german_credit_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3653b2b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, LabelEncoder\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from time import time\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1629887e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"german_credit_data.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0c3335b",
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Saving accounts</th>\n",
       "      <th>Checking account</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>NaN</td>\n",
       "      <td>little</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>moderate</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>little</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "      <td>car</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>free</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9055</td>\n",
       "      <td>36</td>\n",
       "      <td>education</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>quite rich</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2835</td>\n",
       "      <td>24</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>rent</td>\n",
       "      <td>little</td>\n",
       "      <td>moderate</td>\n",
       "      <td>6948</td>\n",
       "      <td>36</td>\n",
       "      <td>car</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>61</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>rich</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3059</td>\n",
       "      <td>12</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>moderate</td>\n",
       "      <td>5234</td>\n",
       "      <td>30</td>\n",
       "      <td>car</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Sex  Job Housing Saving accounts Checking account  Credit amount  \\\n",
       "0   67    male    2     own             NaN           little           1169   \n",
       "1   22  female    2     own          little         moderate           5951   \n",
       "2   49    male    1     own          little              NaN           2096   \n",
       "3   45    male    2    free          little           little           7882   \n",
       "4   53    male    2    free          little           little           4870   \n",
       "5   35    male    1    free             NaN              NaN           9055   \n",
       "6   53    male    2     own      quite rich              NaN           2835   \n",
       "7   35    male    3    rent          little         moderate           6948   \n",
       "8   61    male    1     own            rich              NaN           3059   \n",
       "9   28    male    3     own          little         moderate           5234   \n",
       "\n",
       "   Duration              Purpose  Risk  \n",
       "0         6             radio/TV  good  \n",
       "1        48             radio/TV   bad  \n",
       "2        12            education  good  \n",
       "3        42  furniture/equipment  good  \n",
       "4        24                  car   bad  \n",
       "5        36            education  good  \n",
       "6        24  furniture/equipment  good  \n",
       "7        36                  car  good  \n",
       "8        12             radio/TV  good  \n",
       "9        30                  car   bad  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eeaac84",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6395a253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1029638c",
   "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>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.546000</td>\n",
       "      <td>1.904000</td>\n",
       "      <td>3271.258000</td>\n",
       "      <td>20.903000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.375469</td>\n",
       "      <td>0.653614</td>\n",
       "      <td>2822.736876</td>\n",
       "      <td>12.058814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1365.500000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2319.500000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3972.250000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18424.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age          Job  Credit amount     Duration\n",
       "count  1000.000000  1000.000000    1000.000000  1000.000000\n",
       "mean     35.546000     1.904000    3271.258000    20.903000\n",
       "std      11.375469     0.653614    2822.736876    12.058814\n",
       "min      19.000000     0.000000     250.000000     4.000000\n",
       "25%      27.000000     2.000000    1365.500000    12.000000\n",
       "50%      33.000000     2.000000    2319.500000    18.000000\n",
       "75%      42.000000     2.000000    3972.250000    24.000000\n",
       "max      75.000000     3.000000   18424.000000    72.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e416f9b8",
   "metadata": {},
   "source": [
    "#### Since Risk is the target variable, plotting the histogram to find out the distribution of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dbde9aff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Risk', ylabel='Count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.histplot(data=data, x=\"Risk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "57ed6bf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Risk\n",
       "good    700\n",
       "bad     300\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the number of values per class in \"Risk\"\n",
    "data['Risk'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba22923",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "<li> As you can see, the data is imbalanced as the target variable has more number of values as 'good' than of 'bad'. </li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a635973d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                   0\n",
       "Sex                   0\n",
       "Job                   0\n",
       "Housing               0\n",
       "Saving accounts     183\n",
       "Checking account    394\n",
       "Credit amount         0\n",
       "Duration              0\n",
       "Purpose               0\n",
       "Risk                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495d856b",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "<li> There are missing values in 'Saving accounts' and 'Checking account'. </li>\n",
    "<li> Since there are a lot of missing values in 'Checking account', we are going to remove that column as a part of data cleaning. </li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6394f231",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Checking account', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4be117ef",
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Saving accounts</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>little</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "      <td>car</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>free</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9055</td>\n",
