Predicting Red Wine Quality with Regression Machine Learning Algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Predicting Red Wine Quality with Regression Machine Learning Algorithms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table of Contents\n",
    "\n",
    "1. Introduction:\n",
    "    Scenario\n",
    "    Goal\n",
    "    Features & Predictor\n",
    "\n",
    "2. Data Wrangling:\n",
    "    Missing Values,\n",
    "    Detecting/Handling Outliers with a Z-Score\n",
    "    \n",
    "3. Exploratory Data Analysis:\n",
    "    Correlations\n",
    "    Pairplots\n",
    "    Filtering data by Tasty & Non-Tasty \n",
    "    Kernel Density Estimation (KDE)\n",
    "    Regression Joint Plot\n",
    "    Comparing Tasty & Non-Tasty Red Wine¶\n",
    "    \n",
    "\n",
    "4. Machine Learning + Predictive Analytics:\n",
    "    Prepare Data for Modeling\n",
    "    Modeling/Training\n",
    "    Predictions\n",
    "    K-Fold Cross Validation\n",
    "  \n",
    "5. Conclusions\n",
    "\n",
    "*** Note include link to kaggle data\n",
    "https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009?select=winequality-red.csv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scenario:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You walk into a Pub in northern Paris, France. Where you order vodka and mention to the person sitting next to you that your a Data Scientist. The owner of the Pub, who happens to be a vintner lets you know that he is hiring a Data Scientist. He says he is loosing customers & needs help coming up with a new formula for his red wine collection. The vintner hands you this data to peform Data Analysis and predict the perfect ratio of ingredients to maximize his profit. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goal:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Predict the perfect ratio of ingredients for red wine. This is a numerical discrete outcome. \n",
    "\n",
    "- Explore with various Regression Models & see which yields  greatest accuracy. \n",
    "- Examine trends & correlations within our data\n",
    "- Determine which features are important in determing the quality of red wine\n",
    "\n",
    "Note: Due to the fact that we are predicting a numerical discreet value, we will be training various Regression Models "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features & Predictor:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our Predictor (Y, Wine Quality) is determined by 11 Features (X):\n",
    "\n",
    "    1.Fixed acidity - most acids involved with wine or fixed or nonvolatile (do not evaporate readily)\n",
    "    2.Volatile acidity - amount of acetic acid ( which at too high of levels can lead to an unpleasant, vinegar taste)\n",
    "    3.Citric acid - found in small quantities, citric acid can add 'freshness' and flavor to wines\n",
    "    4.Residual sugar - amount of sugar remaining after fermentation stops\n",
    "    5.Chlorides - amount of salt \n",
    "    6.Free sulfur dioxide - free form SO2 exists equilibrium between molecular SO2 (dissolved gas) and bisulfite ion\n",
    "    7.Total sulfur dioxide - amount of free and bound forms of S02\n",
    "    8.Density - density of water is close to that of water depending on the percent alcohol and sugar content\n",
    "    9.pH - describes how acidic or basic a wine is on a scale from 0(very acidic) to 14(very basic); most wines are between 3-4\n",
    "    10.Sulfates - additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial \n",
    "    11.Alcohol - percent alcohol content "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Our data has only 1 type of data:\n",
    "        Continuous (#): which is quantitative data that can be measured\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>7.4</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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filePath = '/Users/jarar_zaidi/Downloads/wineQuality.csv'\n",
    "\n",
    "data = pd.read_csv(filePath)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Rows, columns): (1599, 12)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
       "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"(Rows, columns): \" + str(data.shape))\n",
    "data.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity            96\n",
       "volatile acidity        143\n",
       "citric acid              80\n",
       "residual sugar           91\n",
       "chlorides               153\n",
       "free sulfur dioxide      60\n",
       "total sulfur dioxide    144\n",
       "density                 436\n",
       "pH                       89\n",
       "sulphates                96\n",
       "alcohol                  65\n",
       "quality                   6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique(axis=0)# returns the number of unique values for each variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summarizes the count, mean, standard deviation, min, and max for numeric variables.\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean quality was 5.6, with its Max (best quality score) being 8.0 & its Min (worst quality score) being 3.0. Now lets see if we have any missing values we need to take care of. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "# Display the Missing Values\n",
    "\n",
    "print(data.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the Number of Missing Values for each column. We luckily have none."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting/Handling Outliers with Z-Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Z-Score is a measure of position that indicates the number of standard deviations a data value lies from the mean.\n",
    "Any z-score less than -3 or greater than 3, is an outlier.\n",
    "\n",
    "Note: From the empirical rule we see that 99.7% of our data should be within three standard deviations from the mean.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.52835961 0.96187667 1.39147228 ... 0.57920652 0.96024611 0.78782264]\n",
      " [0.29854743 1.96744245 1.39147228 ... 0.1289504  0.58477711 0.78782264]\n",
      " [0.29854743 1.29706527 1.18607043 ... 0.04808883 0.58477711 0.78782264]\n",
      " ...\n",
      " [1.1603431  0.09955388 0.72391627 ... 0.54204194 0.54162988 0.45084835]\n",
      " [1.39015528 0.65462046 0.77526673 ... 0.30598963 0.20930812 0.78782264]\n",
      " [1.33270223 1.21684919 1.02199944 ... 0.01092425 0.54162988 0.45084835]]\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "z = np.abs(stats.zscore(data))\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([  13,   14,   15,   15,   17,   17,   19,   33,   38,   42,   43,\n",
      "         45,   57,   81,   81,   83,   86,   88,   91,   92,   95,  106,\n",
      "        106,  109,  120,  126,  127,  142,  144,  147,  151,  151,  151,\n",
      "        151,  163,  164,  169,  169,  181,  199,  226,  226,  240,  243,\n",
      "        244,  258,  258,  274,  281,  291,  324,  325,  339,  340,  347,\n",
      "        354,  374,  381,  391,  396,  396,  400,  400,  442,  442,  451,\n",
      "        459,  467,  480,  480,  494,  515,  517,  544,  554,  554,  555,\n",
      "        555,  557,  557,  568,  584,  588,  591,  595,  608,  614,  636,\n",
      "        639,  649,  649,  651,  652,  652,  652,  672,  672,  684,  690,\n",
      "        690,  692,  692,  695,  723,  724,  730,  754,  776,  777,  795,\n",
      "        821,  832,  836,  837,  889,  899,  911,  917,  923,  925,  926,\n",
      "        982, 1017, 1018, 1043, 1051, 1051, 1071, 1074, 1079, 1079, 1081,\n",
      "       1081, 1111, 1114, 1131, 1154, 1165, 1175, 1186, 1231, 1235, 1244,\n",
      "       1244, 1244, 1260, 1269, 1269, 1270, 1270, 1288, 1289, 1295, 1296,\n",
      "       1299, 1299, 1300, 1312, 1316, 1319, 1319, 1321, 1358, 1367, 1370,\n",
      "       1370, 1372, 1372, 1374, 1374, 1434, 1434, 1434, 1435, 1435, 1435,\n",
      "       1469, 1474, 1474, 1474, 1476, 1476, 1476, 1478, 1493, 1496, 1505,\n",
      "       1558, 1558, 1570, 1574, 1589]), array([ 9,  5,  5,  6,  4,  9,  4,  3,  1,  4,  9,  8,  5,  4,  9,  4,  9,\n",
      "        9,  9,  9,  8,  4,  9,  6,  1,  1,  1, 10, 10,  4,  2,  4,  8,  9,\n",
      "        3,  3,  4,  9,  4,  1,  4,  9,  4,  0,  0,  4,  9,  3,  4,  4,  3,\n",
      "        3,  9,  9,  0,  6,  0,  0,  0,  3,  5,  3,  5,  0,  7,  4, 11, 10,\n",
      "        3,  7,  3,  6, 11,  0,  0,  7,  0,  7,  0,  7,  4,  5, 10,  6,  3,\n",
      "        7,  9,  6,  9,  3,  6,  6,  0,  3, 10,  1,  6,  6,  1, 11,  4,  9,\n",
      "        8,  9,  1,  4,  4,  4,  4,  9, 10, 11,  7,  7,  7, 11,  3,  3,  3,\n",
      "        5,  5,  5,  7,  7,  3,  4,  9,  3,  3,  3,  6,  3,  6,  8,  7,  5,\n",
      "        5,  4,  5,  3,  5,  3,  3,  5,  6,  4,  7, 10,  7, 10,  9,  9,  5,\n",
      "        5,  1, 11,  8,  1,  8,  4,  9,  8,  5,  9,  4,  9,  4,  9,  4, 11,\n",
      "        3,  5,  7,  3,  5,  7, 11,  3,  5,  7,  3,  5,  7, 11,  6,  6, 11,\n",
      "        4,  5,  4,  3,  3]))\n"
     ]
    }
   ],
   "source": [
    "threshold = 3\n",
    "print(np.where(z > 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first array is the list of row numbers & the 2nd array is the corresponding column number of the outlier. For example, the first outlier is in row 13, column 9. Once we calclulated the Z-score, we can remove the outlier to clean our data, by peforming the action below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>7.4</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>1451 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",
       "[1451 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Newdata = data[(z < 3).all(axis=1)]\n",
    "Newdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now succesffully removed 148+ rows which were outliers!\n",
    "\n",
    "Note: Other outlier tools could have been used like IQR Score, Scatter Plots, Box Plots.\n",
    "\n",
    "Lets take a look at our new cleaned up data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</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>count</th>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "      <td>1451.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.310062</td>\n",
       "      <td>0.522950</td>\n",
       "      <td>0.265382</td>\n",
       "      <td>2.387285</td>\n",
       "      <td>0.081425</td>\n",
       "      <td>15.104755</td>\n",
       "      <td>43.735355</td>\n",
       "      <td>0.996710</td>\n",
       "      <td>3.315934</td>\n",
       "      <td>0.642584</td>\n",
       "      <td>10.421089</td>\n",
       "      <td>5.659545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.646458</td>\n",
       "      <td>0.168531</td>\n",
       "      <td>0.190934</td>\n",
       "      <td>0.862078</td>\n",
       "      <td>0.020966</td>\n",
       "      <td>9.309768</td>\n",
       "      <td>29.441284</td>\n",
       "      <td>0.001716</td>\n",
       "      <td>0.141096</td>\n",
       "      <td>0.129801</td>\n",
       "      <td>1.021588</td>\n",
       "      <td>0.781605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.991500</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>4.000000</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>21.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.220000</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>0.250000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.996700</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>0.630000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0.997800</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.500000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>0.226000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>1.002200</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>13.600000</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    1451.000000       1451.000000  1451.000000     1451.000000   \n",
       "mean        8.310062          0.522950     0.265382        2.387285   \n",
       "std         1.646458          0.168531     0.190934        0.862078   \n",
       "min         5.000000          0.120000     0.000000        1.200000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.250000        2.200000   \n",
       "75%         9.200000          0.630000     0.420000        2.600000   \n",
       "max        13.500000          1.040000     0.790000        6.700000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1451.000000          1451.000000           1451.000000  1451.000000   \n",
       "mean      0.081425            15.104755             43.735355     0.996710   \n",
       "std       0.020966             9.309768             29.441284     0.001716   \n",
       "min       0.038000             1.000000              6.000000     0.991500   \n",
       "25%       0.070000             7.000000             21.000000     0.995600   \n",
       "50%       0.079000            13.000000             36.000000     0.996700   \n",
       "75%       0.089000            21.000000             58.000000     0.997800   \n",
       "max       0.226000            47.000000            145.000000     1.002200   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1451.000000  1451.000000  1451.000000  1451.000000  \n",
       "mean      3.315934     0.642584    10.421089     5.659545  \n",
       "std       0.141096     0.129801     1.021588     0.781605  \n",
       "min       2.880000     0.330000     8.500000     4.000000  \n",
       "25%       3.220000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.720000    11.100000     6.000000  \n",
       "max       3.750000     1.160000    13.600000     8.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Newdata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation Matrix aka Heat Maps- let’s you see correlations between all variables.\n",
    "Within seconds, you can see whether something is positively or negatively correlated with our predictor (target)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c3c6b0ad0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate correlation matrix\n",
    "\n",
    "corr = Newdata.corr()\n",
    "plt.subplots(figsize=(15,10))\n",
    "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))\n",
    "sns.heatmap(corr, xticklabels=corr.columns,\n",
    "            yticklabels=corr.columns, \n",
    "            annot=True,\n",
    "            cmap=sns.diverging_palette(220, 20, as_cmap=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there is a strong positive correlation between alcohol & target (our predictor). In fact, this is our most correlated feature in our datast, with a value of 0.5! Note that our alcohol feature was the percent alcohol content in a drink. This makes sense that a higher percent of alcohol content would yield a greater satsification for a customer purchasing red wine!\n",
