wine_quality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary librares\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.utils import resample\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from collections import Counter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": [
    "# Read the data\n",
    "data = pd.read_csv('winequality-red.csv')\n",
    "\n",
    "# See the data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "# Information about the data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fixed acidity</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>8.319637</td>\n",
       "      <td>1.741096</td>\n",
       "      <td>4.60000</td>\n",
       "      <td>7.1000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>15.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatile acidity</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.3900</td>\n",
       "      <td>0.52000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>1.58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citric acid</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual sugar</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>1.9000</td>\n",
       "      <td>2.20000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>15.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorides</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>0.01200</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.07900</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.61100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>72.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>38.00000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>289.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.99007</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>0.99675</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>1.00369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>2.74000</td>\n",
       "      <td>3.2100</td>\n",
       "      <td>3.31000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>4.01000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulphates</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>0.33000</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.62000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>8.40000</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>10.20000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>14.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>5.636023</td>\n",
       "      <td>0.807569</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count       mean        std      min      25%  \\\n",
       "fixed acidity         1599.0   8.319637   1.741096  4.60000   7.1000   \n",
       "volatile acidity      1599.0   0.527821   0.179060  0.12000   0.3900   \n",
       "citric acid           1599.0   0.270976   0.194801  0.00000   0.0900   \n",
       "residual sugar        1599.0   2.538806   1.409928  0.90000   1.9000   \n",
       "chlorides             1599.0   0.087467   0.047065  0.01200   0.0700   \n",
       "free sulfur dioxide   1599.0  15.874922  10.460157  1.00000   7.0000   \n",
       "total sulfur dioxide  1599.0  46.467792  32.895324  6.00000  22.0000   \n",
       "density               1599.0   0.996747   0.001887  0.99007   0.9956   \n",
       "pH                    1599.0   3.311113   0.154386  2.74000   3.2100   \n",
       "sulphates             1599.0   0.658149   0.169507  0.33000   0.5500   \n",
       "alcohol               1599.0  10.422983   1.065668  8.40000   9.5000   \n",
       "quality               1599.0   5.636023   0.807569  3.00000   5.0000   \n",
       "\n",
       "                           50%        75%        max  \n",
       "fixed acidity          7.90000   9.200000   15.90000  \n",
       "volatile acidity       0.52000   0.640000    1.58000  \n",
       "citric acid            0.26000   0.420000    1.00000  \n",
       "residual sugar         2.20000   2.600000   15.50000  \n",
       "chlorides              0.07900   0.090000    0.61100  \n",
       "free sulfur dioxide   14.00000  21.000000   72.00000  \n",
       "total sulfur dioxide  38.00000  62.000000  289.00000  \n",
       "density                0.99675   0.997835    1.00369  \n",
       "pH                     3.31000   3.400000    4.01000  \n",
       "sulphates              0.62000   0.730000    2.00000  \n",
       "alcohol               10.20000  11.100000   14.90000  \n",
       "quality                6.00000   6.000000    8.00000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the statistics of the data\n",
    "data.describe().T"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality values ranges from 3 to 8:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 4, 8, 3])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show output values\n",
    "data[\"quality\"].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the number of rows and columns for the subplot\n",
    "nrows, ncols = 4, 3\n",
    "\n",
    "# Create the subplot using the defined number of rows and columns\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(25, 10))\n",
    "\n",
    "# Define the columns to plot\n",
    "cols_to_plot = data.columns\n",
    "\n",
    "# Loop through each column and plot it against the quality column\n",
    "for i, col in enumerate(cols_to_plot):\n",
    "    row_idx = i // ncols\n",
    "    col_idx = i % ncols\n",
    "    sns.boxplot(x='quality', y=col, data=data, ax=axes[row_idx][col_idx])\n",
    "    \n",
    "# Add a title for the entire plot\n",