       "      <td>36</td>\n",
       "      <td>education</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>quite rich</td>\n",
       "      <td>2835</td>\n",
       "      <td>24</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>rent</td>\n",
       "      <td>little</td>\n",
       "      <td>6948</td>\n",
       "      <td>36</td>\n",
       "      <td>car</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>61</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>rich</td>\n",
       "      <td>3059</td>\n",
       "      <td>12</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>own</td>\n",
       "      <td>little</td>\n",
       "      <td>5234</td>\n",
       "      <td>30</td>\n",
       "      <td>car</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Sex  Job Housing Saving accounts  Credit amount  Duration  \\\n",
       "0   67    male    2     own             NaN           1169         6   \n",
       "1   22  female    2     own          little           5951        48   \n",
       "2   49    male    1     own          little           2096        12   \n",
       "3   45    male    2    free          little           7882        42   \n",
       "4   53    male    2    free          little           4870        24   \n",
       "5   35    male    1    free             NaN           9055        36   \n",
       "6   53    male    2     own      quite rich           2835        24   \n",
       "7   35    male    3    rent          little           6948        36   \n",
       "8   61    male    1     own            rich           3059        12   \n",
       "9   28    male    3     own          little           5234        30   \n",
       "\n",
       "               Purpose  Risk  \n",
       "0             radio/TV  good  \n",
       "1             radio/TV   bad  \n",
       "2            education  good  \n",
       "3  furniture/equipment  good  \n",
       "4                  car   bad  \n",
       "5            education  good  \n",
       "6  furniture/equipment  good  \n",
       "7                  car  good  \n",
       "8             radio/TV  good  \n",
       "9                  car   bad  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e4872d",
   "metadata": {},
   "source": [
    "### Remove all the missing values in 'Saving accounts' using SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da6ebb66",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(missing_values=np.nan, strategy='most_frequent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20018b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data = data.iloc[:, 4:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "26fdc481",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data = imputer.fit_transform(missing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0836884e",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data_df = pd.DataFrame(missing_data, columns=['Saving accounts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d890dc9b",
   "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>Saving accounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Saving accounts\n",
       "0            little\n",
       "1            little\n",
       "2            little\n",
       "3            little\n",
       "4            little\n",
       "..              ...\n",
       "995          little\n",
       "996          little\n",
       "997          little\n",
       "998          little\n",
       "999        moderate\n",
       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "210a0984",
   "metadata": {},
   "source": [
    "#### Remove the original column to add the column obtained using SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d3438028",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop('Saving accounts', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "610e14e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final = pd.concat([data, missing_data_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c268047b",
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Saving accounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>good</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>bad</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>good</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>good</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "      <td>car</td>\n",
       "      <td>bad</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>free</td>\n",
       "      <td>9055</td>\n",
       "      <td>36</td>\n",
       "      <td>education</td>\n",
       "      <td>good</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>2835</td>\n",
       "      <td>24</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>good</td>\n",
       "      <td>quite rich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>rent</td>\n",
       "      <td>6948</td>\n",
       "      <td>36</td>\n",
       "      <td>car</td>\n",
       "      <td>good</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>61</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>3059</td>\n",
       "      <td>12</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>good</td>\n",
       "      <td>rich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>own</td>\n",
       "      <td>5234</td>\n",
       "      <td>30</td>\n",
       "      <td>car</td>\n",
       "      <td>bad</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Sex  Job Housing  Credit amount  Duration              Purpose  \\\n",
       "0   67    male    2     own           1169         6             radio/TV   \n",
       "1   22  female    2     own           5951        48             radio/TV   \n",
       "2   49    male    1     own           2096        12            education   \n",
       "3   45    male    2    free           7882        42  furniture/equipment   \n",
       "4   53    male    2    free           4870        24                  car   \n",
       "5   35    male    1    free           9055        36            education   \n",
       "6   53    male    2     own           2835        24  furniture/equipment   \n",
       "7   35    male    3    rent           6948        36                  car   \n",
       "8   61    male    1     own           3059        12             radio/TV   \n",
       "9   28    male    3     own           5234        30                  car   \n",
       "\n",
       "   Risk Saving accounts  \n",
       "0  good          little  \n",
       "1   bad          little  \n",
       "2  good          little  \n",
       "3  good          little  \n",
       "4   bad          little  \n",
       "5  good          little  \n",
       "6  good      quite rich  \n",
       "7  good          little  \n",
       "8  good            rich  \n",