    "\n",
    "Next, we can see the second strongest positive correlation, 0.39, between sulphates & our quality predictor. It seems that people rate the quality higher when an additive (S02) is contributed to the drink. Sulphates acts as an antimicrobial.\n",
    "\n",
    "Lastly, the strongest negative correlation is the volatile acidity, with a correlation of -0.35! This is as expected because too high acetic acid levels can lead to an unpleasant, vinegar taste!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pairplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pairplots are also a great way to immediately see the correlations between all variables. But you will see me make it with only continuous columns from our data, because with so many features, it can be difficult to see each one. So instead I will make a pairplot with only our continuous features.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we have 11 Features, lets select only significant features that correlate to our predictor, to further examine their correlation on a pairplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f8c3e5163d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subData = data[['volatile acidity','citric acid','sulphates','alcohol']]\n",
    "sns.pairplot(subData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chose to make a smaller pairplot with only the the strongest predictors, to dive deeper into the relationships. Also a great way to see if theirs a positive or negative correlation!\n",
    "Note: This plot supports the correlations stated in the Heat Map.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering data by Tasty & Non-Tasty "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now do a form of Feature Engineering, where we create a new column classifiying if the  the wine quality is tasty or not based on its quality score!\n",
    "\n",
    "This new column will be a binary Categorical Data where 0 or 1 indicates if the wine was considered \"tasty\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jarar_zaidi/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Newdata['tasty'] = [0 if x < 6 else 1 for x in Newdata['quality']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DONT INCLUDE THIS IN ARTICLE!!!!!!!!!!\n",
    "#Newdata.drop(columns=['goodquality']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernel Density Estimation (KDE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Kernel Density Estimation (KDE) estimate the probability density function (PDF) of a continuous random variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(\"quality\", \"volatile acidity\", data=Newdata,\n",
    "           kind=\"kde\", space=0, color=\"red\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Kernel Density Estimation allows us to visualize the distribution of data over a continuous interval. From this plot, we can conclude that lower quality Wine is heavily inclined to higher levels of volatile acidity. This is as we expected because, large quantities of acetic acid yield an unpleasant vinegar taste!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Joint Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Fixed acidity vs. pH')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xh10Foo7gBEAsTJyY/VAjF88JAACA/KmoYDlitB1TdYBGzJolHXmkNHiw7WfNopZCwjkHAACItooKmvuj7QhOgACzZkkTJkgrVkhVVbafMCGcD8/tqZZCwTkHAACIvooKadOm7Zv+Ay1BcAIEmDLFljArL5ecs31pqd1eyLUUCs45AABA9JWX254+J2gLghMgwOLFUllZ/dvKyqQlSwq7lkLBOQcAAIi+igrbE5ygLQhOgACDB9uwvnSbNkmDBhV2LYWCcw4AABB9jDhBNhCcAAGuukrautX+J+u97bdutdsLuZZCwTkHAACIvmRwQoNYtAXBCRBg7Fjpxhulvn2ltWttf+ONdnsh11IoOOcAAADRx4gTZENJ2AUA7dnYsdn/oDxrljUYXbzYpoNcdVXzXiMXtaBxrT3njf2OW/v7BwAAQMsle5ww4gRtwYgTII9Y4jb+Gvsd8/sHAADIr44dpeJiRpygbQhOgDxiidv4a+x3zO8fAAAgv5J/cxGcoC2YqgPk0eLFNtIgHUvcxktjv2Pv+f0DAADkW0UFU3XQNow4AfKIJW7jr7HfMb9/AACA/GPECdqK4ATII5a4jb/Gfsf8/gEAAPKP4ARtRXAC5BFL3MZfY79jfv8AAAD5x1QdtBU9ToA8y8USt7l8bHsxaZI0daq0YYPUpYt0+eXSxIlhV5VZY79jlpUGAADIr2RwUlcXdiWIKkacABHQlmVs47AE7qRJ0uTJ1g+ktNT2kyfb7QAAAEBjune30GTDhrArQVQRnAAR0JZlbOOwBO7UqVJRkVRSYj9DSYldnzo17MoAAADQ3lVW2n7t2nDrQHQRnAARsHixLVubrrnL2Lblse3Fhg1ScXH924qLmasKAACApiWDk3Xrwq0D0UVwAkRAW5axjcMSuF26SLW19W+rrbX5qgAAAEBjGHGCtqI5LBABV11lfUkkGy2yaVPzl7Fty2Pbi8svt54m27bZSJPaWpunevnlYVcGAACA9mT8+O1vq6uTrr46Wl8con1hxAkQAW1ZxjYOS+BOnChdc40FPzU1tr/mmva7qg4AAADaj6IiqV+/aE1VR/vivPdh1xAJo0eP9q+99lrYZQAAAABAe+bCeNGBA0f7X/zitYwjTiRpzBhp9Wpp/vz81oXIyfj+ZcQJAAAAACDWhg2TFi2yaTtASxGcAAAAAABibdgw6/X32WdhV4IoIjgBAAAAAMTasGG2f//9cOtANBGcAAAAAABiLRmcLFoUbh2IJoITAAAAAECs9e4tde8uvfNO2JUgighOAAAAAACx5py0997SK6+EXQmiiOAEAAAAABB7++0nLVhgTWKBliA4AQAAAADE3n77SbW10uuvh10JoobgBAAAAAAQe/vtZ/uXXw63DkQPwQkAAAAAIPZ695YGDiQ4QcsRnAAAAAAACsL++0vz5kneh10JooTgBAAAAABQEL71Lemzz6T33gu7EkQJwQkAAAAAoCAce6ztZ88Otw5EC8EJAAAAAKAgDBgg7b47wQlahuAEiIhJk6TKSqmkxPaTJoVdEQAAABA9Y8ZIc+ZI1dVhV4KoIDgBImDSJGnyZGnTJqm01PaTJxOeAAAAAC01Zoz+P3v3Hh9HXe+P/zXZTZpr2/QCpvQWgYJIsYUWUOklFY8GUQ/eKjRgBUQuhUPUli+XhpCCBylYDqJiufjQkyoIonJxvfZCuZ0WrJabFGhqoS2lhdDm1ja7md8fb+c3m2TnM5vP7OzM7r6ej0cew+7szHw+s9uQ+exn3i8cOiSDJ0Tp4MAJUQ5YsQIoKpLZJoYhy6IieZ6IiIiIiNI3axZQVgY8/njQLaFcwYETohzQ0QFEIv2fi0SAzs5g2kNERERElKtKS4EzzgB+/WsgkQi6NZQLOHBClAOqqgb/Uk8kgMrKYNpDRERERJTLvvpVYPdu3q5D6eHACVEOaGwE+vqAeBwwTVn29cnzREREREQ0NGecIV9C3n9/0C2hXBANugFE5K6pSZYrVsjtOZWVMmhiPU9ERERERMDKlem/9rjjgF/8Apg2TWoIWi66KPPtotzGGSdEjMFsLAAAIABJREFUPojFgHnzgNpaWcZi3vfZ1AS0twO9vbLM1KCJH231g1/tzJX+ExEREVFmzZghkcSvvBJ0SyjsOHBClGGxGLBoEbBrFzBqlCwXLQrnBXmutNWvduZK/4mIiIgo8447DigvBzZsCLolFHYcOCHKsOXLgZISoKJCooMrKuTx8uVBt2ywXGmrX+3Mlf4TERERUeYVF8usk02bZOYJkRMOnBBlWFubjFwnKy8Htm0LpDlKudJWv9qZK/0nIiIiIn/MmiW3wnPWCalw4IQow2prge7u/s91dwOTJwfSHKVcaatf7cyV/hMRERGRPyZOlJ/16yW9kigVDpwQZdjixcChQzLdzzRleeiQPB82udJWv9qZK/0nIiIiIv/MmgXs2MFZx+SMAydEGVZfD9x5J1BTI+k3NTXyuL4+6JYNlitt9aududJ/IiIiIvLPzJlS5279+qBbQmHFgRMiH9TXA6tXA1u3yjLMF+J+tFU34le1nZd2trQA1dVANCrLlhbv+/UjxljVTiIiIiLyR1mZDJ489xzQ0xN0ayiMOHBCRBmlG/HrVzRwSwuwbJnULikpkeWyZd4GJfxoqx/tJCIiIqL0zJkDHDwIPPlk0C2hMDJMVsBJy4wZM8znnnsu6GYQhd68eTKQUFFhP9fVJbfBrF6d+e3cVFfLIEQ0aj8Xj0t6Tnu73j79aKsf7SQiIiIKgBHEQSdNmmFee62367XbbgP27gV27+7/NxkVlJSfX844IaKM0o349SsauKMDiET6PxeJAJ2d+vv0o61+tJOIiIiI0nf66cB77wG//nXQLaGw4cAJEWWUbsSvX9HAVVVAItH/uUQCqKzU36cfbfWjnURERESUvqlTgcMOk5knvDGDknHghIgySjfi169o4MZGoK9PbnsxTVn29cnzuvxoqx/tJCIiIqL0FRXJrJONG4Gnngq6NRQmHDghoozSjfj1Kxq4qQlYulRupentleXSpfK8Lj/a6kc7iYiIiGhoPvpRKf5/221Bt4TCJO+KwxqGUQrgCQDDAEQBPGSa5vUpXvcVAM0ATAD/ME3zHNV+WRyWiIiIiIjIVc4Wh7Vs3w5897vAli3AUUdlZJeUOwqmOOxBAPNM0/wIgGkAPm0YxqnJLzAM42gAVwP4uGmaHwZwZfabSZTbWlokCSYalWW6sbmxmKTS1NbK0mvccFDHzJV9hk0h9JGIiIhy22WXAcXFwP/8T9AtobDIu4ETU1g5FMX//hk4reYbAH5ommb7v7d5J4tNJMp5LS3AsmVSELWkRJbLlrkPnsRiwKJFEuU7apQsFy3y9+LZj2Pmyj7DphD6SERERLmvpgY45xzgvvskZYco7wZOAMAwjIhhGH8H8A6AP5um+X8DXjIFwBTDMJ4yDONZwzA+nf1WEuWuFSukeFY0ChiGLIuK5HmV5ctloKWiQrarqJDHy5f711Y/jpkr+wybQugjERER5YfGRvlycOXKoFtCYZCXAyemaSZM05wGYDyAkw3DOH7AS6IAjgYwF8DZAO4xDGPkwP0YhnGRYRjPGYbx3J49e/xuNlHO6OgAIpH+z0UiQGdn6tdb2tqk6Gmy8nJg27aMNs/3Y+bKPsOmEPpIRERE2ZN8vdbZmdnrtRNOAD75SeAHP5D0RCpseTlwYjFN830AawEMnFHyFoDfmabZa5pmG4BXIQMpA7dfaZrmDNM0Z4wdO9b39hLliqoqIJHo/1wiAVRWqrerrZWR+2Td3cDkyRltnu/HzJV9hk0h9JGIiIiyJ/l6rbIy89dr3/oWsHMn8MADGd815Zi8GzgxDGOsNXvEMIwyAKcD+OeAl/0WQN2/XzMGcuvO1my2kyiXNTYCfX1APA6Ypiz7+uR5lcWLZcS+q0u26+qSx4sX+9dWP46ZK/sMm0LoIxEREeWPT30KOO44iSbOszBaGqJ8jCM+AcDPAEQgA0O/Mk2zxTCMFgDPmab5iGEYBoDbIDNREgBuMk3zftV+GUdM1F9Li9Q06eyUmSaNjUBTk/t2sZjUtNi2TWYaLF4M1Nf721Y/jpkr+wybQugjERFRgcv5OOJkTz4J/O//yt+6xx7bf91FF2X8cBS8lJ/faLZb4TfTNDcDmJ7i+aak/zYBfOvfP0SkYeZMYPp0qVtRWyuPLdbFsbUu1cVxqjHbhgaZChmPS8HZ+fOB1lbvba2vd744T6etQ92nH+3MF4XQRyIiIsofp5wC/Pa3wF/+MnjghApH3t2qQ0T+U8XK6q5raABWrZJBE0CWq1bJ80H0g4iIiIiouBiYOxd44QX5W5EKU97dquMX3qpDZJs3T/7HUVFhP9fVJZn3gN669etlsKQoaTi3r09mnvT2Zr8fq1f7c0wiIiKiPJdXt+oAkih59dXAqaf2/1KPt+rkpZSfX844IaIhU8XK6q6zZpoM5PR8JjAel4iIiIjcVFXJoMmzz8ogChUeDpwQ0ZCpYmV110UdKi45PZ8JjMclIiIionR84hMyC3rduqBbQkHgwAkRDZkqVlZ33fz5su++PvsHsJ/Pdj+IiIiIiCw1NcDxxwNr1/p3GzmFFwdOiGjI6uuBO++U/4G0t8vyzjvtxBSdda2twIIF9gyTaFQeZyJVR6cfRERERETJPvlJuVXn//4v6JZQtnHghChHxGJSzLS2VpbZSH5paQGqq2UQo7paHlvq66WA6tatskwebNBdt2ABMGuW3Coza5Y8trj1X/f8qNoTNg0NUtndMGSZXJwsiM8HERERUSE55hhg/HiJJmbGSmHhwAlRDggiNrelBVi2TGp+lJTIctmy/oMnmaQbY+y2bb5QxTUXQv+JiIiIgmYYMutk1y7gpZeCbg1lE+OI08Q4YgpSELG51dUyWJJcnDUel9SZ9vbMH0834nj16sKIFS4udo5rnjUr//tPREREOSPv4oiTxePANdcA48YBL7/s++Eo+xhHTJSrgojN7egAIpH+z0UiQGenP8fTjTF22zZfqOKaC6H/RERERGEQjQJ1dcArr3DWSSHhwAlRDggiNreqCkgk+j+XSACVlf4cTzfG2G3bfKGKay6E/hMRERGFxaxZMhv4Bz8IuiWULRw4IcoBQcTmNjbKrSDxuBwzHpfHjY3+HE83xtht23yhimsuhP4TERERhUVlJXDyycDPf+7PLewUPhw4IcoBQcTmNjUBS5fKLR+9vbJculSe94NujLHbtvlCFddcCP0nIiIiCpO6OqCnB7j33qBbQtnA4rBpYnFYGopYDFi+XGpP1NbKN//WRaxqXdjaqrtdXR2wbp3MfjAMYM4cYM0a/9ril7C1h4iIiCgH5HVx2GS/+IXUlHvjjcG1ASlnsTgsUTZ4idUNU1t1t6urA9autbPtTVMe19X50xa/hK09RERERBQuV1wB/OtfwKOPBt0S8htnnKSJM04oXV5idbNNN8ZXtV3yoEkyw7DrcmSyLX4JW3uIiIiIckTBzDg5/3zgyCOBD34wvdnVlBM444QoG7zE6mabbntU2zmNxbqN0ebLuSEiIiKiwhCNApddJl8cbt4cdGvITxw4IcowL7G62abbHtV2hsN3DE7Pe22LX8LWHiIiIiIKnwsvBMrKGE2c7zhwQpRhXmJ1w9RW3e3mzEm9jdPzXtvil7C1h4iIiIjCZ9QooKFBkg7ffTfo1pBfOHBClGFeYnXD1Fbd7dasAebOtWeYGIY8drvvM1/ODREREREVlssvBw4cAO65J+iWkF9YHDZNLA5LRERERETkqmCKw150kf3f8+YBr78ObN0qtU8oZ7E4LNFQxWLyS7C2VpZBRtG2tADV1fKLuLpaHod1v6p9qs6p7nZERERERNm2cqX9c+yxwJtvMpo4X3HGSZo446TwxGLAokVASYmkqXR3S42LIG7XaGkBli0DioqASARIJCTad+lSoKkpXPtV7XPmTOdzunGj3na8dYaIiIgoVApmxkmyRAK45hrg1FP5BV+OS/n55cBJmjhwUnjmzQN27QIqKuznurqk1sXq1dltS3W1DBYkT/uLx2UQob09XPtV7XP6dOdzummT3nbZfi+IiIiISKkgB04A4JFHgN//Xm7XYRJjzuKtOkRD0dYmF+3JysuBbduy35aODpmFkSwSATo7w7df1T5V51R3OyIiIiKiMDjtNAlGYJHY/MOBEyIHtbUyAyJZd3cwo8dVVTL9L1kiAVRWhm+/qn2qzqnudkREREREYTBqlNxGfu+9QG9v0K2hTOLACZGDxYuljkZXF2Casjx0SJ7PtsZGqfcRj0tb4nF53NgYvv2q9qk6p7rbERERERGFxTe/Cbz9NovE5hsOnBA5qK+X4qM1NVLvo6YmuGKkTU1SJLW8XEavy8u9F4b1a7+qfarOqe52RERERERhUV8PjB8P3H130C2hTGJx2DSxOCzlslgMWL5caoXU1spMjaAGHYJoix/HVO2zoQF44AGZNRONAvPnA62t3vtBRERElAMKtjgsAFx0EXDddcB//zewfTtwxBFBt4iGiMVhiQqRFau8a5fcd7lrlzwOIiYtiLb4cUzVPhsagFWrZNAEkOWqVfI8EREREeW/r31NbjnnF2f5gzNO0sQZJ5SrwhSrHERb/Dimap/r18tgSVHSsHRfn8w8YZEwIiIiKgAFP+MEAGbNAvbuBV5+WZJ2KGdwxglRIQpTlG8QbfHjmKp9WjNNBnJ6noiIiIjyz8KFwD//CWzYEHRLKBM4cEKU58IU5RtEW/w4pmqf0WjqbZyeJyIiIqL88+UvA2VlwE9/GnRLKBM4cEKU58IU5RtEW/w4pmqf8+fLa/r67B/Afp6IiIiI8t/w4cAXvwjcfz9w8GDQrSGvOHBClOfCFOUbRFv8OKZqn62twIIF9gyTaFQeszgYERERUWFpaAD27QsmlIEyiwMnRJpiMSkSWlsry+RfiC0tQHW1XDRXV8vjMBhqLWjdfjQ0AMXFUgiruLh/okx9vRRl3bpVlpkaNFG1VXVM1fuootpna6sUgjVNWXLQJP+E9d84ERERhccnPgGMHQv84hdBt4S84sAJkQZVHG1LC7BsmdS8KCmR5bJlwV1Y6cbx6vYjiDhe3baGKaqZckfY/o0TERFROEWjwFe+Ajz6KNDREXRryAvGEaeJccSUTBVHu2mTXEglFwONxyV1pb09XG1VxfFWV+v1o7g4+3G8um0NU1Qz5Q7dzxsREVGBYBxxkqeeAk47Dfj5z4Fzzw2mTTQkjCMmyhRVHG1HBxCJ9F8XiQCdnVlrXj+6cby6/Qgijle3rWGKaqbcEbZ/40RERBReH/0oMHEi8MtfBt0S8oIDJ0QaVHG0VVVAItF/XSIBVFZmrXn96Mbx6vYjiDhe3baGKaqZckfY/o0TERFReBUVAWefDfzpT8C77wbdGtLFgRMiDao42sZGuTUlHpd18bg8bmwMX1tVdPsRRByvblvDFNVMuSNs/8aJiIgo3L70JfmS5dFHg24J6eLACZEGVRxtUxOwdKnc8tHbK8ulS+X5sLVVRbcfQcTx6rY1TFHNlDvC9m+ciIiIwu2kk4AJE4CHHw66JaSLAydEmlRxtE1NciHe2yvLoC+odCOAdfuhG8erGw2cbKj1rjdulIK+27fLcuNGe50qVlm3rSecIPuzfk44YWjtzXe5EvMbtn/jREREFF6GAZx1ltyuw5pouYkDJ0QUCl6igXXjYVXbqWKVddt6wgnACy/0f+6FFzh4YmHMLxEREeWrs84CDh4E/vCHoFtCOhhHnCbGERP5y0s0sG48rGq7zk7nWOVZs/TaaijC+firmDG/REREeaKg44idJBJSR+/DHwYuuMD5dQPjjCnrGEdMROHlJRpYNx5WtZ0qVpkxxv5gzC8RERHlq0gE+MhHZLax09+ZFF4cOCGCnYpBwfESDawbD6vaThWrzBhjfzDml4iIiPLZtGlATw/w6qtBt4SGigMnRJBfYP/6l9xuQcHwEg2sGw+r2k4Vq6zb1qlTh/Z8oWHMLxEREeWz444Dhg0D/v73oFtCQ8WBE6J/6+0FduwA3n578Lfe5D8v0cC68bCq7VSxyrpt3bx58CDJ1KnyPDHml4iIiPJbcTFw/PEycMLZ7rmFxWHTxOKw+a2jQ4p9WiIRYOxYYPjw4NpERERERJSDWBxWYcMG4N57ZabyUUcNXs/isIFjcViidCUSMvNkx47wFG+KxSR5prZWlunE9Hrdrx/r/NLSIqks0agskyNsVet026q7nW47vQji/QiTQu8/ERERhcfUqfIlLW/XyS2ccZImzjjJbwNnnCQrKgLGjAFGjsxum5LFYsCiRUBJidy+0N0tNTXSvZVFZ79A5td5aatKSwuwbJm8V5GIDHz19cltHoDzupkz9dqq+37ottPLrSp+fXZyRaH3n4iIKACcceLijjuA3buBG28EjAFnizNOApfy88uBkzRx4CS/qQZOLGVlwOGHywVYts2bJ+2rqLCf6+qS2hqrV/uzXyDz67y0VaW6Wi6Ik5Nw4nE7Mthp3fTpem3VfT9029ne7rxPN359dnJFofefiIgoABw4cfHEE8CqVfIF2fjx/ddx4CRwvFWHyElvr/trrOSd996TxI9samuzL64t5eXAtm3+7dePdX7p6JBZGskiEaCzU71Ot6262+m204sg3o8wKfT+ExERUfhMmyYzTTZtCrollC4OnFDBe/11udfwwQfdq1ubJrB3L7B9O3DwYHbaB0hthu7u/s91dwOTJ/u3Xz/W+aWqanASUiIBVFaq1+m2VXc73XZ6EcT7ESaF3n8iIiIKn+HDgSOPZJ2TXMKBEyp4V18N7NwJXHedxM2++qr7NgcPyuDJ3r3ZmX2yeLHUZejqkuN1dcnjxYv9268f6/zS2CiDXvG4HDMel8eNjep1um3V3U63nV4E8X6ESaH3n4iIiMJp2jTgrbfkeoLCL9Lc3Bx0G3LCypUrmy/iDWd5afZs4M03gZdekloIv/qVXFxNm+Zez6SnR26lKC3tX5si044+GpgyBdi8WdJ+JkwAbrrJe3FL1X79WOeXOXNk+fzzwIEDUs/iqqukqKpqnW5bdbfTbacXQbwfYVLo/SciIgrADUEc9PbbVzbPnp0712sjRki9tVGjZPaJ5aSTgmsTAXD4/LI4bJpYHDa/dXQADz8MNDfbtQ9qamQWyumnp7eP6mpJ3xlYGbsQxWLA8uVSX6K2Vr7dty5UW1qAFSvknFdVyYwKr4MDfrXVD3V1wLp1MvvBMGTAZM2a9LZtaAAeeEBmo0SjwPz5QGur+3a6fcz2ucm19hAREVFKLA6bpmXLgGHDgCVL7Of4XX3gWByWSOWjHwUefRS44gqZabJrF3DZZcDFFwM7drhv394uxWN7evxva5hZ8a+7dskI+q5d8jgWs+N4u7vlHHd3y+OWlvC11Q91dcDatfbtXaYpj+vq3LdtaJDq6/G4PI7H5XFDg3o73T5m+9y4CVt7iIiIiLw68UTgjTe8JShSdnDGSZo44yS/DYwj3r5dLubXr5fHZWXApZcCX/86UFzsvr8RI4CxY4GiAhyaVMW/btrkT+SuH231I6q2qCh1TRzDcC9MXFws5yr5M9XXJ+dSlQql28ewxfiGrT1ERETkiDNO0vTOOxJJ/KUvAZ/8pDzHGSeB44wTonRNnAjcfTdw++0yANLTA9x2G3DWWUA642f79sktP11dvjc1dFTxr35F7urKdlSt0zh1OuPX1kyTdJ+3ZDty2S9haw8RERGRV4cdBkyaBGzYEHRLyA0HTogcGIbUT/jDH4DzzpNv+l97TZJ3rr4aeO899fbxuNzis2vX4JjZfKaKf/UrcldXtqNqnerfpFMXx6n4sFtR4mxHLvslbO0hIiIiyoSZM2W2++7dQbeEVDhwQuSishK49lrgoYeAqVPluYcflkGVBx90v8Wio0O+Fd+/3/emhoIq/tWvyF0/2uoHKzkn3eeTzZ8vy74++yf5eSfZjlz2S9jaQ0RERJQJM2bIl2isChFuHDghStOHPyyJJk1NMnPi/fcldWfBAuDVV9XbJhIShbpjh/utFbmuvh64806pPdHeLss775Tnm5rkPs7ycqnLUV4uj4NK1VG11Q9r1gBz59ozTAxDHqeTqtPaKp81a4ZJNCqP3VJ1dPuY7XPjJmztISIiIsqE6mrgqKOAjRvTu32bgsHisGlicdj81tsrAxvpJuLs2QPcfDPw2GPyOBoFvvY1SfkYWIdhoPXrgZ/9DHjzzXBEqoYp4lXVFrd2ZrsfXtrqxzHzRSH0MVeELTqciIhyBovDDtH69fJl2JIlwPe+F3RrCl7Kzy8HTtLEgZPC0NEhgyLpzgp55hmgudkuUFlTI7NQTj899evXrZOLkZISe9ZFb29w35xbEa9We7q75faHINqjagugbme2++GlrX4cM18GFgqhj7nCig4vKpICzomE3B4W5AwxIiLKGRw4GaIDB2TQ5MQTgaefDro1BY8DJ15w4KRw9PVJ4df29vSmyx06JAk8d90l/w1IdOp11wFHHNH/teedJwMzZWX9tx8/nhGvqrYA6nZmux9e2urHMfMljrcQ+pgrqqvDFR1OREQ5hQMnKbjFDF98scxK37lT/j9MgWEcMVE6ioqAMWMkrSOdtJeSEuCyy+S2ndNOk+dWrwY+8xkZUOnttV/71ltAaWn/7YuLgddfBw4ezFgX0hamiFdVW9zame1+eGmrH8fMF4XQx1wRtuhwIiKifPfNb8rMk5//POiWUCocOCFyUFwMjBsns0aKi91fP2kScM89UhNg7Fipl3LrrcBZZ9lVssePl1+IyQ4ckONs3w7s3ZvdolBhinhVtcWtndnuh5e2+nHMfFEIfcwVYYsOJyIiynfTpwMnnwz85CcsEhtGHDghclFRIRduY8bYaShODAM44wzgD38Azj1XZq+89pqkn1x9NfCVr8itOT098guxp0ceX3CBPH7vPfl2feDFo1/CFPGqaotbO7PdDy9t9eOY+aIQ+pgrwhYdTkREVAi++U3glVeAJ54IuiU0EAdOiNJgGMCoUfKNeFWV++srK6XGyUMPAVOnynMPPyzFFufNk0GY/ftlZkpTEzBnjr1tb6/c0vP224O/8c20MEW8qtri1s5s98NLW/04Zr4ohD7mirBFhxMRERWC+fPlmuPWW4NuCQ0UdX8JEVmiUbmYGzECeOcduxiskw9/GHjgAeD+++UWnvffl/sWTzwR+NGPgGOOcd52/375xv2ww9IbrNFlXeyHwcaNwKZNUl+hvV0eJw+OpNPObE1tVLVH1Q8Vtyhe3f36wa/Y4DB9HgtdUxMHSoiIiLKpogK48kr5/++mTXL7DoUDZ5wQaSgvl5omhx02uIDiQJGI3KoTiwFnninP/e1vUvvkllvUt+UkEpIysmNH+hHJucqKP+3uloK73d3yuKXFfVsrxnbXLhml37VLHsdi/rd7IN1+uPXBy/nJtDCdbyIiIqJ8cvnlwPDhwE03Bd0SSsY44jQxjpicJBLAu+/KbJJ0PPMM0NxsJ4WMGwdcey1w+unq7ay0n5EjvbQ2vLzEn4Ypxla3H259CFM8bJjONxEREYUO44hTcIsjTnbddTJw8uKLMoOdsopxxER+iERk5smkSYOjVFP56EeBRx8FrrhCZg7s3ClxxpdcIjNLnPT1ye1B27cHE13sNy/xp2GKsdXth1sfwhQPG6bzTURERJRvrrxSvqD67neDbglZOHBClCHDhknc8Lhx7vHFJSUyWPLYY8Bpp8lzq1cDn/kMcPfdUozRyYEDwUQX+81L/GmYYmx1++HWhzDFw4bpfBMRERHlmzFj5EvV+++XhE4KHgdOiDKsslIuIEePdo8vnjQJuOceKRw7dqzEE996q9Q/Ud0ZZkUX/+tf2Ysu9puX+NMwxdjq9sOtD2GKhw3T+SYiIiLKR9/+NlBaCvy//xd0SwjgwAmRLwxDBk4mT3afEWAYwBlnAH/4A3DuuVLL5LXXpKDsNdfIAImTQ4ckunj3bv+ji/3mJf40TDG2uv1w60OY4mHDdL6JiIiI8tEHPiB1EB9+GPjLX4JuDbE4bJpYHJa86O5OL74YAF56SS6GX3xRHo8cKd/kf+ELMqjixKq14md0MRERERGRCxaHTWEoxWEtBw5IcdjSUuDvf3cvB0AZweKwREGx4ovHjlUPfgDyy/FXv5LBk8pKSeu59lqgoQHYssV5Oyu6eOfOoUUXx2KSklJbK8tMRMr6sU+/6La1pUWSbqJRWSbHAqvWeTk3qv3qblcI75Uu3fPtl1x6r3QVQh+JiIjSVVoqt/S//DLwox8F3ZrCxhknaeKME8qUeFwKu+7f7/7aPXuAm2+WIrKAXMAtXCiFZVUJPkVFMkgzYoR6/7EYsGiRFKstL5eZMYcOebvtwo99+kW3rS0twLJlcp4jERm06uuTW2cA53UzZ+qfG9UxVbfrqLbz0p5sy/bnSvd8+yWX/l3pKoQ+EhEVCM44SUFnxgkg9eTq64Fnn5UvUQ87LLPtokFSfn45cJImDpxQpvX0yO076UQLP/00cMMNdtzruHGS7/6JT6i3Ky8HDj/ceVrfvHkyS6Wiwn6uq0tqVqxenVY3srJPv+i2tbpaLuqiUfu5eNwezHJaN326/rlRHbO9XW87L+3Jtmx/rnTPt19y6d+VrkLoIxFRgeDASQq6AycA8M9/AlOnAl/7mgRLkK94qw5RmJSVye07hx0m32irfOxjwCOPAJdfLt/G7twJXHqpxJTt2OG8XXe3DLY4Xei1tQ2euVJebg/Q6PBjn37RbWtHx+D3LBIBOjvV67ycG9V+dbcrhPdKl+759ksuvVe6CqGPREREOo49FrjySuDee4G//jXo1hQmDpwQBWzkSEnfcbutZtgwmcb+2GPAaafJc6tXA5/5DHD33ZK0koppyi0/27cPnt1SWzs4zri7W9qjy499+kW3rVVVg1OMEgmpSaNa5+XcqParu10hvFe6dM+3X3LpvdIzc5u/AAAgAElEQVRVCH0kIiLSdcMNwDHHyG37Qcx+LXQcOCEKgUhEbqmZOFGKQKlMmiRT9FaskDomPT3ArbcCZ50FqO4mO3BABk/27pXBFEDSeg4dkunwpinLQ4fkeV1+7NMvum1tbJR6F/G4bBePy+PGRvU6L+dGtV/d7QrhvdKle779kkvvla5C6CMREZGu8nKgtRV4+235MpWyK9Lc3Bx0G3LCypUrmy/ycmMaURqiUZl5Eo3KQIdTCSLDAI4+Gvjyl2Xg5MUXgXfflZz3XbukdkVZWepte3rkdoPSUuBDHwKmTAE2b5ZfwhMmADfd5K0Q49FHZ36fftFt65w5snz+eXmfKiqAq66SoqGqdV7OjWq/utsVwnulS/d8+yWX3itdhdBHIqICcUMQB7399pXNs2eH93rtpJO872PcOLkOuOMO+Tv++OO975MGSfn5ZXHYNLE4LGVbIiGzQ/btc3/tiy8C118vS0Bu/1myRGahqOKPR44Exoxxj0hOJRYDli+XugS1tfKtsNcLnIYG4IEH5Nv9aBSYP19G1v2k6odqXV0dsG6dDG4Zhlxor1kTXD9U/HivKFzC9pkjIqKCxuKwKWTqO/h4XG7b37IFeOEF4IgjMrNf+v8xVccLDpxQUA4elPSdnh716xIJ4P77ge9/3y5gedJJQHOzfIvrpLhYbhNSxRsP5EdsaEMDsGrV4OcXLPDvAlDVD8B53S23AGvXDt7f3LnyP69s90OFEa/5L4h/O0RERAocOEkhkzcvbNkiM8w//nHgD3/Q+xKUHHHgxAsOnFDQ9u+XGSjxuPp1e/YAN98sRWQB+fZ54ULgssvUgyPDh0vNFLeEH8Cf2NDiYulb8i/+vj5pv1PhW69U/QCc161dm/o2KsOQ85ftfqgw4jX/BfFvh4iISIEDJylkuurDXXdJwuaNNwLXXpvZfRc4Dpx4wYETCoO+Pqll8v77zvVPLE8/LdW3rSjPceOA664DPvEJ520iEYlHrqpS77u2Fhg1SgYKLKYpFb63bk2rK4NY+xp48Wft2w+qfpim87q2Nvd9Z7MfKn68VxQuQfzbISIiUuDASRaYJnDffcDGjfIF6dSp2T1+Hpf/TPn55aQeohxSVCSzQiZNcr+15mMfAx55BLj8crlNY+dO4NJL5WfnztTbJBIyO2HnTvXMFj9iQ6PRoT2fCap+qNYZDn8OGEYw/VBhxGv+C9tnjoiIiPxnGMC55wLjx0vi5u7dQbcov3HghCgHlZTIL8maGvXF0bBhUt/iscekiBQA/PWvwBlnAPfe6zyNv7NTZqq8/37q9X7Ehs6fL8u+Pvsn+Xk/qPqhWmclrgw0Z04w/VBhxGv+C9tnjoiIiLKjpERu14lGgR//2L0mIunjwAlRDquqSn0rxkCTJslI9IoVMmOlp0cKnH7hCxK3mkpfnxSlffNNudBOVl8vxUVrauSWj5oa78VGW1ulmKU1EBSN+l/cUtUP1bo1a6QQrHXODUMer1kTTD90+0j5IWyfOSIiIsqe0aPltpndu4Gf/tT+AoUyiwMnRAqxmBTXrK2VZSwWdIsGMwyJFJ48uX8B0FSvO+MM6cO558ptP1u2AOecIwWl2tsHb7NuHfDlLwMf/CAwaxbw+98Pfk2qGgq6523KFKCyUmqtVFb2TwPy8l6ott24Edi0Cdi+XZYbN9rrVq0C1q+Xmibr1/dPLlmzRv7HZJqytKKIAblonTVL3pNZs+SxpaUFqK6Wi9vqanmciT6qqPqokguffxKtrTKDzDRlme6gierzSERERLnhmGPkb/Z//AN4/PGgW5OfWBw2TSwOW3hyNca1q0tmirilabz4InD99bIEgJEjgSVLZBaKYcigSUuL9L+0FDhwQPZ5xx32LUBDjfFVnbeWFmDZMhnQiUSk3kpfH7B0KTBzpv57oXofN250PuaWLXoRr7rH89JHFdV5bWrS60eYP/+UPt3PBhERkQsWhw2AaQI/+xnwzDPA+ecDp5zi7/EKrTgsB07SxIGTwpPLMa6mCbz3nvyo/oknEsD99wPf/77UNQGAGTOA5ma5oNqzBygrs1/f0yO3+kSjEo1cWWmvSyfGV3Xeqqvl4jy5Zks8Lhft06frvxeq93HTJudjdnbqRbzqHs9LH1VU5zXVLKN0+hH2zz+lR/ezQURE5IIDJwHp7QV+8APgtdckaef44/07VqENnPBWHSIHbW2Dk2vKy+143zAzDLnfcfLk/oMbA0UiMoMiFgPOPFOee+454D//E3jpJZltkKy0FNixQ85BNCoDLxbr3Oiet44Oac/A9nV2ensvVNuqjumUKqRKG/JyPL8+b6pjquTy55/So/vZICIionAqLpZisUccAfzkJ/L3HGUGB06IHORDjGtxMTBunCTwDBwESXbYYcBtt0lBqcmTZXCgsxN4/XW5uLIcOCC/iMePl9knvb12XYV0YnxVqqr6D8QA8riy0tt7odpWdUzdiFfd4/n1eVMdUyUfPv+kpvvZICIiovAqKwOuuAIYMUJmn7z9dtAtyg8cOCFykE8xruXlkqwzdmz/W08G+tjHgEceAS6/XAYI4nEpKLp9uwygHDoEXHCB/Bw6JIMn8bjEFh844B7jq9LYKLfCxOOyXTwujxsbvb0Xqm1Vx9SNeNU9nl+fN9UxdftB+UH3s0FEREThNnw48F//JTNJb7+dt+BmQqS5uTnoNuSElStXNl+Uxzdy0WBHHy2pLps3y0jthAnATTflbmFMw5AR6BEj5FvlgwdTvy4aBU4+GfjMZySqeO9euWDu6ABOPx047zxJ2Zk8GXj1VVn/gQ/IL+ePfxw44QTg2GOHft7mzJHl88/LIExFBXDVVVKk0st7odpWdcwvfEFm3Lzyil3b5Oyz3dNKdI/n1+dNdUzdflB+0P1sEBERubghiIPefvvK5tmzeb1mqagAPvQh4IknJG1n5kz1DPShOumkzO0rZFJ+fjnjhEihvl4KYW7dKsswXzSmGx0bichAx8SJUrPEyeTJwG9+I4Vjx46VwYNf/1oGFJ5/3n5dcvHZ7m6pgbFunXP8raqdTU0yIt7bK8tUF3BOxW5V+1W9jzNnSmHW8eNlOXOmvU4Vj6xyyy3A2rVyX+natfI4nT66fd5UfVTFyqr6qBK2zz/jkTMvnX9zRERElJsmTAAuvVQCH+64Y/Bt2JQ+puqkiak6FGZeomP37ZNZIwNrHSTr6JBpfqtW2QMX5eUyoFJRId9WHzokF10vvAD86Ed2xKl1m4uXyF23/un2Xzc6WHVxWVcngyUDzZ0LrFnjvJ2bsMUcZxvjkYmIiHIGU3VC5h//kGKx48fLLPHk1ERdeXwzBuOIveDACYWZ1+jYvj4ZPNm3Tx1f/MILwPXXS+IOIBfphx8OjBwpgydjxwIvvyz/baV1GIbUTqio0I/cdeufbv91o4NV94kWFaU+h4Zh10nREbaY42xjPDIREVHO4MBJCG3eLIMn48YBV17pffCk0AZOeKsOUR7wGh1bVCTJOhMnDt5PsqlTgQcflDopRUUys2HnTvs4O3bIxWxyAVrTlMcdHfrtdNvOj/3qRrU6DTx5HaMOW8xxtuVLP4iIiIiCcMIJwMUXy9/uK1bI3+yUPg6cEOWBTEXHDhsmU/hqaiTKOJVIRIq/jh8vFbutY23dKgMp5eWDZ1b09cnz48YN/iWdTjvd+qfbf93oYBXD4TsWp+fTFbaY42zLl34QERERBWXqVOCSS2QW74oV7l8Iko0DJ0R5INPRsVVVckE6enTqC/4LLpDBkDFjZJaKNciya5e8PpGQH9O0620sXCg/3d3A/v1Da6db/3T7rxsdrGIllaT7fLrCFnOcbfnSDyIiIqIgHX+8FIzl4MnQcOCEKA/U10uRzJoaqb9RU+O9aKZhyMBJba0MpCSbM0cKpFppO9OmAZ/9rAygdHTIc6YpSR2lpfLLedEi2W7pUmDUKKnuffjh6bXTrX+6/Vdt19QkbS0vl36Ul7sXhgWkAOzcufaAk2F4Lwzrpa1+fDaCkC/9ICIiIgrahz8MXHYZsHs3cNtt6vp9JFgcNk0sDkuFrqcHeOcd4OBB59ds2yYxuE89JY/LymTA5GtfS33rj2HIIMqoUd5vZSEiIiKiUGBx2Bzxz39KGmZlpaTtHH54+tuyOCwRUQplZcCkSVJEdmAhUsvkycC99wIXXigDJT09wPLlwH/8B/D88/KadeuA886TlJRzzwUefhj417/ktbGYPF9bK8tYLP32NTTIMQ1Dlg0N3rdTtaelBaiuliSb6mp5bKmrk4K4hiHLurr01nmhaqvuufGjLflC1Ucv/S+Ec0dEREThcOyxwLe/Lbc/L18uf5NTapxxkibOOCGyJRJ2fPFA69bJIEI0KuuTp/6ddhrwxhsyCFNaKrHFhw7Zt7/cdJM8X1EhtVAOHUrvdoyGBmDVqsHPL1gAtLbqbbdggcyWKSmRW1+S27NxI7BsmQx8RCJ2HZelS+WWnLVrB+9z7lxZOq3zcitPLObc1lWr9M6NH23Jl9tqVH0E9PtfCOeOiIgKAmec5Jjdu4H/+R+pIXfJJTKg4qbQZpxw4CRNHDghGuzAAbl958AB+7nzzpP6JWVl8rinB3jrLbkABGSw4QMfAEaOlBkQPT1SKwWQ7crLZdAlEpFf3jU1wOrV6nYUF0tB1OQY5L4+2U9vr952s2ZJ0azkjHurPZs2yUVtNGqvi8el7fv2pY4etm5Fclo3MIloKObNc27r+vV658aPtri9j7lC1UdAv/+FcO6IiKggcOAkB7W3A3fcIX/bX3ABcOKJ6tcX2sBJNNWTRETpKC2VVJ39+2XQI5GQQZIRI+zXlJUBRx4pF4T79slF+86dwPvvywVhaSmwY4cMKIwYYReVTSRk223b3NsRjw/t+XS2a2uT2ivJysulPR0dMisgWSQiVcmdxqJVY9Rex69VbdU9N360JV+o+mia+v0vhHNHRERE4VRdDXznO8APfwisXAmccw4we7a9Po8HStLCGidE5Nnw4VKToboaGD++/wwUQArKHnOMpO9Y36Z3d8ttOzt2yAyUgdv19clAy/jx7sePOgwBOz2fzna1tdLGZN3dUselqkoGdpIlElJYy6nIrWGo13mhaqvuufGjLflC1Ucv/S+Ec0dEREThVVEBXHmlpO6sWgU88oj3L/jyBQdOiCgjiorklptrrpHZDD098ou2p0du07ngArlnsrpaBkqsGRv79sk37TNmyOsGbnfuucCbb9q3+qQyf74s+/rsn+TndbZbvFiO2dUl7enqkseLFwONjfLaeFzWxePyuLFRIpdTmTNHvc4LVVt1z40fbckXqj566X8hnDsiIiIKt5IS4NJLgY9+FHj8cQl+8OP27lzDgRMiyqjPfQ646y7giCPkFp6xY6X4qzVw0NQk6Txjx8prolHg3XdlWuDo0TJ7ZeB2PT1S5fu991KPere2SrFTaxZFNJpe8VPVdvX1UpSzpkbu+aypsYt0NjVJIdjycvkfSXm5PG5qkiKvc+fas0gMwy7+qlrnhaqtuufGj7bkC1UfvfS/EM4dERERhV8kAnzta8BZZ0kowooVclt+IWONEyLKOOsC0jRlUKS93R7wGDjzoq1NUniefhr4xz+krsnll0uR2eJi+3WmKUk+HR2SMV9a2v+Yra3OgwGxmESstbXJ7RCLF9sXowsWSM0Va92CBYO3TzVYM3MmMH26vd3MmfY61UDIkiUyYJLcFktLi/yPqaNDbgdqbLQTh9zWW+c8FdW5Uamrk5Qk05Q2z5lj9011Ti2pzls62+UC1flWrfOy30KXL58dIiKiXGAYwKc/LV9m/vSnwCmnAI89Bhx3XNAtCwZTddLEVB0ifb29UqG7qyv1etOUqYA332yPZk+ZAjQ3AyedlHqb6mpgzBj3+iC60bG661QXcqq2qCKOm5pk0ES1PtPq6pyjk5csye55I2JUMxFRTmGqTp5pa5PBkwMHgIceAk4/PegW+YpxxF5w4ITIu64uGUBxuk+yowO4/XYpRmX9avryl4Fvf1sGSgYqLpbZJ+XlzsfUjY7VXaeKjVW1RRVx3N4u/Vetz7SiIufo5Llzs3veiBjVTESUUzhwkoc+9Sngs58FXn4Z+PGPgW98I+gW+Sbl55c1TogoayoqJCFkzBi5MB+oqkpmUDz4oFTzBuS/6+uBhx8efCHf2yvxx7t320VPB2prGzywYkW8+rFORbVdR4fMJElmRRwD7uszTRWrnO3zRsTPDhERUbAmTQKefBL4j/+QaOLFiwenTOYzDpwQUVYZBjBqlAygDB+e+jVTp8qAydKlEvHb3g5cfTXQ0AC89trg1+/bJxdQqW4F0o2O9SNyVjfiGHBfn2mq6ORsnzcifnaIiIiCN3y4RBQvWgTceivwxS8634qfbzhwQkSBiEYllnjixMGFXgGZTdHQILUNPvMZee6554D//E/gttskaSdZPA7s2CHT+ZMHGHSjY/2InNWNOAbc12eaKjo52+eNiJ8dIiKicIhGgR/8ALjjDuDRR4HZs+Vv8HzHgRMiClRpqQyeHH744FtRAOCww4Dvfx+47z6ZIhiPAytXymBKqvSajg771hdAPzrWj8hZ3YhjwH19pqmik7N93oj42SEiIgqXyy+XgZMtWyRxZ9OmoFvkLw6cEJGWujqpU2IYsqyrS2+7lhYpdBqNyrKlRZ4fMQL45z+B88+XQpDnnSdRuJaPf1x+OV9+uRSF3bEDuPhi4LLLJE74zjuBGTOAD31IYoKvvVaej8ftfaSq27FqFbB+vdRQWL9eHg+Uarv6eilKuXWrLJMv4EpK5LxYPyUl6R2vqUkuCnt7ZTlwUMSKQB4/XpbJEcixmJy32lpZxmKD25yKarslS2SwZPJkO00nnf5bhnreKD/ofhbd8LNDREQULmecATz1lFwLzJolf6vnK6bqpImpOkQ2VVRtqlkgFlWk7syZdtxoaanMGDl4UAYPBt42sm2b7Oupp+RxcbEMkEQi8tPXJz+XXgp85CPAjTfKPgfGmK5alXqgZMEC+dGJPy0pSZ0aVFwMfOUrzsdrbXXeJ6Afq6wbj+zHPnmhm//4/hMREZiqk5cuush53a5dwOc+Bzz/vNxSf+WVzvXycgDjiL3gwAmRTRVV65RuA6gjdadPHxw3un8/MHo08LOfDd6XaQKPPw7cfDOwZ499/OJiaV8iIYMlxx0n6ysqZJ1h2DGm69fL8ZMTfvr6pH2zZunFn6r+JxGNOh/PKaLZohurrBuP7Mc+GRub//j+ExEROHCSl1QDJ4D8jX/uuZKEefHFUgOluDg7bcswxhETUWaoompVVJG6qeJGq6qAt98GRo4cvC/DAM48s/9tAKYp32739sr67m6JKy4tlQGKgwftgZpt2/rfxpMsHvcn/lR1PDfZjkf2Y5+U//j+ExERFabycknFvOoq4K675O/0ffuCblXmcOCEiIZMFVWroorUdYobra2VArGTJgFlZan3WVUlAzDW8RMJGUApLgaOOAI4cMB+fTwuv8QnTOg/8yVZNOpP/KnqeG6yHY/sxz4p//H9JyIiKlxFRTIb/N57Zabpxz4mX6rkAw6cENGQqaJqVVSRum5xo8OGyWDHBz4weNbKwoWyjET6D0IcOCBFVru6JL7YNGV58KBEHX/+8/I6qyaKdZvR/Pn68adOUxKLi2W/Tsdzk+14ZD/2SfmP7z8RERGdfz7wpz9JUMMppwDPPBN0i7zjwAkRDZkqqlZFFambbtzo8OHy7XXy7TuLFkkh2LIyuVirqACOPlrWvfaa1Djp6QHefx8YO9YuOPvd70ohK2sgJhq1C7Xqxp9aM12SFRfL862tsn9rcCf5eG6yHY/sxz4p//H9JyIiIkDCJJ59Vv52r6sD7r8/6BZ5w4ETItKyZo3MmDBNWSYPmqjiSFWRuha3WimRiH37TmmpPLdoEfDcc8DLLwN/+xvw2GMyTXDiRGnfrl0ywPP1r/efGfPcc/btQ/E48OST9rpbbpH0oLY2Wd5yS3p9/N3v5H8QkyfL8ne/s9dNmSK3JkUispwyxV7X0GAXsC0ulsepDDXm1ykCGtCPY1b1v9BjY9N9H1NRvVe5otDffyIiIhLHHCODJyefDJx9tqRr5mo2DVN10sRUHaL06Ebcqta5XXjt2wfs3Tu4fgogt+WsXAn85Cd2cs0nPwlcey1wzjkyhXCgSZNkQMApcnnJEr1+bNzoHMe8ZYs6qlg35lUVAa06piqOWdXHQr9IbmjQj5xWvVdNTf60l4iIyAdM1clDbqk6KgcPAt/4BvC//yvJO/fcI39HhhTjiL3gwAlRenQjblXr0okxTSRk8MSpendbm1yYPv20PLYu+IfKui1Jpx+bNjnHMXd2qqOKdWNeVRHQqmOq4phVfSz0yNniYv3IadV71d7uT3uJiIh8wIGTPORl4ASQmSY33ihfBs2bJ7HFI0Zkpm0ZxjhiIvKfbsSt1xjTSAQ4/HC5NWfYsMHra2uB++4DbrsNGDNGb9AEkF/6uv1QxTG7RRXrnh/dY/r5XuUzL5HTqveKiIiIKJcZhsyi/dnPgCeeAGbPBnbsCLpV6ePACRFllG7EbaZiTEtL5Vabww8ffBFqGJIpH4vJrRM6DEO/H6o4ZreoYt3zo3vMbLxX+chL5LTqvSIiIiLKB+edB/z+9/JF3KmnAi++GHSL0pPGn3JEROlbvFjqXwD9619YcaS664ZqxAi54Hz3Xbl9J/muxOHDZZrgH/8ot/cMVFMj0ccbNgxeN2eOfh+tGifxeP8aFo2Ndr0RK6LYYkUVux3TSWOj3jGtGidD7WOhmz9f/T6qqN4rIiIioiCtXJnZ/f3XfwE/+IEUjr3kEikkm0leby0aiDNOiCijdCNu/YgxTU7fGXhrCQA89ZRdsyPZhAnA9dfLL/Jkp5wC/Pa3+v1QxTG7RRXrnh/dY2b7vcoXXiKnVe8VERERUT6ZMAG46ipg5EjgjjvkC8Ywy7visIZhlAJ4AsAwyIyah0zTvN7htV8C8CCAmaZpKisJsTgsUe7r7AT27EldpHP3buDmm2XqICAXvBdcICPgZWX9X1tWJrcChbgaOBEREVFQWByW0tbVBfzoR8DrrwNf+pKkX2aChxknBVMc9iCAeaZpfgTANACfNgzj1IEvMgyjCsAVAP4vy+0jIh80NEiiiWHIsqFh8GsqK6UOx5gxdurJd74DHHecFKj64x9lVsnEiXK7xE9+AkybJlMHjzlG4tMAoKdHfrFHo6mPp2rLCSfI89bPCSfY62IxqTJeWyvLWKx/+1XrVetaWiSxJRqVZUuLv9t5odtHP44Xpn0SERER5aOKCuDKK4ETTwQeegh4/PGgW5Ra3s04SWYYRjmAJwFcYprm/w1YdzuAvwD4DoDvcMYJUe5qaJC6EgOpbpGIx6XuxMMPD153xhnA3/8O7Nw5eN3JJ8tsk0cfTX08wLktmzcDL7wweN3UqcD3vid1Q0pK+tcNsW6BicWc1wPO66y6KkVF/etmLF0KzJyZ+e283K6j20fdY6qOF6Z9EhER5RjOOKEh6+uTxJ1nn5W/mT7/efmSUVemZ5zk5cCJYRgRAM8DOArAD03TvGrA+ukArjNN84uGYawFB06IclpxsQyEFCXNoevrk5kSqW7LGbhd8i9l07QHCpwUFcn+U21nGM5tUUXS1tUBu3bJqLulq0vqh6xeLTMXnNYDzus2bZKL9+RUl3hcLuqnT8/8dqtXO/fRjW4fdY+pOl6Y9klERJRjOHBCWvr65AvIJ5+UW3a++EX9wZNMD5zkZaqOaZoJANMMwxgJ4DeGYRxvmuaLAGAYRhGAFQAWuu3HMIyLAFwEABMnTvSvwUTkidOAhGqgInm9YfRP3VENmgCDE1PS2c6tLW1twKhR/Z8rLwe2bXNfb5rO6zo6BtdiiUSk3otqn7rbeaHbRz+OF6Z9EhERUWrJ12ujRvF6LdcVFdlF9v/8Z/kCdP78/l9IBta2oBvgJ9M03wewFsCnk56uAnA8gLWGYWwDcCqARwzDmJFi+5Wmac4wTXPG2LFjs9BiItIRdRgCdno+1frk0exIJL3jmmb/AZdIxN524GQ+t7bU1soMj2Td3VKTxW29al1V1eABnURC6r34sZ0Xun3043hh2icRERGllny9VlnJ67V8UFQEfPWrwOmnA2vXygwUpy8tsynvBk4Mwxj775kmMAyjDMDpAP5prTdNc59pmmNM05xsmuZkAM8C+JzbrTpEFF7z58uyr8/+SX4+3e2swY4vfxk4dVBJaXHcccCIEf2fs7Y74wz5sZ4b2JapU1Pvc+pUYPFiqYXR1SXbdnXJ48WL5TWq9ap1jY3Shnhc1sXj8rix0Z/tvNDtox/HC9M+iYiIiAqJYUgQQ3293Lbz858HP3iSdwMnAGoArDEMYzOAjQD+bJrmY4ZhtBiG8bmA20ZEPmhttaf1AbJUFYZ12+6XvwSeeQY47bT+rz/5ZOA3v5F1xx/ff93o0cDFFwO33gp89rP2zJNIBDj7bDnW5s2DB0+mTpXn6+ulgGhNDdDeLsvkgqKq9ap1TU1S0LW8XKY7lpfL46Ymf7bzQrePfhwvTPskIiIiKjSGIQVizzxT/vZ+6KFg25N3AyemaW42TXO6aZonmKZ5vGmaLf9+vsk0zUdSvH4uZ5tQvtONR1XF0YbNggXArFlyS8SsWXbCDaCOAG5tlYEB05Rl8mDLNdcAc+cCEyZITPGFF8rzkYgMJCR791355f797w+uZ9LZKRfRgIyejxwp+xg5Uh5bNm6Uoqzbt8ty48b++3FbDwy+RQgAtmyRNsTjstyyxV53yy0yDbKtTZa33JLePlXrvMTx6vZR95j19VK0detWWaY7wJFO/HWmz02+SOfcERERERmGDJzMmwf89a9S9ySwtuRjqo4fmKpDuUo3HrWlxTmOtqkpe+1Ph6qPV13lHAG8eXP6++zqAnp6gIqCd08AACAASURBVOuuA+65B9iwYWht/OxngaOOAu64w66FknxOAfX5Vr0fqnjgVauc45F37JDBkoHmzpWUH53jAfpxvLp99HJMHar46wULwtPOMNKJDiciopzDVB3KqL4+4O67gb/9Tb7InDnTfRvGEQeEAyeUq3TjUaurneNordkTYaHq45o1ztupfv057XPsWGD9+qG30ZqlcuCAfU6t6GJr9orqfKveD1U88Pr1zvHIiUTqc2AYUsdF53iAfhyvbh+9HFOHKv561qzwtDOMdKPDiYgop3DghDKutxe4/XZJKrziCuCYY9Svz/TASd7dqkNE/bW1Db6tJJ141I6OwekyVhxt2Oj2UWefb72lt79EQi6Si4rsNB7TtM+p2/lWrVf1XxXV7DRwZJr6x/PyXgRxTB2qcxqmdoaRbnQ4ERERFbbiYuDSS+VLzB//WGZOZxMHTojynG48qiqONmyyHStrKL5HUUUZl5b2rwhupdVUVLifb914YFVUs1M/DEP/eF7eiyCOqUN1TsPUzjDSjQ4nIiIiqqiQ2SbDhsnt79mcBc+BE6I8pxuPqoqjDRtVH1URwLr7nDMn9TYnn2zHEafS3S3n0TqnVg2PhQtlBF11vnXjgVVRzU79mDNH/3he4niDOKYO1TkNUzvDSDc6nIiIiAgARo2SmnEHDkjdk4Ffuvkl0tzcnJ0j5biVK1c2X+ThRimioBx9NDBlihRCffttSYi56Sb3YpTWRfXzz8svpooKKbQatsKwgLqPl1wCPPww8M479uvdCsO67XPhQmDdOuBf/5LXGoacr8ceA2bPllsv3njDvhXntNNkNsWePfJ6a9CkrEzuv7zsMmDaNJmC+MILqc+36v1QtfULXwBefx145RW7loQVj5yqH3PnSl0Y3ePpft689NHLMXWozmmY2hlGqnNHRER544YgDnr77SubZ8/m9VohGDECGD1aknYMI3W9k5NO0t59ys8vZ5wQacqlWFHdyNWmJpkC19sry+RBk2z33y0aWRVj+73vSUrM5Mmy/N730tuvap9HHGHflhOJAOPHy/aTJ8sv7/JyqWdSXi6DIg8+KIk8xcV2fZOeHuC99+x9HnMMcOyxwLhxUgzVqWJ4qtokqlhhVVTzkiUyWDJ5siyXLLHXzZwp7Rg/fnB70vlM6cTxqo6p2q/qvfIjVlsVY71qlRTlbWuTZaoUGaf6Mrn0e0WX6tzpKoTzlkv4fhARkd9OPhk45RTg8cflSxm/MVUnTUzVoWS6Eb/5Itv9d4tG1o2x3bjReTvAed2WLc6RqlOmyHaGIdtatyNceqm85oc/tAdOLCedJLM/vvc9aWdpKXDwoGz3wx/KOVX1cc0a51jhJUv04nFV61Tvseqz4bZP3W1130c/Zk/pRhW79b8Qfq/o4nkLF74fRASm6lCW9PQAN94of1cvXSqzuS2MIw4IB04omW7Eb77Idv/dopF1Y2w3bXLeDnBe19npHKlaWWlvZ/16jcdlMASQW1CsC/jkZJthw6Qfo0fb++zpkdknTzwh93M6tWffPudY4blz9eJxVetU77Hqs+G2T91tdd9HPwqK6UYVu/W/EH6v6OJ5Cxe+H0QEDpxQFr3xBnDrrcCMGcAFF9jPM46YKAQKPVY02/33K6pXtZ1qnSpSNXk7w5CfSEQu3q04YmtfJSX2aw8elLoX27fL7QuADLZs2ybP7d/v3B5VrLBuPK7ue+xln7rb6r6PftCNKgb4e0UXz1u48P0gIqJsOvJI4MwzgQ0bgGef9e84HDgh0lDosaLZ7r9fUb2q7VTrVJGqqbbr65Pnq6r6xxFbt/NUVADDh8tzHR1yn+bevTLj5IgjZFCloqL/DJXk9qhihXXjcXXfYy/71N1W9330g25UMcDfK7p43sKF7wcREWVbfT1w1FHAAw/IF5V+4MAJkYZCjxXNdv/dopF1Y2xV26nWqSJVVdt961t2ok5yHPH55wPLl8vtOJGIPLd7N/DWW3KrDSA1UKz9Ji8bG9WxwrrxuLrvsZd96m6r+z76QTeq2K3/5IznLVz4fhARUbYVFQFf/aoM1P/pT/LcypWpf3QxjjhNjCOmZIUeK5rt/rtFI+vG2Kq2U61TRaoOZZ/l5cA3viGFFCdPlrbu2CG35fT2yr6fekpijL/5TamD8uKLMgOlvFwKv15/vTpWWDceV/c99rJP3W1130c/6EYVu/WfnPG8hQvfDyIC44gpACNGAO+8Azz9NPDxj9v1BQdKI6Y45efXYVIxEbmpry/sPwSz3f+mJvXFrmq9qq2q7VTrFiwAdu6U+/lra/tH/FpS1R657z7g/fflv/fvB373O+A735GLekAutEeOlIGU/fuBN9+UaYd//jNw4ol2Io9pSnHTffvkfxRLlsiAidWe5G93rXjceFz2N26cfT5U66yI344OOdbGjem957rHczt3qnVWjLHV/+QYY9X7GIvJbJ/k8+b1c636bLj9u8mH3ystLcCKFfK5qaqS2T1+DVRZ8uG85RO+H0REFITPfQ547jmJKD7nnMzum6k6aWKqDhFZVHGbqmjc++6zZ4UkmzRJqoF/61uSylJaKgMpBw8Cp50GPPaYfb+mYdh1NKyY4xkzgOZmmZEysD2rVjnH4wLuscpDjfFVxfGqjtfaqh9HrFqnG52se9FX6FGsbtHhRERUEJiqQ4H5xS/kS7obbgAOO2zw+jRuImEcsRccOCEiiypuUxWNa800SaWuTmYpDBtmFzPt6QHGjpUZEfPm9U9siURkEKWsDDjuOLmdp6rKft5qjzW7I1U8rtU2t1jlgf1Qxfiq4nhVx+vt1Y8jVq3TjU7WjU0t9ChWt+hwIiIqCBw4ocDs2wdcdx3wkY8AF144eL3uwAmLwxIRDZFuNK7bPisqZOChpEQGP0pLpebJ4YfLAENxsZ2gk0jIBWlXlxSRLS2Vx4cOyWut9qjicdONVR5KP3SPB4QrOllXoUexZjv+mYiIiCjZiBHyRdbGjVLzJFM4cEJENES60bjp7rOoSGae9PZKHDFgz2AoKel/YdrXJ9tZF6amKYMn+/fLLUCqeNyhxiqn0w/d4wHhik7WVehRrNmOfyYiIiIaaM4c+bJxw4bM7ZMDJ0REQ6QbjTtpUur9TZqUep+JhEw1HDHCjiO2bm1JHoRob5dCq7t2yfqeHqmRcu65kvICpI7H1Y1VVlHtU7XO7bxmOzpZV6FHsWY7/pmIiIhooFGjJOVtwwZ14MBQcOCEiGiI6uul2GdNjQxa1NTYxT+bmqQQZnm5zBgpL7cLY27bNnjwZNIked5pn2eeKbfq3HILcMUV9i05ZWVShHT5cmD0aPmfwnvvSRRuaakc77TTpFDnWWfZs1SiUbsYa2ur/Lc1CJO8TtUPFdU+VevczqvuOt33UZcf+8wlup8bIiIiokw65RRg9+7UwQw6WBw2TSwOS0RhsG8fsHdv/9sh9u2T+Nf775cBFMMAvvIV4NvfltkqgDw3erQU7zQCKdlGREREBYLFYSlw3d0y43fOHPm72MLisESUcQ0NdkHS4mJ5bGlpkYvwaFSWLS3ejxeLSTGn2lpZxmKZOZ5q27o6qSliGLKsq/O+T9U6L31saZFaGVOmADNn2lG8I0YAjzxiT0U0TeCBB4BPfxr47W9lAOVDH5KBk+Ji4Ktf9d5W3c+Gajsv51xF1Q/d4/nVFt22+iGItoSp/0RERJS7ysuB44+XIrHW7eFeRJqbm73vpQCsXLmy+aI0hqeI8kVDA7Bqlf2Lpq8PeOEFuRXkxRflFpB4XC5+Dx0C1q2T182Zo3e8WExuPenpAYYPl9scHn1UBgl++Uv947W0OG/b3AysXdv/9du2yfqFC/X2uW6d87rubv0+Djxmb6/ctxmJyMh5V9fgdvb0AH/5C7BlS/9BlZdekvfylVeAG28celtvuEHvs3H33c7bWbVYdM656jOg+lwdfXTm32MvbdFtqx+CaEuY+k9ERJ7cEMRBb799ZfPs2bxeI1skAjz9NHDMMcCYMfLcSSe5bpby88tbddLEW3Wo0BQXy4VhUdK8NKswaWWlXFgnFyiNx2Vkt71d73jz5klxUys9BpDBgJoaYNMm/eNVVztvu29f6oJRhqEemVbtE3BeN326fh9Vx3z/ffU5SCUSkW0PHJD3eihtXb9e77PR2em8XW+vur2q/qs+A6rP1erVescD/GmLblv9EERbwtR/IiLyhLfqUCh0dQHf+pbU+/v0p+U53qpDRBkVjzs/39HRPxIXkMdWJK6Otjb7otRSXi4zQLwcT7Wt07ix23iyap+qdV76mOlznkjI/0yKimTwwupzOm3V/WyotnOj239VP3SP51dbdNvqhyDaEqb+ExERUe6rqADGjs1MgVgOnBBRSsnfpg98vqqqf3FSQB5XVuofr7ZWvsVP1t0tNT28HE+1rVORVLfiqap9qtZ56aPuORh4gZ+srMyeWWOa8pNOW3U/G6rt3Oj2X9UP3eP51RbdtvohiLaEqf9ERESUH6wES684cEJEKc2fL8u+PvvHer6xUR7H43KxHY/L48ZG/eMtXiy1Irq6ZJ9dXfJ48WJvx1Nt61SPwq1uimqfqnVe+qhaP3x46nZWVgJnnOHcj0OHZD/J+0wkgMsvV7dV97Oh2s6N7mdA1Q/d4/nVFt22+iGItoSp/0RERJQfJk8G3nsP2L/f235YHDZNLA5LheYLX5Cina+8YtehOPtsoLXVHlh4/nmpkVFRAVx1FdDUpH+8o4+WIpCbNwNvvw1MmADcdBNQX+/teKptFy6Uop7W9D3DAObOBdas0d+nap2XPqrWX321xBEfPGi30Squ+clPAq++CrzxhlyMFhUB06ZJTZHkW0v6+uS2iG98A7jwQuCDHwQ+8hEp3jqwrbqfDdV2Xt5HFdU51z2eX23RbasfgmhLmPpPRESesDgshUYiATzzjBSIPfxw/eKwaUyQJqJC1drqfFHb1ORtoCSV+nrniyS349XVySCIacoAyJw59gDIzJlS7LStTW4HmDmz/3Z//7vUraiq6h9HHIsBy5fb2y1ebLdvzRq7uOy+ffJ4YPtS1UrZuFEKwXZ0yMDGxo32PrdssWuBdHbK41RS7ffEE/v3/8QT5Tadww4DHnwQeOcdSSux7N8vgy2/+IU98+PAAfkBpODshz4E/O53cl4GmjJFZrR0dMhyyhR7nep8L1gA7Nxpr1uwIHUfU9H9zKk+V6r3WHU81TrVPletsovrvvkmMG5c6raleo9V+/WD6rx5oeqHX8ckIiKiwjRxovxtvH07MHWq/n6YqpMmpuoQhVdd3eBYYUBmjyxZIhGnJSUyo6K7W6b/33mnDFosWyazMCIRGZHu6wOWLpWLfaftbrnF+Xh1dc77BJzXbdkiF9UDLVggg1dWPG6qbdescW5P8uyZ/fuBPXvs+hx33gn88IeDE4ROP13WWbVeKipkAMZK31G1RXXeAOd1QVwsW/G3mWyPap+rVqnfY9W2QLjOnS4/zjkREYUOU3UoVL7zHZlx3dCgn6rDgZM0ceCEKLyKipxjhefO1YsAVsXxrl3rfLwRI/RibN2iejMVq9zXB+zdKzNKZsyQGSZFRTL4kZxuc/LJQHMzcOSR9r5Gj5Z2jBqld96AcMXN+hF/q9qnKsa5t1e9LRCuc6eLkcNERAWBAycUKi0twJgxwKWXMo6YiAqYKlZYNwJYtZ3qeLoxtm5RvZmKVS4qktkjkybZccSGIRfvw4bZF/UbNgCf/7zcztPTI/vau1fqwezfr3fewhY360d7dGOc3bYN27nTlS/9ICIiotwxfLj34rAcOCGinKeKFdaNAFZtpzqeboytW1RvpmOVhw2T/4kkz0gxDBkAKSuTwpy9vcBddwFnnin1UwC5raKiwk6UGdgW1XkLW9ysH+3RjXF22zZs505XvvSDiIiIckdVFQdOiIiUscK6EcCq7VTH042xdYvq9StW2TTtARmrVsmFFwKPPQZcconUNXnrLZnWePnlknaycKHdluSl23kLW9ysH+3RjXF22zZs505XvvSDiIiIcoc148RLlRLGEaeJccRE4aWKFdaNAFZtpzqeboytW1RvtmKVL71UBkyiUeDUU4FPfUratWOHRBr/6lfA7NlSlfyllyQCubxcLnybm9XnLWxxs360R7VPt/c4l86drnzpBxERKTGOmLLmooskYlj18/bbwB//KGmSw4a57jLl55czTogoq1papMBoNCrLlhZ7XSwmxSNra2UZi6W3DpBBgr4+GUnu60s9aKAaZR7qurY2+3mrloqlqUmihnt7ZZkcW6uKHG5tlW1MU5YDo6CtmN/x42WZHPN7xBF23ZFIRB6nI3mfJ54IfOITEpEbjUrSyfz5Uu+ktFRu4enuBv77vyWmeMIE4AMfkNjiiROlr4CdOtTWJstbbrGPV18vRUC3bpXlwAtm3c+Himo7VXtUbUlHqs/NggXArFlya8qsWYPjmN3Oj9N+vfzbCQpr0xMREVE2WEXpe3r098FUnTQxVYfIuyBibFXxp7pxxJdcYs/uSDZpkrrIZUODOo5Wtx9uMbc6+9ywwT43RUVyXhIJ4LjjZKaJ9b+OkSMlSSiRkAGi++4Dnn128LHSmQGj+/lQfQZ0429VbUkeCBvK8QB/Pseq/Xo5ph8YR0xEVBCYqkNZk85NIXfdJX+/79xppxUqMI7YCw6cEHmnitT1K8ZWFX+qG0esGgBQ/UotLlbH0er2wy3mVmef1rmJROw+JRIy82TiROC11+SCF5DXjBolMxo2bEh9rFTRyAPpfj5UnwHd+FtVW6yZNUM9HuDP51i1Xy/H9APjiImICgIHTihr0hk4WbkS+OY3pW5fGrOyU35+HWr8O+zBMO4byuszxDRN84IAjktEGdbRId80J0uOsR01qv+65Phfp3VuVPvVbY8utzhaFVV7dPebzrkxDPkxTRmY6e4G3n8fOPJIGUB45x0ZUNmzR12tPJ0xej/eD93tVG3RPZ5fn2PVfr0c0w9+/LsiIiIiUrFuZx+YUDkUQxo4AbAQQDanqBj/Ph4HTojyQFXV4G/xk2NsB34TnRxTqlqnotpve7tee5LrmQxFNJp6MMMppjbdfrz5pt5+h3JurBkjFRVSD2XPHmD0aKlS/vbbMmhy8KB7P1S8fD50+qjbFi/H8+Nz7LZf3WP6Qff9ICIiItKViYGTbBSHNZJ+iKiA6cb/eokw9SOOeNKk1Mdyet7iFker2w/d/Q713Jgm8O1vA9dcI497emRgYcwY+8fJKaeoZ6QA+u+Hbh9126J7PL8+x7kUYxy29hAREVH+C2rgxBjij5n0M9RtiSiPNDVJcc3ycqm9UV5uF9usr5cCkTU1MtuhpsYuGKla50a1rW57tm0bPEjiVhgWkEKtCxbYsxii0fQKw7r1Q3e/uufmc5+TIltHHCGDIWPHAt/9LvDXv0rhLWPAb+9p04Cf/1xmprz1lnPdFd33Q7ePKqq26B7Pr8+xX8f0Q9jaQ0RERPnP+tuzuFh/H0O9VefrQ3z9GADL//3fpsb2RJSDYjFg+XK5paW2Vr5Nti6MmprcLz51YoNVx1Rta8XxWtslR/xaF56pnH8+sGKF1MKoqpLH6ZgyRW736OiQ5ZQp9rrJk/un9TgNxqTqR2tregMwA6n6qIpO3rgReOEFGTj5/9g78zgpqrPf/2o2mIFhk01ZJwLGBSMqiSYKjDEqiWhCrqJhYlCMuUbfN6ABtysZxiUmxiUJal4S8xoDSTQG9bqM3hgBTWKSkWBA44YMmwubwAyz93TdPx7q7Z6ZrlM9p/pUVXf/vp8Pn6a7+izPOadqup56zvNraABefx2YPh1YsACYNQtYuhT4+98T9Tz0kKgKNTeLTYcdJglYuztZVDaq+qqirk6S3TY2ys16XV2iHt11o9uebp1eZVVjoztuplD1J535yCQ1NV3P44ULva9PhBBCCMkumprkNXmrcG8xqqpjWdY4AE42ANu27UJjjRmGqjqEpIeu3KgfGdegpVp1pWpV5X75S7XEcdAyrirp5EmTetrR2Ql85zuSsRyQm/unngLuuAPYu1c+O/pocah86lPyvqQEGD5c7AH0x1WFrsSxSqpad451JZWB/JDxDdpGE+uNEEIIAKrqkABJR1XnjjuAG24QB4rzu1NB8HLEdJwQkn/oyo36kXENWqpVV6pWVW7/fvdyth28jKtKOrl/f3c7tm4F9uxJJKs9cAC4+27gkUfEDsuS3CvXXAMMHCjfGTBAtvsMHao3rip0JY5VUtW6c6wrqQzkh4xv0DbqnseEEEI8oeOEBEY6jpMlS4Bbb5WHJG7Rzkmk/EYQyWEJIXlEfX1PT266srFu5bzq1C2r29fGxkSSKYd0pGp1ywH6fdVFJXGssmPAANly5GzDGThQokweeUQiTmwb+N3vJILgySflfUOD2NHQoD8+bqj6qhpTE3PsZw6Dnv8wCNpGP+cjIYQQQrIHJ9IkDaeJK3ScEEIySkWFPMVNJl3ZWLdyXnXqltXta3l5z6zc6UjV6pYD9Puqi5uUcVGRtx0FBRJBMnYsUFoqn33qU8Bjj0mYZFmZbN9ZvBj4xjeA996T8v36JVRsUtWrg6qvqjE1Mcd+5jDo+Q+DoG30cz4SQgghJHtIc4uOEjpOCCEZxYRsrFedQUu16krVqsp5SRwHLeOqkjhO1/4+fYAxY2SrRVGR/Js3D3juOeCcc+Q7f/87cP75wL33Sl4Vp954XDKgpzOuKnQljk3MsSk54lwhaBt155gQQggh2cX+/Ykt4roUVldXZ6QzqVi6dOkgAAuc99XV1TXGGjPM8uXLq69IZwMVIXnOxImSPHTDBpGfHTMGuO027+SOqnJedeqW1e3r9Onyum4d0NoqkRLXXeedUFJVbsECUZ45cCDx/WRVHd2+6jJ7NrBpE/Dmm4ncJhdfLMo9vbW/Tx/5Y2XbQFubfH/mTOD44yWXyP79wKuvAjt3AqefLpLFbW0SrbJwoSTx1EXVV9WYmphjP3MY9PyHQdA26s4xIYQQT5aG0ei99y6vnjaN92v5xkkneX/n/vvld2WaKpgp1y+Tw6YJk8MSQoh/2tuBXbsSWzJaW4Gf/Qz4xS8kwgQAzj4buOkmYMQIeV9WJuo7JSXh9JkQQgghvYLJYUlgpBPbcNxx8nBm1aq0qmRyWEKymdpaUZ2oqJDX2tqwe6SHyg4/NpoYHxN9rayUHCCWJa+VlYljNTWSVLWoSF67R1qo2lSVVR1T9Ue3PRUlJcDo0YntO337SrTNk08Cn/mMfOf554Fp04BPflLUd5qbRa3nkUekf0HNscpG3bXhNW5VVaJoZFnyWlXl38ZcIVeugbmA11xwrgghhESJ3bvlIZwfGHGSJow4IWFSWwtcfbXcdJaVyY1kezuwbFl2heqr7AD0bTQxPib6WlkJrFnT8/MZM+TYLbeI86KwUJJUxuPAzTfL1gFVf+rq3MsC7sdWr3bvz+LFeu31ZpuDbQMffyz/bBu49lrg6ad7fu+004BLLhEnQ0mJKPe0tpqdY5WNU6fqrQ2vcauqAlau7NnPuXNli1Q+kyvXwFzAay44V4QQMOKEBIhXxElnp/xNuummtB/0pVy/dJykCR0nJEzOOAP48EPZg+/Q1CRP7V98Mbx+9RaVHYC+jSbGx0RfCwq6KsY4OLK9zc1d1WxiMbnx2LdP3Z/1693LAu7HDhxw78+MGXrt7dvnbr8bHR3yJGDs2J4qJw7Dh8veVEfxpLBQ8qAccYSZOVbZOGWK3trwGrfiYnlfkBQL6uSXcbYx5Su5cg3MBbzmgnNFCAEdJyRAvBwnTrTJT38qjv004FYdQrKV+vqeElplZYnEodmCyg4/NpoYHxN9dfNT2zbQ2CjOgGQKC4GDB737oyqrOqbqj257OhQXiwPEcZpYlvxLZtcuSRq7f7/0r7NTHAqbN+u1CejbqLs2vMYtFkvdT7fP84lcuQbmAl5zwbkihBASJXbskFfnAZcudJwQkgVUVCSSaTo0NwPjx4fSHW1Udvix0cT4mOhrd2dA8ufl5T2jLTo7ExEWqjZVZVXHVP3Rbc8PyZEYTj+c14ICaeP99yXfSVsb0NICjBwpn+lEZOjaqLs2vMatu/0Obp/nE7lyDcwFvOaCc0UIISRKvPeevB55pL966DghJAtYtEj2iDc1ydP2piZ5v2hR2D3rHSo7/NhoYnxM9NWRP031+cKFsi0jFpM6YzF5v3Chd39UZVXHVP3Rbc8Pc+bIq20n/gHAuecC3/++bNUBpC+bNkno5de/Lu+3bAH27nWPokmFro26a8Nr3Bz74/HEv+TP85lcuQbmAl5zwbkihBASJTLlOOlVjhPLsi7pZf1DAfzo0P9tAJf2srwUtO2HdcplEuY4IWFTWwvceafcII4fLz9CszHRnsoOPzaaGB8Tfa2sBNaulRsKyxInxerVcqymBrjnHtm60b+/3FAnJ1tVtakqqzqm6o9ue36oqhL1nFhMIi1mzxanSSwm/bzrLnGaOJEbY8ZIu9OmyfuSEtnH2n2rgBu6NuquDa9x627/nDlMDOuQK9fAXMBrLjhXhOQ9zHFCIsOvfw3861/Aj36U+MwjL0rK9dvbAOCHIA4QHSwA/61ZNnTHCSFhM3NmbvzwVNnhx0YT45NOnb3Nr+04JVIxdaokHq2vl3D3qVPT749XWbe+Ll4sDhOnXPJT4ZUrgZdflpv47dslD0n39lPV6TgHGhtle0pvnCpz5wIffJDoz7x5cuO1ezdQXS3Hktm+HfjmN4EJEyQhZUuLJKX81rfE4ZLuNpdUdqjGVDU2ftZNd/vnzk0cc25Gk+equ7Omt8fCQHd95Mo1UJcozaPXXOT7XBFCCIkOu3cDQ4f6r6e3ESdxiOOkN17E5AZ0vI+RUONhxAkhxCFo+WM/csy6srorV7pL406apCd/7HVzrLLjyislt0l3SkrkOw6FheIIsm3gdjn4DwAAIABJREFUqquApUuBQYN63x6gNzaq6JCaGn3JaVV/dI+FcWPrNQYkNZT4JYRkGYw4IZHhxhtlm878+YnPdCJOdBwnQUPHCSEkUgQtf+xHjllXVteJpkgljdu/v578sZdUscoOVaRO375Aa2vivZNMtqwMePVVoE8fYMQI+V667QF6Y6NKUjt4sL7ktKo/usfCkIb1GgOSGkr8EkKyDDpOSCRobQUWLAC+9CVg1qzE50Fs1flVL79PCCE5R309MGRI188yIX+sW6eqbGOjPKVOJllW162cShpXVadtux/zY4eK9nZps7NT/jlyxQcPAg0NwIABwLZtwMCBwLBhCYeHqj3b1hsbFapx89Mf3WNh4DUGJDUmrjmEEEJIrrN9u/wWyoSyW68cJ7ZtayV3JYSQXKKioufT30zIH+vWqSq7b1/PJ/zJsrpu5bZvT+0IcIs4SZbVVR3TtaO+3r1cv37yRKG4WG7COzoSqjznnANcdx1w3nnAgQNygz5smDhTvMZcZ2xUlJerx0a3P36OBY3XGJDUmLjmEEIIIbmOs8173Dj/dVGOmBBCeknQ8sd+yurK6qqkcXXlj/3Y4fYHb9w44JprpI3OTtmm4+Q5KS4WieLFi4FvfEPk6Do7gY8+AnbskNBNHVlhXdlgP5LTJo6FgSkp61wnavNICCGEZANbtsg24QED/NdVWF1d7b+WPGD58uXVV3hshiKE5AcTJ0qC1A0b5CZ8zBjgttv8JWn0U6eq7PTp8p116yQqo18/icBYskRdbvZskf19881E/o6LL5bkp6o6Vcf82LFgAfDQQxI14jBunPxBnDFD3r/6qrRZViZ7V++4Q57Sv/ce8P77wKOPys3mCSfIzeewYcAxxwBvvdWzPd2xUeE1Nqo2TRwLAz/rI5+J2jwSQogHS8No9N57l1dPm8b7NZLg8cflb2Z3tcmTTlIWS7l+GXFCCHGltlaSElZUyGttbdg90kNlh+6xmTMlKePmzfKafAOjW6cjcVtfL6/dlVtqasRrXlQkrzU16ZV1ZHVHj5bXdKWK584FTj9dtgOcfnpXaVxVucceA/bvl2iC/fvlvUNVlUSCOBEhVVVdy6rscHJ2OP+653dw1HQsS/6NHAn85CeSDMyyZAvPAw+ILS+9JN/9y1/kJn7rVkmkW1eXno26LFki26c6OuTVzWGQqk3VmtM9pkI1V36uDemOQdiEcf1TneO680jyG9WaIoSQXKa5Gdi1KzPbdIBequr4bsyyBgIYDsARh9wPYJdt2wfcS0UDquqQfCNX5C9NSLyq7DclcauScX3nHfeyc+dm3n6VxPFjjwEbN/bsy+TJwPHHq22sqsqczG9nZ2Irzv33J5wqnZ2JchMmyE1oYaGUdbbdLFmiL9XsFXWiIkrnnGouVGsqm64NKsKYC0o1k0zDNUVChKo6JHQ2bpS/2wsWAEcf3fWYcTliHSzLOgfAXACnAqhw+Vo9gFcArLRt+zmjHdKEjhOSb+SK/KUJiVeV/aYkblUyrgcPupc9/fTM26+SON6/331siorUNhYXZ17m17blWGFhoj4neWxyv5y8KJ2dQGkpcOKJsiUik3LEXkTpnFPNhWpNZdO1QUUYc0GpZpJpuKZIiNBxQkLnN78BXnkFuPtu+V2TTBByxGljWdYXANwH4EhVBw7xCYhT5WuWZb0H4Crbtv9oqm+EEG9yRf7ShMSrifa8JG5VMq6qsibs15WU9bLRhMyvbSe2m9i2OAKKi7s6T2IxcZgUF8vxpibg3XclB0oyfuWIvYjSOae7pnKFMGykVDPJNFxThJB8xbaB11+XSJPuThNdjOQ4sSzrbgDPAZiAhMNEFdriHLMOlXnOsqy7TPSNEJIeFRXypCqZbJS/VNmhe8xEe25Sts7n5eVdt5kACRlXVVkT9qv6osLLRq/jbqj6U14ukRJO3hNA/pj27y//nIgK25atGB0dEnEyejTQ0NDVwZLuXOkSpXNOd03lCmHYqHteEeIG1xQhJF/58ENRVjzuuMzVmXHHiWVZywB8B+IESXaINAJ4GcB/A7gXwI8P/f/lQ8ec79uH/r/gUF2EkBDIFfnLoGVcdev0krhVybiqypqwX9WXyZNTj8vkyd42mpD5TT6WXPell8q/ZPli53hnJ3DssUBbmzyZbW0VJ4pfOWIvonTO6a6pXCEMGynVTDIN1xQhJF95/XV5jazjxLKscwF8+9BbxwFSB+B8AENt255u2/Z827avsW174aH/TwcwFMCXAaxDwoFiAbjSsqwvZrKPhJD0mDlTEiodfrjshT788OxM/qiyQ/eYifZWrJCkm8nRF8nJRpcskYR+ZWUSBVFWlkjwpyprwn5VXzZs6Ok8mTxZPvey0eu4G6r+pDq2ZAlw113A9dcD3/62fGZZQJ8+Es7Z0gL88peJqJSGBtm2cfPNQGWlfj+9iNI5p7umcoUwbFStY0J04JoihOQrGzdK9HD3bbd+yHTEyR2HXp3tOUtt2/6MbdtP2bbtuvvbtu2Ybdv/17btTwOoQVfnyQ8y3EdCSJrkmvylKhd2b+VfVdTVSQLVbdt6StxeeSWwerXkUFi9Wt47TJokN+qFhfI6aVL6fV2xIrGtpKOj6028yg5VX1XHVq8GDhyQJ5gHDsh7h+OP73rDffzx6duoOq6Sx1VJ3KY6VlQEHHEEcNttYtubb4pz54UXgLPPlnJvvy2fz5oFLF8OfPazMhY7dwIXXeQt1ZwKLzlmh0zmbVfJkaokd1VrSrU2vDAh82uizjCuf9ki1UyyB64pQki+0dAAbNqU2WgTIIOqOpZlHQdgAxLbc35m2/ZVmnXdD+B/H3prAzjBtu0UApfBQVUdQrITXXlg3ZsklfzjL38JbN3as8y4ccBll6llI03ISqrqBNyPrV4NrFnTs74ZM4BRo9xlbCdN0rdRJbnsN9KjsxPYs0ccQA5r10p/duyQ92PGSB+nTZNjt9wC9O0rOQTSXTdecssmJHBVY6qSXFa152ctmrAxSjLOhBBCAFBVh4TI888Dq1YBS5cCI0em/k6ocsSWZX0dwK8OvW0DcLht2wphSmVdgwB8BMDJgXupbdsP+++lPnScEJKd6MoD60qOquQfVVK9gwapZSNNyEqq6gTcjx04kDoiwskX4iZj27+/vo0qyWU/EsDJtLQAu3ZJbhPn/c9+Bjz4YKKNc86RiJMDBySJbEGB9KGlxXvdeMktm5DAVY3plCl67flZiyZsjJKMMyGEEAB0nJCQsG3ge9+T35yLF7t/T8dxksmtOoduQ2AD+Juu0wQADpV9BYlOu/iKCCFETX19whHg4MiKqo7p0tgozoNk0pF/9CqnW69um6pjbv52J/lgKmIxfzaakgBOprQUGDtWZIgLCuT9woXAk08Cn/60fOe554DXXkskDY3HJbqhuFjWkwovG4Jej7rt+VmLJmw0USchhBBCso/33pMHXJ/7XObrzqTjpDHp/x9koL7kOhpdv0UIIQpMSA6r0JV/9CpnQlbSS8bX7Zjl8hzJstQytn5sNCUB3B3LkoiK8eMT/TrySODhh4Ef/ECSjNm2/FHevDmxfpqaJBy0ocG9bi8bgl6Puu35WYsmbMwHeWRCCCGEePPnP0uy/5NOynzdmXSc7Ej6/6AM1Jdcxw7XbxFCiAITksMqVPKP48alLjNunLdspAlZyXRlfLsfmz49dX3Tp6tlbP3YaEoC2A0neeyoUYlkrl/+suTTmDZNvtPaKtEO27fL/y+7DPjoI3nvbPdJxsuGoNejbnt+1qIJG/NBHpkQQgghalpagHXrJIdb376Zrz+TjpO/QHKbWACmWpZV6PF9VyzLKgJwKDAaHYfqJoSQXmNCcliFSv5xy5aezpNx4+RzL9lIE7KSvZXxdY6tXi2JYJ3IE8uS96tXq2Vs/dhoSgLYi379JHJhyBCxc9Ag4Oc/FyljZ3tIQ4NsU3Fyv7S0iNrM7t0J5wjgbUPQ61G3PT9r0YSN+SCPTAghhBA1r7wiD05MbNMBMpgcFgAsy/odgAsheU6usG37Qc16Lgew/FA9f7Bt+8KMdVITJoclhJD8pr1dtui0tMj7WEycHj/+cWKryCmnSFKyT3xC3hcVSc6U8vJw+kwIIYSEAJPDkkCJxYD/83/kQdeiRe7byh3CTg4LADcBcNLD3WVZ1md7W8GhMncdett0qE5CCNGmtlaUNyoq5LW2Nr1jKmpqJBdGUZG81tT4r1O3nFfZqqrEdpPiYnmfTjnVMRP2e9WrS6bmv6REZIlHjJBkqEVFwLx5Uubss+U7f/sbcN55wL33yvadWEwUX3bsEMeLF7pzVVkpCW0tS14rKzNvfzKm1qqfet0wsaa8MHFeRQnVOiWEEEKC5h//kKjTmTOBb31LHCOqfzpkNOIEACzL+jyAVQDKAbQA+D6Ae2zbbvIo1w/ANQCuB1AKccDMtm37hYx2UBNGnBCSndTWAldfLTe9ZWUSGdDeLqH8gPsxVZh/TQ1wyy1yg1pYKIkx43HZrjB1ql6dqn56bTlQlV25Uv51Z+5c+aczNnV1mbffa1x1tySZmH9A+rZ7d9dksGvXig07DmXlGjNGok9OP13eO4lnDzss9ZOQqiq9ufrhD4E1a3qWmzFDpPgybb+ptarbHxUm1pQXujaqzitTfdVBtU5Nb50jhGQNjDghgRGPA9XV8rf1ppvEceKTlOs301t1DqXMw9EQh8kgyHabFgC1AOoAvAeg4dDnAwEcCWAqgJkQh4kFYB+AGwG8mW7btm2/lBEjXKDjhJDs5Iwz5Il/v36Jz5qaJA8C4H7sxRfd6xw8WG54klVSYjG5EZoyRa9OVT9V5bzKvvyy9K0gKb4wHpe+n3663tisX595+wH1uO7bpy7rhon5T6apCdi1S3J9ALKN52c/Ax58MPHZzJnADTdIpAogT+iHD+/arvO5zlytWZNaItrJPZNp+02tVd3+qDCxprzQtVF1Xpnqqw6qdeqseUJI3kPHCQmMdeuA5cslkuSkk/QjSpIIxHEShzhEUjXs1VC630uFbdt2hkUpu0LHCSHZSUVFIrGng23LjYhtux/bvNm9zqIi8Wp3L9fRAYwerVenqp+qcl5l6+vlffebHKBr0tPu5VRjs21b5u0H1OOqe0NmYv67E48De/d2vbl97z15+vGPf8j7fv2ABQvkqXzhodTp/fuLA8W5UXb60du5cuY4FbpzHMZazdR8JGNiTXmha6PqvIqSQ0K1TjMcxEwIyV7oOCGBYNvAbbdJ9GZ1tfxtMuU4yXSOk1SN2ejqDLG6/Uv3e17/CCGkBxUVicSdDs3NckOpOqaivFzC6JPp7JQbYd06dct5lS1ycSkXFemPjQn7AXW9upiY/+4UFEgC2LFjgT595LMjjwQefhj4wQ/kRrmpSf6wX3ABsGGDfOfgQVFU+vhj+cOvO1duCdAsy4z9ptZqpuYjGRNrygsT51WUUK1TQgghJEj+9S9g+3bJNVdgyrNxCBPVW0mv6Tg46AghhBhj0SLxQjc1yc1pU5O8X7RIfUzFwoXyhDUWk3KxmLxfuFC/Tt1yXmXnzJHvxOOJf4B8rjs2Juz3GlddTMy/G337ivNk2DBxWlgW8OUvS84LZx7eeAO48EJ5KtLQIPbt2QNs3Qp89avynd7O1fTpqfszfboZ+02t1UzPB2BmTXlh4ryKEqprCiGEEBIUnZ3AqlXAyJGiamiaTG/V+UbGKusltm3/ymT93KpDSPZSWwvceac83R8/Xm5SnGSTqmMqamqAe+6RqIH+/eXmxkngqFunbjmvslVVwCOPyI1YUZHc4DhJHHXHxoT9XvXqYmL+vejokNwnTUlp0devl2Sxb78t74cOBa67Dpg1KxE1csMNwJNPyo+B3sxVZaUkp7VtqWv6dGD1anP2m1qrJubDxJrywsR5FSVU1xRCCAG36pAAePFF+Vt01VXA8ccnPje1VSejgZWmnReEEKLDzJnuN1+qYyqWLHG/oamrk5vkxkbJXVBX1/Omqb5ewvaTb5p0++JVdsUK95saVV9Vdb7zjtzcxWLy+s47Pb+Tyi+vsh+Qm/0DB6TsgQPy3hln56aysVG2NaRy1vR2XFeuTCTQ3b4dOOKI9BxOKpL7MmaMSBafdpokzj3zTLlhbmuTSJNFi4A//EEcKp/4BHDOOcD77wMffABMmCA5URxUdlRWAq+9lhibZDliB9Vzkt4+Q1Gtm3RJ1aafc8AN1blqCt1rThh91UF1TSGEEEJM09QEPP008MlPApMnB9NmxuWIcxVGnBBC0sGEVHEYfVXdvOnK5gJq+ysr3WV1KyszP64qOwA9ydVUUrRtbZLf5JVXgPvvFxssS6JSnD/BxcXAWWeJ86NPH9n209Ymdt5/vxl5bCDzctxeN/1+pIwJIYRkDYw4IUZ57DHghRdEfnjMmK7HskJVJ5eh44QQkg4mpIrD6KtK/lRXNhdQ219Q4C6rO3Bg5sdVZYdTf28lV1VStP/8p9jgqOoAUlc8nrC7qAgYNSqRELSlRcquXdu1XDK6aw7IvBy3l2yuHyljQgghWQMdJ8QYu3dLrrjPfAa45JKex7NNVYcQQvKSxsaeN7iFhbKdpb5ebi6TKSuTrRthoOqriljM/XOVjV72