8cChSe4xcvoXAidV1dbAVsCCGWY6ATisve7jZ3DIPavqcVX1oZH17wR+UFXbAMcDG445dk9gIt+u7edYDzgY2CvJ44GPAvtV1e0jxx4BHFBVO46sfxlwffu12QF4eZJNRi+cZP8k85PMP/G4o2fwMSVJ0pJIMquvvlram1SOr6q/tO93BbYY+LBrJVkTeBLwjIlqGrAaTQPs0oHznA18LsmqwNeqasFS5prKl6ZYvzNNA5Cq+laSP47ZZyfgi1V1G3BtW4ncoaqOT/Jy4AzgwKr62eBBSdamaZie3q46Gti9ff8kYMskz2mX1wY2A4a68KvqcOBwgG9ceG3N+NNKkiQtgaVtIN408H4esONAgxFoumaBZ1fVT6Y6SVWdkWRn4B+Ao5McWlWfBwYbQ6vNIM9Chquio8fcxNQW1fCarpn/cOD3wPpTHDfVuQO8rqpOWsS1JUnSLHAexMaynAfxZOC1EwtJtm7fngS8rm0okmSb0QOTbARcV1WfBj4LbNtuujbJQ5LMA541gwzXAvdpxxTeDXjaDLOfAezdZtkduNcU++zVjhtcl6bq+OM2+5uAbYDdkzxy8KCq+hNwfZKd2lV7D2w+CXhVWzklyeZjut8lSZJm1bKcB/EA4D+TXNCe9wzglcC/AP8BXNA2Eq9kcsNtF+DNSf4G/BmYmB7mYOCbwK+Bi4A1pgtQVX9L8m7gf2m6aS+bYfZ3AV9Mci5wOvCrMft8FdgROJ+mIvgWmgbpKcBBVXV1kpcBRybZYeTY/Wi60G+maRRO+AywMXBu+7X5Lc1d2ZIkSZ1JlUPaViR9HIP4qxv+suidZtkad1ul6wiTnPmLG7qOMMnD1rt71xHGeuvrRu8j695a2z2u6wiTvOtlo7+Ldu+6P3c149nU7tLTPsP7r33XriNMsrCHbYKX7rDhrP4F7nfshbP6RTji+Q/v5Teoj9qTJEnSEBuIkiRJrczya5F5kqck+UmSK5IcPGb73u1cyhckOSvJVkv62QfZQJQkSeqhJKsA/0kzPd4WwAuSbDGy2y+Ax1XVljT3fRy+LK69LG9SkSRJWqHN69fk1Y8ArqiqnwMkORbYA7hkYoeqOmtg/x8B918WF7aCKEmS1JHBp6W1r/0HNm9AM5PLhKvadVN5GfDtZZHLCqIkSVJrtguIg09LG2NcmrF3WbeP/H0ZzZPflpoNREmSpH66CnjAwPL9gatHd0qyJc3cyrtX1e+XxYVtIEqSJLXSrzGIZwObJdkE+D/g+cALB3dIsiHwP8CLq+qny+rCNhAlSZJ6qKoWJnktzVPYVgE+V1UXJ3llu/0w4B3A3wGfaBu3C6tq+6W9tg1ESZKkVr8KiFBVJwAnjKw7bOD9PwL/uKyv613MkiRJGmIFUZIkqdWzeRA7YwVRkiRJQ6wgSpIktSwgNqwgSpIkaYgVREmSpFbP5kHsjBVESZIkDbGCuIK55bbbuo4wyW9v+lvXESa56db+fZ1uv33s4zM79ee/3t51hLHW2u5xXUeY5IZzTu86wiTX7bVN1xEm2fhed+s6wiRX33Br1xHG2mCN1buOMMnCHv47pW7YQJQkSWrZtdrw6yBJkqQhVhAlSZJa3qTSsIIoSZKkIVYQJUmSWvMsIAJWECVJkjTCCqIkSVLLCmLDCqIkSZKGWEGUJElqeRdzwwqiJEmShlhBlCRJajkGsWEFUZIkSUOsIEqSJLUcgtiwgihJkqQhVhAlSZJa8ywhAlYQJUmSNMIGoiRJkobYxSxJktSyctbw6yBJkqQh0zYQk9wzyasXdZIkGyd54Qz3u2hxAk5xnkOSHNS+f3CSBUnOS7Lp0p67PeeVSdZp35+1hOd4ZZJ9xqxfJl8DSZK07CWz++qrRVUQ7wkssoEIbAwssoG4nDwT+HpVbVNVP5vJAUlm3LVeVY9eklBVdVhVfX5JjpUkSerSohqI7wM2bSt0h6ZxaJKLklyYZK+B/R7b7ndgWyX7fpJz29e0jawk6yU5oz3+oiSPbdf/eWCf5yQ5cuS4pwJvAP4xyfdGq3NJDkpySPv+tCT/luR04PUj5/m7JCe3VchPARnY9uf2z7GfPclHk7yjff/k9nPMG6lybpfk/CQ/BF4zcO5V2nOeneSCJK9YxN+HJElajuYls/rqq0U1EA8GflZVW1fVm4E9ga2BrYBdgUOTrNfu9/12vw8D1wG7VdW2wF7ARxdxnRcCJ1XVxLkXzCR8VZ0AHAZ8uKoeP4ND7llVj6uqD42sfyfwg6raBjge2HDMsdN99r2SPJ7mc+5XVbePHHsEcEBV7Tiy/mXA9VW1A7AD8PIkm4xeOMn+SeYnmf+dr3xhBh9TkiRpyS3uXcw7AV+sqtuAa9tq3A7ADSP7rQp8PMnWwG3A5os479nA55KsCnytqhYsZq6Z+tIU63emaQBSVd9K8scx+4z97FV1fJKXA2cAB452cydZm6Zhenq76mhg9/b9k4AtkzynXV4b2Az4xeA5qupw4HCALy+4umb8aSVJ0mLpcVFvVi1uA3GmX7YDgWtpqm3zgFum27mqzkiyM/APwNFJDm3H7w02hlabwXUXMlwVHT3mpuliLOLc0332hwO/B9af4ripzh3gdVV10iKuLUmSNGsW1cV8I7DmwPIZNN2pqyRZl6by9uMx+60NXNN2tb4YWGW6iyTZCLiuqj4NfBbYtt10bZKHJJkHPGsGn+da4D7tmMK7AU+bwTETn2vvNsvuwL2m2GfSZ2+zvwnYBtg9ySMHD6qqPwHXJ9mpXbX3wOaTgFe1lVOSbJ7kHjPMLEmSlrF5md1XX01bQayq3yc5s73x49vAW4AdgfNpqmJvqarfJPk9sDDJ+cCRwCeAryR5LvA9pq/cAewCvDnJ34A/AxPTwxwMfBP4NXARsMYi8v4tybuB/6Xppr1sEded8C7gi0nOBU4HfjVmn68y8tlpGqSnAAdV1dVJXgYcmWSHkWP3o+lCv5mmUTjhMzR3gJ+bJMBvae7KliRJ6kyqHNK2IunjGMSLr1tU+3/23X3V/s0B/9Pr/tJ1hEkeuM7qXUcY6yNfPLfrCJPccM7pi95plv3TB97QdYRJNr7X3bqOMMnVN9zadYSxdlh/7a4jTLLw9t79iOHJW6w7q3W2d59yxax+Ed6x29/3so7Yv5+ikiRJ6pTPYpYkSWp5F3PDCqIkSZKGWEGUJElq9fnO4tlkBVGSJElDbCBKkiRpiF3MkiRJrcz4oXErNyuIkiRJGmIFUZIkqeVNKg0riJIkSRpiBVGSJKllBbFhBVGSJElDrCBKkiS14rP2ACuIkiRJGmEFUZIkqeUYxIYVREmSJA2xgriCee3Hz+w6wiRvefF2XUeY5IQLru06wiSr+GvpjL3rZTt0HWGS6/bapusIk7z3Lf/RdYRJfvT193YdYZL5V/1f1xHG2mzrNbuOMMlF11zfdYTOOQSxYQVRkiRJQ6wgSpIkteZZQgSsIEqSJPVWkqck+UmSK5IcPGZ7kny03X5Bkm2XxXWtIEqSJLX6NFw8ySrAfwK7AVcBZyc5vqouGdhtd2Cz9vVI4JPtn0vFCqIkSVI/PQK4oqp+XlW3AscCe4zsswfw+Wr8CLhnkvWW9sI2ECVJkjqSZP8k8wde+w9s3gD49cDyVe06FnOfxWYXsyRJUmu271GpqsOBw6fYPC5NLcE+i80KoiRJUj9dBTxgYPn+wNVLsM9is4EoSZLUmkdm9bUIZwObJdkkyV2B5wPHj+xzPLBPezfzo4Drq+qapf062MUsSZLUQ1W1MMlrgZOAVYDPVdXFSV7Zbj8MOAF4KnAFcDOw37K4tg1ESZKkVt/mya6qE2gagYPrDht4X8BrlvV17WKWJEnSECuIkiRJrT5NlN0lK4iSJEkaYgVRkiSpNa9vgxA7YgVRkiRJQ6wgSpIktSwgNqwgSpIkaYgVREmSpJZjEBtWEMdIckiSg5bh+U5Ics/29epldV5JkqTlwQbiLKiqp1bVn4B7AjYQJUnqqWR2X31lA7GV5G1JfpLkO8CD2nWbJjkxyTlJvp/kwe36I5N8NMlZSX6e5Dnt+vWSnJFkQZKLkjy2XX9lknWA9wGbttsPTXJ0kj0GMhyT5Bmz/uElSZIG2EAEkmwHPB/YBtgT2KHddDjwuqraDjgI+MTAYesBOwFPo2n4AbwQOKmqtga2AhaMXOpg4GdVtXVVvRn4DO1DtZOsDTyakectttv2TzI/yfy/XPadpfuwkiRJi+BNKo3HAl+tqpsBkhwPrEbTYPty7qwB323gmK9V1e3AJUnu2647G/hcklXb7Qumu2hVnZ7kP5Pch6Zh+pWqWjhmv8NpGqvc9x+/XEv4GSVJ0iJYOWv4dbjTaMNrHvCntto38XrIwPa/DrwPQFWdAewM/B9wdJJ9ZnDdo4G9aSqJRyxxekmSpGXEBmLjDOBZSVZPsibwdOBm4BdJnguQxlbTnSTJRsB1VfVp4LPAtiO73AisObLuSOANAFV18VJ+DkmStBSSzOqrr2wgAlV1LvAlmjGDXwG+327aG3hZkvOBi4E9xp7gTrsAC5KcBzwb+MjIdX4PnNnewHJou+5a4FKsHkqSpJ5wDGKrqt4DvGfMpqeM2XffkeU12j+PAo4as//GA+9fOLgtyd2BzYAvLkFsSZK0DPW3pje7rCB2KMmuwGXAx6rq+q7zSJIkgRXETlXVd4ANu84hSZIaPmqvYQVRkiRJQ6wgSpIktawfNqwgSpIkaYgVREmSpJZDEBtWECVJkjTECqIkSVKrz083mU1WECVJkjTECqIkSVLLylnDr4MkSZKG2ECUJEnSELuYJUmSWt6k0rCCKEmSpCFWECVJklrWDxtWECVJkjTECuIK5peHPbfrCJO8/Evndx1hkmP22b7rCJN892fXdh1hkgeutUbXEcY6+Re/6zrCJBvf625dR5jkR19/b9cRJnnUHv/UdYRJjv+vQ7qOMNYhJ/+06wiT7Lzp2l1H6JxjEBtWECVJkjTECqIkSVLLylnDr4MkSZKGWEGUJElqOQaxYQVRkiRJQ6wgSpIktawfNqwgSpIkaYgVREmSpJZDEBtWECVJkjTECqIkSVJrnqMQASuIkiRJGmEDUZIkSUPsYpYkSWp5k0rDCqIkSZKGWEGUJElqxZtUACuIkiRJGmEFUZIkqeUYxIYVREmSJA2xgihJktRyouyGFcSOJDktyfYDyxsnuajLTJIkSWAFUZIk6Q6OQWxYQVzO2srgZUmOSnJBkuOS3L3rXJIkSVOxgTg7HgQcXlVbAjcAr27XH5NkQZIFwAlTHZxk/yTzk8z/7KcPX/5pJUmao5LZfS1d1tw7ySlJLm//vNeYfR6Q5HtJLk1ycZLXz+TcdjHPjl9X1Znt+y8AB7Tv966q+dBUGoFvjju4qg4HDge4ZSG1fKNKkqQVxMHAqVX1viQHt8tvHdlnIfCmqjo3yZrAOUlOqapLpjuxFcTZMdqos5EnSVIPZZb/W0p7AEe1748Cnjm6Q1VdU1Xntu9vBC4FNljUiW0gzo4Nk+zYvn8B8IMuw0iSpH4YHEbWvvZfjMPvW1XXQNMQBO6ziGttDGwD/O+iTmwX8+y4FHhJkk8BlwOfBJ7ebSRJkjRq3izfxTw4jGycJN8B7jdm09sW5zpJ1gC+Aryhqm5Y1P42EGfH7VX1ypF1uwwuVNWVwMNmK5AkSeq/qtp1qm1Jrk2yXlVdk2Q94Lop9luVpnF4TFX9z0yuaxezJEnSiul44CXt+5cAXx/dIUmAzwKXVtW/z/TENhCXs6q6sqqsDEqStAJYwW5SeR+wW5LLgd3aZZKsn2Ri+rzHAC8GnjAxtV6Spy7qxHYxS5IkrYCq6vfAE8esvxp4avv+B7D4LVEbiJIkSS0ftdewi1mSJElDrCBKkiS1lsG4wJWCFURJkiQNsYIoSZLUmu2JsvvKCqIkSZKGWEGUJElqOQaxYQVRkiRJQ6wgSpIktZwHsWEFUZIkSUOsIEqSJLUsIDasIEqSJGmIFURJkqTWPAchAlYQJUmSNCJV1XUGLYZXHHdx7/7C9t16g64jTPLe717edYRJdt1ina4jTPK1c3/TdYSxdn3ofbqOMEkfn65w9Q1/6zrCJE9/UP++z5/xwkO6jjDWG//tgK4jTPLz397cdYRJvrjP1rP6f98Pr/jTrP6c3fHv79nDf13sYpYkSbpDL1trHbCLWZIkSUOsIEqSJE2whAhYQZQkSdIIK4iSJEmtWEIErCBKkiRphBVESZKklvNkN6wgSpIkaYgVREmSpJYFxIYVREmSJA2xgihJkjTBEiJgBVGSJEkjrCBKkiS1nAexYQVRkiRJQ6wgSpIktZwHsWEFUZIkSUNsIEqSJGmIXcySJEkte5gbVhAlSZI0ZM43EJPsm+TjS7vPmGPekOTuS5dOkiTNqszyq6fmfANxOXoDYANRkiStcFbKBmKSeyT5VpLzk1yUZK8kVyZZp92+fZLTxhx3ZJLDknw/yU+TPG1g8/pJTkxyeZIPDBzzySTzk1yc5F3tugOA9YHvJfleu+5JSX6Y5NwkX06yRrv+fUkuSXJBkg8uv6+KJElalMzyf321UjYQgacAV1fVVlX1MODExTh2Y+BxwD8AhyVZrV2/NbAX8HBgryQPaNe/raq2B7YEHpdky6r6KHA18PiqenzbMH07sGtVbQvMB96Y5N7As4CHVtWWwL+OC5Rk/7YROv/SU768GB9FkiRp8a2sDcQLgV2TvD/JY6vq+sU49r+r6vaquhz4OfDgdv2pVXV9Vd0CXAJs1K5/XpJzgfOAhwJbjDnno9r1ZyZZALykPf4G4BbgM0n2BG4eF6iqDq+q7atq+4fs9tzF+CiSJGlxJLP76quVcpqbqvppku2ApwLvTXIysJA7G8SrTXkw1BTLfx1YdxtwlySbAAcBO1TVH5McOcW5A5xSVS+YtCF5BPBE4PnAa4EnTPfZJEmSlreVsoKYZH3g5qr6AvBBYFvgSmC7dpdnT3P4c5PMS7Ip8EDgJ9PsuxZwE3B9kvsCuw9suxFYs33/I+AxSf6+zXf3JJu34xDXrqoTaG5q2XrGH1KSJC1z3sTcWCkriDTjBA9NcjvwN+BVwOrAZ5P8P+B/pzn2J8DpwH2BV1bVLZmiBlxV5yc5D7iYpjv6zIHNhwPfTnJNOw5xX+CLSe7Wbn87TSPy6+04xwAHLtGnlSRJWoZWygZiVZ0EnDRm0+Zj9j0SOHJg1ZlVdeB0+1TV0wbe7ztFho8BHxtY/i6ww5hdHzHueEmS1IE+l/Vm0UrZxSxJkqQlt1JWEJfUVNVASZI0N/R5bsLZZAVRkiRJQ6wgSpIktfo8N+FssoIoSZKkIVYQJUmSWhYQG1YQJUmSNMQGoiRJkobYxSxJkjTBPmbACqIkSZJGWEGUJElqOVF2wwqiJEmShthAlCRJaiWz+1q6rLl3klOSXN7+ea9p9l0lyXlJvjmTc9tAlCRJWjEdDJxaVZsBp7bLU3k9cOlMT2wDUZIkqZVZfi2lPYCj2vdHAc8c+5mS+wP/AHxmpie2gShJkrRium9VXQPQ/nmfKfb7D+AtwO0zPbF3Ma9gHr3xWl1HmOTPf1vYdYRJtrj/2l1HmOTmW2f8/+Ws+ecnb951hLF+fePNXUeYZIM1Vu86wiSbbb1m1xEmOeTkn3YdYZI3/tsBXUcY69//30e7jjDJQ561Z9cRujfLNzEn2R/Yf2DV4VV1+MD27wD3G3Po22Z4/qcB11XVOUl2mWkuG4iSJEkdaRuDh0+zfdeptiW5Nsl6VXVNkvWA68bs9hjgGUmeCqwGrJXkC1X1ouly2cUsSZLUyiz/t5SOB17Svn8J8PXRHarqn6rq/lW1MfB84LuLahyCDURJkqQV1fuA3ZJcDuzWLpNk/SQnLM2J7WKWJElqLe3chLOpqn4PPHHM+quBp45Zfxpw2kzObQVRkiRJQ6wgSpIktVagAuJyZQVRkiRJQ2wgSpIkaYhdzJIkSRPsYwasIEqSJGmEFURJkqTWMpi8eqVgBVGSJElDrCBKkiS1VqSJspcnK4iSJEkaYgVRkiSpZQGxYQVRkiRJQ6wgSpIkTbCECFhBlCRJ0ggriJIkSS3nQWxYQVyEJFcmWWcJjjsyyXMWY/+Nk1y0uNeRJEla1qwgSpIktZwHsWEFcUCSryU5J8nFSfYfs32fJBckOT/J0e26jZKc2q4/NcmGA4fsnOSsJD+fqCamcWiSi5JcmGSvWfp4kiRJM2IDcdhLq2o7YHvggCR/N7EhyUOBtwFPqKqtgNe3mz4OfL6qtgSOAT46cL71gJ2ApwHva9ftCWwNbAXsChyaZL3pQiXZP8n8JPNP+59jlvIjSpKkqWSWX31lA3HYAUnOB34EPADYbGDbE4Djqup3AFX1h3b9jsB/te+PpmkQTvhaVd1eVZcA923X7QR8sapuq6prgdOBHaYLVVWHV9X2VbX9LnvuvRQfT5IkadEcg9hKsgtNRW/Hqro5yWnAaoO7ADWDUw3u89eR4wf/lCRJ6iUriHdaG/hj2zh8MPCoke2nAs+b6HZOcu92/VnA89v3ewM/WMR1zgD2SrJKknWBnYEfL4sPIEmSlpJ9zIAVxEEnAq9McgHwE5pu5jtU1cVJ3gOcnuQ24DxgX+AA4HNJ3gz8FthvEdf5Kk239Pk01ca3VNVvkmy8DD+LJEnSErOB2KqqvwK7j9m08cA+RwFHjRx3Jc34xNHz7TuyvEb7ZwFvbl+j53nYEkSXJEnLiBNlN+xiliRJ0hAriJIkSS0nym5YQZQkSdIQK4iSJEktC4gNK4iSJEkaYgVRkiSp5RjEhhVESZIkDbGCKEmSdAdLiGAFUZIkSSOsIEqSJLUcg9iwgihJkqQhVhAlSZJaFhAbVhAlSZI0xAaiJEmShtjFLEmS1PImlYYVREmSJA2xgriCueg3N3cdYZIdtr131xEmufpPt3QdYZL7PWDNriNM8sdbbu06wlgLq7qOMMnC2/uX6aJrru86wiQ7b7p21xEmOenSP3QdYayHPGvPriNMculX/6frCJO94wmzerl4mwpgBVGSJEkjrCBKkiRNsIAIWEGUJEnSCCuIkiRJLQuIDSuIkiRJGmIFUZIkqeU8iA0riJIkSRpiBVGSJKnlPIgNK4iSJEkaYgVRkiRpggVEwAqiJEmSRlhBlCRJallAbFhBlCRJ0hAbiJIkSRpiF7MkSVLLibIbVhAlSZJWQEnuneSUJJe3f95riv3umeS4JJcluTTJjos6tw1ESZKkVmb5v6V0MHBqVW0GnNouj/MR4MSqejCwFXDpok5sA1GSJGnFtAdwVPv+KOCZozskWQvYGfgsQFXdWlV/WtSJbSBKkiS1ktl+Zf8k8wde+y9G3PtW1TUA7Z/3GbPPA4HfAkckOS/JZ5LcY1EntoG4HCTZOMlF7fvtk3y0fb9Lkkd3m06SJPVFVR1eVdsPvA4f3J7kO0kuGvPaY4aXuAuwLfDJqtoGuImpu6KHDtJyVFXzgfnt4i7An4GzOgskSZJWGFW161TbklybZL2quibJesB1Y3a7Criqqv63XT6OGTQQrSCOSPK2JD9pW+xfTHJQktOSbN9uXyfJle37jZN8P8m57WtSdbCtGn4zycbAK4EDkyxI8tgkv0iyarvfWkmunFiWJElahOOBl7TvXwJ8fXSHqvoN8OskD2pXPRG4ZFEntoE4IMl2wPOBbYA9gR0Wcch1wG5VtS2wF/DRqXasqiuBw4APV9XWVfV94DTgH9pdng98par+NibXHeMTzj/xS4v3oSRJ0ozN9hjEpfQ+YLcklwO7tcskWT/JCQP7vQ44JskFwNbAvy3qxHYxD3ss8NWquhkgyfGL2H9V4ONJtgZuAzZfzOt9BngL8DVgP+Dl43ZqxyMcDvDmb/6kFvMakiRpJVRVv6epCI6uvxp46sDyAmD7xTm3DcTJxjXAFnJntXW1gfUHAtfSzCk0D7hlsS5UdWbbTf04YJWqumgJ8kqSpGVkGcxNuFKwi3nYGcCzkqyeZE3g6e36K4Ht2vfPGdh/beCaqrodeDGwyiLOfyOw5si6zwNfBI5YitySJEnLjA3EAVV1LvAlYAHwFeD77aYPAq9KchawzsAhnwBekuRHNN3LNy3iEt+gaYAuSPLYdt0xwL1oGomSJKlDK9gYxOXGLuYRVfUe4D0ASQ5p110GbDmw29vb9ZePrP+ndv2VwMPa96fR3IxCVf10ZH+AnYDjZjKruSRJ0mywgdihJB8DdmdgIKkkSepOj4t6s8oG4jSq6pDlfP7XLc/zS5IkLQnHIEqSJGmIFURJkqQJ9jEDVhAlSZI0wgqiJElSy4myG1YQJUmSNMQKoiRJUqvPk1fPJiuIkiRJGmIFUZIkqWUBsWEFUZIkSUOsIEqSJE2whAhYQZQkSdIIK4iSJEkt50FsWEGUJEnSECuIkiRJLedBbFhBlCRJ0pBUVdcZ1JEk+1fV4V3nGGSmmTHTzPUxl5lmxkwz18dcfcykmbOCOLft33WAMcw0M2aauT7mMtPMmGnm+pirj5k0QzYQJUmSNMQGoiRJkobYQJzb+jg2xEwzY6aZ62MuM82MmWauj7n6mEkz5E0qkiRJGmIFUZIkSUNsIEqSJGmIDUSp55LMS/LornNIkuYOG4hzSJL5SV6T5F5dZ5nQx0x9U1W3Ax/qOseKJMlGSXZt36+eZE0zSctXklW6zqBlxwbi3PJ8YH3g7CTHJnly0vlTJ3uTKcmFSS6Y6tVFpgEnJ3l2D/6+hiT5QJK1kqya5NQkv0vyoo4zvRw4DvhUu+r+wNc6C0Q/MwEkeVoSfw4sQh+/z3vqiiSHJtmi6yBaet7FPAe1PxCeBnwSuB34HPCRqvrDXM6UZKP27WvaP49u/9wbuLmq3j1bWUYluRG4B7AQuAUIUFW1VleZ2lwLqmrrJM8CngkcCHyvqrbqMhPwCOB/q2qbdt2FVfVwM03K9QVgR+ArwBFVdWmHWS4Exv1Amvhe33KWI90ZoEff5z3/Oq1J80v/fjQFqM8Bx1bVDV1l0pK7S9cBNLuSbEnzP+9TaX4oHAPsBHwX2HouZ6qqX7Z5HlNVjxnYdHCSM4HOGohV1dfuyFXbP58KfLGq/tCDIudfq+rWiRxJ7sL4H6izqY+ZqKoXJVkLeAFwRJICjqD5u7xxluM8rf0zwLdovqf6ok/f509b9C7daL9nPg18OsnOwBeBDyc5DviXqrqi04BaLDYQ55Ak5wB/Aj4LHFxVf203/W+Sx0x54BzLBNwjyU5V9YM246NpqnedasdpbgasNrGuqs7oLhEA30hyGfAX4NVJ1qWpcHbp9CT/D1g9yW7Aq4FvmGm8qrohyVeA1YE3AM8C3pzko1X1sVnM8cuJ90n+OrjcA735Ph/5Ot0X2KFd/HFVXddFpoE8qwD/QPML/8Y0Y6ePAR4LnABs3lk4LTa7mOeQJA+sqp+PrNukqn5hpqHrb0fTNbJ2u+pPwEur6twOM/0j8HqasWsLgEcBP6yqJ3SVaULbcL2hqm5Lcg9gzar6TYd55gEvA55EU406CfhMdfiPXR8ztbmeQfPDfFOaIRVHVdV1Se4OXFpVG017guWX69yq2raLa0+lh9/nzwMOBU6j+Z56LPDmqjquw0w/B74HfLaqzhrZ9tGqOqCbZFoSNhDnkHH/6CY5p6q2M9Nkbddbqur6HmS5kKZS8KN2LNSDgXdV1V4d57o78EZgw6raP8lmwIOq6ptd5tLMJPk8TUN1UiU6yROr6tRZzDL478AxNGN/79DxL2i9+z5Pcj6w20TVsK1qfqfj8b939LwMrHtMVZ3ZVSYtObuY54C2MfFQYO0kew5sWouB7koz5UVV9YUkbxxZD0BV/XsXuVq3VNUtSUhyt6q6LMmDOswz4QjgHGBinsargC8Ds/6Dc5rB+wB0MXi/j5lGXDPaOEzy/qp662w2DluDUzn9BvjgRCSar2GX1fLefJ8PmDfSpfx7up+Z5KPAaOX3Y2PWaQVgA3FueBDNwOZ7Ak8fWH8j8PIuAtHPTBPjDPt4Q8hVSe5JMzXKKUn+CFzdaaLGplW1V5IXAFTVXzqcimdi8P7Yu9BnPw7Qz0yDdgPeOrJu9zHrlruqejw0c0TSjNHciaZh+H2a2Q261Kfv8wknJjmJ5kYQgL1oxvnNuiQ70jSe1x35BXstwLkRV1B2Mc8hSXasqh92nWNQHzP1XZLH0YyPPLGqbu04y1nAE4Ezq2rbJJvS3OX5iA4znTlyF/rYdXM5U5JX0TTCNgUG7yxdk+bvsrM5/pL8N3ADTTczNHdY37Oqntdhpt59n7e5ng08hqbKekZVfbWjHI8DdgFeCRw2sOlG4BtVdXkXubR0bCDOAUneUlUfSPIxxnR3dTFwuI+ZJiQ5Cnh9Vf2pXb4X8KGqemmHme49ZvWNVfW3WQ8zoL0j9+3AFsDJND+s9q2q0zrMtAB47chd6J+oqq3NdEeetYF7Ae8FDh7YdGOX86FCM7ZudBzduHWznOlJwNsY/j7fr6q+11WmPkqyUc/uPtdSsIt5bpiY/HZ+pymG9THThC0nGocAVfXHJNt0mAfgXOABwB9pqgX3BK5Jch3w8qo6p4tQVXVKknNp7qoOTcP6d11kGfAy4HNtIwjau9C7iwP0L1NV1ZVJXjO6Icm9O24knpfkUVX1ozbPI4FOb3KoqpPbKbl6833ejt1+P3CfNlNnk+cn+Y+qegPw8TRzaQ6pqmfMdiYtPSuI0oj27sBdquqP7fK9gdOr2ydxHAZ8tapOapefBDwF+G+aJ848cpbzTDvovMs7Tif06S70CX3JlOSbVfW0JL+gqeAPjqerqnpgR9FIcinNGOVftas2pPmF8nY6elJIklOr6omLWjfLma4Anl4dPv1mIMt2VXVO29U8SVWdPtuZtPRsIM4BSb7B9HdSzvpvd33MNCHJPsA/0Tw7F+C5wHuq6uipj1rumeZX1fbj1qV9DNgs55noWlsN2B44n6aRsSXN4+R2ms08baaxd6FP6OIu9D5m6rvc+cjLsWazCzPJasDdaeb224U7G9JrAd+uqofMVpZRXY+r1crPLua5YWK6iD2B+wFfaJdfAFzZRSD6mQmAqvp82530eJofCHtW1SVdZgL+kOStwLHt8l7AH9M8ueD22Q4zcMfpscD+VXVhu/ww4KDZztPq413ofcx0hzRPK1pQVTcleRHNdCT/UVW/WsShy03PxrC9gubpMuvTTHMz0UC8AfjPLgINTAs2P8mXaGY2mHgCFVX1Px1k6vt0TloCVhDnkCRnVNXOi1o31zMN5LgPw4+16+yHZpJ1gHfSTP0R4AfAu4DraSbv7eQZp+Oql11UNBclyV27vuN7VB8yJbkA2Iqm8ns0zSMv96yqsV2Fc1WS19UsPnZwOkmOmGZzdXEzXZ+qvlp2rCDOLetm4NF2STYB1jXTsDSPH/sQTdXgOmAjmjFQD+0qUzsg/nVTbO6kcdi6NMlnaCrABbyIO29A6kSS02jupL6yXd4B+AxNQ8hMwxZWVSXZg2Ys62eTvKTjTL1TVR9rq+NbMPxL4+c7yLLfbF9zUWwArpxsIM4tBwKnpXleJjQPU39Fd3GAfmb6F5q7Fb9TVdskeTxN13dn2jF/4+4O7PpZzPsBr6J5TjTAGXQ/qfF7aSYR/iiwAc3Ez13/UO1jJoAbk/wTTcN+53bIwqodZ+qdJO+kGYO4Bc1k1LvTVPFnvYE4kOn+NE8peQzNvw0/oLm7+qoOMz2qzfQQ4K40k2Tf1MWd1Vp6djHPMUnuBjy4Xbysqv463f6zoW+ZBm7+OB/YpqpuT/LjLifFTTL4bOrVgGfTVH/e0lGkXkuyC3AK8Duav8PfdBqI3ma6H/BC4Oyq+n6SDWnu4O+s4dNH7Ri7rYDzqmqrJPeleYb10xdx6PLMdArwX9z5dJ4XAXtX1W4dZpoPPJ/mMYTbA/sAf19Vb+sqk5acFcQ5IMkTquq7GX7mMcCmSboa1Ny7TAP+lGQNmmrYMe1cgws7zMOYeQ7PTNLZ1BFJ/ruqnjfV4PQuB6Un+WfgecDONGPrTkvypqr6lpmGtY3Ufx9Y/hUdVsV67C/tL4oL26mKrgM6mwqotW5VDY5HPDLJG7oKM6GqrkiySlXdBhyR5ik0WgHZQJwbHgd8l+FnHk8ooIvGWB8zTdgD+AtN9/feNI+1e3eHeUafpDIP2I7m7u+uTHQpP23avbqxDvCIqvoL8MMkJ9KM9+uyMdbHTL2abLnn5qd5Fvqnae5m/jPw404Twe/aO88nnsX8AuD3HeYBuDnJXYEFST4AXMOdd/JrBWMXs7QCGJnQeCHwC+Dd1T66raNMqwAnVdWuXWWYStsFuEO7+OOquq7LPNDbTL2ZbHlFkWRjYK2quqDjHBsCHwd2pPm34SyaMYid3TDS3s18Hc041gNpfrn+RFezLGjp2ECcQ5L8G/CBGn7G8Juq6u1m0pJIcjzw4q6fDDIoyXNp5tk8jaZB/VjgzVV13HTHzbVMbS4nW56hJBvQzGhwR89bVZ3RXSJp+bKBOIckOa+qthlZd25VTfvYtLmWqY/aBsaJVXVjkrfTTGj8r9XxI+2S/DfNHd+nADdNrK+qAzrMdD6w20SFLsm6NHekdznNTe8ytTk+QjNU4Wt0PNlynyV5P83k9JcAt7Wrq+MnPh1FUzH8U7t8L+BDXcyDOJBpoqdjSHX46EYtOccgzi2rJLnbxF3CSVYH7mamYUnuQTsovV2eB6xWVTd3GOufq+rLSXYCnkxTjfokMKvPYB7jW3Q8jm6MeSPdt7+nGbfZpT5mguaRcTcDTxpY1/UY4D56JvCgrmdYGLHlROMQoKr+mGSbafafDYOPA12N5jGl955iX/WcDcS55QvAqe1M/AW8FDiq20i9zHQqsCvNQHRonsV6MvDozhLdWbX4B+CTVfX1JId0mAeAqjqqHZS+ebvqJ1X1ty4z0cw3eBJ3Dt7fi2buui71MVMvJ13uqZ/TjKvrUwNxXpJ7VdUf4Y4b2Tr9mV5VozfJ/EeSHwDv6CKPlo5dzHNMkt2BJ9KMgzq5qk7qOFLvMo17VNy4dbMpyTeB/6NpuG5Hc5f1j3vQRbkLTYP+Spq/vwcAL+l6bFaSZ9NMIBzgjKr6apd5oLeZNqepRN+3qh6WZEvgGVX1rx1H64UkH6P5xXUDmnkQT2W4K77LoRT7AP8ETIxjfS7wnqo6euqjlnumwaFB82gqiq/q+t8pLRkbiNKIJGcCr5sY39dOUv3xqtqxw0x3B54CXFhVlydZD3h4VZ3cVaY21znAC6vqJ+3y5sAXq2q76Y9UH7Rzab4Z+NTEWOAkF1XVw7pN1g9ZxGMHq6rT3o4kWwBPoPml49SquqTjPINPfFpI84vjB6vqp52F0hKzgTiH9PExSD3NtANwLHB1u2o9YK8xk1XPeUkuGJ0Ue9y6Wcryg6raKcmNDA+U72xuvz5mGpTk7KraYfBmsa6r5X3VDqV4MM3f40+q6taOckw7pq+q/jBbWUYleRN3TscFIzesVNW/TzpIveUYxLnl44x5DFKniXqYqarOTvJg4EE0/9Bd1oNxdX01P8lnufNxX3vTTCQ866pqp/bPNbu4/jh9zDTid0k2pf1BnuQ5NJMba0CSpwKfAn5G82/CJkleUVXf7iDOOYxvhKV93+Udw9vRzPX59TbP02meSPXrDjNpCVlBnENy5zOG76jwJDmrqjq7+aJPmTL14/8Ap/4YJ81ztF8D7EQ7to5mYtxZH8zfx8pKHzMNSvJA4HCaG7D+SDMB+95dTrbcR0kuA542MeFz26j+VlU9ePojl3uuewOb0dwxDEBVdfkIzpOBZ1fVje3ymsCXq+opXWXSkrOCOLf08TFIfcrU58f/9VLbEPx3Bp7n26HBysqGNA2eAPcEfgVsYqZGkjcOLJ4AfI/mpoKbgGfTj7/PPrlu5GkgP6d5YkhnkvwjzSMv7w8soJmP9CyaG/66siEw2PV+K