       "9   bad          little  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44d4f6dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                0\n",
       "Sex                0\n",
       "Job                0\n",
       "Housing            0\n",
       "Credit amount      0\n",
       "Duration           0\n",
       "Purpose            0\n",
       "Risk               0\n",
       "Saving accounts    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c798d3",
   "metadata": {},
   "source": [
    "#### We can observe that there are no missing values in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1d54abc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 int64\n",
       "Sex                object\n",
       "Job                 int64\n",
       "Housing            object\n",
       "Credit amount       int64\n",
       "Duration            int64\n",
       "Purpose            object\n",
       "Risk               object\n",
       "Saving accounts    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "def88a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_final.drop('Risk', axis=1)\n",
    "Y = data_final['Risk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3f6e8d96",
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Saving accounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "      <td>education</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>free</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "      <td>car</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>free</td>\n",
       "      <td>9055</td>\n",
       "      <td>36</td>\n",
       "      <td>education</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>own</td>\n",
       "      <td>2835</td>\n",
       "      <td>24</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>quite rich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>rent</td>\n",
       "      <td>6948</td>\n",
       "      <td>36</td>\n",
       "      <td>car</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>61</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>own</td>\n",
       "      <td>3059</td>\n",
       "      <td>12</td>\n",
       "      <td>radio/TV</td>\n",
       "      <td>rich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>own</td>\n",
       "      <td>5234</td>\n",
       "      <td>30</td>\n",
       "      <td>car</td>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age     Sex  Job Housing  Credit amount  Duration              Purpose  \\\n",
       "0   67    male    2     own           1169         6             radio/TV   \n",
       "1   22  female    2     own           5951        48             radio/TV   \n",
       "2   49    male    1     own           2096        12            education   \n",
       "3   45    male    2    free           7882        42  furniture/equipment   \n",
       "4   53    male    2    free           4870        24                  car   \n",
       "5   35    male    1    free           9055        36            education   \n",
       "6   53    male    2     own           2835        24  furniture/equipment   \n",
       "7   35    male    3    rent           6948        36                  car   \n",
       "8   61    male    1     own           3059        12             radio/TV   \n",
       "9   28    male    3     own           5234        30                  car   \n",
       "\n",
       "  Saving accounts  \n",
       "0          little  \n",
       "1          little  \n",
       "2          little  \n",
       "3          little  \n",
       "4          little  \n",
       "5          little  \n",
       "6      quite rich  \n",
       "7          little  \n",
       "8            rich  \n",
       "9          little  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f14ebe23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    good\n",
       "1     bad\n",
       "2    good\n",
       "3    good\n",
       "4     bad\n",
       "5    good\n",
       "6    good\n",
       "7    good\n",
       "8    good\n",
       "9     bad\n",
       "Name: Risk, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8276f4",
   "metadata": {},
   "source": [
    "### Age, Job, Credit Amount and Duration are numerical features whereas remaining are categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa50258f",
   "metadata": {},
   "source": [
    "### Perform One Hot Encoding for categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a63cc253",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = X.select_dtypes(include=['object']).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8df28bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sex', 'Housing', 'Purpose', 'Saving accounts']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "54549f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "en = OneHotEncoder(sparse_output=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0501ea86",
   "metadata": {},
   "outputs": [],
   "source": [
    "oh_encoded = en.fit_transform(data_final[categorical_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "841e9ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "oh_encoded_df = pd.DataFrame(oh_encoded, columns=en.get_feature_names_out(categorical_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5aae6c27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded = pd.concat([X, oh_encoded_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9f58cb89",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded = df_encoded.drop(categorical_columns, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6a700512",
   "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>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Housing_free</th>\n",
       "      <th>Housing_own</th>\n",
       "      <th>Housing_rent</th>\n",
       "      <th>Purpose_business</th>\n",
       "      <th>...</th>\n",
       "      <th>Purpose_domestic appliances</th>\n",
       "      <th>Purpose_education</th>\n",
       "      <th>Purpose_furniture/equipment</th>\n",
       "      <th>Purpose_radio/TV</th>\n",
       "      <th>Purpose_repairs</th>\n",
       "      <th>Purpose_vacation/others</th>\n",
       "      <th>Saving accounts_little</th>\n",
       "      <th>Saving accounts_moderate</th>\n",
       "      <th>Saving accounts_quite rich</th>\n",
       "      <th>Saving accounts_rich</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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>995</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1736</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>3857</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>804</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>1845</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>4576</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Job  Credit amount  Duration  Sex_female  Sex_male  Housing_free  \\\n",
       "0     67    2           1169         6         0.0       1.0           0.0   \n",