u/nxbdvMuKrsUB1ToerLwYPS52QVnKIief+//3ei/q1bZdtQR4dEnmzdKuUbGlK3qTs2uuOmu26A6J0DhBBCCMkuVq2SB1vnnRdsu3ScEEJIBjEl1WsCXflTExLHgFpW18S4quzQlVz1kpuNxxOKO4C879dP8rUcf7w4SgBxkmzaBOzcKXlXOjuBjz4Sh0pbW9f6dcfGhBy3F1E7BwghhBCSPWzaJBG8Z50FDBoUbNt0nBBCSAYxJdUbdF9VmJA4BtSyuibGVWWHruRqunKzyXVfdpm8v/pq4LDDRM64sFCO7d0rW1s2bJDvtLQA27ZJmKrTJ92xMSHH7UXUzgFCCCGEZAfxOPD734vD5Kyzgm+/sLq6OvhWs5Dly5dXX5GBDVOEkNzGuflftw5obZVoguuuk6SZEycCkybJTfBHH0kyq9tuCy8ppqqvKmbPFo//m28m8n5cfLEkTVXZ6GX/vHmSy2PrVnlvWZIYdvVqM+OqskN1TIWqL6lsuP564NZbpY2RIyXyYssWieAoLRXHQkODJEHbuxc48URJHtvaKp8XFSV+PPR2bHTHTXfdeI0PIYSQnGFpGI3ee+/y6mnTeL+Wq9TVyW/COXMkgtWNk07y3VTK9cuIE0IIcaG2VpJZVlTIa21teuWWLJEkmR0d8prqhtJNqlfVnup4ZWVCraWgoKscraqcSla4qkoSqFqWvFZVJY6tWJFQheno6OpQcKRqt22T17q69Ox37Bg4UCIuBg7sasdjjwH790tf9++X9+nW64bKjrlzJdnt+PHymiwNrBobVV9SrY3CQmDECGDsWOCZZ4BXXxU54r17gc99DjjqKKnrN78BPvtZ4JRTgK9/HfjTnyQaZccO4IQTJBHs6NHyOnVqos2ZMyXx6ubN8prKSdHbcZs61b09L1TrQ/ec06WmRpLdFhXJa02N2XK5Qr7bTwghJFja24HHH5cHLqecEk4fGHGSJow4ISS/cGRTW1qAAQPkJvepp+Rp+cSJma9z0yZ1e6qyV1zRU8Z3yxaJ3hgxwr3c0qUiuets+YjHgY0bpS+rVrkfmz3b3UZHqjYWE4dCe7v0A5BcFiobVWX/4z+k/WR27ZJ+jh8f7Fypxq20VL8v8+ZJCKrjxLBtyWlyyiniGHnzTYlEaW0FPv4Y+OtfgU98Qtq9/nqJThk4MP02dde4n3PDz/rINKq+uG0b81MuV8h3+wkhacGIE5JR/vhHedgyf75saVZhKuKEcsRpQjliQvILE7KpqjoBdXuqsmvWuMv4zpjhXu7llzMvx+tHjllVdv9+9zYrK4OdK9W4qeSYvfqSLI+c7DwpLAROPlnq3bdPVG0cRo2S5LF794rTxrKkH62t3m3qrnE/50aU5Lp1ZZX9yDHnAvluPyEkLShHTDJGQwNw883yIOWqq7y/TzliQggJEBOyqX6kYVXHVTK+qnIm5Hj9yAbrytwGPVe6csxeJNebrLzT2Snbcfr3l+08Y8eKkwUA3n9fsss7ZZ0tR0VF0hddG02UA6Il16273vzIMecC+W4/IYSQYHnqKYlu/OpXw+0HHSeEEJICE7KpfqRhVcdVMr6qcibkeP3IBuvK3AY9V7pyzF641VtYKOVbW+V9eTkwYYJklbcsGaNt22RbjxMN1Nws27T27nV3rOn21Y+NUZLr1l1vfuSYc4F8t58QQkhwfPCBRPpOmyZJ9MOEjhNCCEmBCdlUP9KwquMqGV9VORNyvH5kg1VlJ09O3d7kycHPla4csxdu9V50EXDTTVJfa6u8trXJjer3vpfI/9HQIPlOPvpIjs+fL46TLVukH72xUXdsvIiSXLeurLIfOeZcIN/tJ4QQEhx/+APQty8wa1bYPWFy2LRhclhC8gsTsql+pGFVx1UyvqpyJuR4/cgGq8peeaUkgt21K9HW5MlSV9BzpSvH7EU69b7xhozByJHAggXAuefKdw4eBF5/XZ78t7RIHorzz5eok3hctle0tkoeFGebhW5f/dgYJbluXVllP3LMuUC+208ISQsmhyW++fe/ZZvO+ecDRx+dfjlTyWE9gq4JISR/mTnT/aattha4807Jy1BRIU/Fe3ODl2r7hKo9r+OrV+uVmztXwiAdO5IldydNkqiGxkZ5nTQpcaymBrjnHjlWXi5Pm50bpyVL3G+iHCnaxkZJJFlXl7487g9+0HPM07HRL6n6smKFtxNJJ/e6qt5kG+NxYM8eSZprWTIHffsm8kxs2wZceKE4VRYuFJWapiaJPhkyRP4lb/HqbV/9jLdqfZicRxW9tV9lQxj4vR71lqjZHzWCng9CCMk14nHgsceAoUNFBCAKUFUnTaiqQwhxcORYS0okeWVzs2wpWLZM/eNYt5wpVP2pqxPJ0YICiVDo7JQ/YjffLGXdjqluphwZU7dyquNTpwY7dtkyx62twI03Aj/5iYxbQYFsnUjOQTF0qMgVn3tuwllSXAy89hrw3e9GZz0Gjdd6zBaidl3JdzgfhACgqk5OEcamiwcfBC6/HHj0UeCCCwJvPuX6peMkTeg4IYQ4hCHjagJVf9avd5ccBczIuEZJqjab5njwYGkjWekkFpP3hYWJhKunnio5USoq5P0ll0jUysCBCYdKmOsxaHJFVjdq15V8h/NBCAA6TnKKoB0nra2yffeII4C//c1dBMEglCMmhJBMEIaMqwlU/VFJjpqScY2SVG02zXFjo9z8J/+wKCwUJ8CzzwJnnSWfvfKKJFf7yU8keeyOHUCfPvJ/J9FnmOsxaHJFVjdq15V8h/NBCCH+eOAB+Y3y/e+H4jRxhY4TQgjpJWHIuJpA1R+V5KgpGdcoSdVm0xw742ZZsu3EsmTLSVmZPOX+6U+B//ovYNQokSu+7z7ZttO/f0LiOBaT7QSNjeGtx6DJFVndqF1X8h3OByGE6NPYCNx+O3DmmRLBFyXoOCGEkF4ShoyrCVT9UUmOmpJxjZJUbTbNcfdxc3J1fOtbie/MmAE884x8VlQkyWPfflu2FDQ2SrnmZlHkmTdPolBynVyR1Y3adSXf4XwQQog+99wj24hvvz3snvSEcsRpQjliQohDGDKuJlD1RyU5akrGNUpStdk0x6nG7frrpd3SUnGGxOOSEPbUU4GzzwbefVfUlGIxcZy0tQFjx4rT4LTTgAMHxAHTt69EseQiuSKrG7XrSr7D+SAEAOWIc4oMyPumxZ49wJw5EhUb8kOMlOuXyWHThMlhCSGEZCO2DezdKwlPnT/5tg088YRIPDuJUI89FqiuBo4/PlG2sBA47DBg0KDAu00IISR7YXLYHCKo2IFFi4C77wY2bgSOOSaYNl1gclhCCAmC2lrZl1lRIa+1tZkppzqu22ZNjaiLFBXJa01NesdUVFVJdINlyWtVVXrl/NhhgjD6ojvHqrmyLJEkHjtWIkicz77yFZHeHTZMPnvjDeDCC4GlS4GGBmDtWmDuXOC444DPflYcLemgu268MDEfJs4pL0yMT5TOG1Pkg42EEJKv7N0rSWEvvjh0p4krjDhJE0acEELSobYWuPpqoKREknM2N8v+9mXL1KHaXuVUxwG9NmtqgFtuka0YhYWJ3Bg33yzH3Y6ptjJUVQErV/b8fO5cYMUKM2NngjD6ojvHdXW9m6v9+yUcdvVqWQMlJfL9Dz6QOgFgwAC5sR84ULb7tLbKsTvukDDaZAnfZFRrys8WGBPzYeKc8sLE+ETpvDFFPthISI7BiJMcIoiIk+pqeXDz+usSARsyKdcvHSdpQscJISQdzjhDEm7265f4rKlJ1E1efFG/nOo4oNfm4MFyA5J8ExyLJaQ03Y45WztSUVws30vOixGPSz0dHe7lAP2xM0EYfdGd4/Xrez9XsZjk+PjoI3GMALJ956OPxLESj8tn/fpJG336SK6UYcPEAeZs3+kuE6haU6p144WJ+TBxTnlhYnyidN6YIh9sJCTHoOMkhzDtOGlsBMaNA6ZNSz+61TDcqkMIIaapr084HhzKyoAtW/yVUx3XbbOxUZ56J1NYCBw8qD6mIhbr3efJ6NphgjD6ojvHOnNVVCQRJuXlCeeHZQEjRwLDhye29DQ1Ae+9B+zaJU/7339fnCq7dwNbt/aUXdVdN16YmA8T55QXJsYnSueNKfLBRkIIyVeWL5eHBzfcEHZP1NBxQgghGaSioufNZHMzMH68v3Kq47ptlpfLVoFkOjuB/v3Vx1S4beFw+zwZXTtMEEZfdOdYd64qKkRNp6QkcTPf2ipPfT71KWDECIkgsm1xlGza1PWJf3s7sGOHRAI4jjHdvnhhYj5MnFNemBifKJ03psgHGwkhJB9pawPuuksiCz/zmbB7o4aOE0KFu7TbAAAgAElEQVQIySCLFskNZVOT3HA2Ncn7RYv8lVMd121z4UKJHojFpFwsJu8XLlQfUzFnjrzG44l/yZ+bGDsThNEX3TnWnSunTmfrSEeH/Js/X/4VFwOjRsm2HEDqffttqXfnzkQ9jY0SEfDxx8CCBXp98TM2Juo0Nf+6c6VrR66QDzYSQkg+8vDD8gAm6tEmAFBYXV0ddh+yguXLl1dfEZQWEyEka5k4EZg0CdiwQfJFjBkD3HabdwJDr3Kq47ptTp8ur+vWSaRBv37AdddJkkrVMRWzZ0tkwptvJnKbXHyxd2JYP2NngjD6ojvHunPVvc6xY6XOykrZsjN+PPDOO5LbZNw4SRT78cfAu+8Cjz4qWyWOOy6Rz6a5GTjxRMmZ8tprveuLn7ExUaep+dedK107coV8sJGQHGNpGI3ee+/y6mnTeL+WaU46yUy9tg184xvA6NGSfL573rQQSbl+GXFCCCEZZuZMSVi4ebO8Jv+4r6yUG03LktfKyvTKeR33KutFqjzhS5bIntOODnlNvrlTSYOuWCFlbFtek50mXpKidXWS7HTbNnmtq0scC1rGVTWmJmSDk0k1H6qxmToVmDJFfnxMmSLve4PTXkGBbNEZM0YiTpxjZWXAd78LfP/70u+mJuDWW4ELLgA2bkzU09EhDpdjjpFold72RTU+qvlQSWDrznH3sUnGjzSu6rzSxe/5nw3kg42EEJJPvPwy8MYbopoWIaeJK1TVSROq6hBC/FJZCaxZ0/PzGTNEGjZodKVRTUkum5BHVmHCDkBfNlhVr6rs1Klm7LjqKnFgFBcn5IiXLAGOP172I//+9/I9ywK+9jXZprN+fULiuG9fKdPZCdx3n/eNru56VElgz50b7BzzZp4QQgBQVSenMLXp4qKLgOefl8Tz3ROAhwzliP1AxwkhxC8FBamfXltWIhdIkOhKo5qSXDYhj6zChB2Avmywql5V2SlTzNrh5ORoahI54ocfluPr1gHV1bKdBwCGDpXtPPF41x9ALS0SffLSS+59AfTXo0oC+/TTg51jSuMSQggAOk5yChOOE2fb5X/8B3D33Zmv3yeUIyaEkDBx81OH5b/WlUY1JblsQh7ZT390yvmRDdYta9qOggKJrigvl6dCDiedBKxaBSxeLN/dsychXdzWlvhe375S3/btXT/vjgkJ7KDnmBBCCCHe/OIX8nf6yivD7kn60HFCCCEB4bZ/M6x9nX5kbE1ILpuQR/bTH51yfmSDdcsGZUdbGzBhgvTFobhYFHieeQY480z5rKkp4UCJx2Wbz6hREnmydat83t0WwIwEdtBzTAghhBA1sRjwX/8FnHWWJP/OFug4IYSQgHAUNdL93DR+ZWwzLblsQh45aDv8yAbrlg3SjsWLZUvKqFFdHRZHHCF5TK66SqJEbBvYvVsUeBoaxLnisH+/RGccONC1PyYksIOeY0IIIYSoeeopYMcO4NvfDrsnvYNyxGlCOWJCiF/mzQPWrpWn7oBEmoSVGBbInIxtpiSXTcgjB22HH9lg3bJB2wHI1h0nn0lra6LOz3xGyr72mmyxicclGiMel1wsTl4Qx+HQ1AT06SNOGBMS2GGMDSGEEMoR5xKZliP+z/8UZbn77uuanyxCUI6YEEIyha4c6erVcnNn2/IahNNEJfGqKznskCo/ix/JZVW9qr6q5GhV6EqcqqSBVXV6yQaryqrsV/XHhBwvIHM7fLg4DkpKEp+fdZaoRz39NHDyyfLZM89I3StWdN2O09oqff7oI/lcNT6qOZ47VxLBjh8vr3PnpmeHqk5T8t+ZkKvuTZ26x7KJXLGDEEJynbffBl54AfjWt9y32kYVquqkCVV1CCEOujK2YWBCchhwP/bDH+pLLpuQo12xQt2mDkHLOPvpjwmpYjfnyr59wN69XR0Utg08/risC0cZ59hjgaVLgcmTu9bx8stiR9++Em2S3ObKlXqSw4D7MVWdJtYNoC85beJcVR2L2nVMRTZdjwnJI6iqk0NkctPFwoUSabJtGzByZObqzTCUI/YDHSeEEAddGdswMCE5DLgfW7NGX3LZhBxtR4e6TR2ClnH20x8TUsWqch0dkvy1qanr5/v2AXfdBfz+9/LesoCvfU1+QDnJZi+5RPKilJXJnBYUJNp8+WU9yWHA/ZiqThPrBtCXnDZxrqqORe06piKbrseE5BF0nJAetLXJFtzjjgMuvzz1dyKSGYNyxIQQkgmySY7UhOSw6pgfyWUTcrQmCFrG2U9/TElHu1FcLIljDz+8qwNg8GDg1luB3/xG8oPYtkR7zJwpW3psWxLF9e0r/29vF+dFaam0qSs5rDoW9LoB/MlV69SZD7LKuWIHIYTkOnV1orA3Y0bYPdGDjhNCCOkl2SRHakJyWHXMj+SyCTlaEwQt4+ynP6ako9Pp0/jxwKBBXT8/6SRg1SpR5yktlQiTa68FLrsMGDq0a6LZzk5R3hk1qqdDwcFLclh1LOh1A/iTq9apMx9klXPFDkIIyWVsW6KSR40Cjjwy7N7oQccJIYT0kmySIzUhOaw65kdy2YQcrQmClnH20x9T0tHp4JY8trhY5ImffRY480z57K9/Bd54QxwpTpstLdLmJZcA554r3+ut5LDqWNDrBvAnV61TZz7IKueKHYQQksvU1wPbt8vvwXQepkURyhGnCeWICSEO2SRHakJyWHXMj+SyCTlaEwQt4+ynP6ako3tDcbFIF1tW14iS8nLgS1+SZLGvvSbRJe3t4jCJxURZZ+FCse8LX5A19d57cnOcruSw6ljQ6wbwJ1etU2c+yCrnih2E5BiUIyZdeOIJYM8e+Z2oiuzMtPSxJinXL5PDpgmTwxISLrW1wJ13ise6okKeJmbjD2NTdqjqraoCHnlEbkaLiuSJunNzqFuupga45x7Jy1BeLje4zg2eHxt1+xM0umPjhcp+E2sn6POqrQ244QbgF7+QyIB+/eRH1GWXAfffDzz4YCLy47DDxHFw/vld67AsyZly2GFmn1qZWuP5QNDrOJvId/tJ3sDksOR/OHhQ/p6fdpo8oFARkTiFlOuXESdpwogTQsLDkZtsaQEGDBCFiaeekqeMEyeG3bv0MWWHqt6lSyUJp3MzGo8DGzfKk/bSUr1yr78usqmxmEQTtLdLlAkguQV0bdS1Y/Zs/bHTwZE/7u3YeG1XUtm/aVPm104Y59Xtt8tNY2dnQr3mH/+Q/0+ZAvz973IsFpN+vfCCbN+ZNq2r+k1Lizg0iou7bgPKFI7Ec6bXeD4Q9DrOJnLlbxkhacCIE/I/rF4t23G/8Y2Ekp4bjDjJARhxQkh45IrcpCk7VPXqyriqyvXvn3n5Wz92mJKOdUMlf6waG5WkLKAvK6u7dsI4r5Klk21b/nV2iprOMceIk6RvX2D/fmDnzkSy1GOPFefZ5Mk96ywrk1wqmXSgmJB4zheCXsfZRK78LSMkDRhxQgDI76ObbwaGDJFk8F5EJE6BcsSEkOwkV+QmTdmhK7mqW86E/K0fO4JGd2y8CFo6NozzKnl8LEucT4WF4qRw5IidrTgTJiRUed54A7jgAokCaWzsWmdzs+Q/2bWrpypNJvrpkIk1ng/kgwSyLvluPyEk/9i4UXKbZKsEcTJ0nBBCIk+uyE2askNXclW3nAn5Wz92BI3u2HgRtHRsGOdVqvGJx2V8xo/vmjy2qEieUB1zjGxlsG3JIzNzJvDMM/LewbYlSmXLFkkya6KfmVjj+UA+SCDrku/2E0LyjxdekL/lJ5wQdk/8Q8cJISTy5IrcpCk7dCVXdcuZkL/1Y0fQ6I6NF0FLx4ZxXrmNzzXXADfdJP9vaekqR7xgAbBqFbB4seTl2b1bvn/ZZT2f1Hd2yhafrVulfKb76XeN5wP5IIGsS77bTwjJL7ZtA955B6is7BnFmY0wOWyaMDksIeGRK3KTpuzQlVzVLWdC/taPHUGjOzZeBC0dG8Z55bV2jjpKEuzu3AmMGJGQIy4sBE48ETjvPOD994HNm4Ht24FHHxVnyQkndI0E6uwEGhrkprS0tGs+mkz0MxeuR6bIBwlkXfLdfpJXMDkswapV8rDjssskP1w6MDlsgFiW1RfASwD6ACgC8Jht29/r9p1rAFwOIAZgN4DLbNveqqqXyWEJIYSQ4GhrEwdK8vYdhxdfBG69VZwoADBuHPC97wGf+1zP71qWSBcPHmxWvpgQQsj/wOSwec6BA8ANN4gq3kUXpV8uInEKeZMctg3AGbZtfwrACQDOsSzrlG7fWQ/gZNu2jwfwGIAfBtxHQggJjNpaUXOoqJDX2lr/5aqq5OmBZclrVVVm+qpbb02N3BgXFclrTU16dlRWSjSCk6i0sjK9cipU5VT99FOvCt02ddvzQ3JfR44EHnpIFHO6R4yccQbw9NPAN78p3926VZ5onXCC/EBzpIMB2RKxZ49s60knSa9DGPYHjZ/1qMLEuRM0UeoLoH+NI4SQMFi9WqJyP//5sHuSOXIu4iQZy7LKAPwZwJW2bf/d5TtTACyzbTvFc6oEjDghhGQjtbXA1VeLVGtZmSQibG8Hli1Th4eryq1cKf+6M3euv60zVVV69dbUiNqKo9DS2ZmQv5s61d2OH/4QWLOmZ30zZkg+jUyPW12dez+9tvLozqNqbFRt6rbnB1Vfb7xRok+amnqW++1vgdtvl/4B4gQbOBC4446ujjCHdOSLw7A/aHTXhhcmrjlBj3mU+gLoX+NyZa2SrIQRJ3lMa6v83Z4wAfj2t3tXNsoRJznpOLEsqxDAOgATANxn2/Z1iu8uA/CRbdu3quqk44QQko2ccQbw4YeSo8GhqQk4/HDZ7qBT7uWXJVlmchSAk+ujo0O/r8XFevUOHiw3C8k5LmIxuYmYMsXdjjVruiqzOFiWOE8yPW7r17v3c98+9zq96lX1RzU2qjZ12/NDOn1tbOwpO3zJJfKZs7XHOdavH/Dww8Bxx/Vsy7JE6viww1LnPwnD/qDRXRtemLjmBD3mUeoLoH+Ny5W1SrISOk7ymNpa4IknZKtOb1XDouw4ycWtOrBtu9O27RMAjAbwacuyUvxsAizLqgJwMoA7XY5fYVnWq5Zlvbp7925zHSaEEEPU18uP62TKynqqkfSmXCyWuozb5+miW29jY89s7YWFsi1DZYfbcwPbNjNuqn56odsf3TZ12/NDOn0tL5cfYQMGJD7bsUMSwA4eLE+3Bg2Sz5uagAsukCf1jY1d67VtcQ7U10sS2e6EYX/Q+FmPKkycO0ETpb4A+tc4QvKB5Pu1gwd5vxY2ra3AH/8oDy1yTWo9Jx0nDrZt7wewBsA53Y9ZlnUmgJsAnGfbdptL+eW2bZ9s2/bJw4YNM9pXQggxQUWFPKlMprnZ+4+ZqlzyU89k3D5PF916y8u7RiAA8r5/f7UdbolCLcvMuKn66YVuf3Tb1G3PD+n2tbBQ8p+MHi1RSqNHJxLIFhUBo0YBRxwhzpR4XLZ5zZwJPPtsT2dZZ6eom2zb1jUJbRj2B42f9ajCxLkTNFHqC6B/jSMkH0i+X+vfn/drYbN2rTy4OPfcsHuSeXLOcWJZ1jDLsgYd+n8pgDMBvNXtO1MA/BfEabIr+F4SQkgwLFok+92bmuSmsalJ3i9apF9uzhz5Tjye+AckPtdFt96FC+W7sZj0NRaT9wsXqu1wJGe7M326mXFT9dML3f7otqnbnh9629eyMrk5XLBA+tbSIuVaWmT7zV13Sa6a0lKRQ1y4EJg/P/WT+NZWcZ44W33CsD9o/KxHFSbOnaCJUl8A/WscIYQESVubRJscc4w4dXONnHOcADgcwGrLsjYAqAPwR9u2n7Ysq8ayrPMOfedOAP0B/N6yrNcsy/q/YXWWEEJMMnOmJAk8/HDZmnD44eklDVSVW7FCErY6kSBFRf4TwwL69S5ZIkkSy8okF0pZWSLBpcqO1asll4kTeeLkNlm92sy4qfrphW5/dNvUbc8POn21LFHRue8+YMQI2XYzbJiU+fznxVHy7LPAmWfK9//yF2DWLLGlLUWs6YED4lg59dTg7Q8aP+tRhYlzJ2ii1BdA/xpHCCFB8tJLsrUwF6NNgBxNDmsCJoclhKRLbS1w552y97yiQp78ZeOP2Koq4JFH5OlmUZFEfqTjHPFjv27ZmhrgnnvkD3Z5uTyJ9XsDqEJ3bMJAd2xMzYXJudq3D9i7NxGtBMhN5EMPJXJ3OD97xo8Hvvxl4JVXJE/K6NHiaHEikfr0EfWd0tKe7eTKOR4G2TJ2QV9TCMkxmBw2z2hpEYfu6NESCapLlJPDFlZXVwfcj+xk+fLl1VdEZCYJIdHFkbFsaZEElvv2AU89BUyaBEycGHbv0seRBnZuQONxYONGYNMmYPZs93J+7Nct60h1xmKS86K9XfbYAu7bcfygOzZhoDs2pubC9FyVlkp/29vlyfyyZcD998v2GyeSybZlK8++fcDf/gbs3w8MGSKOlRdfFIfK+PFSpqFB6unbN6G+kyvneBhky9gFfU0hJAdZGkaj9967vHraNN6vhcHTTwP//jfwzW8mkrTrcNJJmeuTD1KuX0acpAkjTggh6RA1GUtddKWB/dgftOSuLrpjEwZByxF7tRfkXDU2SqLY1tauiiSdnTKHhYUJtZ2CgkR0yfDhImOcTEGBSBcPGiRbgHLhHA+DbLk+Bn1NISQHYcRJHrF3r0TknXwycOmlenVELD4hf+SICSEkLHJFGlJXGtiP/UFL7upiSo7ZBEHLEXu1F+RclZfLTXn39goKJL9Jebmo7xQWiuPro4/kpr6+vmdd8bgkmN26FXjvvdw4x8MgW66PQV9TCCEkm3n8cck5dv75YffELHScEEJIBskVaUhdaWA/9gctuauLKTlmEwQtR+zVXtBzNWCAOD2SpafjcblZHzNGtuBMmJAIK25tBXbtkm0aTjRKMu3tEh1x4EBXaeNsPMfDIFuuj0GvU0IIyVbq64G6OuALX5Btr7kMHSeEEJJBckUaUlca2I/9QUvu6mJKjtkEQcsRe7UX9Fw57Tk3wZ2d8n7ePEkE6+RCOeII+ec4v1askISlzzzT1UECSLm2NsmN0tEhUQjZeI6HQbZcH4Nep4QQko3YNvD738tDirPPDrs35mFy2DRhclhCSDpMnCiJDjdskND/MWOA226LpmqEitmzJdnpm28m8ndcfLG3cowf+3XLOska162TiIF+/YDrrjOngKE7NmGgOzam5iLouereXv/+wLXXAlddJTaNHw+8/TawZ48oAdxwA3D00cD69RJV8vzzwD//CZxwQiIqxUke+/bbEp0yYgTwve8B551nxoZcIluuj0GvU0JyECaHzQPq6oA//Qm44ALgyCP91RWRpLAOKdcvI04IISTDzJwpiQ43b5bXqN0UpMvcucDpp8tN4umny/t08GO/qmxtrSSXrKiQ19raxLElSyRpY0eHvKZ7g1NTI4kgi4rktaYmvXIrVkhbti2vmXKaVFZKDg7LktfKyvTK6Y6NqpxDb3PIpzsXqerVtV/F1KnAlCniGJkyBfjsZ4Fx4yTZq7OFx+lLUZEoAjzzDPCpT8lnf/0rcM45wHe/K5EmgNxYP/yw/GD81a+AyZNF0tg5ns649hYTdeqie94AZq6PJsZG95pC1ERpHRNC9Dl4EHj0UfmN+LnPhd2bYKCqTppQVYcQkk84sqElJZIPorlZQuqXLQvHEWSiP47kaEGBJH50tnHcfHM4N0mVlcCaNT0/nzEDWL3avZzu2KjKAWbmXzXmq1fr2a/Cy8arrpK+9OkjkQXt7Ym5r6mRm+aPP06oJQ0fDtxxh/pH4rp1wE03SZ2ZGrsonY9RO2+iNDZEDecqr6CqTo7z0EPA3/8uf+9Gj/ZfX8Q2dqRcv3ScpAkdJ4SQfCJqsqEm+hM1ydGCgtRRGJaVyKOSCt2xUZUDzMy/asy7J1x18LJfRbo2xmLyr6UFGDZMju3eLfLE8bhsydm7N1HHuecC11+f+G4yl1wiZQcMSCiz+B27KJ2PUTtvojQ2RA3nKq+g4ySHeest4J57JBrzK1/JTJ3Z4DjhVh1CCCE9iJpsqIn+RE1y1O05htfzDd2xUZUzNf+qMde1X0W6NhYVJSJE3n9ftt307SvHCgqAkSOBT3xCnpQDwNNPyw/GlSt7qq84ZTs65Gm6o+LjZ+yidD5G7byJ0tgQNZwrQrKf9nbZnjx8OPClL4Xdm2Ch44QQQkgPoiYbaqI/UZMctVyez7l97qA7NqpypuZfNea69qvojY2WJZETFRWStLS1tWd9J5wgyUwHDRJHQU0NcOGFwOuvJ74zenSibDwuPzIbGiSvigk7giZq502Uxoao4VwRkv0884xEVc6dm3iYkC/QcUIIIaQHUZMNNdGfqEmOOkoe6X7uoDs2qnKm5l815rr2q9Cx8YYbZM+2s3XHtuW1vR24/HLgf/0vydXw1a9KG6+/LooCt9wi0RiOzHFy2dZW4Otfl+1ImbYjaKJ23kRpbIgazhUh2c3WrcD/+3+SZP2Tnwy7N8FDOeI0oRwxISSfiJpsqIn+RE1ydN48YO1a+WECSAREOolRdcdGVc7U/KvGXNd+EzZOmiQ/Cv/1L2DnTtmqk+zcKS0FPv954NRTgY0bJf/Jhg3A448Dp5wCnHkm8M47InPslJ02TW4Um5pkW1ByjhA/dgRN1M6bKI0NUcO5yisoR5xjtLUBP/4xUFwMXHll5qNNskGOmMlh04TJYQkh6VJbC9x5p+znrqiQp2lh/jCsqZEkXo2NEma/cKH/mxzdOv2MjapNVb1Bz0fU5j9XUM1/VRXwyCMS/VBUBMyZk55EdDrrOB4XB8j+/anr6OgQSeJlyyS6BBDVnSVLUm9BWLsWePBB4IMPgCOPBBYv9r8+dM8NFVFbx1Hrjy65YgchHjA5bI7x618Df/mL/H056qjM1x+x+ISU65cRJ2nCiBNCSDo4costLaKqsW8f8NRT8pRt4sTg++NIh8Zi8pSgvV1u3AD9LRC6dfoZG1Wbzc3u9W7aFOx8RG3+cwXV/P/855Kk1VHeicclCmTTJmD2bL06k9exZUlURb9+EmHRPb9HYSFw4onArFmSGLa+Hti+HXj0UenLpz6ViC5Zu1babWuT+vbulUSzRx2lvz50zw1Ve1Fbx1Hrjy65YgchacCIkxzin/+UiMpzzgFOO81MG4w4ySEYcUIISYeoyS2akA7VrdPP2KjanDIleFldN6I2/7mCav4PHpT/FyRlbYvH5bsdHXp1qtbxvn3i8HCTSP7Tn4Bbb5WIEkCiTr73PdkT7kgVl5Ymvt/SAowYIVuS+vRxb1fHDtW5oStVnSty5GGQK3YQkgaMOMkR9u0T5/xhh8m2zN5sM+0NEYtPoBwxIYSYJmpyiyakQ3Xr9DM2qjbDkNV1I2rznyuo5j8WS13G7fN06lQxeLA4Q5JvfpP5/OdFdeDyy+UH5pYtwKWXAtdeK/lbHJljh759JUJl61Zg166eES1e6J4bKqK2jqPWH11yxQ5CSH4QjwP//d/yEML5m5bP0HFCCCEZJGpyiyakQ3Xr9DM2qjbDkNV1I2rznyuo5t/th5zXDzw/50ZRETBqlEQKdHdaAHIzvGiRhDY74cdPPy3RJjt3iqKIQ2ur1AVIHpUtW3qnvqN7bqiI2jqOWn90yRU7CCH5wfPPA2+/LXnDRowIuzfhQ8cJIYRkkKjJLZqQDtWt08/YqNoMQ1bXhI3EHdX8z5kj34nHE/+AxOc6daZLebnc9A4YkPr4pEmSpPa224BBg8ShsXcv8N57csPsyBzPn58o09kpzpVt28Sp4oXuuaEiaus4av3RJVfsIITkPv/+N/Dkk8DJJ0vCc8LksGnD5LCEkHSImtyiCelQ3Tr9jI2qzTBkdU3YSNxRzf/s2ZII9s03E7lNLr7YW1UnU+dGQYFEd5SWiiOke+4TywKOOQb46lclouTNN8U5sn+/yDled53IF3cnFpPIk1hM6i5wedSle26oiNo6jlp/dMkVOwhJAyaHzWL27BHp4aFDgauuCmaLDpPD5hBMDksIIYQQFbYtPzhVyWVffRWorgbefVfeDxsG3Hij3DxbLukUCwrkB+ygQRnvMiGEmIDJYbOU9nbghz+U6MgbbgCGDw+m3YjFJzA5LCGEZDM1NZKYsqhIXmtq/NdZWSk3ZZYlr5WViWNVVSJvalnyWlXlvz0/1NaKKkVFhbzW1qZ3zMS46aLqZ64Qho2qOVYd0+2rWznLEkfI2LESTZKKk0+W3CeLFkkkye7dsq3m8sslQWwq4nFJHLt1q0S19IZcWXNBn8de7eXKuJqAY0NIdmLbwMqVwI4dwGWXBec0yRa4VSdNuFWHEBImNTUiBxeLiROjvR1Yu1aOOaH6vaWyElizputnW7ZIvS+8IH88nW0H8TiwcaNsiZg9W9cKfWprgauvlpvGAQPkif5TT0nY+6ZN7sd++9vMj5sJGyZODLYvpgjDRtW5sXat+7HmZr2+pmNjUREwcKD8P1WeksJC4MQTgVmzRFGnvl5ymjzyiJxrJ5yQOulsZyfQ0CB2qLbv9Kav2YCJ65+f9nJlXE3AsSGH4FadLGTNGuC55+Rv0+mnB9s2t+rkENyqQwgJk8GD5UYveZ9pLCbqHaptASoKCrqqezhYlty0xWJdb8yc/BEdHXrt