    "fig.suptitle('Box plots of variables against quality')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4G/18Vf24qn4AfAV46mCjG01z9Jn/Dza3L/Ci9u/vPOC5Sf52Wp2h/z+YtATgFcAeSVYkeShwJHDxkGNasJL8QpJHTT2nufn5tTR9ekxb7RjgouFEOBZm68uLgSOT7JBkBbAHcPkQ4luwps23/GKazy7Yt/PSXj1wBnBDVb23Y5Wf3W00W9/62d12SRYneUz7/OHAbwE34ue2G1scSyV5TLsO4PeBr0ydTZ5kl/ZxOc0Ufee29cZl7NCX/klyEM0Z0C+qqnsH8k76p+d9VFXfqqpdqmr3qtqd5kvW06vq+4N5Sz3Vr7+xC4HntuueCDwU+EF/30pf9Kt/1gHPaZ8/l47k+xi6GHhFGs8E/q2d1nOuvh2XfXQ35t0/Y7aP7sa8+mjM9tFjZY7vcp1uAw5o6+8KPAm4ZTARjqY5vkt0mu3vZGJ102/t/+fPAEdX1bcHHuSI6abPqmpFx/71U8AfVNWFAw51pHT5N3oRzfT4i9Jcqf4MmvugTqQu+8z/B9NU1UlVtVv793ck8I9V9bvTqg39/8GiQb7YsFXVpiQnAl8AtgfOrKrrhhzWQrYrcEEzZmQRcE5VfT7JFTTTCh1Ls3M4bIgxLhhJzgX2A3ZOshZ4G80URpv1ZVVdl2QVcD3NNDMneLn/7Gbp2/2S7EVz2fWtwPFg326FfYGjgW+lmWoL4C342e2F2fr2KD+722wJcHaS7WlOhlpVVZ9N8nX83M5ptrFUkte06z9Mc1+Ajye5n6bPju1o4tNJHgv8lKYf727LZ9xnLDR97J/3AzsAl7Tjrsuq6jUDeVM91sc+Ggt97J8zgTOTXAv8BDhmIV6l1Mf+eTXw10kWAf8JHMcCNcu49yHws/75HM39D1cD99Kczb2l78pjsY+GvvXP2OyjoW99pNE02/eN5fCz3/c7gbOSfItmGrM3tVfLTLLZvkt0/i+a8e9kwnXTbycDj6W5ig2aKbxXDivgEdBNn2lzW+y3qrohyeeBa2hmxvhoVV07e5Njr5vPmv8PujRq/w+yAL/3SZIkSZIkSZIkSZrFpE0BKkmSJEmSJEmSJI01E4CSJEmSJEmSJEnSGDEBKEmSJEmSJEmSJI0RE4CSJEmSJEmSJEnSGDEBKEmSJEmSJEmSJI0RE4DSmEtyeJJXbuW2z0/yhq3c9pVJKskjt2b7eb5WJTmxY3m7JB9Icke77u39jqHjtU9MUh3L+7UxPKXHr9NV/yb5VJIv9/K1JUnSwpXkKe0YYr8Bv+6DxkRJHprk7Un2GmQckiRJoyzJrUnePcu6Bx3/kqQtMQEojb/DgVdu5bbPB97Qs0gG5yXAHwAnAc8CPjrEWL7ZxvCdHrf792279/a4XUmSpH6YPiZ6KPA2YK9hBSRJkiRJ42zRsAOQpD74ZeDuqjpzWxtK8vCq+o+t3b6q7gEu29Y4Zmh3I7Cx1+1KkiT1Q7/GRJIkSZKkmXkFoDTGkpwF/A7wnHaagAdNh9lOV3lzkvuSrE7yRx3r3g68Efiljm3Patc9K8nFSdYl+XGSq5O8fCvie0ySj7bt/GeS25J8pDP+JFdO22b3NpYXztLml4F3Ajt2xL17O8XUD2aoP3360FuTvCfJnyVZC9wzR/w7JHl/kh8muSvJ+4CHTKuz2RSgSR6R5H8l+X77vq9I8vyO9R9IsjHJLh1lv9O287x2ebMpQJMsS/K5JP/Rvo/fnyXupyT5+yQ/an8+meQXZ3ufkiRp4UryB0lub8dsfwcsmbZ+uyRvbseC9yX5dpJjptX5cjut+Mvaevck+Ycku02rd1K7/j/bqdg/PzXGmGFM9KP28WPTxmxXJPnYDO/j7CTf7FnHSJIkDdjUca4khya5sR0zfTXJk4cdm6Tx5BWA0nh7J7AceAzNlJgAawGSvBr4G+C9wBeA/YH3JNmhqt5FM23mHsBzgRe3205dcfZLwD8DHwb+E9iX5uDNA1V17jziey+wD/BHwPeBZcCz5/0uH+wPgD8GXgoc1Jatn2cbLwOua9uaaz/5LuD3gbcC1wOvBg7rov2PAC8C3gKsbrf7+yT7V9VXgT8FDgROA17cJgI/BHy4qi6ZqcEkAS4CdgaOpfm9vAPYCbi5o95/pfndXQkcDWxP8zn5uyR7V1UhSZLGQpJDgA/QjNkuBJ4DTJ8h4W+AY4BTaKbpfB5wZpI7q+qzHfWeATyO5gSxhwN/DZwOvKB9rVfQjG3eRDOOeizNOPIXZgnvucA/An9OM7U5NGO2j9KMSV9XVf/etv1ImpPaTppvH0iSJI2YX6I5HvZnwH/QHLv5QpI9quo/2zpJ4nF7SdvMHYk0xqrqO0nuArarqp9NuZRkO+DtwFlV9ca2+ItJ/gtwUpK/qqq1SdYD93Vu27Z7XkdbAb4C7EaTyJpPAnBv4ANVdX5H2d/OY/vNVNX17ZV7m6a95/k29cKOgddmkjwWeA3wtqp6T1v2BZpE4KyS/ApwFPCqqjq7Y7traAZ/B1bVj9sz77+S5GjgUJqz5P9kjqYPBp4GPLOqvtG2exXNfXZu7qj3Nppk68FV9ZO23jXAjTQH8P4eSZI0Lt4KfL6qXtsufyHJYpoTmKZODHotHeMS4P8kWUIzZuhMAD4a+O2qurvd9heB9+Xn06XvDXyxqj7Ysc1n5ojtivbxO9PGbOfSHBQ7DJi6EvBwmlkWzun+rUuSJI2knYFDqupr8KBjN6+kOWkLmhPb/3go0UkaK04BKk2m3WjO4P7ktPLzaQ7u/D9zbZxkx3YKy+8CP21/jgOeOM84rgb+pJ2aar7b9tOlcyX/Wv8P8DCaq+4AqKoHOpdn8etA6Oj7drtPAr/RUfbPNAe/PkKTAHzl1Fnws9gbuGMq+de28V3gqmn1fgu4AHggyaL2jLI1wK3Ayi3ELkmSFogk29OcHDR9bNKZlDsAeAC4YGpc0I4NLgX2atuYcsVU8q81ddLT0vbxauAFSd6RZO9p23atvVfgp2gOgk15JXBxVd25NW1KkiSNkA1TyT940LGbvTvq/C3N8aPpP5I0LyYApck0de+XO6aVTy3vtIXtzwKOAP4SeD7NIORMmoTYfJxIMx3VycBNae5HeOQ82+iH6f0yk6l75m2YVj59ebolwL9X1b0zvOYjkuzQUXYusANwbVX93y7imem1p5ftTDM110+n/TyeZgpWSZI0HhbTzPgy11hlZ5rpwP+NB48Lzmq37bxf4A+ntfOT9nFq/HcmzRSghwPfAO5I8s6tTASeAfxmkickeQLwm2w+dakkSdJCNNuxm85x1x1VdeX0nwHFJ2mMOAWoNJmm7om3y7TyXdvHu2bbMMnDgN8GTqyqD3eUz/uEgqr6IfCHwB8m+VWae999Isk1VXU9zX3sHjptsy0lJ2ezWVtJdpwttC7a+377uAsP7q/pfTrdeuCRSR4xLQm4K3BvVd3XxraI5r463wL2THJcVZ2+hXhmeu1daOaUn3IXzRWAH52h7g+2ELskSVo4NgKb2Hx80Ll8V1tnX5orAafb0olNP9POaPA+mmlBlwEvB04FvsfPp7Pqtq2vJLmZ5t6EAdYBX5xPG5IkSSNqtmM31w06EEnjzysApfH3Eza/Mm8tzYGUw6aVHw7cQ5N0mm3bHWjOFL9vqiDJo4AXbUuQVXUNzT3utgN+uSPO3duk45TnbeVLrAUelWRpR9nzt7ItaProP4FDpgraJOghs27RuIImwfjSju3SLn+1o95bgCe17f1P4N1Jdt9Cu7smeUZHu8uBp0+rdynwFOCqGc4mu3ULsUuSpAWiqu6nmZZz+tjkJR3P/5FmXPdfZjrLfOp+wVvx2rdX1buA1cCTZ6k2/QrC6c6kSQC+Avh4+34kSZIWul2S7DO10HHs5vLhhSRpXHkFoDT+bgQOSXIobeKvqtYleTtwWpI7gUuA5wCvBd7Scf+7G2mSSq8ErgV+UFW3JrkCODnJPTRni7+ZZuqoR88nsCRfpbka7VqapNirgR/z80HPhcApwEeTnEVzH5tXzfP9T/k8zZVwZyZ5D7ACeM1WtkVV3ZnkdOAdSTbRnKn1auCRW9juhiTnAu9P8miaA2Ovpkl6vhYgydOA/w68rqrWJHkH8MI29gOqaqYrFD8H/CvwySRvoklOnsLmZ+6/naZ//z7JmTRX/S2lSayeVVVfnl9PSJKkEfb/AZ9J8iGaMddzgIOmVlbVTUk+DJyX5C+AK2kScnsCT6yq3+/2hZKcRnNF4WU048L9gT1oph7fTFX9JMka4PAk19KMXa7pSDqeDfw5zXfWs7p+x5IkSaPtB8D/TvJnNMeppo7dnDXMoCSNJ68AlMbfB2mmTDqT5iqx4wCq6iM002++GPgscBTwxvZs7SmraAYgf9Fu+/a2/GXAGuDjwF8Dn26fz9fXgVcCn2pfa2fg4Kpa28Z4LfB7wLOAi2kOWv3eVrwOVfUD4HeA3WgSi7/bvo9t8ac0/Xoyzf361gHv7WK7V9Mc1Poz4CLgl4AXVtVXkzy0Xfelqjqtjf0nNGfA/wbNfRM30yYFXwRc38b0V8D7afq4s963gWcC99JMMfoPwDtoruhc3d3bliRJC0FVXQC8DvhvNOOfpwHHTqt2AvBOmivtPkcz9vtt4CvzfLmvA88GPta282Lg1VV14RzbvIZm/Pd/aMaaj+uI/fs09xL856q6aZ6xSJIkjarv0syA9XbgPJqZuA7sOBlfknomM19IIkmSJEnScCTZieb+gSdW1RnDjkeSJGlbtbNbPaWqVg47FkmTwSlAJUmSJEkjob239JOB1wM/opllQZIkSZI0TyYAJUmSJEmj4teAL9FMj/WKqrp3yPFIkiRJ0oLkFKCSJEmSJEmSJEnSGNlu2AFIkiRJkiRJkiRJ6h0TgJIkSZIkSZIkSdIYMQEoSZIkSZIkSZIkjRETgJIkSZIkSZIkSdIYMQEoSZIkSZIkSZIkjRETgJIkSZIkSZIkSdIYMQEoSZIkSZIkSZIkjRETgJIkSZIkSZIkSdIYMQEoSZIkSZIkSZIkjZFFww5gW+y88861++67DzsMSZI0Aa666qofVNXiYcfRT46tJEnSIDiukiRJ6p3ZxlYLOgG4++67c+WVVw47DEmSNAGSfHfYMfSbYytJkjQIjqskSZJ6Z7axlVOASpIkSZIkSZIkSWPEBKAkSZIkSZIkSZI0RkwASpIkSZIkaSIkeVKSqzt+7knyhiQ7Jbkkyc3t444d25yUZHWSm5IcOMz4JUmSumUCUJIkSZIkSROhqm6qqr2qai/g14B7gQuANwOXVtUewKXtMkmeDBwJ7AkcBHwwyfbDiF2SJGk+TABKkiRJkiRpEh0AfKeqvgscApzdlp8NHNo+PwQ4r6ruq6o1wGpg70EHKkmSNF8mACVJkiRJkjSJjgTObZ/vWlXrAdrHXdrypcDtHdusbcs2k+S4JFcmuXLjxo19ClmSJKk7JgAlSZIkSZI0UZI8FHgR8MktVZ2hrGaqWFWnV9XKqlq5ePHibQ1RkiRpm5gAlCRJkiRJ0qQ5GPhmVd3RLt+RZAlA+7ihLV8LLOvYbjdg3cCilCRJ2komACVJkiRJkjRpjuLn038CXAwc0z4/Brioo/zIJDskWQHsAVw+sCglSZK20qJhByBJkiRJkiQNSpJHAM8Dju8ofhewKsmxwG3AYQBVdV2SVcD1wCbghKq6f8AhS5IkzZsJQEmSJEmSJE2MqroXeOy0sjuBA2apfypw6gBCkyRJ6hmnAJUkSZIkSZIkSZLGiAlASZIkSZIkSZIkaYw4Bai2yaGHv4z1G++es86SxTty4apzBhSRJEnSeFu6bDnr1t7e0zYft9syvnf7bT1tU5IkSRqUXoyRHRNLGjcmALVN1m+8mxVHnTxnnTXnnjKgaCRJksbfurW3c8RpX+tpm+cfv09P25MkSZIGqRdjZMfEksaNU4BKkiRJkiRJkiRJY8QEoCRJ0ohLsizJl5LckOS6JK9vy9+e5HtJrm5/XtCxzUlJVie5KcmBw4tekiRJkiRJg+YUoJIkSaNvE/DGqvpmkkcBVyW5pF33vqp6d2flJE8GjgT2BB4H/J8kT6yq+wcatSRJkiRJkobCKwAlSZJGXFWtr6pvts9/BNwALJ1jk0OA86rqvqpaA6wG9u5/pJIkSZIkSRoFJgAlSZIWkCS7A08DvtEWnZjkmiRnJtmxLVsK3N6x2VpmSRgmOS7JlUmu3LhxY7/CliRJkiRJ0gCZAJQkSVogkjwS+DTwhqq6B/gQ8ARgL2A98J6pqjNsXjO1WVWnV9XKqlq5ePHi3gctSZIkSZKkgTMBKEmStAAkeQhN8u8TVfUZgKq6o6rur6oHgI/w82k+1wLLOjbfDVg3yHglSZIkSZI0PCYAJUmSRlySAGcAN1TVezvKl3RUezFwbfv8YuDIJDskWQHsAVw+qHglSZIkSZI0XIuGHYAkSZK2aF/gaOBbSa5uy94CHJVkL5rpPW8FjgeoquuSrAKuBzYBJ1TV/QOOWZIkSZIkSUNiAlCSJGnEVdVXmfm+fp+bY5tTgVP7FpQkSZIkSZJGllOASpIkSZIkSZIkSWPEBKAkSZIkSZIkSZI0RkwASpIkSZIkSZIkSWPEBKAkSZIkSZIkSZI0RkwASpIkSZIkSZIkSWOkbwnAJGcm2ZDk2o6y85Nc3f7cmuTqtnz3JP/Rse7D/YpLkiRJkiRJkiRJGmeL+tj2WcD7gY9PFVTVEVPPk7wH+LeO+t+pqr36GI8kSZIkSZIkSZI09vp2BWBVfQW4a6Z1SQIcDpzbr9eXJEmSJEmSJEmDs3TZcpJs9c/SZcuH/RaksdHPKwDn8pvAHVV1c0fZiiT/AtwD/Peq+r/DCU2SJEmSJEmSJM3XurW3c8RpX9vq7c8/fp8eRiNNtmElAI/iwVf/rQeWV9WdSX4NuDDJnlV1z/QNkxwHHAewfLlnA0iSJEmSJEmSJEmd+jYF6GySLAJeApw/VVZV91XVne3zq4DvAE+cafuqOr2qVlbVysWLFw8iZEmSJEmSJEmSJGnBGHgCEPgt4MaqWjtVkGRxku3b548H9gBuGUJskiRJkiRJkiRJ0oLWtwRgknOBrwNPSrI2ybHtqiN58PSfAM8Grknyr8CngNdU1V39ik2SJEmSJEmSJEkaV327B2BVHTVL+StnKPs08Ol+xSJJkiRJkiRJkiRNimFMASpJkiRJkiRJkiSpT0wASpIkSZIkSZIkSWPEBKAkSZIkSZIkSZI0RkwASpIkSZIkSZIkSWPEBKAkSZIkSZIkSZI0RkwASpIkSZIkSZIkSWPEBKAkSZIkSZImRpLHJPlUkhuT3JDkWUl2SnJJkpvbxx076p+UZHWSm5IcOMzYJUmSumUCUJIkSZIkSZPkr4HPV9UvA08FbgDeDFxaVXsAl7bLJHkycCSwJ3AQ8MEk2w8lakmSpHkwAShJkiRJkqSJkOTRwLOBMwCq6idV9UPgEODsttrZwKHt80OA86rqvqpaA6wG9h5kzJIkSVtj0bAD0HAcevjLWL/x7jnrLFm8IxeuOmdAEUmSJEmSJPXd44GNwMeSPBW4Cng9sGtVrQeoqvVJdmnrLwUu69h+bVu2mSTHAccBLF++vD/RS5IkdckE4IRav/FuVhx18px1Ljnld3nG/gfPWec7t6xhRS8DkyRJkiRJ6p9FwNOB11XVN5L8Ne10n7PIDGU1U8WqOh04HWDlypUz1pEkSRoUE4Ca1abKFpOEN77j6AFFI0mSJEmStM3WAmur6hvt8qdoEoB3JFnSXv23BNjQUX9Zx/a7AesGFq0kSdJW8h6AkiRJkiRJmghV9X3g9iRPaosOAK4HLgaOacuOAS5qn18MHJlkhyQrgD2AywcYsiRJ0lbxCkBJkiRJkiRNktcBn0jyUOAW4FU0J8mvSnIscBtwGEBVXZdkFU2ScBNwQlXdP5ywJUmSumcCUJIkSZIkSROjqq4GVs6w6oBZ6p8KnNrPmCRJknrNKUAlSZIkSZIkSZKkMWICUJIkSZIkSZIkSRojJgAlSZIkSZIkSZKkMWICUJIkSZIkSZIkSRojJgAlSZIkSZIkSZKkMWICUJIkSZIkSZIkSRojJgAlSZIkSZIkSZKkMWICUJIkSZIkSZIkSRojJgAlSZIkSZIkSZKkMWICUJIkSZIkSZIkSRojJgAlSZIkSZIkSZKkMWICUJIkSZIkSZIkSRojJgAlSZIkSZIkSZKkMbJo2AFo/K2++ds8Y/+D56yzZPGOXLjqnAFFJEmSJEmSJEmSNL5MAKrvNlVYcdTJc9ZZc+4pA4pGkiRJkiRJkiRpvDkFqCRJkiRJkiRJkjRG+pYATHJmkg1Jru0oe3uS7yW5uv15Qce6k5KsTnJTkgP7FZckSZIkSZIkSZI0zvp5BeBZwEEzlL+vqvZqfz4HkOTJwJHAnu02H0yyfR9jkyRJkiRJkiRJksZS3xKAVfUV4K4uqx8CnFdV91XVGmA1sHe/YpMkSZIkSZIkSZLG1TDuAXhikmvaKUJ3bMuWArd31Fnblm0myXFJrkxy5caNG/sdqyRJkiRJkiRJkrSgDDoB+CHgCcBewHrgPW15ZqhbMzVQVadX1cqqWrl48eK+BClJkjRKkixL8qUkNyS5Lsnr2/KdklyS5Ob2cceObby/siRJkiRJ0oQaaAKwqu6oqvur6gHgI/x8ms+1wLKOqrsB6wYZmyRJ0gjbBLyxqn4FeCZwQnsP5TcDl1bVHsCl7bL3V5YkSZIkSZpwA00AJlnSsfhi4Nr2+cXAkUl2SLIC2AO4fJCxSZIkjaqqWl9V32yf/wi4gWa69EOAs9tqZwOHts+9v7IkSZIkSdIEW9SvhpOcC+wH7JxkLfA2YL8ke9FM73krcDxAVV2XZBVwPc0Z7idU1f39ik2SJGmhSrI78DTgG8CuVbUemiRhkl3aakuByzo2m/P+ysBxAMuXL+9T1JIkSZIkSRqkviUAq+qoGYrPmKP+qcCp/YpHkiRpoUvySODTwBuq6p5kptsoN1VnKJv1/srA6QArV66csY4kSZIkSZIWloFOASpJkqStk+QhNMm/T1TVZ9riO6amWG8fN7Tl3l9ZkiRJkiRpgpkAlCRJGnFpLvU7A7ihqt7bsepi4Jj2+THARR3l3l9ZkiRJkiRpQvVtClBJkiT1zL7A0cC3klzdlr0FeBewKsmxwG3AYeD9lSVJkiRJkiadCUBJkqQRV1VfZeb7+gEcMMs23l9ZkiRJkiRpQjkFqCRJkjTptltEkp79LF22fNjvSJIkSZKkieYVgJIkSdKke2ATR5z2tZ41d/7x+/SsLUmSJEmSNH9eAShJkiRJkiRJkiSNEROAkiRJkiRJkiRJ0hgxAShJkiRJkiRJkiSNEROAkiRJkiRJmhhJbk3yrSRXJ7myLdspySVJbm4fd+yof1KS1UluSnLg8CKXJEnqnglASZIkSZIkTZr9q2qvqlrZLr8ZuLSq9gAubZdJ8mTgSGBP4CDgg0m2H0bAkiRJ82ECUJIkSZIkSZPuEODs9vnZwKEd5edV1X1VtQZYDew9+PAkSZLmxwSgJEmSJEmSJkkBX0xyVZLj2rJdq2o9QPu4S1u+FLi9Y9u1bZkkSdJIWzTsACRJkiRJkqQB2req1iXZBbgkyY1z1M0MZTVjxSaZeBzA8uXLtz1KSZKkbeAVgJIkSZIkSZoYVbWufdwAXEAzpecdSZYAtI8b2uprgWUdm+8GrJul3dOramVVrVy8eHG/wpckSeqKCUBJkiRJkiRNhCS/kORRU8+B5wPXAhcDx7TVjgEuap9fDByZZIckK4A9gMsHG7UkSdL8OQWoJEmSJEmSJsWuwAVJoDkudk5VfT7JFcCqJMcCtwGHAVTVdUlWAdcDm4ATqur+4YQuSZLUPROAkiRJkiRJmghVdQvw1BnK7wQOmGWbU4FT+xyaJElSTzkFqCRJkiRJkiRJkjRGTABKkiRJkiRJkiRJY8QEoCRJkiRJkiRJkjRGTABKkiRJkiRJkiRJY8QEoCRJkiRJkiRJkjRGTABKkiRJkiRJkiRJY8QEoCRJkiRJkiRJkjRGTABKkiRJkiRJkiRJY8QEoCRJkiRJkiRJkjRGFg07AAlg9c3f5hn7Hzzr+iWLd+TCVecMMCJJkiRJkiRJkqSFyQSgRsKmCiuOOnnW9WvOPWWA0UiSJEmSJEmSJC1cTgEqSZIkSZIkSZIkjZG+JQCTnJlkQ5JrO8r+MsmNSa5JckGSx7Tluyf5jyRXtz8f7ldckiRJkiRJkiRJ0jjr5xWAZwEHTSu7BHhKVf0q8G3gpI5136mqvdqf1/QxLkmSJEmSJEmSJGls9S0BWFVfAe6aVvbFqtrULl4G7Nav15ckSZIkSZIkSZIm0TDvAfh7wD90LK9I8i9J/inJb862UZLjklyZ5MqNGzf2P0pJkiRJkiRJkiRpARlKAjDJW4FNwCfaovXA8qp6GvDHwDlJHj3TtlV1elWtrKqVixcvHkzAkiRJkiRJkiRJ0gIx8ARgkmOAFwIvr6oCqKr7qurO9vlVwHeAJw46NkmSJEmSJEmSJGmhG2gCMMlBwJuAF1XVvR3li5Ns3z5/PLAHcMsgY5MkSZIkSZIkSZLGwaJ+NZzkXGA/YOcka4G3AScBOwCXJAG4rKpeAzwbOCXJJuB+4DVVdVe/YpMkSZIkSZIkSZLGVd8SgFV11AzFZ8xS99PAp/sViyRJkiRJkiRJkjQpBn4PQEmSJEmSJEmSJEn9YwJQkiRJ6qOly5aTpGc/kiRJkiRJW9K3KUAlSZIkwbq1t3PEaV/rWXvnH79Pz9qSJEmSJEnjySsAJUmSJEmSJEmSpDFiAlCSJEmSJEmSJEkaIyYAJUmSJEmSJEmSpDFiAlCSJEmSJEmSJEkaIyYAJUmSJEmSJEmSpDFiAlCSJEmSJEmSJEkaIyYAJUmSJEmSJEmSpDFiAlCSJEmSJEmSJEkaIyYAJUmSJEmSJEmSpDFiAlCSJEmSJEmSJEkaIyYAJUmSJEmSJEmSpDFiAlCSJGkBSHJmkg1Jru0oe3uS7yW5uv15Qce6k5KsTnJTkgOHE7UkSdJoSrJ9kn9J8tl2eacklyS5uX3csaOu4ypJkrTgmACUJElaGM4CDpqh/H1VtVf78zmAJE8GjgT2bLf5YJLtBxapJEnS6Hs9cEPH8puBS6tqD+DSdtlxlSRJWrBMAEqSJC0AVfUV4K4uqx8CnFdV91XVGmA1sHffgpMkSVpAkuwG/Dbw0Y7iQ4Cz2+dnA4d2lDuukiRJC44JQEmSpIXtxCTXtFOETk1VtRS4vaPO2rZsM0mOS3Jlkis3btzY71glSZJGwV8Bfwo80FG2a1WtB2gfd2nLHVdJkqQFyQSgJEnSwvUh4AnAXsB64D1teWaoWzM1UFWnV9XKqlq5ePHivgQpSZI0KpK8ENhQVVd1u8kMZY6rJEnSyFs07AAkSZK0darqjqnnST4CfLZdXAss66i6G7BugKFJkiSNqn2BFyV5AfAw4NFJ/ha4I8mSqlqfZAmwoa3vuEqSJC1IXgEoSZK0QLUHp6a8GLi2fX4xcGSSHZKsAPYALh90fJIkSaOmqk6qqt2qanfgSOAfq+p3acZPx7TVjgEuap87rpIkSQuSVwBKkiQtAEnOBfYDdk6yFngbsF+SvWimoboVOB6gqq5Lsgq4HtgEnFBV9w8hbEmSpIXiXcCqJMcCtwGHgeMqSZK0cJkAlCRJWgCq6qgZis+Yo/6pwKn9i0iSJGlhq6ovA19un98JHDBLPcdVkiRpwelqCtAk+3ZTJkmSpLk5rpIkSeoNx1WSJEmz6/YegH/TZZkkSZLm5rhKkiSpNxxXSZIkzWLOKUCTPAvYB1ic5I87Vj0a2L6fgUmSJI0Tx1XStlm6bDnr1t7es/Yet9syvnf7bT1rT5I0OI6rJEmStmxL9wB8KPDItt6jOsrvAV7ar6AkSZLGkOMqaRusW3s7R5z2tZ61d/7x+/SsLUnSwDmukiRJ2oI5E4BV9U/APyU5q6q+O6CYJEmSxo7jKkmSpN5wXCVJkrRlW7oCcMoOSU4Hdu/cpqqe24+gJEmSxpjjKkmSpN5wXCVJkjSLbhOAnwQ+DHwUuL9/4UiSJI09x1WSJEm94bhKkiRpFt0mADdV1Yfm03CSM4EXAhuq6ilt2U7A+TRnZt0KHF5Vd7frTgKOpRmw/WFVfWE+rydJkrRAzHtcJUmSpBk5rpIkSZrFdl3W+7skf5BkSZKdpn62sM1ZwEHTyt4MXFpVewCXtsskeTJwJLBnu80Hk2zf7ZuQJElaQLZmXCVJkqTNOa6SJEmaRbdXAB7TPv5JR1kBj59tg6r6SpLdpxUfAuzXPj8b+DLwprb8vKq6D1iTZDWwN/D1LuOTJElaKOY9rpIkSdKMHFdJkiTNoqsEYFWt6NHr7VpV69s21yfZpS1fClzWUW9tWyZJkjRWejiukjRili5bzrq1t/esvcfttozv3X5bz9qTpHHjuEqSJGl2XSUAk7xipvKq+niP4shMzc8Sy3HAcQDLly/v0ctLkiQNxgDGVZKGZN3a2znitK/1rL3zj9+nZ21J0jhyXCVJkjS7bqcA/fWO5w8DDgC+Ccx3QHVHkiXt1X9LgA1t+VpgWUe93YB1MzVQVacDpwOsXLlyxiShJEnSCOvVuEqSJGnSOa6SJEmaRbdTgL6ucznJfwH+91a83sU087O/q328qKP8nCTvBR4H7AFcvhXtS5IkjbQejqskSZImmuMqSZKk2XV7BeB099Ik6WaV5FxgP2DnJGuBt9Ek/lYlORa4DTgMoKquS7IKuB7YBJxQVfdvZWySJEkLyRbHVZIkSeqK4ypJkqRWt/cA/Dt+fk++7YFfAVbNtU1VHTXLqgNmqX8qcGo38UiSJC1UWzOukiRJ0uYcV0mSJM2u2ysA393xfBPw3apa24d4JEmSxp3jKkmSpN5wXCVJkjSL7bqpVFX/BNwIPArYEfhJP4OSJEkaV46rJEmSesNxlSRJ0uy6SgAmORy4nOaefYcD30jy0n4GJkmSNI4cV0mSJPWG4ypJkqTZdTsF6FuBX6+qDQBJFgP/B/hUvwKTJEkaU46rJEmSesNxlSRJ0iy6TQBuNzWYat1Jl1cPSr2w+uZv84z9D56zzpLFO3LhqnMGFJEkSVvNcZUkSVJvOK6SJEmaRbcJwM8n+QJwbrt8BPC5/oQkbW5ThRVHnTxnnTXnnjKgaCRJ2iaOqyRJknrDcZUkSdIs5kwAJvmvwK5V9SdJXgL8BhDg68AnBhCfJEnSWHBctTAsXbacdWtvH3YYkiRpDo6rJEmStmxLVwD+FfAWgKr6DPAZgCQr23X/rY+xSZIkjZO/wnHVyFu39naOOO1rPW3z/OP36Wl7kiTJcZUkSdKWbGle9N2r6prphVV1JbB7XyKSJEkaT46rJEmSesNxlSRJ0hZsKQH4sDnWPbyXgUiSJI05x1WSJEm94bhKkiRpC7aUALwiyaunFyY5FriqPyFJkiSNJcdVkiRJveG4SpIkaQu2dA/ANwAXJHk5Px9ArQQeCry4j3FJkiSNmzfguEqSJKkX3oDjKkmSpDnNmQCsqjuAfZLsDzylLf77qvrHvkcmSZI0RhxXSZIk9YbjKkmSpC3b0hWAAFTVl4Av9TkWSZKksee4SpIkqTccV0mSJM1uS/cAlCRJkiRJkiRJkrSAdHUFoLQQrL752zxj/4PnrLNk8Y5cuOqcAUUkSZIkSZIkSZI0eCYANTY2VVhx1Mlz1llz7ikDikaSJEmSJEmSJGk4nAJUkiRJkiRJkiRJGiMmACVJkiRJkjQRkjwsyeVJ/jXJdUne0ZbvlOSSJDe3jzt2bHNSktVJbkpy4PCilyRJ6p4JQEmSJEmSJE2K+4DnVtVTgb2Ag5I8E3gzcGlV7QFc2i6T5MnAkcCewEHAB5NsP4zAJUmS5sN7AI6hQw9/Ges33j1nne/csoYVA4pHkiRJkiRpFFRVAf/eLj6k/SngEGC/tvxs4MvAm9ry86rqPmBNktXA3sDXBxe1JEnS/JkAHEPrN97NiqNOnrPOje84ekDRSJIkSZIkjY72Cr6rgP8KfKCqvpFk16paD1BV65Ps0lZfClzWsfnatkySJGmkOQWoJEmSJEmSJkZV3V9VewG7AXsnecoc1TNTEzNWTI5LcmWSKzdu3NiDSCVJkraeCUBJkiRJkiRNnKr6Ic1UnwcBdyRZAtA+bmirrQWWdWy2G7BulvZOr6qVVbVy8eLF/QpbkiSpKyYAJUmSJEmSNBGSLE7ymPb5w4HfAm4ELgaOaasdA1zUPr8YODLJDklWAHsAlw80aEmSpK3gPQAlSZIkSZI0KZYAZ7f3AdwOWFVVn03ydWBVkmOB24DDAKrquiSrgOuBTcAJVXX/kGKXJEnqmglASZIkSZIkTYSqugZ42gzldwIHzLLNqcCpfQ5NkiSpp5wCVJIkSZIkSZIkSRojJgAlSZIkSZIkSZKkMWICUJIkSZIkSZIkSRojJgAlSZIkSZIkSZKkMbJo0C+Y5EnA+R1FjwdOBh4DvBrY2Ja/pao+N9joJEmSJEmSJEmSpIVt4AnAqroJ2AsgyfbA94ALgFcB76uqdw86JkmSJEmSJEmSJGlcDHsK0AOA71TVd4cchyRJ0khLcmaSDUmu7SjbKcklSW5uH3fsWHdSktVJbkpy4HCiliRJkiRJ0jAMOwF4JHBux/KJSa5pD3DtONMGSY5LcmWSKzdu3DhTFUmSpHF0FnDQtLI3A5dW1R7Ape0ySZ5MM87as93mg+3MC5IkSZIkSZoAA58CdEqShwIvAk5qiz4EvBOo9vE9wO9N366qTgdOB1i5cmUNJFhNlEMPfxnrN949Z50li3fkwlXnDCgiSZKgqr6SZPdpxYcA+7XPzwa+DLypLT+vqu4D1iRZDewNfH0gwUqSJEmSJGmohpYABA4GvllVdwBMPQIk+Qjw2WEFpsm2fuPdrDjq5DnrrDn3lAFFI0nSnHatqvUAVbU+yS5t+VLgso56a9uyzSQ5DjgOYPny5X0MVZIkSZIkSYMyzClAj6Jj+s8kSzrWvRi4drMtJEmS1I3MUDbjzAlVdXpVrayqlYsXL+5zWJIkSZIkSRqEoVwBmOQRwPOA4zuK/yLJXjQHp26dtk6SJEmbuyPJkvbqvyXAhrZ8LbCso95uwLqBRyeNsu0WkcyUK5ckSZIkaeEbSgKwqu4FHjut7OhhxKLJsvrmb/OM/Q+es853blnDigHFI0nSNroYOAZ4V/t4UUf5OUneCzwO2AO4fCgRSqPqgU0ccdrXetrk+cfv09P2JEmSJEnaWsO8B6A0cJsqW7y/343vMBctSRo9Sc4F9gN2TrIWeBtN4m9VkmOB24DDAKrquiSrgOuBTcAJVXX/UAKXJEmSJEnSwJkAlCRJWgCq6qhZVh0wS/1TgVP7F5EkSZIkSZJGlQlASZIkSb3l/fUkSZIkSRoqE4CSJEmSesv760mSJEmSNFTbDTsASZIkSZIkSZIkSb1jAlCSJEmSJEmSJEkaIyYAJUmSJEmSJEmSpDFiAlCSJEmSJEmSJEkaI4uGHYDm59DDX8b6jXfPWec7t6xhxYDikSRJkiRJkiRJ0mgxAbjArN94NyuOOnnOOje+4+gBRSNJkiRJkiRJkqRR4xSgkiRJkiRJkiRJ0hgxAShJkiRJkiRJkiSNEROAkiRJkiRJkiRJ0hgxAShJkiRJkiRJkiSNEROAkiRJkiRJkiRJ0hgxAShJkiRJkiRJkiSNEROAkiRJkiRJkiRJ0hgxAShJkiRJkiRJkiSNEROAkiRJkiRJkiRJ0hgxAShJkiRJkiRJkiSNEROAkiRJkiRJkiRJ0hgxAShJkiRJkiRJkiSNEROAkiRJkiRJmghJliX5UpIbklyX5PVt+U5JLklyc/u4Y8c2JyVZneSmJAcOL3pJkqTumQCUJEmSpAmxdNlykvTsZ+my5cN+S5I0X5uAN1bVrwDPBE5I8mTgzcClVbUHcGm7TLvuSGBP4CDgg0m2H0rkkiRJ87Bo2AFIkiRJkgZj3drbOeK0r/WsvfOP36dnbUnSIFTVemB9+/xHSW4AlgKHAPu11c4Gvgy8qS0/r6ruA9YkWQ3sDXx9sJFLkiTNj1cASpIkSZIkaeIk2R14GvANYNc2OTiVJNylrbYUuL1js7Vt2UztHZfkyiRXbty4sW9xS5K0Jb2Y+cPZPhY+rwCUJEmSJEnSREnySODTwBuq6p4ks1adoaxmqlhVpwOnA6xcuXLGOpIkDUIvZv5wto+FzysAJUmSJEmSNDGSPIQm+feJqvpMW3xHkiXt+iXAhrZ8LbCsY/PdgHWDilWSJGlrmQCUJEmSJG2d7RZt89RCTjUkaZDSXOp3BnBDVb23Y9XFwDHt82OAizrKj0yyQ5IVwB7A5YOKV5IkaWs5BagkSZIkaes8sGmbpxaazqmGJPXZvsDRwLeSXN2WvQV4F7AqybHAbcBhAFV1XZJVwPXAJuCEqrp/4FFLkiTN01ASgEluBX4E3A9sqqqVSXYCzgd2B24FDq+qu4cRn7Qlq2/+Ns/Y/+A56yxZvCMXrjpnQBFJkiRJkqQtqaqvMvN9/QAOmGWbU4FT+xaUJElSHwzzCsD9q+oHHctvBi6tqncleXO7/KbhhCbNbVOFFUedPGedNeeeMqBoJEmSJEmSJEmSfm6U7gF4CHB2+/xs4NDhhSJJkiRJkiRJkiQtTMO6ArCALyYp4LSqOh3YtarWA1TV+iS7zLRhkuOA4wCWL/fm8BpdThMqSZKkbbLdIpLZZqmTJEmSJGl2w0oA7ltV69ok3yVJbux2wzZZeDrAypUrq18BStvKaUIlSZK0TR7YxBGnfa2nTZ5//D49bU+SJEmSNJqGMgVoVa1rHzcAFwB7A3ckWQLQPm4YRmySJEmSJEmSJEnSQjbwBGCSX0jyqKnnwPOBa4GLgWPaascAFw06NkmSJEmSJEmSJGmhG8YUoLsCF7T3slgEnFNVn09yBbAqybHAbcBhQ4hNkiRJkiRJkiRJWtAGngCsqluAp85QfidwwKDjkSRJkiRJkiRJksbJUO4BKEmSJEmSJEmSJKk/TABKkiRJkiRJkiRJY8QEoCRJkiRJkiRJkjRGTABKkiRJkiRJkiRJY8QEoCRJkiRJkiRJkjRGTABKkiRJkiRJkiRJY8QEoCRJkiRJkiRJkjRGTABKkiRJkiRJkiRJY8QEoCRJkiRJkiRJkjRGTABKkiRJkiRJkiRJY2TRsAOQJtnqm7/NM/Y/eM46SxbvyIWrzhlQRJIkSZIkSZIkaaEzASgN0aYKK446ec46a849ZUDRSJIkSZIkSZKkcWACUJIkaYFLcivwI+B+YFNVrUyyE3A+sDtwK3B4Vd09rBglSZIkSZI0ON4DUJIkaTzsX1V7VdXKdvnNwKVVtQdwabssSZIkSZKkCWACUJIkaTwdApzdPj8bOHR4oUiSJEmSJGmQTABKkiQtfAV8MclVSY5ry3atqvUA7eMuM22Y5LgkVya5cuPGjQMKV5IkSZIkSf3kPQBHyKGHv4z1G+e+Nc93blnDigHFI0mSFox9q2pdkl2AS5Lc2O2GVXU6cDrAypUrq18BSpIkSZIkaXBMAI6Q9RvvZsVRJ89ZEOy8MgAAHKhJREFU58Z3HD2gaCRJ0kJRVevaxw1JLgD2Bu5IsqSq1idZAmwYapCSJEmSJEkaGKcAlSRJWsCS/EKSR009B54PXAtcDBzTVjsGuGg4EUqSJEmSJGnQvAJQkiRpYdsVuCAJNGO7c6rq80muAFYlORa4DThsiDFKkiRJkiRpgEwASpIkLWBVdQvw1BnK7wQOGHxEkiRJkiRJGjanAJUkSZIkSZIkSZLGiAlASZIkSZIkSZIkaYyYAJQkSZIkSZIkSZLGiAlASZIkSZIkSZIkaYyYAJQkSZIkSZIkSZLGiAlASZIkSZIkTYwkZybZkOTajrKdklyS5Ob2cceOdSclWZ3kpiQHDidqSZKk+TEBKEmSJEmSpElyFnDQtLI3A5dW1R7Ape0ySZ4MHAns2W7zwSTbDy5USZKkrWMCUJIkSZIkSROjqr4C3DWt+BDg7Pb52cChHeXnVdV9VbUGWA3sPYg4JUmStoUJQEmSJEmSJE26XatqPUD7uEtbvhS4vaPe2rZsM0mOS3Jlkis3btzY12AlSZK2ZOAJwCTLknwpyQ1Jrkvy+rb87Um+l+Tq9ucFg45NkiRJkjR+li5bTpKe/SxdtnzYb0nS4GSGspqpYlWdXlUrq2rl4sWL+xyWJEnS3BYN4TU3AW+sqm8meRRwVZJL2nXvq6p3DyEmSZIkSdKYWrf2do447Ws9a+/81z6bZKacwNZ73G7L+N7tt/W0TUnzckeSJVW1PskSYENbvhZY1lFvN2DdwKOTJEmap4EnANtpFKamVPhRkhuYZeoESZIkSdKE2W5Rz5NrPffApp4mFAHOP36fnrYnad4uBo4B3tU+XtRRfk6S9wKPA/YALh9KhJIkSfMwjCsAfybJ7sDTgG8A+wInJnkFcCXNVYJ3z7DNccBxAMuXO+2KJEmSJI0Vk2uS+izJucB+wM5J1gJvo0n8rUpyLHAbcBhAVV2XZBVwPc2sVidU1f1DCVySJGkehpYATPJI4NPAG6rqniQfAt5JM4/6O4H3AL83fbuqOh04HWDlypUzzrkuSZIkSZIkzaSqjppl1QGz1D8VOLV/EUmSJPXedsN40SQPoUn+faKqPgNQVXdU1f1V9QDwEWDvYcQmSZIkSZIkSZIkLWQDTwCmuZnDGcANVfXejvIlHdVeDFw76NgkSZIkSRqK9t6HvfpZusxbZkiSJEmTbBhTgO4LHA18K8nVbdlbgKOS7EUzBeitwPFDiE1akA49/GWs37jZLTMfZMniHblw1TkDikiSJEnSvPT43ofe91CSJEmabANPAFbVV4HMsOpzg45FWghW3/xtnrH/wXPW+c4ta/itt35szjprzj2ll2FJkiRJkiRJkqQRNYwrACXNw6YKK446ec46N77j6AFFI0mSJEmSJEmSRt3A7wEoSZIkSZIkSZIkqX9MAEqSJEmSJEmSJEljxASgJEmSJEmSJEmSNEZMAEqSJEmSJEmSJEljxASgJEmSJEmSJEmSNEZMAEqSJEmSJEmSJEljxASgJEmSJEmSJEmSNEZMAEqSJEmSJEmSJEljxASgJEmSJEmSJEmSNEZMAEqSJEmSJEmSJEljxASgJEmSJEmSJG2FpcuWk2Srf5YuWz7styBJGlOLhh2AJEmSJEmSJC1E69bezhGnfW2rtz//+H16GI0kST/nFYCSJEmSJEmSJEnSGDEBKEmSJEmSJEmSJI0RE4CSJEmSJEmSJEnSGPEegJJ+5tDDX8b6jXfPWWfJ4h25cNU5A4pIkiRJkiRJkiTNlwlAST+zfuPdrDjq5DnrrDn3lAFFI0mSJEmSJEmStoYJQEljzysbJUmSJEmSJEmTxASgpLHnlY2SJEmSJEmSpEliAnBAurkC6Tu3rGHFgOLR5Fl987d5xv4Hz1lnkJ9Br8qTJEmSJEmSJKk/TAD2QLfJvd9668fmrHPjO47uZVjSg2yqbPEquEF+Br0qT5IkSZIkSZKk/jAB2APdJDJM7kmSJEmSJEmSJGkQTABK6jmnvJUkSZIkSZIkaXhMAErqOa+KlSRJkiRJkiRpeLYbdgCSJEmSJEmSJEmSescrACXNy+qbv80z9j94zjqDnN7T6UYlSZKkwVi6bDnr1t7es/Yet9syvnf7bT1rDxZGjJIkSVo4tnV8OczxpAlASfOyqTJS03s63agkSZI0GOvW3s4Rp32tZ+2df/w+PWtrykKIUZIkSQvHto4vhzmeNAEoaWSN2tWGo2ZLVz8uWbwjF646Z4ARSZIkSfOw3SKSDDsKSZIkaSyZAJQ0skbtasNRs6WrH9ece8oAo5Gk4ej1VG+SpAF6YFNPr9YDr9jT5OjFGMgpbiVJGm8mACVJkjQQ/UrWOdWbJM3Aq+uksdaL6W4d90iSNN5GLgGY5CDgr4HtgY9W1buGHJIk9dSWpu4EuP22NSxbPvfkppM8/WkvdPN7cBrVwejV34S/r82N2riq1/dlAg9cSdKsvLpO6qlRG1dJkiRtyUglAJNsD3wAeB6wFrgiycVVdf2wYurmoKQH4aWFr5v7DXaTgOg2cfdbb/3YnHVufMfRTn86i14mULf0e3Aa1cHY0nS20N3fhL+vBxvFcZUkSdJC5LhKkiQtRCOVAAT2BlZX1S0ASc4DDgGGNqDq9qCkpIWt2/sN9qqOtl6vkkX+HjQBRm5cJUmSeq8fU2x7b7jNOK6SJEkLTqpq2DH8TJKXAgdV1e+3y0cDz6iqEzvqHAcc1y4+CbgT+MGgY12AdsZ+6ob91B37acvso+7YT92xn7rT7376papa3Mf2e6qbcVVbPn1sddNAA10Y/Bvsnn01P/bX/Nhf82N/zY/91b1e9JXjqm3nZ3Z0+LsYDf4eRoe/i9Hg72F0DOJ3MePYatSuAJzpDuUPylBW1enA6T/bILmyqlb2O7CFzn7qjv3UHftpy+yj7thP3bGfumM/bWaL4yrYfGylzfnZ6p59NT/21/zYX/Njf82P/dW9Ce2rkRtXTejvYST5uxgN/h5Gh7+L0eDvYXQM83ex3TBedA5rgWUdy7sB64YUiyRJ0kLmuEqSJKk3HFdJkqQFZ9QSgFcAeyRZkeShwJHAxUOOSZIkaSFyXCVJktQbjqskSdKCM1JTgFbVpiQnAl8AtgfOrKrrtrCZU1Z1x37qjv3UHftpy+yj7thP3bGfumM/ddjKcZVm5mere/bV/Nhf82N/zY/9NT/2V/cmrq9GdFw1cb+HEebvYjT4exgd/i5Gg7+H0TG030WqNpuyXJIkSZIkSZIkSdICNWpTgEqSJEmSJEmSJEnaBiYAJUmSJEmSJEmSpDEy0gnAJAcluSnJ6iRvnmH9y5Nc0/58LclTO9bdmuRbSa5OcuVgIx+sLvppvyT/1vbF1UlO7nbbcdFFH/1JR/9cm+T+JDu16ybps3Rmkg1Jrp1lfZL8r7Yfr0ny9I51k/JZ2lIfuV+iq36a+P0SdNVPE79vSrIsyZeS3JDkuiSvn6HOxO+b1B9JXt/+7V2X5A3DjmfUzLQPS7JTkkuS3Nw+7jjMGEfJLP11WPv5eiDJymHGN2pm6a+/THJju6+/IMljhhjiSJmlv97Z9tXVSb6Y5HHDjHFUzDX+SvL/JqkkOw8jtlE0y2fr7Um+1zFOfcEwY5w0SR6W5PIk/9r+D3nHsGOaZEm2T/IvST477Fgm2aR8Px51SR6T5FPteO2GJM8adkyTKMmTOv5HX53kHr/PDkeSP2r/V1+b5NwkDxt0DCObAEyyPfAB4GDgycBRSZ48rdoa4DlV9avAO9n8Zor7V9VeVTW2X6a77CeA/9v2xV5Vdco8t13QunmfVfWXU/0DnAT8U1Xd1VFl7D9LrbOAg+ZYfzCwR/tzHPAhmJzPUuss5u6jid8vtc5i7n6CCd4vdTiLOfrJfRMAm4A3VtWvAM8ETpjhM+G+ST2X5CnAq4G9gacCL0yyx3CjGjlnsfk+7M3ApVW1B3Bpu6zGWWzeX9cCLwG+MvBoRt9ZbN5flwBPacdZ36b536jGWWzeX39ZVb/ajiM+C5w8faMJdRYzjL+SLAOeB9w26IBG3FnMPF59X8dY/nMDjmnS3Qc8t6qeCuwFHJTkmcMNaaK9Hrhh2EEImIzvx6Pur4HPV9Uv03yH8m9jCKrqpo5jSb8G3AtcMNyoJk+SpcAfAiur6inA9sCRg45jZBOANAdbVlfVLVX1E+A84JDOClX1taq6u128DNhtwDGOgi32U5+2XUjm+z6PAs4dSGQjpqq+Atw1R5VDgI9X4zLgMUmWMDmfpS32kfulRhefpdlMzGcJ5t1PE7lvqqr1VfXN9vmPaL5ALJ1WbeL3TeqLXwEuq6p7q2oT8E/Ai4cc00iZZR92CHB2+/xs4NBBxjTKZuqvqrqhqm4aUkgjbZb++mL79wgTPM6aySz9dU/H4i8ANdCgRtQc46/3AX+K/fQg2zCuV5+0Y95/bxcf0v74uR2CJLsBvw18dNixSMOW5NHAs4EzAKrqJ1X1w6EGJYADgO9U1XeHHciEWgQ8PMki4BHAukEHMMoJwKXA7R3La9n8gF+nY4F/6Fgu4ItJrkpyXB/iGxXd9tOz2ukh/iHJnvPcdqHr+n0meQTN2Y2f7iielM9SN2bry0n5LM3XpO6XujXJ+6V5cd/USLI78DTgG9NWuW9SP1wLPDvJY9u/wRcAy4Yc00Kwa1WthyaBD+wy5Hg0vn6PB4+zNIMkpya5HXg5XgE4qyQvAr5XVf867FgWkBPbKWbPdLrnwWunnbwa2ABcUlXTx8cajL+iOXHggSHHoQn8fjyCHg9sBD7WTov70SS/MOygxJFM4Mnko6Cqvge8m2Z2ifXAv1XVFwcdxygnADND2YxnNCXZn+ZA+5s6ivetqqfTTPt1QpJn9z7EkdBNP30T+KV2eoi/AS6cx7bjYD7v878B/zxtir1J+Sx1Y7a+nJTPUtcmfL/UjUnfL83XxO+bkjySJgH6hmlXNID7JvVBVd0A/E+aKQc/D/wrzZS0koYsyVtp/h4/MexYRl1VvbWqltH01YnDjmcUtSd5vBUTpPPxIeAJNNNPrgfeM9RoJlBV3d9O7bYbsHc7dbkGKMkLgQ1VddWwYxEwYd+PR9Qi4OnAh6rqacCP8XYAQ5XkocCLgE8OO5ZJ1J4gdQiwAngc8AtJfnfQcYxyAnAtDz7LejdmuEQyya/SXGp/SFXdOVVeVevaxw00c9zu3ddoh2eL/VRV90xND9HOzf+Q9qbmXfXxGJjP+9zsrIgJ+ix1Y7a+nJTPUlfcL22Z+6V5m+h9U5KH0CT/PlFVn5mhivsm9UVVnVFVT6+qZ9NMf3bzsGNaAO5op+Clfdww5Hg0ZpIcA7wQeHlVeVJH984BfmfYQYyoJ9AcmPnXJLfSjBe+meQXhxrVCKuqO9oE1APARxjjceioa6fX+zJbvv+6em9f4EXtfuM84LlJ/na4IU2uSfp+PMLWAms7rkj+FE1CUMNzMPDNqrpj2IFMqN8C1lTVxqr6KfAZYJ9BBzHKCcArgD2SrGiz1UcCF3dWSLKcpuOOrqpvd5T/QpJHTT0Hnk8zjdM46qaffjFJ2ud70/ze7+xm2zHR1ftM8l+A5wAXdZRN0mepGxcDr0jjmTSXLq9ncj5LW+R+qTvul7o36fum9nNyBnBDVb13lmrum9QXSXZpH5cDL8GpU7pxMXBM+/wYOvZd0rZKchDN7Aovqqp7hx3PqEuyR8fii4AbhxXLKKuqb1XVLlW1e1XtTnMA8+lV9f0hhzaypk70aL2YMR2Hjqoki5M8pn3+cJoDjP59D1hVnVRVu7X7jSOBf6yqgV/Zocn6fjzK2v+btyd5Ult0AHD9EEMSHIXfYYfpNuCZSR7RHts6ALhh0EEsGvQLdquqNiU5EfgCsD1wZlVdl+Q17foP00zR8Vjgg+1x5E1VtRLYFbigLVsEnFNVnx/C2+i7LvvppcBrk2wC/gM4sj1bdsZth/JG+qjLPoLmi8sXq+rHHZtPzGcJIMm5wH7AzknWAm+juaH4VD99juYeSKuBe4FXtesm4rMEXfXRxO+XoKt+muj90pQu+gncN+0LHA18K819TgDeAiwH903qu08neSzwU+CEqrp72AGNkln2Ye8CViU5luYLz2HDi3C0zNJfd9FMhb0Y+PskV1fVgcOLcnTM0l8nATsAl7T/Ay+rqtcMLcgRMkt/vaA9CPcA8F3AvmLmvqqqM4Yb1eia5bO1X5K9aKZWvxU4fljxTaglwNlJtqc5kXJVVX12yDFJwzRJ349H3euAT7Qn4N5C+91cg5dmivPn4f/ooamqbyT5FM1tkDYB/wKcPug44qwpkiRJkiRJkiRJ0vgY5SlAJUmSJEmSJEmSJM2TCUBJkiRJkiRJkiRpjJgAlCRJkiRJkiRJksaICUBJkiRJkiRJkiRpjJgAlCRJkiRJkiRJksaICUBJC0aS/ZJUkqfMc7tXtts9sgcxPD/JG7a1HUmSpGHr5RhpWrtnJbmyR219OcmnetGWJEnSQjLTcbB2+cSO5eOSHDqUACWNPBOAkjQ/zwfeMOwgJEmSJEmSNHGeBXyyY/k44NDhhCJp1C0adgCSJEmSJEmSJGluVXXZsGOQtHB4BaCkgUmyZ5LPJ7kryY+T3JDkhHbdrUnePa3+Fqelatf/cZK/btv9YZK/SfLQGaqvSHJJ+9o3JnnJtLZ+u12/Ick9SS5L8vyO9W8H3gj8Uvu6leSsjvW/keSfktyb5M4kH0nyqI71j0ny0STrkvxnktuSfGSe3ShJktSVJM9KcnE79vhxkquTvHwL2zw8yV8k+W6S+5KsSfI/OtZvn+Tt7TjmviTXJXnZLG09L8k17Wt/Ncme09Y/Isn/SvL9dmx0RefYS5IkaRQl+YMkt7djnL9rxzzVTtm5e/v8hdO2edAU6Ul+Ocl5bTv3tmOqNySZ83h95xSgSb4M/BpwTMdxqlcm+csktyTJtG1fleQnSXbuWWdIGmleAShpkC4GbgR+F7gPeBLw6B60+0bgMuDlwJ7AqcB/An8yrd45wOnAXwKvA85L8viqWtuuXwH8HfBu4AHgYOAfkjy7qv4Z+CiwB/Bc4MXtNhsBkuwLXApcCLwUeCzwLmDHdhngvcA+wB8B3weWAc/uwfuXJEmayS8B/wx8mGZstC/wsSQPVNW50yu3B4kuopla6p3AVcBS4Dc7qp0C/CnwDuAK4HeATySpaW0upxlznQr8B834alWSp1RVtXU+ArwIeAuwGng18PdJ9q+qr/bg/UuSJPVUkkOAD9CMry4EngOcuRVNLQVuAj4B/AjYi2Z89XDgf8y+2YP8AfBp4BaasRvAd4CvA/9vG9uXO+q/Evi7qvrBVsQraQEyAShpINqzix4PHFpV32qLL+1R8z8CDquqB2gSdjsAb03yP6rqro5676uqM9t4rgLuAF5IM2ijqt7fEe92wJdoEorHAv9cVWuTrAfum2HKhXcBX6uqIzra+B5waXug61pgb+ADVXV+x3Z/24sOkCRJmq6qzpt63ib3vgLsRpNo2ywBSHOv4+cBh1TVxR3lH2/b2InmXsh/XlV/3q77QpLdgLdPa3MnYN+qurnddjvgApoTwG5M8ivAUcCrqursts4XgGuAPwMO3Oo3LkmS1D9vBT5fVa9tl7+QZDHw+/NppKoupT0u1o7Tvgo8gmac1lUCsKquT/JjYOO041Qbk/wz8CraBGCSx9Oc1PWi+cQpaWFzClBJg3IXcDvw4SRHJNmlh21f1Cb/pnyG5oypp0yr98WpJ1V1J7CB5iAYAEl2S3J2m7jbBPyU5kDYE+d68SSPoDlTflWSRVM/NIO3n9JMxwBwNfAn7VQRc7YpSZK0rZLs2E6x+V2aMclPgeOYfWzzXOCuacm/Tk+hOTD1yWnl5wNPnDa+u3Uq+de6vn2cGnv9OpDOttrx3CeB35jzjUmSJA1Bku2Bp9HMmNDpM1vR1sOSvCPJappZsn5KM3PCivaY0rY6A/id/Py2Oq+kORH+8z1oW9ICYQJQ0kC0B3SeTzP15ZnA95P83yRP60HzG2ZZXjKt/IfTln8CPAx+dlb6xTRTdJ4M7E9zYOofpurMYUdge+CD/Pzg2k9pBnAPoZnqE+BEmukhTgZuSnJzkiO30LYkSdLWOgs4gmYqzufTjG3OZPaxzWOB9XO0NzW2umNa+dTyjh1lP5xW5yft49RrLwH+varunaGtR7QzOkiSJI2SxTQz6s12HGo+/ifNNJ2nAy+gGadNzbCwpeNQ3VhFc3ubw9srDF8BfLyqNvWgbUkLhFOAShqYqrqR5uyjh9BMO/A/ae7zshvNfWkeOm2TnbpsevrVhFPLcx3Amu6/0pzFdXBV/exsqCQP72LbHwJFM/XV52ZYvw6gqn4I/CHwh0l+leb+OZ9Ick1VXT/DdpIkSVslycOA3wZOrKoPd5TPdRLonWx+AlWnqbHVLm3dKbu2j3fRvfXAI5M8YloScFfg3qq6bx5tSZIkDcJGmhmjZjsOBc3xLdjyMa7DgL+pqr+YKkjy270IEqCqfpzkPJor/75Lc2/os3rVvqSFwSsAJQ1cVf20qv4ReC/NQabHAGuBX5lW9XldNnnItINZLwH+A7h2HmFNJfp+drApyS8B+06r97OrBqdU1Y+By4AnVdWVM/ysm/5iVXUN8Cc0++FfnkeckiRJ3diBZoaCzrHNo5j7vi+XAjsleeEs668F7qU5YNXpcODbVbVxHvFdQXMC1Us74ku7/NV5tCNJkjQQVXU/ze1dDpm26iUdzzfQzAr1s2Nc7TScz5q2zcN58Dhte2BrZona7DhVhzNoTsB/O3BZVd2wFe1LWsC8AlDSQLRXvL2b5h4xt9BMEfUm4F+r6q4kFwB/k+QtNAeEXgLs2WXzjwI+meQj7TYnA++vqvmchX4jTRLyPUn+rG3zHcD3Zqi3a5JX0hwE+0FV3UpzNd+lSR4APgX8CFhOc+b9W6vq20m+ClzQblc0N3b+MXD5POKUJEnaoqr6tyRXACcnuYdmCqg3A/8GPHqWzS4BvgCck+QU4Js0J2s9u6qOb8dsfwX89ySbgCtpxmwvAI6aZ3w3JDkXeH+SRwOracZGvwy8dn7vVpIkaWD+P+AzST5Ec4znOcBBUyur6oEkFwF/1N6H+YfAG2lOVO90CXBCew/Au4ATaE7gmq8bgQOTHEgzQ8OaqrqzjeUbSa6jub/y8VvRtqQFzgSgpEH5Ps09Xd4KPI5mAPQlmiQgNHOeP4FmiswdgI/TzH1+Whdtvwd4PHAuzRV1HwXeMp/gquq+JC8BPkCTwFtLc/Pl/YCndFRdRXN/wL+gmfv9bOCVVfXVJM+mSRr+b5oz7r9Lc3PlqfvifJ1m6oXdgfuBf6GZcnTtfGKVJEnq0stoxlgfpzkg9H7gETT3Jd5MVVWSFwPvBN5AM9ZZB5zTUe1kmqmvXkszXedq4Her6rytiO/VNFPC/xnNjBDfAl5YVV4BKEmSRlJVXZDkdTQnVh0DfBk4luYkqikn0ozBPgjcTXN8aR8efHzpdcCHaY5D/QfN8aUL2u3m489pTkBfRXOS16t48FSfF9IcM9uasZqkBS5VNewYJGmrJSngdVX1/mHHIkmSJEmSpMmS5Ck0JzLtX1VfHnI4D5LkcuCmqjp62LFIGjyvAJQkSZIkSZIkaUwkWQk8F/h1mulFJU0gE4CSJEmSJEmSJI2PK2huv3NSVV0x5FgkDYlTgEqSJEmSJEmSJEljZLthByBJkiRJkiRJkiSpd0wASpIkSZIkSZIkSWPEBKAkSZIkSZIkSZI0RkwASpIkSZIkSZIkSWPEBKAkSZIkSZIkSZI0Rv5/Htm1BzGDdYsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1800x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "plt.figure(figsize=(25, 20))\n",
    "\n",
    "# Initialize plot counter\n",
    "plotnumber = 1\n",
    "\n",
    "# Iterate over the columns of the dataframe\n",
    "for col in data.columns:\n",
    "    \n",
    "    # Limit the number of subplots to 12\n",
    "    if plotnumber <= 12:\n",
    "        \n",
    "        # Create a new subplot\n",
    "        ax = plt.subplot(4, 3, plotnumber)\n",
    "        \n",
    "        # Plot the histogram using seaborn\n",
    "        sns.histplot(data[col], ax=ax)\n",
    "        \n",
    "        # Label the x-axis with the column name\n",
    "        plt.xlabel(col, fontsize=15)\n",
    "        \n",
    "        # Increment the plot counter\n",
    "        plotnumber += 1\n",
    "    \n",
    "# Adjust the layout of the subplots\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pearson Correlation\n",
    "plt.figure(figsize=(25, 20))\n",
    "sns.heatmap(data.corr(), annot=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Findings from Data Analysis\n",
    "* Based on the data analysis performed, it was found that certain columns contained outliers which could negatively impact the accuracy of the model. To address this issue, min-max scaling will be used as a normalization technique. By squeezing the data values between 0 and 1, this method effectively removes the impact of outliers and allowes for uniformity of scales across all features. This is important because the different value ranges of the features could lead to problems during model training.\n",
    "\n",
    "* Additionally, the Pearson Correlation plot was used to investigate the presence of highly correlated features. It was observed that none of the features were highly correlated (using a threshold value of 0.8), indicating that each feature provided unique and valuable information that should be retained in the model.\n",
    "\n",
    "*  Also, since some features have low correlation (< 0.3), it is reasonable not to use PCA, as it is generally used to reduce dimensionality when there are too many features or when there is high correlation between them."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "good    855\n",
       "bad     744\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert continuous output to categorical output:\n",
    "# Bad: 3-5\n",
    "# Good: 6-8\n",