7BxN1G0tGwgzi0vpvkh8FqaxyA9gOaHQZd6k6mq3tnOefjtqvrvLjKMGjN27Y5NdDiGLcmFjM8FQBdjEKtqE4AkhwHHV9UJ7fLuNHd/z7o+ZmpNdHk/iOEuwRfTVIE17OIkJwD/TfN9/1zg7Ineho56F15P83f3o6p6fDss5l0d5Bh0NPDjJF+l+To9i+6nLdMSsotZGpHkjKrauescfdY+c3VKXXZRJjln9C7qiaEMZhpml+DMtPO0TqW6eHrJwA1GC4BHVtVf+3CDUTvVzWPbxTOq6rwu82jJWUGUJjslyUHAlxh+fFyn48UAktyH4fFGv+oiR8/HqP2ufRzhF2iqGC+ieXJJl/qYCewSnJGeTih+VZJ70jwm8ZQkf+TOmRc6004P1ukjQLVsWEGURrTPEx1VXT5PNMkzgA8B69OMfdoIuLSqHtpVpjbXnsD7gfvQdFF2Pn1LO3D/ncDONI2xM4B3dzz9R+8ytbneBjwPGOwS/FJVvbfLXH0xMFH2WF1OlD0oyeOAtWme197J9Dta+dhAVC8kuUdV3bToPZe/JKtV1S2LWjfLmc6nmRD3O1W1TZLHAy+oqv27ytTmugJ4elVd2mUOLTm7BKfW94mypeXJBuIckOQbTP9b8DNmMc6QJI8GPgOsUVUbJtkKeEVVvbrDTOdW1baLWjfLmSamAzof2Kaqbk/y46p6RFeZ2lxnVtVjuswgSVr2HIM4N3yw/XNP4H4046AAXkDzKKQufRh4MnA8QFWdn6STG0SS3I/mmaurJ9mGOyeiXQu4exeZBvwpyRo0XZPHJLmO5lFWnRiYK3J+ki/RjIMafEatUwJppTHyCLk7VNUTOogjzQobiHPAxMSpSf5l5O7cbyTpfEqLqvp1ksFVt3UU5cnAvjTzig3OA3cj8P+6CDRgD+AWmqmA9qYZb/TuDvNMzBVZwM3Akwa2dTZnZJJVgAOq6sNdXH+cPmbSYjto4P1qNFNxdfYLmjQbbCDOLesmeWBV/RwgySbAuh1n+nXbzVzthNkHAJ2MZ2vHEx2V5NlV9ZUuMkxlZHxm5+OeJu7qTHIU8Pqq+lO7fC+am2m6ynVbkj1oKtO90MdMWjxjnsN+ZpLOnlgizQYbiHPLgcBpSX7eLm8MvKK7OAC8EvgITdfuVcDJNI9um3VJXlRVXwA2HnnSBNDtg+ZHJsy+K7AqcFOXdwu3tpxoHAJU1R/b7vkunZnk40yepqjLqTf6mEkzNPLIxHk0z42/X0dxpFlhA3EOqaoTk2wGTDw/9LIunpk7kul3NF2mfTDxiL81Ok0xRlWtObic5JlApzeotOYluVdV/RHu+EHa9b8rE8/xHuyCL5q7wLvSx0yaucFHJv6NZuz2y7oMJC1v3sU8hyS5O/BGYKOqennbWHxQVX2zgywrxPxifZbkR1X1qI4z7AP8E3Aczd/n84D3VNXRXeaSlqUkz6OZY/CGJP8MbAv8ixVgrcy6/k1fs+sImt+Ed2yXrwK+DMx6AxGY38E1Z2SqcXVdPE5rINOeA4sTXVyd/3ZXVZ9PMp+mEhZgz6q6pMtMSd4xbn1VdXZTTx8zabG8var+O8lOwG4042w/CTyy21jS8mMDcW7ZtKr2SvICgKr6S0ZuH54tPZ9gto/j6p4+8H4hTRfXHt1EGdY2CDttFI4YvKFnNeBpdHTj04A+ZtLMTcys8A/AYVX19SSHdJhHWu5sIM4ttyZZnbbylGRTBuaum01J/qOq3jDVJN5dTt5ND8fV9fRZsL1UVUN3USf5IO08m13pYyYtlv9L8ilgV+D9Se5GU8mXVlo2EOeWQ4ATgQckOQZ4DNBVw2NijNoHp92rGx8CzkoyNK6uiyCO1Vwm7g509hztKfQxk6b2POApwAer6k9J1gPe3HEmabmygTiHVNXJSc4BHkUzXuz17V3EXWSZmFds66r6yOC2JK8HOptjrGfj6ibGaj4G2IJmmhSA59KMJ9WIJBdyZ6N6FZq5Pjsd69fHTJq5qrqZgcnfq+oa4JruEknLn3cxzyFJjgZeW1XXt8sbAZ+rqid2mGncc4/Pq6qux/z1SvuorydV1d/a5VWBk6vq8d0m648km1TVL9rv6wkLgWurqpOnXvQxkyTNhBXEueUHwP+2k0BvQNNF8qYugrQ3yrwQ2CTJ4FisNYHfd5Gp59an+dr8oV1eo12nOx0HbEfHv/SM6GMmSVokG4hzSFV9KsnFwPeA3wHbVNVvOopzFk0XzToMP5rtRuCCThL12/uA89pKIsDjaMaU6k7zkrwT2LxHT8LpYyZJWiQbiHNIkhcD/wzsA2wJnJBkv6o6f7azVNUvgV9y55yMmkZVHZHk29w579rBHTbu++r5wDNp/l1bc/pdZ00fM0nSIjkGcQ5J8jVg/6q6rl1+BHB4VW3dYaZHAR8DHkLzjOFV6MczhnshyYOr6rIk247b7pMcJkuye1V9u+scg/qYSZKmYwNxjkty16q6tcPrz6epsnyZ5ukg+wB/X1Vv6ypTnyQ5vKr2H+haHlRV5bN8W+O6cAd10Z3bx0ySNBN2Mc8BSd5SVR+YZk69TufSq6orkqxSVbcBRyQ5q8s8fVJV+7d/erfyovWxC7ePmSRpkWwgzg0Tc/j18fnHNye5K7AgyQdobly5R8eZeifJc4ETq+rGJG8HtgX+parO6zhab1TVu7rOMKqPmSRpJuxingOSHF1VL07y+tFJqbvWzg93HbAqcCCwNvCJqrqi02A9k+SCqtoyyU7Ae2meQPP/quqRizh0zklyBOMf3/jSDuIA/cwkSdOxgjg3bNc2xF6a5PM0Twe5Q1X9Yfxhy197NzPAXwCrLVO7rf3zH4BPVtXXkxzSYZ4+++bA+9WAZwFXd5RlQh8zSdKUrCDOAUkOAF5F8+zX/2O4gVhVNevPhB159NgkVbXlLMbpvSTfpPm725Vm4uW/AD+uqq06DbYCSDIP+E6fbujpYyZJGmQDcQ5J8smqelXXOeCOruUpDVQWBSS5O/AU4MKqujzJesDDq+rkjqP1XpIHAd+qqr/vOsuEPmaSpEF2Mc8hfWkcwnADMMl9gR3axR9PzNOoO1XVzUmuA3YCLqd5nu/l3abqpyQ3Mlyd/g3w1o7iAP3MJEnTsYKoTiV5HnAocBpN1/djgTdX1XFd5uqb9nFt2wMPqqrNk6wPfLmqHtNxNEnSSmhe1wE0570