+eGMM4APP+wqAdvUJKomgPux9eszP266qGx48cVg+2KKMGxUnRuA+7EpU/T62lsb29okn0Vbm3udL7wA3Hqr1AtIwtnqauDUU93LFBQAhx0m23fctvjkypozcf3z016ujKsJODbkENyqk2W88w5wzz3AcccBV17p7ZjPNBGLT0i5fuk4SRM6TgghYVJUJKH/yTdIti1ODF1HhtvNVjLdHSdOu0FTUQEMGdLT/n375NXt2LZtmR83XVQ2bN4cbF9MEYaNqnPDtt2PjR6t11cdG53je/e6nz9NTcB99wG/+pXcpAPAuecC1xSTu4IAACAASURBVF8v23/cKCmRcGrHUeS3r1HExPXPT3u5Mq4m4NiQQ9BxEhHScUi8/bY46keMAF55hfm0wBwnhBCSvZSXS5h+Mp2douahi5vjxLLcM6gHkVk9FRUV8gQ4meZmeTKvOmZi3HRR9TNXCMNG1Ryrjun2VaecZcnN5LhxssUmFf36AYsXA6tWyTYeAHj6aUkau3JlTzsc2ttlP/qHHyYcLn76GkWCPo+92suVcTUBx4aQ7GL3buCLX5Tfd88+S6eJCjpOCCEkC1i4UCI+YjF5eheLyfuFC/XrdMsNMH06MGeO/D8eT/wDEp8HzaJFcoPY1CT2NzXJ+0WL1MdMjJsJG3KFMGxUzbHqmG5f/dhYUiIStMOGuTsujzpKHCW33SY/YBsbJefGhRcCr7/uXndjo+RK+fjjRFRLrqy5oM9jr/ZyZVxNwLEhJHtoaQHOPx/44APJRVRREXaPog2Tw6YJk8MSQsLEcXKsWyfJJvv1A667DliyRL/OefMk4aGj5GFZwIwZwOrVkgB20ybgzTcTuU0uvhhYscKvJXpMnCjJBTdskHwRY8bIjeXMmepjJsbNhA25Qhg2quZYdUy3r5mwsbRUEme2t6feamJZwDHHAF/9KrB/v5yHu3YBjz0m76dMAfr0SV13c7M4UUpKpI5cWHNBn8de7eXDuawLx4YcgslhI4Jb0tV4HPj614Hnnwd+9zvgC18Itl8RJ+X6ZcQJIYRkCVOnyg3T6NHyOnVqeuVU0pCLF4uzZPx4eV28OHFsxYpEnoiOjvSdJqakKOvqJNnrtm3yWleXOLZyJfDyy/LE/eWX5b3DkiWyv76jQ14zcbOlK406c6YkSNy8WV57czOh26ZuOb/z6JbLQ1WvrgS2ao5Vx1RrSjVuXvOYjh3FxXIujxjhnoRvyBDg9ttlPU+cKD90f/1rae/ZZ93HuKMDeP99+Xfmme591ZX49oNuvbrXP128rhuqNZDvcrx+rnOEkGC46Sbg0UeBH/5QnPTEGyaHTRMmhyWEhIkj8VhSIkkgm5vlafWyZeofpapygF6dJvrphSMPWlAgij+dnXITefPNkgk+2VHiMHeumQgZVV9MPQHXbVO3nIn1NnOm+vjKldGZR0B/jquqem9HLCYRJQcPutfb0QE89JAkkG1pkc9OO036M26cezknv0r3hJ1BXxu82vSzrqJENvWVEIMwOWxESLVhYvly4Fvfkn8PPJCeWECeQVUdP9BxQggJE12JR10ZX13ZSFNSlCp50IMHg5VODloa1U+buuVMrLcXX1Qff/nl6MwjoD/HxcX6djQ2igPFLREsIFEkt96amIeSEvnxe8UV8n9Vv4YNSyQ4Dfra4NVmJiWgwySb+kqIQeg4iQjdHSePPSb56s4+G3jySfnbQHpAVR1CCMlW6ut7yo2WlQFbtuiX063TRD+9aGyUJ//JFBYmnCapcPvcL6q+mEK3Td1yJtab1/EozaOfOfZjR3m5bJsbMMD9O6NGyRPC++6Tm/H2duCnPwVmzRIZSTc6OiQB4Pvvy/+DvjYA5tZVlMimvhJC8osXXpDox1NOEQcKnSa9g44TQgjJAkxIp5qQjTQlRamSBw1aOjkMiWPdNnXLmZLqVR2P0jz6mWO/dhQWAiNHSi4P1Y/aM88EnnkGmD9f6t6yRRI+X3utyEu60dQk3x09Wv6fjMlrAxCsBHRYZFNfCSH5wz/+AXz5y6Lc9vTTPR28xBs6TgghJAswIZ1qQjbSlBSlSh40aOnkMCSOddvULWdKqld1PErz6GeOM2VHWZnkLhk82P07/fpJQufHHwdOPFE+e/ppyaWxcqX7lh/bBi65RG7oGxqCuTYA4UhAB0029ZUQkh+8+ab8XRg+XFR0VH9XiDuUI04TyhETQsLEhHSqCdlIU1KUKnnQoKWTw5A41m1Tt5wpqV7V8SjNo585zqQdliVt9+sn/XBzhBx2mLR7xBHAP/8pzpC1a4GXXgKOPVZ+LHdn/HhxzLz1FrBzp8zF7bebuzYA4UpAB0U29ZUQg1COOCIMHSqqiQUFwJo1wNixYfcoK0i5fpkcNk2YHJYQEmVqa4E775T99RUV8nSTP9QF3bFRlaupAe65R3JhlJdLJIJJx4kfdO1X2ag65tWeibUa9HyEMf+2LYlp9+51lyEGgI8/Bn70I+APf5D3BQWyp33BAu+tRoMGiROme36X3hL09ShXrn/ZdF0hxAMmh40ADQ3Az38uzvGXXgKOPz7sHmUNKdcvI07ShBEnhJCo4shftrRIUsl9+4CnnpKnnhMnht27cNEdG1W53/5WpGpjMclB0d4uT/eBRKRCVNC135HqTWXj2rXux5qb1e2ZWKuqvpqYj6Dbc7AsoLRUbqjb2tyTzZaWAp//PHDqqcDGjeJo2bBBtvOMHAlMmOAuPdnaKj+0CwqAvn31+hn09ShXrn9hrStCDMGIk5BpaQF+/GNR+XruOWDq1LB7lFUw4sQPjDghhEQVyl+6Y0L+dP364OWIddG1X1eqd8oUfTli3bUatDx0GHLUqdi/H9izJ5FDJRXt7cBDD4kCT2urfHbaaRLFMG6cuv4+fUS+uLcJBIO+HuXK9S8q64qQDMGIkxDp6AB+8hPZNvrUU8AXvxh2j7IOyhETQkguQvlLd0zIn4YhR6yLrv26Ur1+5Ih1CXo+ojL/gwZJnpJkh0F3SkqAK64Q9Z3KSvnsz38Gzj0XWLZMHCtutLUBO3aIU6I3ktBBX49y5foXlXVFCMluOjtle8677wKXXkqnSSah44QQQrIcyl+6Y0L+NAw5Yl107deV6vUjR6xL0PMRpfkvKgJGjZLoClVektGjgZ/9TCJPDj9cHCY//SkwaxbwyivqNhobxTnhlVvFIejrUa5c/6K0rggh2Uk8Dvz618C//iVKbp/+dNg9yi3oOCGEkCyH8pfumJA/DUOOWBdd+3Wlev3IEesS9HxEcf7Ly8VRMGCA+ntnninRJ/Pni6NlyxZg3jzg2muB3bvdy9m2OE62bPGOgAj6epQr178oritCSPZg25IU/JVXJKrQiTIkmYPJYdOEyWEJIVGF8pfumJA/DUOOWBdd+3Wlev3IEesS9HxEdf4LCiQ6obRUkgK65T4pKQE+9zlxorz9tmzFeecd4Pe/F1uOPVbqSkU8LhEoLS2SPDZVlEvQ16Ncuf5FdV0RogmTwwbMc88Bzz4rDpPZsxNJwE86Kdx+ZSlMDusHJoclhBDz6MpxquRIoyRVGqW+RJF8GB8TNnav89prJUTbK6loPC5PKH/0I0k2CwCTJwNLl0ri2QcflDwno0dLlEqyuotlJeSLuztasmUeKf9LiDGYHDZAXnoJWLlSrvuXXtr1mszn/lqkXL90nKQJHSeEEGIWR46zoECeZHd2yo3dzTerb2YcOdKSEkkK2dwsofrLlslxt2NB38ip+hnFm8qgyYfxMWGjqs7KSmDnTkn0quLjj8XRsWqVvLcsiXgYOlTqbG2VOpMjjhwKC0V9x9kmlC3zqHu9IYSkBR0nAbFunSSDPe444More0YC0nGiBR0nfqDjhBBCzKIrx6mSIwWiI1WaK7KppsiH8TFho1edti3nTzrJXevqgOpqkbAE5FwcOVKcIq2t4iB5+OHUZfv2BYYPFwWHbJhHyv8SYhQ6TgLg3/8Wp3RFBfCd74jDujt0nGhBOWJCCCHRRVeOUyVHGiWp0ij1JYrkw/iYsNGrTssChgwBxo2T/Ccqpk4FHn9cHCWWJY6EHTuAbdskMuP9993LtrbK9959t2c7UZxHyv8SQrKZzZuBBx4Qp/RVV6V2mpDMUuT9FUIIIcQ85eU9nwCnI8dZUdHzCXeyHKnqWJB49TPfyYfxMWFjunWWlEji1P37JX+JKnns0UcDH3wgkRcHD8q/994T6eP2dvUP9FGjRKGnvFwcEZYVzXnUvd4QQkim0I0GeeMN4IYb5Jr+5z9LZCAxDyNOCCGERAJdOU6VHGmUpEqj1Jcokg/jY8LG3tY5aJA4MZIdLd2ZP1/qGjZMEsMWFsr7HTuAWbNE7lJVtr1dIjra2uQ1ivNI+V9CSDaydStw1llAnz7AH/9Ip0mQ0HFCCCEkEixZIokZy8qAjg55TSdR48yZssf38MPlCfnhhycSUaqOBU2U+hJF8mF8TNioU2dRkUSGHH54aknh6dPlvBs+XN5PmSI/1AsLZcvNvHnAd78rkStuZYcNAw4ckFwiS5eK/HGU0L3eEEJIWHz8MXDOORIt9/zzEnFIgoPJYdOEyWEJIYQQkmt0dsrWmoYG7+++9ZYkj12/Xt6XlwPXXAPMmZPaAZOMk2tlyBD5PyEkp2Fy2DTozVad1lbgC18A/vEPiTSZNs1cvwiTwxJCSA9qa0WVoqJCXmtrw+6RO7p9zSYbc4GaGnnKXlQkrzU15ttUzbHusVxBNR+m7A96XP20V1good6jRwPFxYnP164FLrlE6rvkEnn/yU8Cv/kNcOutsuWnsVGiSS66SPbcq7BtUfapr5dyQdpogqj1hxCSu8TjwNe/LvlMfv1rOk3CghEnacKIE0Jyj9pa4OqrJdFhWZmEPra3R3N7gG5fs8nGXKCmBrjlFlEgKSyUp/nxuNktAKo5BvSO5craUM3H1Klm7A/6nMtke/G4ODeeeELGrqREZIZbW6XOJUtkKw4gIeN33gmsWiXvCwqAuXOBBQvSS7BaViZbgdJRgojadSxq/SEkgjDiJA3SjTi55hrgnnuAH/0IuPZas30iAFzWLx0naULHCSG5xxln9FSjaGqSff8vvhhev1Kh29dssjEXGDy4p1JHLCY3V/v2mWlTNceA3rFcWRuq+ZgyxYz9QZ9zJtqbPl2kh/v2TXzW0iJ5Sx5+uOt36+pk+86mTfJ+2DDgxhvFgeC1JceygIEDgaFDxfHiRtSuY1HrDyERhI6TNEjHcXLvvZK0+j//U/7PrY6BwK06hBCSTH293EAlU1YmyQ+jhm5fs8nGXKCxsWeuh8JCkXM1hWqOdY/lCqr5MGV/0ONqor1t24ABA7o6nPr2FWdKd6ZOBR5/XJ6C9u0r+VIWLgQuv1zqUWHbIo9cXy+JZN2I2lqNWn8IIbnJH/4g0SZf+Qpw9910moQNHSeEkLylokKeRifT3CxSnVFDt6/ZZGMuUF4u20GS6exMb+uCLqo51j2WK6jmw5T9QY+rifYqKiTCpKhItqMUFMh2nVGjUn+/pESenD7zDFBZKZ/9+c/Al74E3HefbGNR0dkJ7NwpjpbW1tT9idJajVp/CCG5x7p1QFUVcMopwMqV3gm4iXnoOCGE5C2LFskP+qYmefLZ1CTvFy0Ku2c90e1rNtmYCyxcKHkiYjEZ71hM3i9caK5N1RzrHssVVPNhyv6gx9VEe8l1WpbI9XZ2At/8prrc6NHAAw+Is2TkSKnjJz8BZs0CXnnFu93WVnGefPRRV4dX1NZq1PpDCMktdu4Evvxl2fr4xBNAaWnYPSIAUFhdXR12H7KC5cuXV1/RG80oQkjkmTgRmDQJ2LBBfqiPGQPcdls0k/vp9jWbbMwFnMSZ69bJTWC/fsB115lLDAuo51j3WK6gmg9T9gc9ribaS1Xn7bcDs2eLg6Cjw72sZQGf+ARw4YXyvQ0bJL/PE08AW7cCJ57Yc5tLd9raZOuOZcn2n6it1aj1h5AIsjSMRu+9d3n1tGnZc7920kk9P2trk2i9zZtFdnjChOD7RVKvXyaHTRMmhyWEEPPU1opSR329hMMvWpTezYhuOeIOxzS/Uc3/H/4g6g7bt0uUyfz5CSdVd956S5LHrl8v78vLZc/+nDk9Q8/XrgUefBDYsSNR7xe+IOo7Xs4WQkikYHLYNOj+TN625bNf/AL43e/kOklCgclhCSGERBdH4vPDD4EhQ+T16qvlcxPliDsc0/xGNf+1tcDixRJFMmSIJIOtqRGnRyo++UngN78Bbr0VGDRIEvYuXQpcdBHw738nvrd2rdSze7co7Tj1/vGP4kj54AN1pAshhGQ7990nTpMbb6TTJIow4iRNGHFCCCFmoeRydOCY5je9kbju7AQaGlJLFXfn448limXVKnlfUCDJD7/zHeDb3xZnSfJe/u4SyJYlEtOHHUZ1CUIiDiNO0iA54mT1aomw++IXZWujSqKdGIcRJ4QQQqILJZejA8c0v+mNjHVhoUSIfPihd71DhgDf/74oREyYIIl6H35YtgC9+y7Qp0/X73eXQLZtcb7U10vkCiGE5AIffCARJkcdBaxYQadJVOG0EEIIiQSUXI4OHNP8prcy1i0twJFHSpLUkhLv+k8+GXj8ceDaa8U5smuXOES2bOkqXewmgRyLiaNm+3ZJpEgIIdlKZ6dE3jU1Sf6oAQPC7hFxg44TQgghkYCSy9GBY5rf6MpYl5YC48alt5WmpETC1J95BqislM9aWoBNm8SR0tws9c6f715HS4so9ezc2VW+mBBCsoXbbpNtOvfdJzmhSHSh44QQQkgkmDkTWLZM8ijs2yevy5Z5K7noliPucEzzG9X8e60NyxLHydixEk3ixejRwAMPyE3D4MHijNm9WyJKLrzQXa0nmQMHZPvOvn1SnhBCsoG1ayVZdlUV8I1vhN0b4gUdJ4SQnKe2VpIdVlTIay4qg1RVAcXFctNSXCzvs5GZMyX56ObN8prujbpuORU1NXIjV1QkrzU16ZVTrbdsWosmxtQUuuOaTfOhi66NqvlPZ2306SPOk+HDvffrWxZw5pnAn/4EXHaZ5E1pbQXuvhv47neBPXvEOXPyycDRR8vrsmWJ8mvXyjVv8mTgs5+VbUDpYGr+TdQbxlrVvQYSQrw5eBD42tdkm+P99zPhdTZAVZ00oaoOIdmJI6tZUiIJDZ3w71x6el5VJckWuzN3riQZI72npga45Ra54SsslG0A8Thw883AkiXu5VTrDcj9tRgGuud4PlwbomJjR4dsv2lqSu/7b70FVFcD69fL+5ISqaOwUP7F4/Lv298WZ0lNjXynb19xuLS3A7ffLpLHxcWp2zA1NibqDWMeda+BhByCqjoKbFui7N5+G/j734ETTgi7R6QbKdcvHSdp8v/bu/c4ucoq3//f1d3pXDqde0ICuRBQySBHwEHloIITAWlguOkIOCIiGGEmKlEioBIw8QgYFcEoF0euCuhPozBCIygieI7KgNwGQSUkJCEJBBJI0p2mb8/vj1U9Xel0daqqa/e+1Of9evVr195dl7V37apkr36etUicAOlUDW1Vhw3zYon5f9Xt7va/EnZ0xBdXmo0f7xcndXW92zo7/aJl8+bCjyuljWv+77JyLsaBNtaFJW0ft2zxaTjF1CPp7vZCid/4hvTaa76tZ0RdTY0/x4gR0r77Fm5jfMst0rhxPnWo76iXqI5NFM8bx/tY7ncgkEPiZAAPPCDddpv0ne94UhSJQztiANWnGtqqdnaWth27tnWr/5U1X22tD60dSCltXPN/h/LRxrqwpO3jmDHemaeYrhE1NdK//MuOU1JC8JEWHR2eRGltldau3bmWSk8b4xD8In/VKk/a5Ivq2ETxvHG8j+V+BwIY2CuvSMuXe9L33/897mhQChInADKtGtqq5v9FsJjt2LXGxp3/Kt7VJY0ePfDjSm3jmrVzMQ60sS4siftYWytNnepthov5jpowwT+PtbW9NQC6ujyBMmyYP09b246P6dvGuLNT2rBBWr26975RHZsonjeO97Hc70AAhXV3Szff7N9lp51GXZO0IXECINOqoa3qySf7smfef3f3jttRugUL/Dh2dvp509np6wsWDPy4ctu4ony0sS4syfvY0OAX/uPG7fq+H/+4L3tqnPRoa/N9am316Tkh+LJQG+O2Nk+ebNggfe5z0RybKI55HO9jud+BQLWaN2/XP5LXNbnqKumCC+KNF6UjcQIg06qhreoPf+iFYHv+eltXR2HYwVq0yIsgjhrl0wJGjSquKOJg2riiPLSxLizp+1hT4113Zs70wqeFzJ/vhWB76pj0JF0k6emnpVdf9UTC6697bZNFiwZuY7xlizRnjnTppT76pZLHJopjHsf7WO53IID+vfCCJzsPP1w666y4o0E5KA5bJIrDAkByNTdLS5d6LYDZs/0/J8VcVJT7OJRv8WLpiiu8hkJjo/8FO4kXY2mJczCS9LkJQdq0yX/6+6/p734n/eAHXtNk+nRvW9zRIX31qz6CRPJYLrlEOvjg4l932DBPtqR5CgrfY0igqisO2zOipD8hSB/4gPSHP0hPPZWtKaEZRXFYAED29LTqXL/e6yGsX+/r+UUlK/k4lK+nxWlrq48waG319cWL445sR2mJczCS9rkx8+43s2btXOz1d7/zY79xozR2rC+XLPH35u67PYlSW+uJg9NP98TBK68U97odHdK6dZ6QeeONwe1DHPgeA5Lv5pul++6Tvv51kiZpxoiTIjHiBACSiXa06ZGWFqdpiXMwkv65ee01T350d0sf+1jhlsM33+zrzz7ro00ee8zXx4zxOiYnn7xzK+KB9LQv7ttRJqn4HkNCMeIk57XXpLe8RXrTm6Tf/7607yPEhhEnAIDsoR1teqSlxWla4hyMpH9uxo3z0ScNDQO3HO4xZ450660+EmXsWK9jcsklnjj5y1+Kf93XXvN9ee21SuxF9PgeA5Lt4os9Cfzd75I0STvePgBAqtGONj3S0uI0LXEORho+Nz3thvfaa9cthyW/KPnwh6V77pFOPNG3Pfmk9MEPSl/7WvGJr64u6eWXvZhj331NGr7HgOR68kkv5Hz22dKBB8YdDQaLxAkAINVoR5seaWlxmpY4ByNNn5sLL/TXamvbdcthyWt9XHaZdMst0t57+3t3001eMLW5uf/is/154w0f7bJunddCSSK+x4BkCsHrDY0f70WskX4kTgAAqUY72vRIS4vTtMQ5GGn63DQ1+TD3mTM9MTBlyq5bDkvSO98p/eIX0uc/71N7Xn5ZOvdc6ZOflFavLv71t23zqS89NVeShO8xIJluvVV66CFP4k6YEHc0qASKwxaJ4rAA8mWl/eNQ70fSjlvS9j9pxwf9G8z7VA1tjqPW1eXFYrdsKf4xa9b4X30feMDXhw/34fNnneXdeYpVVydNmuTFZwvhcwxUd3HYlhbpzW/21ul//CO1TVKI4rAAUAlZaf841PuRtOOWtP1P2vFB/wbzPlVDm+OhUFsrTZ3qNU7yOx8NZMYM6ZprfDTG1Kk+DefKK6XjjvMLm2J1dkobNviIlb51VyQ+xwA8Ob5+vfTtb5M0yRJGnBSJEScAemSl/eNQ70fSjlvS9j9pxwf9G8z7VA1tjodad7dPoSmlC05Li/Sd73gr454iwMcdJ51/vo8mKcWYMd4WuacLE59jQFIVjzjZuNFrKx1+uLR8eSyhYPAYcQIAlZCV9o9DvR9JO25J2/+kHR/0bzDvUzW0OR5qNTVe82TGjOKn3DQ0SBdc4Bc1PZ0u7rzTp9PcfntpdUy2bPFzYvNmLwbJ5xiobj2jCi+9NO5IUGkkTgCgRFlp/zjU+5G045a0/U/a8UH/BvM+VUOb47iMHCnNmlVaEcY5c7yA45Il0tixngS5+GLplFOkZ54p/nm6u/2vzC+84AVs+RwD1WnFCp8SeNZZ0j77xB0NKo3ECQCUKCvtH4d6P5J23JK2/0k7PujfYN6namhzHCczn2ozc6YXfy1GTY304Q97DZITT/RtTzwhnXSS9LWvlTYaqL1d+uhHPVGybRufY6DafOlL0rBhnoBF9pA4AYASZaX941DvR9KOW9L2P2nHB/0bzPtUDW2Ok2DECE+eTJzoyZRiTJzobUNvucXrE3R3SzfdJB19tHTPPZ4EKcZhh0lf/rI0bpzXXpk6lc8xUA0efVT68Y+9/fm0aXFHgyhQHLZIFIcFAABIl/Z274LTXwecgR5zww3Sd7/r3Xck6dBDPcE1Y0Zprz9smBePZToWqkzVFYe9807pD3/wOkcDtStHKlAcFgCAatPc7J0+Zs/2JW1Rk2vxYu+8U1fny7S2KR7q/RjoHK+v99EnkycXP/qkvl761Keku+7yESSS9OCD0jHHSFdf7YmVYnV0SOvWSS++6I8r9/OYlXMDyKKVK/374rzzSJpkGYkTAAAyqrlZmj/f26NOmODL+fNJniTR4sW93Rjq6325ZEn6LpCHej+KPcfHj/cCrX073gxkxgzp2mu9dfHUqT765Nvflo4/XvrTn0qLs6XF2x+fc44nUkr5PGbl3ACy6pe/9Ol+8+fHHQmixFSdIjFVBwCQNnPn+sVZQ0PvtpYWn399//3xxYWdjR/vF8R1db3bOjv9Qn/z5vjiKtVQ70c55/jrr3sXnFLaDre0eK2Sm27q7Yx0/PHS+ef7BVMxPvYxf91Ro/z41NYW93nMyrmBqlI1U3VWrvT6SJde6m3OkQlM1QEAoJqsXLnzX9hHjZJWrYolHAxg61a/kM5XW1taV5ckGOr9KOccHzvWR5+UUnekocGTJMuXSwce6NvuuEM66ijp9tuLS8KsXeuFa0PwKTzt7b6+q89jVs4NIIv+8z/9+4HRJtlH4gQAgIyaPdv/Up2vtdUvGpEsjY29Ixl6dHWlr6joUO9Hued4XZ20++4+2qNvUmIgc+ZIt97qU2XGjpW2bPHWo6eeKj3zzMCPnT59xyK13d3++N13HzjxkpVzA8ialSulp5+WjjySz2M1IHECAEBGLVzof9VuafG/cre0+PrChXFHhr4WLPCL585Of686O319wYK4IyvNUO/HYM/xxkZPsjQ2Fv+aNTXShz/stUlOOMG3Pf64dNJJPly/0EiQM8/02LZv91i3b/f100/3C7DXX+//cVk5N4A0mzdv55+nn/Z6RTffHHd0GAokTgAAyKimJq/LMG2a10KYNs3Xm5rijgx9LVokXXSRTzPp6PDlRRf59jQZ6v2oxDleW+uP22OPHeuI7MrEidLll/tF0157eTLjxhulPl6FCAAAIABJREFUo4+WfvUrT3LkO+wwPw6TJ/tIk8mTff2ww3wEyUsvSS+84AmVfFk5N4Aseeopn6bz2c8y2qRaUBy2SBSHBQCgejQ3S0uX+kiA2bN9BEPPxfjixdIVV3jticZG/8t/Ui9iB9qPNBmq/eju9gKuhUZ/FNLeLl1/vfS973n3HUk69FA/L2bMKD2OxkZPrJSSyBlKWTmvEJnMFYedN2/H9X/9V+nOOz3ZOWFCJC+J+FAcFgAAYFcGanGbptawWWlHPZT7UVMj7bab1yMZNqz4x9XXS2efLd11l48gkaQHH5SOOUa6+mpPrJRi61YvGrtp084jV+KWlfMKKNfzz3tR6LPPJmlSTRhxUiRGnAAAUB0GanH72GPpaQ2blXbUce1HCNIrr5T+voYg3Xef9NWv+vQbyafyXHKJ9K53lR5Hfb2PPsnf/zhl5bxCpDI94uTss6UbbvDk5rRpkbwc4sWIEwAAgF0ZqMVtmlrDZqUddVz7YeYJi5kzPXlRyuOOPFK6+27pjDP8/Hj+eeljH5O+8AXp1VdLi6O9XXrxRf/p6CjtsVHIynkFlGP9ek+anHEGSZNqQ+IEAAAgz0AtbtPUGjYr7ajj3o8RI6RZs7wYrJXwd/TRo6ULLpCWL5cOOMC33XGH1wK5/faBWxD3p6XFkxOvvFL6Yysp7vcDiNOyZZ7ApDtd9SFxAgAAkGegFrdpag2blXbUSdgPM0+czJzpiZRSzJkj3Xab18EZO9YLz158sXTqqdIzz5T2XCF43ZNVq7wzTxyS8H4AcWhpka65RjrxRGnvveOOBkONxAkAAECegVrcpqk1bFbaUSdpP4YP9+TJ5MmljT6pqZFOPtkLqJ5wgm97/HHppJOkSy8tfapXZ6e0YYO0Zk1vF5+hkqT3AxhKN93kicvPfS7uSBAHEicAkBLNzV6Ub/ZsX9LBAHBRfDaamrzQ5fPP+zL/onDRIr9g7OjwZSlJk6H+HA+0H1GJch+T0tNg/HifmtK31ock/e53Xs9k7lxf/u53vb+bOFG6/HLp5pu9YGx3t3TjjdLRR0u/+lXp+7d9u7dDfemlnaeQSdGfb0l5P4CodXd7G/p3vlM65JC4o0EcSJwAQArQ/hHoX5o+G2mKtVxR7GNSj9uwYd62eLfdfESJ5EmSxYuljRt9Ws7Gjb6enzyRvLvOHXf4FK/hwz3x8ZnPeOeONWtKj+X1171o6+bNvcmMqI5bUt8PIEq//KX03HPS5z9f2mgzZAftiItEO2IAcaL9I9C/NH020hRruaLYxzQct85OT3588IOeLBk5svd327f71J6bb+7/sWvWSEuW9CZXhg+XzjlHOvPM0rr59Kivl6ZMkY49Nprjlob3A7HLXDviH/3IawutWLFjO3pkEu2IASCtaP8I9C9Nn400xVquKPYxDcetrk7aYw9PKOQnTSQvJvvii4UfO2OGdO210ne+I02d6jVLvv1t6fjjpT/9qfRY2tultWulv/9951gqcdzS8H4AlbRqlfTgg9JnP0vSpJqROAGAFKD9I9C/NH020hRruaLYxzQdt7339tEntbW929raPKkyEDPpyCOlu+6SzjjDH//8814j5QtfkF59tfRY9tjDp/D0dICSKnPc0vR+AJXw6197K/qzzoo7EsSJxAkApADtH4H+pemzkaZYyxXFPqbpuC1c6EWD29u9Bsr27X77zDOLe/zo0dIFF0jLl0sHHODb7rhDOuoo6cc/9gKVxTrzTH/trVt9FMuWLZU5bml6P4DB2rRJevRR6ZOflMaMiTsaxInECQCkAO0fgf6l6bORpljLFcU+pum45cf6+uveunjpUul97yvteebMkW67zQvLjhnjSY9Fi6RTT5Wefba45zjsMH/M5Mkey4QJ0sUXS//0TyXv1g7S9H4Ag/Xb3/ryM5+JNw7Ej1laAJASTU38xzQpmpv9YmjlSh+2vnAh702xyj12xTxuqOrdL17sbSm3bvXh2wsWFN+SuBo+x1HsY9KO20DnY3+xvvGGF49tayv+NWpqpJNPlg4/3FsY33GH9Pjj0kkn+RSe+fN9hMpADjvMf/KtXu0dfyZN2nFKUTnoMYEsa2uTHnpIevvbpVmz4o4GcWPECQAAJaAVZ/nKPXYDPW6o34/Fi70DSmurdy9pbfX1xYujeT0kTznn3PDhPvpk8uTSW5lOnCh9/evelWevvaSuLumGG6RjjpHuvbe85EV/7YuLxXcgqsX//b8+3e7ww+OOBElAO+Ii0Y4YACDRinMwyj12Az1OGtr3Y/x4T5bkd1bo7PSuIps3V/71kDyD/Q7o6PDRJ30LrBajvV26/nrpe9/zUSySTwP68pe9O085hg3zhM6uRq/04DsQRUh9O+Lubp/a1tjoBZrnzavI0yIdaEcMAMBg0YqzfOUeu4EeN9Tvx9atO09vqK2Vtm2L5vWQPIM954YNk6ZPl3bbzafjlKK+Xjr7bO++c+ihvu2BB6Rjj5WuucYTK6Xq6JDWrfMWxj3JmIHwHYhq8Oyz0ssvl16fCNlF4gQAgBLQirN85R67gR431O9HY6NPlcjX1VX8X+uRfpU658aO9ceUc+7MmCFdd5101VWegGlr87o7J5wgPfxw6c8n+T688IJfLPY9x/PxHYhq8MAD/n1/4IFxR4KkIHECAEAJaMVZvnKP3UCPG+r3Y8ECH8Ld2emv19np6wsWRPN6SJ5KnnN1ddLuu/s0l1ILtZpJH/iAdPfd0sc/7o9fsUI67TTp/POlV18tPR5Jeu01Hz3y2mv9/57vQGTdpk3Sk09K73mPjxADJBInAACUhFac5Sv32A30uKF+PxYtki66yKcmdHT48qKLiu+qg/SL4pxrbPQRG42NpT929Gjpwguln/1M2n9/3/aLX3g8P/6xJ/ZK1dXlI09eeGHn0SV8ByLrHnzQl+99b7xxIFkoDlskisMCAAAgai0tXjy2s7P0x3Z3Sz/5ifTNb0pbtvi2Aw6QvvIVac6c8mMaPdoLyPLXdxQp8cVhCxV7bW/3qXAHH+wtwFGVKA4LAAAQteZm7zwye7Yvs9amNU37l8ZY99tPOvNMqZy/19XUSKecIt1zj9c7kaTHH5dOOkm67LLyixhv2+bTd159tbz2x0BaLF/uo63+7d/ijgRJQ+IEAACgQpqbpfnzvV3rhAm+nD8/2RfspUjT/qU51g0bfArY00+XN8pj4kTp8sulm2+W9trLp97ccIN0zDHSvfeWl/wIwRMnK1d6dykgi773PWnvvaUjjog7EiQNiRMAAIAKWbrUW8Y2NHjxzoYGX1+6NO7IKiNN+5eFWL/zHa99Mn58ec/7rnf5dIMFC6Thwz0h8+lPe0vjNWvKe87OTk/wrFlTXPtiIC2eekp66CHpnHNKbxWO7OOUAAAAqJCVK71gbL5Ro3yaQxakaf+yEquZ1xeZOdOTKaWqr/dEyV13SYce6tseeEA69ljp2mu9pkM5tm/34rEvvTRw+2IgLa6+WhoxwrtUAX2ROAEAAKiQ2bN37kLS2uqjBrIgTfuXtVhHjJBmzfJpOFZG6c0ZM6TrrpOuukrabTeprU361re8FsrDD5cf++uv97Yvpv4J0mrLFumWW7xG0MSJcUeDJCJxAgAAUCELF/pf8Fta/CKypcXXFy6MO7LKSNP+ZTFWM7+omznTp96Uykz6wAeku+/2v6rX1EgrVkinnSZdcIG0aVN