       "1     22    2           5951        48         1.0       0.0           0.0   \n",
       "2     49    1           2096        12         0.0       1.0           0.0   \n",
       "3     45    2           7882        42         0.0       1.0           1.0   \n",
       "4     53    2           4870        24         0.0       1.0           1.0   \n",
       "..   ...  ...            ...       ...         ...       ...           ...   \n",
       "995   31    1           1736        12         1.0       0.0           0.0   \n",
       "996   40    3           3857        30         0.0       1.0           0.0   \n",
       "997   38    2            804        12         0.0       1.0           0.0   \n",
       "998   23    2           1845        45         0.0       1.0           1.0   \n",
       "999   27    2           4576        45         0.0       1.0           0.0   \n",
       "\n",
       "     Housing_own  Housing_rent  Purpose_business  ...  \\\n",
       "0            1.0           0.0               0.0  ...   \n",
       "1            1.0           0.0               0.0  ...   \n",
       "2            1.0           0.0               0.0  ...   \n",
       "3            0.0           0.0               0.0  ...   \n",
       "4            0.0           0.0               0.0  ...   \n",
       "..           ...           ...               ...  ...   \n",
       "995          1.0           0.0               0.0  ...   \n",
       "996          1.0           0.0               0.0  ...   \n",
       "997          1.0           0.0               0.0  ...   \n",
       "998          0.0           0.0               0.0  ...   \n",
       "999          1.0           0.0               0.0  ...   \n",
       "\n",
       "     Purpose_domestic appliances  Purpose_education  \\\n",
       "0                            0.0                0.0   \n",
       "1                            0.0                0.0   \n",
       "2                            0.0                1.0   \n",
       "3                            0.0                0.0   \n",
       "4                            0.0                0.0   \n",
       "..                           ...                ...   \n",
       "995                          0.0                0.0   \n",
       "996                          0.0                0.0   \n",
       "997                          0.0                0.0   \n",
       "998                          0.0                0.0   \n",
       "999                          0.0                0.0   \n",
       "\n",
       "     Purpose_furniture/equipment  Purpose_radio/TV  Purpose_repairs  \\\n",
       "0                            0.0               1.0              0.0   \n",
       "1                            0.0               1.0              0.0   \n",
       "2                            0.0               0.0              0.0   \n",
       "3                            1.0               0.0              0.0   \n",
       "4                            0.0               0.0              0.0   \n",
       "..                           ...               ...              ...   \n",
       "995                          1.0               0.0              0.0   \n",
       "996                          0.0               0.0              0.0   \n",
       "997                          0.0               1.0              0.0   \n",
       "998                          0.0               1.0              0.0   \n",
       "999                          0.0               0.0              0.0   \n",
       "\n",
       "     Purpose_vacation/others  Saving accounts_little  \\\n",
       "0                        0.0                     1.0   \n",
       "1                        0.0                     1.0   \n",
       "2                        0.0                     1.0   \n",
       "3                        0.0                     1.0   \n",
       "4                        0.0                     1.0   \n",
       "..                       ...                     ...   \n",
       "995                      0.0                     1.0   \n",
       "996                      0.0                     1.0   \n",
       "997                      0.0                     1.0   \n",
       "998                      0.0                     1.0   \n",
       "999                      0.0                     0.0   \n",
       "\n",
       "     Saving accounts_moderate  Saving accounts_quite rich  \\\n",
       "0                         0.0                         0.0   \n",
       "1                         0.0                         0.0   \n",
       "2                         0.0                         0.0   \n",
       "3                         0.0                         0.0   \n",
       "4                         0.0                         0.0   \n",
       "..                        ...                         ...   \n",
       "995                       0.0                         0.0   \n",
       "996                       0.0                         0.0   \n",
       "997                       0.0                         0.0   \n",
       "998                       0.0                         0.0   \n",
       "999                       1.0                         0.0   \n",
       "\n",
       "     Saving accounts_rich  \n",
       "0                     0.0  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                     0.0  \n",
       "..                    ...  \n",
       "995                   0.0  \n",
       "996                   0.0  \n",
       "997                   0.0  \n",
       "998                   0.0  \n",
       "999                   0.0  \n",
       "\n",
       "[1000 rows x 21 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cc40c66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                            0\n",
       "Job                            0\n",
       "Credit amount                  0\n",
       "Duration                       0\n",
       "Sex_female                     0\n",
       "Sex_male                       0\n",
       "Housing_free                   0\n",
       "Housing_own                    0\n",
       "Housing_rent                   0\n",
       "Purpose_business               0\n",
       "Purpose_car                    0\n",
       "Purpose_domestic appliances    0\n",
       "Purpose_education              0\n",
       "Purpose_furniture/equipment    0\n",
       "Purpose_radio/TV               0\n",
       "Purpose_repairs                0\n",
       "Purpose_vacation/others        0\n",
       "Saving accounts_little         0\n",
       "Saving accounts_moderate       0\n",
       "Saving accounts_quite rich     0\n",
       "Saving accounts_rich           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eadd2820",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_numerical = pd.DataFrame(df_encoded, columns=['Age', 'Job', 'Credit amount', 'Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e0778235",