N2KGqXlJV+wCPoHkcoIY9C3gGcBNAVV2Nc+yNleQxSe7Rvn9Rkn9f1JCGuZhJkqZjA1FdmzfSpfx7/L4c59Zqyv0FMNHY0FifpJlfcyvgLTTP/P58t5F6mUmSpuQPYnXtxCQnJdk3yb7At4ATOs7UR/+d5FPAPZO8HPgO8OmOM/XVwrYxvQfwkXbuz66rrX3MJElTcgyiOpdkT5qbLwKcUVVf7ThSryQJcH/gwcCTaL5OJ1XVKZ0G66kkpwMnAvsBOwO/BRZU1cPNJEkzYwNRnUpyIM3NFld1naXPkpxTVdt1nWNFkOR+wAuBs6vq+0k2BHapqs66dPuYSZKmYwNRnWrvzn0e8AfgWOC4qrq221T9k+Q/gSOr6uyus0iSVn42ENULSbYE9gKeDVxVVbt2HKlXklwCbE5zc8NNNN3M5RNnJEnLgxNlqy+uo5k8+PfAfTrO0ke7dx1AkjR3WEFUp5K8iqZyuC5wHPClqrqk21Ra0SVZHdiwqn7SdRZJWhFZQVTXNgLeUFULug6ilUOSpwMfBO4KbJJka+DdVfWMDrJcyPAj9u7YhEMEJPWYFURJK5Uk5wBPAE6rqm3adRd00Rhb1NNSBp9JLkl9YgVR0spmYVVd30wf2S0bgJJWVD5JRdLK5qIkLwRWSbJZko8BZ3UZKMmjkpyd5M9Jbk1yW5IbuswkSdOxgShpZfM64KHAX4H/Aq4H3tBlIODjwAuAy4HVgX8EPtZpIkmahmMQJa2Uktyjqm7qOgdAkvlVtf3gWMgkZ1XVo7vOJknjWEGUtFJJ8uh2YvFL2+Wtknyi41g3J7krsCDJB9pHTN6j40ySNCUbiJJWNh8Gnkwz6TpVdT6wc6eJ4MU0/96+luZJOA8A9uw0kSRNwwaipJVOVf16ZNVtnQS50zOr6paquqGq3lVVbwSe1nEmSZqSDURJK5tfJ3k0UEnumuQg2u7mDr1kzLp9ZzuEJM2U8yBKWtm8EvgIsAFwFXAy8JougiR5AfBCmie6HD+waS3aLnBJ6iMbiJJWGklWAf6jqvbuOkvrLOAaYB3gQwPrbwQu6CSRJM2ADURJK42qui3JuknuWlW39iDPL4FfAjsmuS+wQ7vp0qpa2F0ySZqeDURJK5srgTPbLt075kGsqn/vKlCS5wIfBE4DAnwsyZur6riuMknSdGwgSlrZXN2+5gFrdpxlwtuBHarqOoAk6wLfAWwgSuolG4iSVgpJjq6qFwN/qqqPdJ1nxLyJxmHr9ziLhKQes4EoaWWxXZKNgJcm+TxNV+4dquoP3cQC4MQkJwFfbJf3Ar7dYR5JmpbPYpa0UkhyAPAq4IHA/zHcQKyqemAnwVpJ9gR2osl1RlV9tcs8kjQdG4iSVipJPllVr+o6x6Ak76+qty5qnST1hQ1ESVrOkpxbVduOrLugqrbsKpMkTccxiJK0nCR5FfBq4IFJBifGXhM4s5tUkrRoVhAlaTlJsjZwL+C9wMEDm27s+KYZSZqWDURJkiQNcR4uSZIkDbGBKEmSpCE2ECVJkjTEBqIkSZKG/H+mRvDyiGcw1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(correlation,cmap='Blues',fmt='.1f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
      "0               7.4             0.700         0.00             1.9      0.076   \n",
      "1               7.8             0.880         0.00             2.6      0.098   \n",
      "2               7.8             0.760         0.04             2.3      0.092   \n",
      "3              11.2             0.280         0.56             1.9      0.075   \n",
      "4               7.4             0.700         0.00             1.9      0.076   \n",
      "...             ...               ...          ...             ...        ...   \n",
      "1594            6.2             0.600         0.08             2.0      0.090   \n",
      "1595            5.9             0.550         0.10             2.2      0.062   \n",
      "1596            6.3             0.510         0.13             2.3      0.076   \n",
      "1597            5.9             0.645         0.12             2.0      0.075   \n",
      "1598            6.0             0.310         0.47             3.6      0.067   \n",
      "\n",
      "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
      "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
      "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
      "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
      "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
      "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
      "...                   ...                   ...      ...   ...        ...   \n",