58Q/UvhhIgx/+0IsgUxQWhZA4AQAAqJCmJmnZMmnaNGnzZl8uW+bbsyBN+5flWIcP9+TJpEnljT4ZPVq68ELvILL//r7t5z+XjjrK2xl3d5e3H+3t0tq10rp1UkdHec8BDLUQpGuukf7xH6V3vCPuaJBUFhhTV5SDDjooPFJOXzgAGALNzV5ccOVKH/K9cGEyLw4AVN7ixdIVV3ink8ZGLwS6aBHxRClJ37nt7V70ta2tvMd3d3uy5Jvf9OkKknTggdIll0hz5pQfl5kXtZ0wgUKbVaiMdN7gzZp1UPjSl4q7Xps3r/f2o49KBx3kHXXOOSei4JAm/Z6/fI0BQMqlqeUmgMpavFhassSnR9TX+3LJEt9OPNFI2nduff3gRp/U1Hhdh+Zm6fjjfdtjj0knnSRddplPGypHCD71Z9Wq3oQMkEQ33OA1hE49Ne5IkGSMOCkSI04AJNXcuf4f94aG3m0tLT7U+/7744sLQPTGj/fkRF1d77bOTu/Isnkz8UQhyd+57e3e5Wb79vKf449/9NEmK1f6+tSp0pe/LB1+eHmJmR4jRkhTpvgSmZeaESdtbf7ZbWqSbr01wuCQJow4AYAsSlPLTQCVtXWrVFu747baWi9ySDzRSPJ3bn29d8+ZPLn8JMfBB0t33imde67XUtmwwUfUnH22tGZN+bG1tUmrV3tiB0iKO+7wjlCf+ETckSDpSJwAQMqlqeUmgMpqbPSOJvm6urz4J/FEIw3fuePHezwjR5b3+Pp6r/Vw113SoYf6tgcekI49Vrr2Wh/ZUq5yp/4AUbj+ep/qNndu3JEg6UicAEDKpanlJoDKWrDAi3t2dvrnv7PT1xcsIJ6opOU7d9gwH30yZUr5xVlnzJCuu0666ip/nrY26Vvfkk44QXr44crGCwy1F1+U7rtPOv10Chhj1zhFACDl0tRyE0BlLVokXXSRTxXp6PDlRRfF18UmafFEIW3fuePG+eiT/JospTCTPvABLx7bc4G5YoV02mnSBRd4AVggjX78Y09+fvSjcUeCNKA4bJEoDgsAAJCsVrwozZYt0saNO0+nKsVf/uLFY594wtfHjpXOO0/60IeK+6t9XZ20117lvz5SIRXFYd/xDh8R9+ijEQeGtKE4LAAAAMqXtFa8KM2YMT76pLGx/OfYd1/p9tulr3zFn+/1131U0Uc+Ij37bMVCBSL1979Ljzzi5y1QDBInAAAAKMrSpV44tKHBp3A0NPj60qVxR4Zi1db69KLdd9+xbXQpamqkU07xhNlxx/m2xx6TTjpJuvxyCsAi+W67zb/DTj457kiQFiROAAAAUJQkt+JFaUaP9tEnY8eW/xyTJnnS7KabfNpWV5d3KTn6aC+6SUUAJFEI0q23eseo6dPjjgZpQeIEAAAARUlDK14Ur6ZG2m03v3gcNqz85zn4YOnOO6Vzz5WGD5c2bPApXGefLa1ZU7l4gUpYu1b661991BRQLBInAAAAKEpaWvGiNKNGSbNmeQeectXXS+ecI911l/Te9/q2Bx6Qjj1WuvZaP0+AJHj8cZ+mc+KJcUeCNCFxAgAAgKKkrRUvildTI02ZIs2Y4UmQcs2YIX3/+9KVV/rztbVJ3/qWdMIJ0sMPVy5eoFxPPCEdcoiPtgKKlbnEiZmNMLOHzewJM3vazL7Sz32Gm9mPzew5M/uTme059JECAACkT1OTdP/90vPP+3IokibNzdLcuT5VaO7cbHbxSco+jhzpo08mTPC/ypfDTDrqKN+H00/3pMyKFdJpp/nopI0bKxszsCvz5vnPkUf69LETTog7IqRN5hInkt6QNDeEsL+kAyQdZWYH97nPmZI2hxDeJOkKSZcPcYwAAAAoQjW0QE7aPpp54deZM71mSblGj5a++EXpZz+T3vY237Z8ubTPPtKf/1yZWIFS3HGHL0mcoFSZS5wEty23Oiz307em9/GSbsrd/qmk95uVm1MHAABAVKqhBXJS93H4cE+eTJpU/ugTSdp3X+n226VLLpHGjJEmT5be+taKhQkU7ec/l/bbT3rTm+KOBGmTucSJJJlZrZk9LullSfeFEP7U5y57SFojSSGETkmvS5rYz/PMM7NHzOyRjYwpBAAAGHLV0AI5yfto5qNgZs3yaTzlqq2VTj3V2xT/5CeDG8kC9Mi/Xtu2beDrtU2bpIceko4/foiCQ6ZkMnESQugKIRwgabqkd5rZfn3u0l/OfKdO8yGE60IIB4UQDpo8eXIUoQIAAGAA1dACOQ37WF/vhV+nTPGaJeWaNEnaf//KxYXqln+9Nnr0wNdrv/mN1N0tHX30EAWHTMlk4qRHCOE1SQ9IOqrPr9ZKmiFJZlYnaaykTUMaHAAAAHapGlogp2kfx43z0Sd9R8gASXfffT5V7J3vjDsSpFHmEidmNtnMxuVuj5R0uKRn+9ztTkmn525/SNL9IYSdRpwAAAAgXtXQAjlt+zhsmDR9ujR1qk/BAZIuBOnee71jVV1d3NEgjbJ42kyTdJOZ1coTQz8JIfzSzBZLeiSEcKekH0i6xcyek480OSW+cAEAADCQpqbkJhEqJY37OGaMjzx5+WVp27Zd3x+Iy3PPSS+8IJ1/ftyRIK0ylzgJITwp6cB+ti/Ku90m6V+GMi4AAABkX3Ozd8NZudJrlyxcmL6ESCnq6qTdd/fEycsvS52dcUcE7Ozee315xBHxxoH0ytxUHQAAACAOzc3S/PnS+vXeiWb9el9vbo47suiNHu21T8aMiTsSYGe//rUXW95777gjQVqROAEAAAAqYOlS7z7T0OBtfBsafH3p0rgjGxq1tV73ZI89vA4KkAQhSL//vfS+9/nnEigHiRMAAACgAlau3LnbzKhR0qpVsYQTm4YGH30yblzckQA+heyVV6R3vzvuSJBmJE4AAACACpg9W2pt3XFba6tPEag2NTXSlCnSjBk+6gaIy3PP+ZLECQaDxAkAAABQAQsXSu3tUkuLTw9oafH1hQvjjiw+I0f66JMJE5gmgXg8/7w0fry0zz5xR4I0I3ECAAAAVEBTk7RsmTRtmrR5sy+XLct2V51imEmTJkkzZ0rDh8cdDarNihXSIYf4KCigXJw+AAAgEZqbpblzfbrD3LnV0YkkjZL2PiUtnqYm6f77/a/c99+f3qRJFMd1+HAvHAsMlZYW7251yCFxR4K0I3ECAABiV81tXNMkae9T0uLJCo4rsmL1al++4x3xxoH0I3ECAABiV+1tXNMiae9T0uLJCo4rsmLtWl8ecEC8cSD9SJwAAIDY0cY1HZL2PiUtnqzguCIr1qzxttiTJ8cdCdKOxAkAAIgdbVzTIWnvU9LiyQqOK7JizRpviQ0MFokTAAAQO9q4pkPS3qekxZMVHFdkQUeHtGGDNH163JEgC0icAACA2NHGNR2S9j4lLZ6s4LgiC9atk7q7GXGCyqiLOwAAAADJL8q4MEu+pL1PSYsnKziuSLv16325++7xxoFsIHECAAAAAEi1yZOlefN619eulWpqpAsvjC8mZAdTdQAAAAAAmfK3v3mh4/r6uCNBFpA4AQAAAABkyt/+Jr3lLXFHgawgcQIAAAAAyIwQSJygskicAAAAAAAyY/16b6NN4gSVQuIEAAAAAJA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      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(x= 'fixed acidity',y= 'pH', data=Newdata,\n",
    "             kind = 'reg',height=15,color='blue')\n",
    "\n",
    "plt.xlabel('Fixed acidity',size=30)\n",
    "plt.ylabel('pH',size=40)\n",
    "plt.title('Fixed acidity vs. pH',size=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From interpreting the Regression Join plot above, we can see a STRONG Negative correlation between pH level & fixed acidity. According to the HeatMap, these two features have a correlation coefficent of -0.71! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Tasty & Non-Tasty Red Wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(26.411744791666678, 0.5, 'Volatile Acidity Level')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"quality\", y=\"volatile acidity\", hue=\"tasty\", kind=\"bar\", data=Newdata);\n",
    "\n",
    "plt.title('Tasty & Non-Tasty Wine with Volatile Acidity Level',size=19)\n",
    "plt.xlabel('Wine Quality',size=16)\n",
    "plt.ylabel('Volatile Acidity Level',size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot illustrates that high volatile acidity levels yields a terrible tasting wine. This is as we expected because, large quantities of acetic acid yield an unpleasant vinegar taste!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The advantages of showing the Box & Violin plots is that it shows the basic statistics of the data, as well as its distribution. These plots are often used to compare the distribution of a given variable across some categories.\n",
    "It shows the median, IQR, & Tukey’s fence. (minimum, first quartile (Q1), median, third quartile (Q3), and maximum).\n",
    "In addition it can provide us with outliers in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sulphate Level')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x=\"quality\", y=\"sulphates\", hue=\"tasty\", data=Newdata )\n",
    "plt.title(\"Tasty & Non-Tasty Wine with Sulphate Level\", fontsize=20)\n",
    "plt.xlabel(\"Wine Quality Level\",fontsize=16)\n",
    "plt.ylabel(\"Sulphate Level\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we examine this boxplot which helps further conclude that tasty red wine exhibit a heightened median for Sulphate Levels. If we recall back from our chemistry lessons, we remember that sulfates act as a additive which can contribute to sulfur dioxide gas (S02) levels, which is an antimicrobial!\n",
    "\n",
    "Note: A antimicrobial is an agent that kills microorganisms & stops their growth. \n",
    "\n",
    "We can see now why these high sulfate levels would enhance a customers preference!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percent alcohol content ')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.violinplot(x=\"quality\", y=\"alcohol\", hue=\"tasty\", inner='quartile',data= Newdata )\n",
    "plt.title(\"Tasty & Non-Tasty Wine with Percent alcohol content\",fontsize=20)\n",
    "plt.xlabel(\"Wine Quality Level\", fontsize=16)\n",
    "plt.ylabel(\"Percent alcohol content \", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After analyzing this violin plot we can conclude that the overall shape & distribution for tasty & non-tasty wine differ vastly. Tasty red wine exhibit a surplus median for percent alcohol content & thus a great distribution of their data is between 10 & 13,  while non-tasty red wine consists of a lower median Alcohol level content between 9.5 & 11.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>tasty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.485642</td>\n",
       "      <td>0.477541</td>\n",
       "      <td>0.295286</td>\n",
       "      <td>2.400762</td>\n",
       "      <td>0.079785</td>\n",
       "      <td>14.560356</td>\n",
       "      <td>36.626429</td>\n",
       "      <td>0.996481</td>\n",
       "      <td>3.311868</td>\n",
       "      <td>0.682579</td>\n",
       "      <td>10.833990</td>\n",
       "      <td>6.275731</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.748845</td>\n",
       "      <td>0.162388</td>\n",
       "      <td>0.197117</td>\n",
       "      <td>0.871710</td>\n",
       "      <td>0.022654</td>\n",
       "      <td>9.061416</td>\n",
       "      <td>21.715608</td>\n",
       "      <td>0.001856</td>\n",
       "      <td>0.137032</td>\n",
       "      <td>0.127242</td>\n",
       "      <td>1.054157</td>\n",
       "      <td>0.490581</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.991500</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.105000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.067000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.995220</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.077000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.996400</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.650000</td>\n",
       "      <td>0.582500</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>0.087000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.997600</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.400000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>0.226000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>1.002200</td>\n",
       "      <td>3.710000</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count     787.000000        787.000000   787.000000      787.000000   \n",
       "mean        8.485642          0.477541     0.295286        2.400762   \n",
       "std         1.748845          0.162388     0.197117        0.871710   \n",
       "min         5.100000          0.120000     0.000000        1.200000   \n",
       "25%         7.200000          0.360000     0.105000        1.900000   \n",
       "50%         8.100000          0.460000     0.310000        2.200000   \n",
       "75%         9.650000          0.582500     0.450000        2.550000   \n",
       "max        13.400000          1.040000     0.760000        6.700000   \n",
       "\n",
       "        chlorides  free sulfur dioxide  total sulfur dioxide     density  \\\n",
       "count  787.000000           787.000000            787.000000  787.000000   \n",
       "mean     0.079785            14.560356             36.626429    0.996481   \n",
       "std      0.022654             9.061416             21.715608    0.001856   \n",
       "min      0.038000             1.000000              6.000000    0.991500   \n",
       "25%      0.067000             7.000000             20.000000    0.995220   \n",
       "50%      0.077000            13.000000             31.000000    0.996400   \n",
       "75%      0.087000            19.000000             48.000000    0.997600   \n",
       "max      0.226000            45.000000            114.000000    1.002200   \n",
       "\n",
       "               pH   sulphates     alcohol     quality  tasty  \n",
       "count  787.000000  787.000000  787.000000  787.000000  787.0  \n",
       "mean     3.311868    0.682579   10.833990    6.275731    1.0  \n",
       "std      0.137032    0.127242    1.054157    0.490581    0.0  \n",
       "min      2.880000    0.400000    8.700000    6.000000    1.0  \n",
       "25%      3.220000    0.590000   10.000000    6.000000    1.0  \n",
       "50%      3.310000    0.660000   10.800000    6.000000    1.0  \n",
       "75%      3.400000    0.760000   11.600000    7.000000    1.0  \n",
       "max      3.710000    1.160000   13.600000    8.000000    1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yummy = Newdata[Newdata['tasty']==1]\n",
    "yummy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>tasty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.101958</td>\n",
       "      <td>0.576770</td>\n",
       "      <td>0.229940</td>\n",
       "      <td>2.371310</td>\n",
       "      <td>0.083369</td>\n",
       "      <td>15.750000</td>\n",
       "      <td>52.161145</td>\n",
       "      <td>0.996981</td>\n",
       "      <td>3.320753</td>\n",
       "      <td>0.595181</td>\n",
       "      <td>9.931702</td>\n",
       "      <td>4.929217</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.490911</td>\n",
       "      <td>0.159709</td>\n",
       "      <td>0.177049</td>\n",
       "      <td>0.850899</td>\n",
       "      <td>0.018601</td>\n",
       "      <td>9.562567</td>\n",
       "      <td>34.721033</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>0.145722</td>\n",
       "      <td>0.116208</td>\n",
       "      <td>0.723074</td>\n",
       "      <td>0.256656</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.039000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.992560</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.996100</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.996900</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.825000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.250000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>0.997823</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.500000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>0.186000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>1.001800</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count     664.000000        664.000000   664.000000      664.000000   \n",
       "mean        8.101958          0.576770     0.229940        2.371310   \n",
       "std         1.490911          0.159709     0.177049        0.850899   \n",
       "min         5.000000          0.180000     0.000000        1.200000   \n",
       "25%         7.100000          0.460000     0.080000        1.900000   \n",
       "50%         7.800000          0.580000     0.220000        2.200000   \n",
       "75%         8.825000          0.670000     0.350000        2.600000   \n",
       "max        13.500000          1.040000     0.790000        6.600000   \n",
       "\n",
       "        chlorides  free sulfur dioxide  total sulfur dioxide     density  \\\n",
       "count  664.000000           664.000000            664.000000  664.000000   \n",
       "mean     0.083369            15.750000             52.161145    0.996981   \n",
       "std      0.018601             9.562567             34.721033    0.001489   \n",
       "min      0.039000             3.000000              6.000000    0.992560   \n",
       "25%      0.074000             8.000000             23.000000    0.996100   \n",
       "50%      0.080000            14.000000             43.000000    0.996900   \n",
       "75%      0.090000            21.250000             72.000000    0.997823   \n",
       "max      0.186000            47.000000            145.000000    1.001800   \n",
       "\n",
       "               pH   sulphates     alcohol     quality  tasty  \n",
       "count  664.000000  664.000000  664.000000  664.000000  664.0  \n",
       "mean     3.320753    0.595181    9.931702    4.929217    0.0  \n",
       "std      0.145722    0.116208    0.723074    0.256656    0.0  \n",
       "min      2.880000    0.330000    8.500000    4.000000    0.0  \n",
       "25%      3.220000    0.520000    9.400000    5.000000    0.0  \n",
       "50%      3.320000    0.570000    9.700000    5.000000    0.0  \n",
       "75%      3.410000    0.640000   10.300000    5.000000    0.0  \n",