   "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>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1169</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>5951</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2096</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>7882</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>4870</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>9055</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>2835</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>6948</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>3059</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>5234</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Job  Credit amount  Duration\n",
       "0   67    2           1169         6\n",
       "1   22    2           5951        48\n",
       "2   49    1           2096        12\n",
       "3   45    2           7882        42\n",
       "4   53    2           4870        24\n",
       "5   35    1           9055        36\n",
       "6   53    2           2835        24\n",
       "7   35    3           6948        36\n",
       "8   61    1           3059        12\n",
       "9   28    3           5234        30"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numerical.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328c1209",
   "metadata": {},
   "source": [
    "### Histograms of numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7b12e1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_numerical.hist(figsize=(15,10))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c8801c",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "<li> Here we can observe that there is skewness in data for features - Age, Credit Amount and Duration. </li>\n",
    "<li> Log transformations cannot be applied for the hot encoded features since all of them have 0 as one of the values and we cannot log transform it. So, log transformation is applied only to the required features instead of applying to the whole feature set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071266b8",
   "metadata": {},
   "source": [
    "### Apply Log Transformations to reduce skewness in numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fab20ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded['Age'] = np.log(df_encoded['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e2a84d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded['Credit amount'] = np.log(df_encoded['Credit amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ac54dd92",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded['Duration'] = np.log(df_encoded['Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fd618db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_encoded.hist(figsize=(15,10))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344aee07",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "<li> Compared to the previous histograms, there's no skewness in Age, Credit Amount and Duration as tail of the graph is not pointing towards left or right. </li>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf9e021",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7725e2a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scaled = StandardScaler().fit_transform(df_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "30b66d6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.27100592,  0.14694918, -0.933901  , -1.86486906, -0.67028006,\n",
       "         0.67028006, -0.3479601 ,  0.63444822, -0.4669334 , -0.32774947,\n",
       "        -0.71294854, -0.11020775, -0.2503982 , -0.47010767,  1.60356745,\n",
       "        -0.14998296, -0.11020775,  0.52178983, -0.33886163, -0.25929878,\n",
       "        -0.22454436],\n",
       "       [-1.44615223,  0.14694918,  1.16304581,  1.7083687 ,  1.49191369,\n",
       "        -1.49191369, -0.3479601 ,  0.63444822, -0.4669334 , -0.32774947,\n",
       "        -0.71294854, -0.11020775, -0.2503982 , -0.47010767,  1.60356745,\n",
       "        -0.14998296, -0.11020775,  0.52178983, -0.33886163, -0.25929878,\n",
       "        -0.22454436],\n",
       "       [ 1.22669602, -1.38377145, -0.1815588 , -0.67378981, -0.67028006,\n",
       "         0.67028006, -0.3479601 ,  0.63444822, -0.4669334 , -0.32774947,\n",
       "        -0.71294854, -0.11020775,  3.99363901, -0.47010767, -0.62360956,\n",
       "        -0.14998296, -0.11020775,  0.52178983, -0.33886163, -0.25929878,\n",
       "        -0.22454436],\n",
       "       [ 0.94245501,  0.14694918,  1.52514806,  1.47891314, -0.67028006,\n",
       "         0.67028006,  2.8738927 , -1.57617276, -0.4669334 , -0.32774947,\n",
       "        -0.71294854, -0.11020775, -0.2503982 ,  2.12717227, -0.62360956,\n",
       "        -0.14998296, -0.11020775,  0.52178983, -0.33886163, -0.25929878,\n",
       "        -0.22454436],\n",
       "       [ 1.48861975,  0.14694918,  0.90474274,  0.51728944, -0.67028006,\n",
       "         0.67028006,  2.8738927 , -1.57617276, -0.4669334 , -0.32774947,\n",
       "         1.40262577, -0.11020775, -0.2503982 , -0.47010767, -0.62360956,\n",
       "        -0.14998296, -0.11020775,  0.52178983, -0.33886163, -0.25929878,\n",
       "        -0.22454436]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011a66dd",
   "metadata": {},
   "source": [
    "#### A graph to find out if the variables are normally distributed after feature scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8fb046e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHWCAYAAACL2KgUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAADfl0lEQVR4nOy9ebycZX33/77ubWbOmo1skEBkERSFCKKIC7jgL1qKW6HFh03wkQc3TKUVfUoRF9RWGi0FxSJIFaRVQO2DYqyFQFUkgQiKAoFAyL6ebbZ7u35/XNd9z8xZkpOQk8nyfb9e55WZe+7lmnnlPuczn++mtNYaQRAEQRCE/QSn3QsQBEEQBEHYnYi4EQRBEARhv0LEjSAIgiAI+xUibgRBEARB2K8QcSMIgiAIwn6FiBtBEARBEPYrRNwIgiAIgrBf4bV7AXuaNE1Zu3Yt3d3dKKXavRxBEARBEMaB1prBwUFmz56N42zfmzngxM3atWuZM2dOu5chCIIgCMIu8MILL3DIIYdsd58DTtx0d3cD5sPp6elp82oEQRAEQRgPAwMDzJkzJ/87vj0OOHGThaJ6enpE3AiCIAjCPsZ4UkokoVgQBEEQhP0KETeCIAiCIOxXiLgRBEEQBGG/oq05N0uWLOEf/uEfWLZsGevWreOuu+7iXe9613aPqdfrXH311Xz3u99l/fr1HHLIIXzmM5/hAx/4wJ5ZtCAIgiBMAGmaEoZhu5fRVoIg2GGZ93hoq7gpl8scd9xxXHjhhbz3ve8d1zFnnXUWGzZs4KabbuKII45g48aNxHE8wSsVBEEQhIkjDENWrlxJmqbtXkpbcRyHefPmEQTBizpPW8XNggULWLBgwbj3/9nPfsb999/Ps88+y5QpUwA47LDDJmh1giAIgjDxaK1Zt24drusyZ86c3eJc7ItkTXbXrVvH3LlzX1Sj3X2qFPzHP/4xJ554Il/5ylf4t3/7Nzo7O/nzP/9zPve5z1EqlUY9pl6vU6/X8+cDAwN7armCIAiCsEPiOKZSqTB79mw6OjravZy2ctBBB7F27VriOMb3/V0+zz4lbp599lkefPBBisUid911F5s3b+bSSy9l69atfPvb3x71mGuuuYbPfvaze3ilgiAIgjA+kiQBeNGhmP2B7DNIkuRFiZt9yvtK0xSlFN/73vc46aSTeMc73sG1117LLbfcQrVaHfWYK664gv7+/vznhRde2MOrFgRBEIQdI/MOd99nsE85N7NmzeLggw+mt7c333bMMcegtWb16tUceeSRI44pFAoUCoU9uUxBEARBENrIPuXcnHLKKaxdu5ahoaF821NPPYXjODscoiUIgiAIwoFBW8XN0NAQy5cvZ/ny5QCsXLmS5cuXs2rVKsCElM4777x8/3POOYepU6dy4YUX8sQTT7BkyRIuv/xyPvCBD4yZUCwIgiAIwoFFW8XN0qVLmT9/PvPnzwdg4cKFzJ8/nyuvvBKAdevW5UIHoKuri8WLF9PX18eJJ57I+9//fs444wy+/vWvt2X9gjAa6dAA6bYt7V6