    "data[\"quality\"] = pd.cut( data[\"quality\"], bins = [2, 5, 8], labels = [\"bad\", \"good\"])\n",
    "# Show output frequency\n",
    "data[\"quality\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = LabelEncoder()\n",
    "data[\"quality\"] = lb.fit_transform(data[\"quality\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate the dataset as features and output variable\n",
    "X = data.drop(\"quality\", axis = 1)\n",
    "y = data[\"quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 676, 0: 603})\n"
     ]
    }
   ],
   "source": [
    "print(Counter(y_train))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, we have imbalances classes. Since the class imbalance is not very high (855 vs 744), undersampling may not be the best option, as it may lead to a significant loss of information. However, oversampling or SMOTE can be used to increase the number of samples in the minority class. Therefore, we will try both oversampling and SMOTE techniques, and compare their performance."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Models with Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate the training data back together\n",
    "X_resample = pd.concat([X_train, y_train], axis = 1)\n",
    "good_quality = X_resample[X_resample[\"quality\"] == 1]\n",
    "bad_quality = X_resample[X_resample[\"quality\"] == 0]\n",
    "\n",
    "# Upsample minority\n",
    "bad_quality_oversampled = resample(bad_quality, replace=True, n_samples=len(good_quality), random_state=42)\n",
    "oversampled_data = pd.concat([good_quality, bad_quality_oversampled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    676\n",
       "0    676\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See class counts\n",
    "oversampled_data.quality.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have balanced classes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the oversampled data into features and output variable\n",
    "X_train_oversampled = oversampled_data.drop(\"quality\", axis = 1)\n",
    "y_train_oversampled = oversampled_data[\"quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "scaler = MinMaxScaler()\n",
    "X_train_oversampled = scaler.fit_transform(X_train_oversampled)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Random Forest Classifier (on oversampled data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_oversampled = RandomForestClassifier(n_estimators=100)\n",
    "rfc_oversampled.fit(X_train_oversampled, y_train_oversampled)\n",
    "rfc_y_hat = rfc_oversampled.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.68      0.75       141\n",
      "           1       0.78      0.89      0.83       179\n",
      "\n",
      "    accuracy                           0.80       320\n",
      "   macro avg       0.80      0.78      0.79       320\n",
      "weighted avg       0.80      0.80      0.79       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, rfc_y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 96  45]\n",
      " [ 20 159]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, rfc_y_hat))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Decision Tree (on oversampled data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_oversampled = DecisionTreeClassifier()\n",
    "dt_oversampled.fit(X_train_oversampled, y_train_oversampled)\n",
    "y_hat = dt_oversampled.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.68      0.71       141\n",
      "           1       0.76      0.80      0.78       179\n",
      "\n",
      "    accuracy                           0.75       320\n",
      "   macro avg       0.75      0.74      0.74       320\n",
      "weighted avg       0.75      0.75      0.75       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 96  45]\n",
      " [ 35 144]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_hat))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Logistic Regression (on oversampled data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_oversampled = SGDClassifier(loss=\"log\")\n",
    "lr_oversampled.fit(X_train_oversampled, y_train_oversampled)\n",
    "lr_y_hat = lr_oversampled.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.89      0.71       141\n",
      "           1       0.86      0.53      0.66       179\n",
      "\n",
      "    accuracy                           0.69       320\n",
      "   macro avg       0.73      0.71      0.68       320\n",
      "weighted avg       0.74      0.69      0.68       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, lr_y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[125  16]\n",
      " [ 84  95]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, lr_y_hat))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 Models with SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({1: 676, 0: 603})\n",
      "Resampled dataset shape Counter({1: 676, 0: 676})\n"
     ]
    }
   ],
   "source": [
    "# Now, apply SMOTE to balance the classes\n",
    "sm = SMOTE(random_state=42)\n",
    "X_train_sm, y_train_sm = sm.fit_resample(X_train, y_train)\n",
    "print(\"Original dataset shape %s\" % Counter(y_train))\n",
    "print(\"Resampled dataset shape %s\" % Counter(y_train_sm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data\n",
    "X_train_sm = scaler.fit_transform(X_train_sm)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Random Forest Classifier (using SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote_rfc = RandomForestClassifier(n_estimators=100)\n",
    "smote_rfc.fit(X_train_sm, y_train_sm)\n",
    "smote_pred = smote_rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.79      0.76       141\n",
      "           1       0.82      0.77      0.79       179\n",
      "\n",
      "    accuracy                           0.78       320\n",
      "   macro avg       0.77      0.78      0.77       320\n",