       "max      3.750000    1.120000   13.000000    5.000000    0.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notYummy = Newdata[Newdata['tasty']==0]\n",
    "notYummy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Tasty Wine Sulphates level): 0.6825794155019054\n",
      "(Non-Tasty Wine Sulphates level): 0.5951807228915662\n"
     ]
    }
   ],
   "source": [
    "print(\"(Tasty Wine Sulphates level): \" + str(yummy['sulphates'].mean()))\n",
    "print(\"(Non-Tasty Wine Sulphates level): \" + str(notYummy['sulphates'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Tasty Wine Alcohol content level): 10.833989834815748\n",
      "(Non-Tasty Wine Alcohol content level): 9.931701807228912\n"
     ]
    }
   ],
   "source": [
    "print(\"(Tasty Wine Alcohol content level): \" + str(yummy['alcohol'].mean()))\n",
    "print(\"(Non-Tasty Wine Alcohol content level): \" + str(notYummy['alcohol'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Tasty Wine Total Sulfur Dioxide level): 36.62642947903431\n",
      "(Non-Tasty Wine Total Sulfur Dioxide level): 52.161144578313255\n"
     ]
    }
   ],
   "source": [
    "print(\"(Tasty Wine Total Sulfur Dioxide level): \" + str(yummy['total sulfur dioxide'].mean()))\n",
    "print(\"(Non-Tasty Wine Total Sulfur Dioxide level): \" + str(notYummy['total sulfur dioxide'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, the averages between the tasty & non - tasty wine features differ vastly. For example, Tasty Red Wine consists of minimized levels of total sulfur dioxide. In addition, Tasty Red Wine consists of sulphate & alcohol levels that are substantially higher. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "If I was to tell the vinter the perfect ratio for each ingredient to maximize his Red Wine sales, I would tell him to include low levels of sulfur dioxide, and high levels of sulphates & alcohol."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Machine Learning + Predictive Analytics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Data for Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To prepare data for modeling, just remember ASN (Assign,Split, Normalize)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To swap the the indexes of last 2 columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = Newdata[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
    "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
    "       'pH', 'sulphates', 'alcohol','tasty','quality']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
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       "  <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",
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       "      <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>0</td>\n",
       "      <td>5</td>\n",
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       "    <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>0</td>\n",
       "      <td>5</td>\n",
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       "    <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",
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       "      <td>6</td>\n",
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       "      <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>0</td>\n",
       "      <td>5</td>\n",
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       "    </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>0</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>1</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>1</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>0</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>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1451 rows × 13 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  tasty  quality  \n",
       "0         9.4      0        5  \n",
       "1         9.8      0        5  \n",
       "2         9.8      0        5  \n",
       "3         9.8      1        6  \n",
       "4         9.4      0        5  \n",
       "...       ...    ...      ...  \n",
       "1594     10.5      0        5  \n",
       "1595     11.2      1        6  \n",
       "1596     11.0      1        6  \n",
       "1597     10.2      0        5  \n",
       "1598     11.0      1        6  \n",
       "\n",
       "[1451 rows x 13 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assign the 11 features to X, & the last column to our continuous  predictor, y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split: the data set into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize: Standardizing the data will transform the data so that its distribution will have a mean of 0 and a standard deviation of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling /Training\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we’ll Train various Regression Models on the Training set & see which yields the highest accuracy. We will compare the accuracy of Multiple Linear Regression, Polynomial Linear Regression, SVR (Support Vector Regression), Decision Tree Regression, Random Forest Regression, and XGBoost.These are all supervised learning models, for predicting continous values. \n",
    "\n",
    "Note: There are a few metrics for measuring accuracy for a regression model like Root Mean Squared Error (RMSE), Residual Standard Error (RSE), and Mean Absolute Error (MAE). But we will be measuring our models with R-squared. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Model 1: Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7513164915658546"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model on whole dataset\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train,y_train)\n",
    "\n",
    "#Predicting Test Set Results\n",
    "y_pred = regressor.predict(x_test)\n",
    "\n",
    "# Evaluating Model Performance\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 2: Polynomial Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#assign\n",
    "X2 = df.iloc[:, :-1].values\n",
    "y2 = df.iloc[:, -1].values\n",
    "\n",
    "#split\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train2, x_test2, y_train2, y_test2 = train_test_split(X2,y2,test_size = 0.2, random_state = 1)\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "poly_reg = PolynomialFeatures(degree = 4)\n",
    "x_poly = poly_reg.fit_transform(x_train2)\n",
    "lin_reg_2 = LinearRegression()\n",
    "lin_reg_2.fit(x_poly,y)\n",
    "\n",
    "y_pred2 = lin_reg_2.predict(poly_reg.transform(x_test2))\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test2,y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 3: SVR (Support Vector Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7731226625204863"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assign\n",
    "X3 = df.iloc[:, :-1].values\n",
    "y3 = df.iloc[:, -1].values\n",
    "\n",
    "#split\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train3, x_test3, y_train3, y_test3 = train_test_split(X3,y3,test_size = 0.2, random_state = 1)\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "regressor3 = SVR(kernel='rbf')\n",
    "regressor3.fit(x_train3,y_train3) # replace by x_train , y_train if we split\n",
    "\n",
    "regressor3.predict(x_test3)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test3,y_pred3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 4: Decision Tree Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7412566686425608"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign\n",
    "X4 = df.iloc[:, :-1].values\n",
    "y4 = df.iloc[:, -1].values\n",
    "\n",
    "# Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train4, x_test4, y_train4, y_test4 = train_test_split(X4,y4,test_size = 0.2, random_state = 4)\n",
    "\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor4 = DecisionTreeRegressor(random_state = 0)\n",
    "regressor4.fit(x_train4,y_train4) # replace by x_train , y_train if we split\n",
    "\n",
    "y_pred4 = regressor4.predict(x_test4)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test4,y_pred4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 5: Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8273283765249404"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign\n",
    "X5 = df.iloc[:, :-1].values\n",
    "y5 = df.iloc[:, -1].values\n",
    "\n",
    "# Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train5, x_test5, y_train5, y_test5 = train_test_split(X5,y5,test_size = 0.2, random_state = 6)\n",
    "\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor5 = RandomForestRegressor(n_estimators = 10, random_state=0)\n",
    "regressor5.fit(x_train5,y_train5) # replace by x_train , y_train if we split\n",
    "\n",
    "y_pred5= regressor5.predict(x_test5)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test5,y_pred5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 6: XGboost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6805024182487549"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "model7 = XGBClassifier(random_state=1)\n",
    "model7.fit(x_train, y_train)\n",
    "y_pred7 = model7.predict(x_test)\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test5,y_pred7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Intepreting R^2 Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R^2 is a statistical measure of how close the data are to the fitted regression line.\n",
    "Higher R squared, the better. The best R^2 value is 1.0\n",
    "\n",
    "From examining our regression models, we can conclude that Model 5: Random Forest Regression yields the highest accuracy, with an accuracy of 83%!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataa = df.drop(['quality','tasty'], axis=1)\n",
    "# DONT include"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "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>tasty</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>0</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>0</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>0</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>1</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>0</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",
       "      <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>0</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>1</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>1</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>0</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>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1451 rows × 13 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  tasty  quality  \n",
       "0         9.4      0        5  \n",
       "1         9.8      0        5  \n",
       "2         9.8      0        5  \n",
       "3         9.8      1        6  \n",
       "4         9.4      0        5  \n",
       "...       ...    ...      ...  \n",
       "1594     10.5      0        5  \n",
       "1595     11.2      1        6  \n",
       "1596     11.0      1        6  \n",
       "1597     10.2      0        5  \n",
       "1598     11.0      1        6  \n",
       "\n",
       "[1451 rows x 13 columns]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scenario: Lets predict the quality of a red wine a company makes based on its ratio of each ingredient. We will now input the ratio of each ingredient into our Machine Learning Algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.5]\n"
     ]
    }
   ],
   "source": [
    "print(regressor5.predict(([[7.9,0.59,0.004,1.9,0.062,49.0,33.0,0.99513,3.21,0.53,8.9,1]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The red wine ingredients consisting of...\n",
    "\n",
    "7.9 of Fixed Acidity, \n",
    "\n",
    "0.59 of Volatile Acidity,\n",
    "\n",
    "0.004 of Citric Acid,\n",
    "\n",
    "1.9 of Residual Sugar after fermentation stops,\n",
    "\n",
    "0.062 of Chlorides Salt, \n",
    "\n",
    "49.0 of Free Sulfur Dioxide,\n",
    "\n",
    "33.0 of Total Sulfur Dioxide,\n",
    "\n",
    "0.9915 Density of water,\n",
    "\n",
    "pH of 3.21 (acidic), \n",
    "\n",
    "0.53 grams of sulfates,\n",
    "\n",
    "and 8.9 percent of alcohol content\n",
    "\n",
    "yields a Red Wine Quality Score of 6.5!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Fold Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-Fold Cross Validation is a statistical method that ensures us to have a better measure of our model pefromance. We run our modeling process on different subsets of the data to get multiple measures of model quality. We divide our data by a specific amount of \"folds\". \n",
    "\n",
    "K-Fold Cross Validation allows every observation from the original data set to appear in our train & test set.\n",
    "\n",
    "When we create 20 different Test folds, we reduce risk of getting lucky. Final accuracy we get will be the average of the 20 test folds!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 80.44 %\n",
      "Accuracy: 4.48 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = regressor5, X = x_train5, y = y_train5, cv = 20)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100)) # float w 2 decimals after comma\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.std()*100)) # float w 2 decimals after comma\n",
    "# the 20 accruaceis lie within  % , we have a high std. \n",
    "#(Mean - Std, Mean + Std) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 20 accruaceis lie within (80.44-4.48,80.44+4.48)% = (75.96,84.92)% Confidence Interval. We have a high standard deviation, which means that our numbers are spread out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Out of the 11 features we examined, the top 3 significant features that help the vinter brew a delicious Red Wine are low levels of sulfur dioxide, and high levels of sulphates & alcohol.\n",
    "\n",
    "2. Our Random Forest algorithm yields the highest R2 value, 83%! Any R2 above 70% is considered good, but be careful because if your accuracy is extremly high, it may be too good to be true (an example of Overfitting). Thus, 83% is the ideal accuracy!\n",
    "\n",
    "3. Our machine learning algorithm can now predict the red wine quality given it's ingredients. By detecting these important features, we may prevent our vintner from going out of business or loosing any profit! This is extremly powerful because now we can properly see which components of wine people prefer & thus maximize our profit!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is access to the data set & code from my GitHub page:\n",
    "https://github.com/jzaidi143/Project-Predicting-Heart-Disease-with-Classification-Machine-Learning-Algorithms\n",
    "Recommendations & comments are welcomed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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