      "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
      "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
      "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
      "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
      "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
      "\n",
      "      alcohol  \n",
      "0         9.4  \n",
      "1         9.8  \n",
      "2         9.8  \n",
      "3         9.8  \n",
      "4         9.4  \n",
      "...       ...  \n",
      "1594     10.5  \n",
      "1595     11.2  \n",
      "1596     11.0  \n",
      "1597     10.2  \n",
      "1598     11.0  \n",
      "\n",
      "[1599 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "#Data Preprocessing\n",
    "#Separate the data and labels\n",
    "X=data.drop('quality',axis=1)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       0\n",
      "1       0\n",
      "2       0\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "1594    0\n",
      "1595    0\n",
      "1596    0\n",
      "1597    0\n",
      "1598    0\n",
      "Name: quality, Length: 1599, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Label Binarization\n",
    "Y=data['quality'].apply(lambda y_value:1 if y_value>=7 else 0)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train and Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test,Y_train, Y_test=train_test_split(X,Y,test_size=.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1599,) (1279,) (320,)\n"
     ]
    }
   ],
   "source": [
    "print(Y.shape,Y_train.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1599, 11) (1279, 11) (320, 11)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Accuracy Score\n",
    "#accuracy on test data\n",
    "X_test_prediction=model.predict(X_test)\n",
    "test_data_accuracy=accuracy_score(X_test_prediction,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.934375\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy:',test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "The quality is good\n"
     ]
    }
   ],
   "source": [
    "#Building a Predictive System\n",
    "\n",
    "input_data=(7.3,0.65,0.0,1.2,0.065,15.0,21.0,0.9946,3.39,0.47,10.0,)\n",
    "#changing the input data to a numpy array\n",
    "input_data_as_numpy_array=np.asarray(input_data)\n",
    "#reshape thedataas we are predicting the label for only one instance\n",
    "input_data_reshaped=input_data_as_numpy_array.reshape(1,-1)\n",
    "prediction=model.predict(input_data_reshaped)\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0]==1):\n",
    "    print('The quality is good')\n",
    "else:\n",
    "    print('Bad quality wine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Red_Wine_Quality_Prediction_Class.pk1']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Saving Model \n",
    "import joblib\n",
    "joblib.dump(model,'Red_Wine_Quality_Prediction_Class.pk1')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LOAD and RUN \n",
    "RED_WINE =joblib.load('Red_Wine_Quality_Prediction_Class.pk1')\n",
    "RED_WINE_Results = RED_WINE.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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