      "weighted avg       0.78      0.78      0.78       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, smote_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[111  30]\n",
      " [ 42 137]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, smote_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Decision Tree (using SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_smote = DecisionTreeClassifier()\n",
    "dt_smote.fit(X_train_sm, y_train_sm)\n",
    "dt_smote_y_hat = dt_smote.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.66      0.62       141\n",
      "           1       0.70      0.64      0.67       179\n",
      "\n",
      "    accuracy                           0.65       320\n",
      "   macro avg       0.65      0.65      0.65       320\n",
      "weighted avg       0.65      0.65      0.65       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, dt_smote_y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 93  48]\n",
      " [ 65 114]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, dt_smote_y_hat))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Logistic Regression (using SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_smote = SGDClassifier(loss=\"log\")\n",
    "lr_smote.fit(X_train_sm, y_train_sm)\n",
    "lr_smote_y_hat = lr_smote.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.68      0.70       141\n",
      "           1       0.76      0.78      0.77       179\n",
      "\n",
      "    accuracy                           0.74       320\n",
      "   macro avg       0.73      0.73      0.73       320\n",
      "weighted avg       0.74      0.74      0.74       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, lr_smote_y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 96  45]\n",
      " [ 39 140]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, lr_smote_y_hat))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting the optimal model and conducting additional fine-tuning"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The comparison of the models using oversampling and SMOTE techniques suggests that Random Forest Classifier model appears to be the most promising to fine-tune further.\n",
    "\n",
    "Comparing the oversampling and SMOTE results for RFC, the f1-score for the class (1) is higher when using oversampling (0.83) compared to using SMOTE (0.79). Additionally, the recall for the class (1) is also higher for the oversampling results compared to SMOTE results. The confusion matrix also indicates a better performance for the RFC model when using oversampling. When we compare the class (0), there is almost no difference between the RFC models.\n",
    "\n",
    "Similarly, the Decision Tree model appears to perform better when using oversampling. The f1-score for the class (1) is higher when using oversampling (0.78) compared to using SMOTE (0.67). The confusion matrix also indicates a better performance for the Decision Tree model when using oversampling.\n",
    "\n",
    "The Logistic Regression model does not appear to perform as well as the RFC or Decision Tree models, regardless of the oversampling or SMOTE techniques used.\n",
    "\n",
    "In summary, RFC with oversampling method appears to be the most promising model to fine-tune further as it consistently outperformed the other models when using oversampling and SMOTE techniques."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter search for Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'entropy', 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "rfc_oversamp = RandomForestClassifier(n_estimators=50)\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 150, 200, 250, 300],\n",
    "    'max_features': ['sqrt', 'log2', 0.2, 0.5, 0.8],\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "}\n",
    "\n",
    "CV_rfc = GridSearchCV(estimator=rfc_oversampled, param_grid=param_grid, cv= 5, verbose=3)\n",
    "CV_rfc.fit(X_train_oversampled, y_train_oversampled)\n",
    "print(CV_rfc.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the best parameters to train the Random Forest\n",
    "rfc_finetuned = RandomForestClassifier(n_estimators=300, max_features='sqrt', criterion='entropy')\n",
    "# Train the model\n",
    "rfc_finetuned.fit(X_train_oversampled, y_train_oversampled)\n",
    "# Make predictions\n",
    "y_hat_finetuned = rfc_finetuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.73      0.78       141\n",
      "           1       0.81      0.89      0.85       179\n",
      "\n",
      "    accuracy                           0.82       320\n",
      "   macro avg       0.82      0.81      0.81       320\n",
      "weighted avg       0.82      0.82      0.82       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_hat_finetuned))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[103  38]\n",
      " [ 20 159]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_hat_finetuned))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon comparing two Random Forest Classifier (RFC) models, it was observed that the fine-tuned RFC model outperformed the default RFC model in terms of accuracy, precision, recall and f1-score. The fine-tuned RFC model achieved an accuracy of **82%** compared to the default RFC model's accuracy of **80%**. The precision and recall scores for both classes were also improved in the fine-tuned RFC model, leading to higher f1-scores in comparison to the default RFC model. The confusion matrices of both models indicated that the fine-tuned RFC model had 38 false positives and 20 false negatives, while the RFC model without tuning had 45 false positives and 20 false negatives. In conclusion, the fine-tuned RFC model proved to be a better choice for this classification task, achieving an accuracy of 82%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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