GIAjCHuH6669n3rx5FItFTjjhBB544IF2L6m9OTennnoqWusxX7/llltGbDv66KNZvHjxBK5KEHadZM0LbPyXR9DaZ+blJ+FMmdbuJQmCIEwYd9xxB5dddhnXX389p5xyCt/85jdZsGABTzzxBHPnzm3buvaphGJB2Nup/OJBktTkf1Xv/x86331mm1ckCMK+htaaapS05dol392piqVrr72Wiy66iIsvvhiARYsWce+993LDDTdwzTXXTNQyd4iIG0HYjYQbGnNhwuf76GzjWgRB2DepRgkvu/Letlz7iavfTkcwPmkQhiHLli3jU5/6VMv2008/nV/96lcTsbxxs09VSwnC3ozWmv4tDTkTD8rtJQjC/svmzZtJkoQZM2a0bJ8xYwbr169v06oM4twIwm7i+d89AnoSWEc3rolvIwjCzlPyXZ64+u1tu/bOMjyMpbVue0NCETeCsJt44dFHmee8NH+eJJPRcYLydv6XhSAIBy5KqXGHhtrJtGnTcF13hEuzcePGEW7OnkZ8c0HYTVTXbgQgTiO7xSPtk5JwQRD2T4Ig4IQTThhRwbx48WJe97rXtWlVhr1fGgrCPkK0rQIelON+Sl4ngVMi3bwJd9r0di9NEARhQli4cCHnnnsuJ554IieffDI33ngjq1at4pJLLmnrukTcCMJuIion0AthWkXFEAQlki1b2fW5toIgCHs3Z599Nlu2bOHqq69m3bp1HHvssdxzzz0ceuihbV2XiBtB2A3EYYiOTJQ3TGporekB0r6B9i5MEARhgrn00ku59NJL272MFkTcCMJuYGDzJgLHTJ9PVZ16mprHA5V2LksQBOGARMSNIOwGKv3b8J2ieeJGRKEVN7VoO0cJgiAIE4FUSwnCbuD6hzZyRc9R/IEYx0+J0joAutaeFuqCIAgHMiJuBOFFMlCLuO1PVbY4Pt8hxCnoXNyk9bTNqxMEQTjwEHEjCC+Sh57dmj9+mgRVUA1xE451lCAIgjBRiLgRhBfJs5uG8seb0IRFvxGWCuUWEwRB2NPIb15BeJE88NwfW55vLHQSZs5NLKMXBEEQ9jQibgThRfLohtZeNluDrkZYKpYWfoIgCHsaETeC8CJIdcpgONk8sXfTQNDRCEulQZtWJgiCcOAi4kYQXgR/6l+Hsq1s0m7j0gx4DXGTpsV2LU0QBGHCWbJkCWeccQazZ89GKcXdd9/d7iUBIm4E4UXx660bIWvY12XETZ9uSiimAx3HbVufIAjCRFIulznuuOO47rrr2r2UFqRDsSC8CJ7cNojS5rG24mZb2CgFB9CD/ajJU9uxPEEQhAllwYIFLFiwoN3LGIGIG0F4ETw3UAVAuwpdNJVRg2FKSkKSxriOR9rfhyPiRhCE8aI1RG2aS+d3gFLtufZuRMSNILwI1g3ZkJPvgG9+IQyGKShFpOu4eKSDg21coSAI+xxRBb44uz3X/vRaCDrbc+3diOTcCMKLoM8YN/hOivbM7dRfjQiKxUbezeDQWIcLgiAIE4A4N4LwIqjUPSCmpGPKvhU3lZCgWCK2sxfSWn07ZxAEQRiG32EclHZdez9AxI0gvAii0MMhpieJ2GSdm2qU4hQ7iLWpEdcibgRB2BmU2i9CQ+1ExI0gvAjSxMEBeuMoz7kBSIvdxBUrbuoyPVMQhP2ToaEhVqxYkT9fuXIly5cvZ8qUKcydO7dt6xJxIwi7SC1JITaCZkoYglJoT6FiTVToIi5nzk3UzmUKgiBMGEuXLuW0007Lny9cuBCA888/n1tuuaVNqxJxIwi7zJKtA5CYJjfT4hg31eA5ECfEQSdJasVNKE38BEHYPzn11FPRWrd7GSOQailB2EX+Y90GVGS6E3dp6Io12iYVh34pz7lJbQdjQRAEYc8g4kYQdpG47+e5c9OJQ3cM2jNhqtBtiBsdJW1boyAIwoGIiBtB2EUOjh5Dxda5Cep0RTYsBcRegTgLS0V7n2UrCIKwP9NWcfNipon+z//8D57ncfzxx0/Y+gRhLPrCCi4pxEa4dHRvozvWYJ2b2AlIcuembcsUBEE4IGmruNnVaaL9/f2cd955vOUtb5mglQnC9nlm7R+oqI7cuSl1bTE5N64NSzl+w7mRfGJBEIQ9SlurpXZ1muiHPvQhzjnnHFzX3Sm3RxB2F5s3PEmZzty5KXX00xUBrvm+ECm/4dzEEv0VBEHYk+xzv3VvvvlmnnnmGf7+7/9+XPvX63UGBgZafgThxTJU2ciQ7kbZhOJiR19LWCrEbVRLJfvcbSYIgrBPs0/91n366af51Kc+xfe+9z08b3ym0zXXXENvb2/+M2fOnAlepXAgUI83MRh35c8LnZtawlJ17eSzpXTitmWNgiAIByr7jLhJkoRzzjmHz372sxx11FHjPu6KK66gv78//3nhhRcmcJXCgUKSbqaSlABwVIrq2EZ3U7VUXTuNUvBUemUKgiDsSfaZ37qDg4MsXbqURx99lI985CMApGmK1hrP8/j5z3/Om9/85hHHFQoFCoXCnl6usJ+j2EY9KQLguTFpMERHnObOTS1RjQ7Fqd+2dQqCIByI7DPOTU9PD48//jjLly/Pfy655BJe+tKXsnz5cl7zmte0e4nCAYSvtlFLjGj23RiUpkQdrLipxrrh3GgRN4Ig7H9cc801vPrVr6a7u5vp06fzrne9iyeffLLdywLa7NzsaJroFVdcwZo1a7j11ltxHIdjjz225fjp06dTLBZHbBeEiabg9DfEjWNETEFVwDOhqkqcNhKKddCeRQqCIEwg999/Px/+8Id59atfTRzHfOYzn+H000/niSeeoLOzs61ra6u42dE00XXr1rFq1ap2LU8QRiUNE4pqiDAJAN0kbspo9yDAiJssLAUFdByjxpkELwiCsC/ws5/9rOX5zTffzPTp01m2bBlvfOMb27QqQ1t/2+5omuiOxqVfddVVXHXVVbt3UYKwA+KhIXwVESY+EFJQmbjpb4SlokZYCkBXhlA9k9qwWkEQ9jW01lTjaluuXfJKKKV26dj+/n4ApkyZsjuXtEvIV0lB2EnCwW0ARImPIiTAlHwHTl/e56YapyQ6RmuNUgpdLoOIG0EQxkE1rvKa29qTR/rQOQ/R4Xfs9HFaaxYuXMjrX//6vSJVRMSNIOwkQ4NbSVHEqY8PFKxDE7h9jfELMaQoEh3hqQBda8+3MEEQhD3BRz7yER577DEefPDBdi8FEHEjCDtNf3kLIQVIzFypohU3vrctH78AELsBiY7xCNC1WlvWKgjCvkfJK/HQOQ+17do7y0c/+lF+/OMfs2TJEg455JAJWNXOI+JGEHaSoXo/NYpgRy8U7GRM3+sHB7QCpYFiJ4lOAND1eruWKwjCPoZSapdCQ3sarTUf/ehHueuuu7jvvvuYN29eu5eUI+JGEHaSctxPnUI+V6qQJcW7VVxik1Qca9KgszE8U8SNIAj7GR/+8Ie57bbb+NGPfkR3dzfr168HoLe3l1Jp5x2g3ck+08RPEPYWaslgq3OTmvBU4tXwVJJXTKWFjibnJmzPYgVBECaIG264gf7+fk499VRmzZqV/9xxxx3tXpo4N4Kws9SSfmpuQ9wE1rhJvBo+CaHnoOopiV8iSaxzE0ZjnU4QBGGfZHutXNqNODeCsJPEeog6pUZYKrVOjVfHVwk45rn2Cg3nJhTnRhAEYU8h4kYQdpKYoZawVDETN26IT9oIS/lFktQkG+swbs9iBUEQDkBE3AjCTqKHiZtCam4j7dXxlUY7mZNTaCQUi7gRBEHYY4i4EYSdRFOlRhGV9blJTOqadkPToNg6N4kbkGZhqShpy1oFQRAORETcCMLOomrUm8NSVtzgRnhKN8SNZ5r4gYgbQRCEPYmIG0HYCdI0RLnDcm4SP3/dV2meUJw4XpO4Sff8YgVBEA5QRNwIwk7wzH9fxaTBDTYsZcRNpw5QqXFvPNJ8vlSi/FzcEIu4EQRB2FOIuBGEnaD6zz8hILSzpYy4KSkHJzbdOF3i3LmJm52beO/tByEIgrC/IeJGEMZJfWAD/p9idAFC7UNqBEuHcnHiIgAujQ7FiRJxIwiC0A5E3AjCOBl88tdoNDqAug7McEygg4a48XSYh6Ui5Tb63EgluCAIwh5DxI0gjJPyU78zA0tcCJMg396hPJzEhKU8ojwsFeE2nBsplhIEYT/jhhtu4JWvfCU9PT309PRw8skn89Of/rTdywJE3AjCuAmfe5a0YB7XE/NAoSkoD2XFjqejPCwVatUkbtSeX7AgCMIEcsghh/ClL32JpUuXsnTpUt785jdz5pln8oc//KHdS5PBmYIwXuKNm9FW3ISxETOuSnGVh2PFjqfDvENxqBVpLm7ke4QgCPsXZ5xxRsvzL3zhC9xwww385je/4eUvf3mbVmUQcSMI4yTdOoAumESbKDa3jqsSXOXn4sbRYcO5SSHOxE0q4kYQhPGhtUZXq225tiqVUGrnneYkSfiP//gPyuUyJ5988gSsbOcQcSMI40RvLefOTZSaxn0eKY5y87BUoOu5uKklNJwbETeCIIwTXa3y5KtOaMu1X/rIMlRHx7j3f/zxxzn55JOp1Wp0dXVx11138bKXvWwCVzg+5DeuIIyX/rAhbmxXYlcnrWEpanlCcZjqRs5N6u759QqCIEwwL33pS1m+fDm/+c1v+D//5/9w/vnn88QTT7R7WeLcCMJ40Frj9Ceks83zOHWBGFcnOMrNxU2gK2irY2oJDXGj5VYTBGF8qFKJlz6yrG3X3hmCIOCII44A4MQTT+Thhx/ma1/7Gt/85jcnYnnjRn7jCsI4iPo3oiLynJs49YAYT8d4jt8IS1EGxxiiYdLs3Oz4VqvX6zz66KMcdthhzJw5c2LeiCAIez1KqZ0KDe1NaK2p1+vtXoaIG0EYD/UNzwOQdNrRCtpDAV4Sg0fu3BQpg3Vuwhbnxh9xzuHcd999/PrXv6azs5NPfOITeJ7cnoIg7L18+tOfZsGCBcyZM4fBwUG+//3vc9999/Gzn/2s3UsTcSMI4yHs2whA3GVcmUQ7eIBvOxBn4qZEBe2aferN4oYdi5ssTl0ul1mzZg2HHnrobn0PgiAIu5MNGzZw7rnnsm7dOnp7e3nlK1/Jz372M972tre1e2kibgRhPER9mwBIOh0SHLQ2AsZPTethlZqwVBdDeZp+kkKUZq2JA3ScoLzRE4ur1Sr9/f3587Vr14q4EQRhr+amm25q9xLGRKqlBGEcxP2bAUg6XCKCfCJ4Jm6cxIiWDsotd1VL5DmsjXn+jRs3tjzfsmXLi1+0IAjCAYqIG0EYB3H/NgCSokNIkE8Ez50bK24K1HGcxiCpOo1p4LpaHvP827Zta3ku4kYQBGHXEXEjCOMgGegz/xYcInyUFTeBFS9uk7jxVIKNWhE5DqlOAdC1sZ2bwcFBACZPngxAX1/f7n4LgiAIBwwibgRhHCQDJh8mLdiwVObcWGPGsR2IC9RwSPNGftovNZKKtyNuBgYGADj44IMBGBoaQms95v6CIAjC2LRV3CxZsoQzzjiD2bNno5Ti7rvv3u7+d955J29729s46KCD8vHq9957755ZrHBAkw4MAZD4ihC/kXOTOTdp5tyEeCrJRzDooLhT4mbWrFkARFFEGIYT8E4EQRD2f9oqbsrlMscddxzXXXfduPZfsmQJb3vb27jnnntYtmwZp512GmeccQaPPvroBK9UONBJh0y+TOo7Lc5NFpZybFvigDouKdqKm9QrNuZLbaexVSZupk2bRhCYyquhoaEJeCeCIAj7P20tBV+wYAELFiwY9/6LFi1qef7FL36RH/3oR/zkJz9h/vz5u3l1gtBAD1VRgPYgJECZNBoCGznKnBsHjdcUlkr9JuemPrYTUy4b8dTV1UVXVxdbt25laGiIqVOnTswbEgRB2I/Zp/vcpGnK4OAgU6ZMGXOfer3e0go6+4YsCDuDHjL/h7QHEX4j58a+7upG/xqPRlgq9QKSdMfOTaVSAaCjo6NF3AiCIAg7zz6dUPzVr36VcrnMWWedNeY+11xzDb29vfnPnDlz9uAKhf2GcgSA9tKWUvDAaBgcBxI7YsFTKTpLKPYKDecmjEY9dRRFxLHZp1Qq0dnZCUhYShAEYVfZZ8XN7bffzlVXXcUdd9zB9OnTx9zviiuuoL+/P/954YUX9uAqhf0FVTbiA0cb58YmFGdhKeVqYm1GMLjDnZsdiJtqtWrOoRSFQoGSncpb204CsiAIgjA2+6S4ueOOO7jooov493//d9761rdud99CoUBPT0/LjyDsLKpqkmwcNyEiaOpzk1k3mjQ14sYjaeTcOH5D3ETxqOfOxE2pVEIplYubbLsgCMK+wDXXXINSissuu6zdS9n3xM3tt9/OBRdcwG233cY73/nOdi9HOADQUYSyucCOE7fk3BSsuFEupHa+lKeaEordJnET7ljcABSLRUCcG0EQ9h0efvhhbrzxRl75yle2eylAmxOKh4aGWLFiRf585cqVLF++nClTpjB37lyuuOIK1qxZw6233goYYXPeeefxta99jde+9rWsX78eMH8Uent72/IehP2fuNxIQndVZHNuzPOCNW6UB1obcePqJM+5aXFu4nTU8w8XN+LcCMKBjdaaOBz998VE4wUOSqmdOmZoaIj3v//9fOtb3+Lzn//8BK1s52iruFm6dCmnnXZa/nzhwoUAnH/++dxyyy2sW7eOVatW5a9/85vfJI5jPvzhD/PhD384357tLwgTQTy4FYDUSfFIhvW5Mean8hRY58YlznNuEtcj1Uak6CgZfmpAxI0gCK3EYcqNH7+/Ldf+3197E37B3fGOTXz4wx/mne98J29961tF3ACceuqp220xP1yw3HfffRO7IEEYhWjIiBvdAY4yCcUqGRaW8hQ6C0vpJA/4Jo6XOzeM4dxkZeASlhIEYV/j+9//Po888ggPP/xwu5fSwj7d50YQ9gTxkJnYnXYZIdPs3BQy58Z3UGlWCh7lOTeJ8ki1cWx2FJbq6OgAxLkRhAMdL3D43197U9uuPV5eeOEFPv7xj/Pzn/88/1K2tyDiRhB2QGLFje40N31zn5uCaoSlVFYKruM85yZRblPOzegupYSlBEFoRim106GhdrBs2TI2btzICSeckG9LkoQlS5Zw3XXXUa/Xcd32vA8RN4KwA+IhMxFcdxghU2sSN8Vc3Lg4NqHYI86dm3gc4iYLP2XffLJ/4zgmjmM8T25TQRD2Pt7ylrfw+OOPt2y78MILOfroo/nbv/3btgkbEHEjCDskHhpAo0g7zI1a14V8tlTBjl1QfkPcuER5QnFMk7gZPZ84Hw9SKBRa/gXj3nR3d+/eNyQIgrAb6O7u5thjj23Z1tnZydSpU0ds39Psc31uBGFPUhkI+fmvj+Whk/6OsMOIjFoS5K83OzcuRpT4upFzE+M0cm7GKW4cx8kng4fh2MM2BUEQhNER50YQtsNTv11PLSxCR5EtXcdQZB21tOGsFB1ru/oebt6hOETbrw2RVjvt3GSPwzBsGfoqCIKwt7O3VDWLcyMI22H1n7blj8sFM8MsTDPnRuMr8/1ABR4eZrtPPXduwiZxQzL67Za5M5lb00xfX9+LfQuCIAgHHCJuBGE7bF1Xzh+HvinVDm3Jt0uKh825CXx8TCKwT5jn3ESaRlgqHb3r53DnplqtMjg4CMBvfvOb3fp+BEEQDgRE3AjCGMRRwuDWRiO91DHOStQkbtzcufHxlREnAdUm54ZGWCodebtprUeIm+eeey5/fd26daRpe9qwC4Ig7KuIuBGEMejfVAUNHjW8aAjlRgDEVtw4pDgqq5byCay4KVDN+9xEKSS5czPydovjOO/SnYWlNmzYkL8eRRH9/f0T8fYEQRD2W0TcCMIYDG6x/WfSbRTrfQ1xkxi3xiPFdaxzU/AJHNunhlrDuUkhzZ2bkT0fmhOGRxM3AJs2bdpt70kQBOFAQKqlBGEMKgM20TcZRNVDUtc8j1MXSHBV0ghLFQoUXOPcdFDJxY1xbqy40WOLmyAIcBzzXWPLli0t+wx/LgiCIGwfcW4EYQxycRMNUGhybhJtnRud4tiEYgIfz7XjE6iQbY6SZnEz8rvEaJVSAwMDLftIWEoQBGHnEOdGEMYgEzd+vR+vXqXumtyYJHVxMdO/M+cm9cBxTViqRCXPuQmbcm4YRdwMTyYOw3DENPCsckoQBEEYH+LcCMIYDG0zIiOo9RGEgzjWuUltvxpPJ3nOTeIkuNa56aTc6FDc4tz4I64xXNxkQqZ5Jkvftq27940JgiDs54i4EYQx2LxqCICgMoBvq6USHNBGuPhZcz4gSWu4nhE3RWqgjMujUUQ6e+yjh5V1Dw9LZSGpwG8Ioc0bN+729yYIgrA/I+JGEMagOmiFR63fipuQkACsPvHTxjyFOK7i+iYsVSDEcRsiJiSbBu5CHLVcY7hzk4kblTaEUz1qlIsLgiDsLVx11VUopVp+Zs6c2e5lAZJzIwijorUmjoxAKVQGQDs4Ti8RASRGaPhJDD5onRLXhnC9yWY7IY5qCJ+GuAFdq6KCxgyp4eKmXDYdkVUUAbajsVJUymU6u7om5s0KgiDsIi9/+cv5xS9+kT9vDqm3ExE3gjAKWY8bgCAcAhyUGxHhQ2rFTZYonMak1QGCwISlPBJclaIdUClENMYu6FoNehrXGR6WqlQqACTVChQ7QWtQig3Pr+QlL3/FRL1dQRD2IrTWxG0amusVCig1+qiYUff3vL3GrWlGxI0gjMLmNUP5Yy+uAlhxE6CsuPES4+zoJCYe6sedWcqPcUlIHAWpJnY8Ep3gKhc97BfWaHOlAOLKkBE3lo1r14i4EYQDhLhe5+vnv68t1/7Yd36AXyyOe/+nn36a2bNnUygUeM1rXsMXv/hFXvKSl0zgCseH5NwIwihsXWvDQ0rj6BRFgvIim3OTOTc2ryaNSMoDec4NmLlTWcWUDgp5l2LqrWXeUWRycIY7N05i97ffoLZt3ryb36EgCMKL4zWveQ233nor9957L9/61rdYv349r3vd6/aKxqPi3AjCKGQDMz3PVjoFgGfDUjbnxrO5NDqJSKtlnMAn1j6einBJ0K4yASm/SKJjfAojnJtM3Pi2OipzblQct+w30LdtIt6mIAh7IV6hwMe+84O2XXu8LFiwIH/8ile8gpNPPpnDDz+c73znOyxcuHAiljduRNwIwiiU+4wI8TwbevJNWCqkI3dugixROI3R1QrKd0h0kIub3LnxCqTZ8Mxxi5uwZb8haeQnCAcMSqmdCg3tLXR2dvKKV7yCp59+ut1LkbCUIIxG2XYnLtvwkPYUystybsw+eSeaNCKtVVG+Q2ob9XmqEZZK/aDRyC9sLQXPxI3nme8ZWVhKDSsZr9gqKkEQhL2Ver3OH//4R2bNmtXupYi4EYTReKLPiIlNNSNytK9QTkTYUi1ld06axY3JnTHOjXlZu4V8BIMOWx2ZsZwbJ4lxmioW6sNEkSAIQrv55Cc/yf3338/KlSt56KGHeN/73sfAwADnn39+u5cmYSlBGI1NYcR03Ibj4iuUG7aUgmejLnUaoWs1lOegU+vckKAds0fqBaTh9p0b3/cJw5DY5tqoJMZzXUL7PBqWgyMIgtBuVq9ezV/91V+xefNmDjroIF772tfym9/8hkMPPbTdSxNxIwjD0VpTS1PAhdQ4Lann4NhS8CyhOJ/jnUSktRrKd9Gp2eqRgGvDUp5PUrfiJhpb3GSuDWhIUzyvIW5irUmTBGcvaZAlCILw/e9/v91LGBMJSwnCMF7YWMa186O0FTfac3AcUwqucucm2ydG1+oo30HnYam4kXPj+I3J4GGrA9MsbrJp4I7WKMD3GvOltONSHRyYgHcrCIKw/yHiRhCG8ey6QYp5Po2pbgpdD89pDUvlBZNJhK6FKEeBdW5c3aiWSt2mhOKoVdxkYSjf9/OGfsrOrMp635gTulQH+nfn2xQEQdhvEXEjCMNYtalMwTo3RccIjppnyjKjpsGZmXNDGqFrVpjoLCzV7Nx4eRM/HTVmTkGrc5OJG6zgCZr6TWjHpTIgzo0gCMJ4EHEjCMN4YWuFohU33a7Jg6l6Rmi0ODe2mkknMToLN+XiJkLbuyux4xdgvOLGbCs29bkwYSlxbgRBEMaDiBtBGMbqviqBDUt1YvrO1ApGtIQUUHlCsb190ghqRpA4mbjRDecmaXZu4oa4SZKENDU20PCwlFIOhVJjVhWu5NwIgiCMl7aKmyVLlnDGGWcwe/ZslFLcfffdOzzm/vvv54QTTqBYLPKSl7yEb3zjGxO/UOGAYtNQPe9h02HFTd2z4iYtNOXcZOImBlsN5Wrj8HjEebVUrLxGzk2cJfM0XBswTfyyhGLSlEJXV2vODTA0IF2KBUEQxkNbxU25XOa4447juuuuG9f+K1eu5B3veAdveMMbePTRR/n0pz/Nxz72MX74wx9O8EqFA4lttRjf5tN0aOvcWKER6Ya4KeZhqQgdGkfGJetzEzWcG+U2wlJxml9nuLjJnZskodTVnTf2yyiXRdwIgiCMh7b2uVmwYEHL4K0d8Y1vfIO5c+eyaNEiAI455hiWLl3KP/7jP/Le9753glYpHGj01yN8bW6NjtR0Kq7asuxQj+7cqHomboxz4xOhncy5aTQDpNIQKM35NkqplrBUsbchbhylSLWmWq5MyPsVBEHY39incm5+/etfc/rpp7dse/vb387SpUtbvgU3U6/XGRgYaPkRhLHQWjMQJnjWuSkmQwBUfSN26rqYz5YqKHP76DSC0Gz0yMJSYe7cxLiNnJtta2H1UmDk6IU8oTg1zk0WlnLseaoVETeCIAjjYZ8SN+vXr2fGjBkt22bMmEEcx2zevHnUY6655hp6e3vznzlz5uyJpQr7KJUwAd3IiynFRtzU7GDLetrIgylo2y04iSE0x2TiJiBs5NzgkNjeNVoH8Ie7gLHFjUoTip1dTc6NuU3znBxBEARhu+xT4gbMKPhmtP1DNHx7xhVXXEF/f3/+88ILL0z4GoV9ly1DYWMgJhAkWUKxuVXqSUPclFSjWkqFGq1TAseUb/uE+d0VoRoJxfiw6jdAawM/oCXnJujobHJu7LXrIm4EQdi7WLNmDf/rf/0vpk6dSkdHB8cffzzLli1r97L2rdlSM2fOZP369S3bNm7ciOd5TJ06ddRjCoUChaZmaIKwPTaX6/i2x43rO/iRGb+QBGZbqPNxmfjK3D46jVERpGkdX5nXfVXPc26iJCXFOjcEsOEPkKbbdW4KHR2NhGIr3GUyuCAIexPbtm3jlFNO4bTTTuOnP/0p06dP55lnnmHSpEntXtq+JW5OPvlkfvKTn7Rs+/nPf86JJ544orJEEHaFrUMh2f8kz3dQWXM+e6dEduq3i8ZTWVgqQoUQxxUCZZybIrU85yZMkibnJoC4CoPrRoibRil4QlBqiJvMlRwrr0wQhP0LrTU6Sne84wSgfGfMSMhwvvzlLzNnzhxuvvnmfNthhx02QSvbOdoqboaGhlixYkX+fOXKlSxfvpwpU6Ywd+5crrjiCtasWcOtt94KwCWXXMJ1113HwoUL+eAHP8ivf/1rbrrpJm6//fZ2vQVhP2NrJcydGy9w8/412saqotQFUhyV4jY7N6kiDYcoOMYlLFHNxU2UpCRpJm6si7hlBVE0CRgjLFXqGNHnJgtjCYKwf6OjlLVX/qot15599etQgTuufX/84x/z9re/nb/4i7/g/vvv5+CDD+bSSy/lgx/84ASvcse0Nedm6dKlzJ8/n/nz5wOwcOFC5s+fz5VXXgnAunXrWLVqVb7/vHnzuOeee7jvvvs4/vjj+dznPsfXv/51KQMXdhsD1SjPufELbl4Fldk5kS0Rd9E4Tc4NQFwewPeanBs3c27SRrWUDVvRv3q7YamgVMq35zM8gTgMd+v7FQRB2FWeffZZbrjhBo488kjuvfdeLrnkEj72sY/lhkQ7aatzc+qpp+YJwaNxyy23jNj2pje9iUceeWQCVyUcyPRXojwsZcSNHZeQOTfaA0I8mpwbK1yS6gCuY0YmdFBp5Nxo8iZ+aHv2wXVE/ksA08AvSZJG2ClNW5yb/B5xHOqVMt4wR0cQhP0L5TvMvvp1bbv2eEnTlBNPPJEvfvGLAMyfP58//OEP3HDDDZx33nkTtcRxsU/l3AjCRNNXboSlgqKbl3grz/ybJObGd3WCo6xQcaxzU+nH9QqgoUQl90XDVLXm3AAMrifqbjg3YZMjo9KEQpNzk82f0sqlVh6ic9LkiXjrgiDsJSilxh0aaiezZs3iZS97Wcu2Y445Zq+YGrDPlYILwkTSX2kKSxU9VC5ujPOS2N42nk5wbVhKO0a4pJUhXNc4NyXKjZwb7TTCUplzM7S+JSyVixutUVq3ODdJYiutHIewKo38BEHYOzjllFN48sknW7Y99dRTHHrooW1aUQMRN4LQxECTuPECB2VzeB3PuCeJNreMpxMcG5bCNeIjrg7i+CbnppNqnnMTaa+1zw0Y56ZJ3GSPlXVpgqZS8My5wXGJpJGfIAh7CZ/4xCf4zW9+wxe/+EVWrFjBbbfdxo033siHP/zhdi9NxI0gNNNfi/KhmZ7byAdzvJQYF211hpfGjZwbq4bS6hCuTSguUW7MlsIlzgZn6oa4aW7i18i3MfsVmkrBM4xzU92db1cQBGGXefWrX81dd93F7bffzrHHHsvnPvc5Fi1axPvf//52L01ybgShmcFaRJfVNK6T5NtdNyHCR9mhmV4a47g2Ju5aV6daxrXOTYEIpbLGfU5D3GTOzdCGMcJS1rkpdeB6rbendlzCmogbQRD2Hv7sz/6MP/uzP2v3MkYgzo0gNDFYjxsdim1MKnEVnhsTEUBWGZ7G+cynrGlxWqng+qX8XI7TaMLVSBf2zeiqJCSqG6EyPCzluB6unRTuNQsccW4EQRDGhYgbQWhisJ7kpeCOlSSpr/DdiAgfksy5abg62NEMaa1V3LhOo+levfkiTicAUc0kB7eGpVKCjo68Q2hzaEoSigVBEMaHiBtBsCSpphIneFlbGW2Sd7Wv8J2YkACysJRuiBtVsOKmWsEJAlKbdOwqjc6HZzbyd3RxmtkWmvM3h6VUmlIoNQRSS96NhKUEQRDGhYgbQbAM1WyCrw1LNYubwImICPKcGz8XNzGqaOJSaa2G8l0SG6dyiBvl4MrJm/HpQiZujJ/jeV4jLKXN6IWMlrCUUtQq4twIgiDsCBE3gmAZqBmBUcgSilNbdl1Q+G5owlK5c5Pl00Rgp87rWg3lKRJbEeWR5uImcdxGOXgwyRw5Sil41p04Y3jFVK0izo0gCMKOEHEjCJZM3AS2FFylRkiooCkslWTOjRE3ihin2CRufJfUOjcuSS5utOuRZhVTXi/AqNVSKk0pdIwUN47NwalJWEoQBGGHiLgRBMtA1TgrQZZzE5sQkAqcPCyVOTe+zaFRKkbl4iZE+Q6pdW5ckrzXDV7QcG68bgDi2AikFudGj+7cOI65VbPhmoIgCMLYiLgRBEvm3GQdip3EipuCi+9GhPiofEi4FTdOjFM0CcBpvY7yHHTa7NyY/VPXbxI3tloqaYibZucmaEooznJuXFfEjSAIwngRcSMIlsEsodiGpZyobF4IPHwnasm5ydwdpRKckmnch3VudJ5zk+QjGLRfyMNSeSl4JpSGdSgezblxHdMwsHnApiAIgjA6Im4EwVINjbjJSsFVk7gJ3IiQwsiwlJPgFI0Y0WEMrsqdG68p5yZ1m8JSqkiCItGNXjbNTfz8QjFf0/CwVCTiRhAEYYeIuBEESzlMQIMdqtAQN76L78QtTfzypGMnxSkZJ4Z6bJrv2YRiT0d5zk3qtYqbuGnyyfDxC36xIW4aYSmzqihuNAYUBEFoJ4cddhhKqRE/++zgzJUrV+7udQhC26nUY1xAZcIltOKmaNyT5tlSQe7cpLlzQ90ID9WUUJxXS3mFpsngAVGTuGnpc7Mj5yZp6owsCILQRh5++GHWrVuX/yxevBiAv/iLv2jzynZxcOYRRxzBG9/4Ri666CLe9773UWz6pikI+yqVMMmTiQGc+pCRMCVzmzRXS2XODY7GzZyb0AgPlTk3xHnOTeo1cm60do0LhBE2SqmWPje+7ZsDTeLGOjdJmpKmCY7jIgjC/onWuvE7YQ/j27l24+Gggw5qef6lL32Jww8/nDe96U0TsbSdYpfEze9+9zu+/e1v89d//dd85CMf4eyzz+aiiy7ipJNO2t3rE4Q9RjlMGsnErkLVa0bcFMxtUm8anJnJD+Vo3FKXeRKaFx0ycRPl3mjiNjk3qZc7N5l4yaulhoWlstfzXzbKIarVKHR07rb3LQjC3kUURXzxi19sy7U//elPEwTBTh8XhiHf/e53Wbhw4bjF0USyS2GpY489lmuvvZY1a9Zw8803s379el7/+tfz8pe/nGuvvZZNmzbt7nUKwoRTDePcufF8hzQruw6MEKnpQpNzY24d5WqcTNzUrbjJc27iRs6NGzScm9TJnZtMvDTCUklLWCofv2B/WWiZDC4Iwl7I3XffTV9fHxdccEG7lwLsonOTH+x5vPvd7+Yd73gH119/PVdccQWf/OQnueKKKzj77LP58pe/zKxZs3bXWgVhQik3haW8wIUhm+SbOTe6gLIJxblz44LTYZryqci85tpXfaKm8QseibaiJHXyhGLfs4nCOwhL5Yi4EYT9Ht/3+fSnP922a+8KN910EwsWLGD27Nm7eUW7xosSN0uXLuXb3/423//+9+ns7OSTn/wkF110EWvXruXKK6/kzDPP5Le//e3uWqsgTCiVMM7DUl7BRdciFJD6tsdMc86NypwbcEtG3BBqtE5x85ybcJi4sWGpBCKnA1LwbXO+5iZ+oyUU50M3lUMkIxgEYb9GKbVLoaF28fzzz/OLX/yCO++8s91LydklcXPttddy88038+STT/KOd7yDW2+9lXe84x15Rce8efP45je/ydFHH71bFysIE0m51ghLBQUXXTfiJvEazk0mbopZRZULbsnMilIRpGkdjwIa69zYhOJENc2WilMivxvq4LvDkgfHKAXPxA2OQ1SvTdRHIAiCsNPcfPPNTJ8+nXe+853tXkrOLombG264gQ984ANceOGFzJw5c9R95s6dy0033fSiFicIe5JyvRGW8osu2KZ+sXVP6s0dirN0NU/hdfYAoEKI4wqeKhBhEoqznJtYNU0FjzWR12XEjQNxHOfiZSznJk1NPo92XCIZwSAIwl5CmqbcfPPNnH/++Y0cwb2AXVrJ4sWLmTt3bu7UZGiteeGFF5g7dy5BEHD++efvlkUKwp6gEsZMyroGF9y8b01q82JC3ZgtVczCUp6Tl4IrrUjDoVzcBKopLDVC3JhjfJW2lnzuQNygxLkRBGHv4Re/+AWrVq3iAx/4QLuX0sIuVUsdfvjhbN68ecT2rVu3Mm/evBe9KEFoB5UwIUul8wI371sTZ+ImbST6FrJqKU+hmsJISWWQgpMlFIf5HRbhtObcuKbxX4u40SkKjV8cmVDccG5MKbggCMLewOmnn47WmqOOOqrdS2lhl8RNHv8fxtDQkDT0E/ZZqlGCnzk3gZv3rcnETT1tVBE0nBsX5fvYw4gr/fjK3ANF6rlzE2nVyLlpEjeeihujF9IUx3VxvcZ1Mps3yToTS86NIAjCDtmpsNTChQsBk8l95ZVX0tHRmF6cJAkPPfQQxx9//G5doCDsCdJUU4tTfG1Ei1doiJvEVjTFubjR+PbWUZ5rGlYFCuqapDpIwTXOS0HV85ybSKth1VJGAPnEY45egIZzk4kb7TiScyMIgrADdkrcPProo4Bxbh5//PGWUrUgCDjuuOP45Cc/uXtXKAh7gGpkxEMmX/zAQdmmfLFrbpTIzoxy0HjKjj/wjfDRgULVtQlLuZlzU8urpSIazg2pIlI2dKXDMXvcQEPcxNnATMm5EQRB2CE7JW7++7//G4ALL7yQr33ta/T09EzIogRhT1O2lVFZWMoruGA1R+IoPCBOPSDGJcVR1rnx7S0UOEBKUh3Cc6cCUKKSh6XClCbnxiFWAaDx03DM0QvQCEtJzo0gCML42aVqqZtvvnl3r0MQ2kqlblyVQlYK7pFXRiXWpEmsuHF0imudm4a4cYGYpDqEWzgYEihRa8q5aRI3+fiFEF/XWkYveGOEpXIcl1Ca+AmCIGyXcYub97znPdxyyy309PTwnve8Z7v77k1dCgVhPFTCTNzYwZmZsgGSbPhl6qIAlxR3uHNTMGInrVYIOotW3FTQWbVUqkhaZkt5QIifVFvDUh2jOzfN1CXnRhAEYbuMu1qqt7c3n/TZ29u73Z+d4frrr2fevHkUi0VOOOEEHnjgge3u/73vfY/jjjuOjo4OZs2axYUXXsiWLVt26pqCMJyKDUsF1rlxtREQWmkSZURJqo2A8XSCkzk3Qebc2KqmWhnHMwKlg3KecxOmkObOjUtkz+Wnldaw1LCcG8dxRgicsCbiRhAEYXuM27lpDkXtrrDUHXfcwWWXXcb111/PKaecwje/+U0WLFjAE088wdy5c0fs/+CDD3LeeefxT//0T5xxxhmsWbOGSy65hIsvvpi77rprt6xJODDJnJtstpSDyWvRPmgSNMa5cQFXJ7jKJNMrm1Sv7HBNXavieiXAipssLJU2VUtpl8haOn5codaSUDyylYLnecRxjKMUqdaEoYgbQRCE7bFLfW6q1SqVSiV//vzzz7No0SJ+/vOf79R5rr32Wi666CIuvvhijjnmGBYtWsScOXO44YYbRt3/N7/5DYcddhgf+9jHmDdvHq9//ev50Ic+xNKlS8e8Rr1eZ2BgoOVHEIZTyROKzXOV2LwWH7QKSXAhNULFTePcucG3FYMF22yvWsH1M+em2iRuaPS50R6RPZcfD7UOzRylT1SWd+O6tpmgiBtBEITtskvi5swzz+TWW28FoK+vj5NOOomvfvWrnHnmmWMKk+GEYciyZcs4/fTTW7affvrp/OpXvxr1mNe97nWsXr2ae+65B601GzZs4Ac/+MF2h3Vdc801LSGzOXPmjPNdCgcSZZtQnIkbJzXiXQcKrUKTAGznSnk6buTcFIKWf9N6Dcc3zo1HjFbmmLipWgrtEWXnigabcm6SEWEpaBI3dtxJWI9G7CMIgiA02CVx88gjj/CGN7wBgB/84AfMnDmT559/nltvvZWvf/3r4zrH5s2bSZKEGTNmtGyfMWMG69evH/WY173udXzve9/j7LPPJggCZs6cyaRJk/jnf/7nMa9zxRVX0N/fn/+88MIL43yXwoFExfa58bJp36lxbnTggAqJCBripsm50V6ruNHVWu7cKMBx7HwqrYhy58YnSsy5/HioUS2lxw5LATjWuWmZRSUIgtAm4jjm//7f/8u8efMolUq85CUv4eqrr27MwmsjuyRuKpUK3d3dAPz85z/nPe95D47j8NrXvpbnn39+p86VJSlnaK1HbMt44okn+NjHPsaVV17JsmXL+NnPfsbKlSu55JJLxjx/oVCgp6en5UcQhlOxQzK9zLmJhswDK25Cgrw03NNJ7tykrnFVlJ0HldZrOEFAYhOGHZuMDJAFkzQ+UWydomSoZfzC9sJSnh0DEcUibgRBaD9f/vKX+cY3vsF1113HH//4R77yla/wD//wD9s1HPYUu9Tn5ogjjuDuu+/m3e9+N/feey+f+MQnANi4ceO4xcO0adNwXXeES7Nx48YRbk7GNddcwymnnMLll18OwCtf+Uo6Ozt5wxvewOc//3lmzZq1K29HEPKEYptJg4ob4kapiLApLNWccxNnicU2nKRrIcpzSHSAq6q4TkOIRGQz2VziTNwQEdWNSzTa+AVoDkvZa2bdigVB2C/RWpOm7eln5TilMQ2G4fz617/mzDPPzFNDDjvsMG6//fbt5sHuKXZJ3Fx55ZWcc845fOITn+Atb3kLJ598MmBcnPnz54/rHEEQcMIJJ7B48WLe/e5359sXL17MmWeeOeoxlUplRFlslmQ51jBPQRgPlTBGaXCzsFRUNi8ELioLSyVZzk2CYwdnJtbBcYpFEkDX6ijfIdU+UMUlIVGgNIQ0/o9moSWfuDFOYZTxC9AQN1lYKk6SEfsIgrD/kKZV7rv/FW259qlvehzX7djxjsDrX/96vvGNb/DUU09x1FFH8bvf/Y4HH3yQRYsWTewix8EuiZv3ve99vP71r2fdunUcd9xx+fa3vOUtLUJlRyxcuJBzzz2XE088kZNPPpkbb7yRVatW5WGmK664gjVr1uTJy2eccQYf/OAHueGGG3j729/OunXruOyyyzjppJOYPXv2rrwVQQCMc9PcC9jJxE3Bw1EhIZ25c+PrhriIMc6NUzKOi65HKN8l0Wa7S0ziKEg0oRVOGp0LFJ+YMDTiZkc5N5mQT4EkjnFHafAnCIKwp/jbv/1b+vv7Ofroo3FdlyRJ+MIXvsBf/dVftXtpuyZuAGbOnMnMmTNbtp100kk7dY6zzz6bLVu2cPXVV7Nu3TqOPfZY7rnnHg499FAA1q1bx6pVq/L9L7jgAgYHB7nuuuv467/+ayZNmsSb3/xmvvzlL+/q2xAEwIqbZvOvNgiYJn2OExHho6y4CXQjWS7KpoMXTYUU9RDlKevcgKtjcFxINLFySHSSVZQDNiyVl4In2825yZybbHim63W96PctCMLeh+OUOPVNj7ft2uPljjvu4Lvf/S633XYbL3/5y1m+fDmXXXYZs2fP5vzzz5/AVe6YXRI35XKZL33pS/zXf/0XGzduHJEZ/eyzz477XJdeeimXXnrpqK/dcsstI7Z99KMf5aMf/ehOrVcQdkS5HjeGZgYO2ubBEHi4aqilWipzblKtwZo4btHYuLoeo3wXnTs3CbgeRJA4HqmOSZri2R4xUbj9Jn65uLGl4Nox4qbYKeJGEPZHlFLjDg21k8svv5xPfepT/OVf/iUAr3jFK3j++ee55ppr9k1xc/HFF3P//fdz7rnnMmvWrHEnHwnC3ko1aoSlvMAlzYZTBgEuWUKx2eTbBymQhuaxY8UNYYzyHHRqK5xI0I4pC9d+kUQnxLZI0SPBAcK4uRR87JybHOUQyQgGQRDaTKVSyb90Zbiuu1eUgu+SuPnpT3/K//t//49TTjlld69HENpCs3PjBy66ZvNgAh93WEJxYJPXEw2prbJySp3mRPUE5apc3LgkeZdivAKpjoltpZXvaEghsj12tjd+ARptE7TjNpKQBUEQ2sQZZ5zBF77wBebOncvLX/5yHn30Ua699lo+8IEPtHtpuyZuJk+ezJQpU3b3WgShbRhxYx77BZe0bJ2RwMdTse1zY8NS9pgUiLMS8kzc2OfYsJTXJG5SLyDRMVkht2fFTWjLwnc0fiGrCMzCUoIgCO3kn//5n/m7v/s7Lr30UjZu3Mjs2bP50Ic+xJVXXtnupe1aE7/Pfe5zXHnllS3zpQRhX6ZSb4Sl/IKLrltxY2dGNY9f8G1Jd6ohtmEpt2SaWmKfK52FpeJc3GivYMNSZh/fAQ15t2IzfmFscZPjOMQ1ETeCILSX7u5uFi1axPPPP0+1WuWZZ57h85//PIEdKNxOdsm5+epXv8ozzzzDjBkzOOyww0b88n3kkUd2y+IEYU9RDmO6soTigouu267BRXOLRPiNsJQ9JgUiO5PK6bDJvZm4SW35tkrQbsO5SdOY2PbI8V1FnLcNNDk33ig5N1lYKndulENUl5wbQRCEsdglcfOud71rNy9DENpLNUwaOTdW3ChMKThATReaSsEbOTeNsJRxblRoXnOsuPGHh6XqMYm97XzXMYnKGTsIS+VJehKWEgRB2C67JG7+/u//fnevQxDaRppqqnGKb+dB+YEDNVPBpAvmFqmrYh6WCmwzvhRNZJ0ar2TGjqgQ0jTC0eY4k3Njr+P4NixlG/i5jnGEzCJQSuH5I+1cybkRBEHYOXYp5wagr6+Pf/3Xf+WKK65g69atgAlHrVmzZrctThD2BLV8zlNTWCrrPWOFRb25Wsru1+LcdPQCoCJI0xpOmpV7Rw3nxvVttZTNufG9FnHjF4qjtlUY4dwoh0hybgRBEMZkl5ybxx57jLe+9a309vby3HPP8cEPfpApU6Zw11138fzzz+fjEgRhX6Bs82byaqnABTslXNtJ3HVdyJ2bLCsmBeJ6Jm5sWCpWxFEFLzXHmT43DXHT4tz4fh6WGqvHDTRybhI7ssE4N5JzIwiCMBa75NwsXLiQCy64gKeffppiU47AggULWLJkyW5bnCDsCarWfSk0JRRjRUvqZc5NAWu4ULDmSgp5WMptug+Saj9ebJOGVQQ2oThxPFsKnjk3fmN8wxijF7L9oCFukD43giAI22WXxM3DDz/Mhz70oRHbDz74YNavX/+iFyUIe5JyaFyaIHdunLxfTeRb56Zp/EJgb5u0KSylmoRJXBnEzbsZN8JS2fiFWGXOTTAiLDUaw8WNVpJzIwiCsD12SdwUi0UGBgZGbH/yySc56KCDXvSiBGFPUrHiJnduAjcv6Y5d46yETaXgmQRJaBI3rovNISap9hPEVgipsCFuVObcJPY6hZ0KS8Wxbf/nOIRVETeCIAhjsUvi5swzz+Tqq68miuxMHKVYtWoVn/rUp3jve9+7WxcoCBNNxQqUrE7JLzSLG3OLNA/OLDQ5N1mfGwDtWxFTHcJLsiTlep5zkyiXRCckWViqUNwp5yYXN0oRinMjCIIwJrskbv7xH/+RTZs2MX36dKrVKm9605s44ogj6O7u5gtf+MLuXqMgTCiNhOKGc5P1q4ltvkw99VE2bFW0TfhSNHHUNCAuMNuT8hCBnRdVICTr0xcrt8W58QslwjznZvQeNzBKzg1Ql4RiQRCEMdmlaqmenh4efPBB/vu//5tly5aRpimvetWreOtb37q71ycIE041Mo5IVi3lBQ6prQSP7cTbOG0028tybpJhzg0FB0hIa0MUYtPhuEAdbHl3jEOqk0YpeKGjybkZffQCtI5fUJiRDeLcCIKwNzA4OMjf/d3fcdddd7Fx40bmz5/P1772NV796le3dV07LW7SNOWWW27hzjvv5LnnnkMpxbx585g5cyZa61H7dAjC3szwUnDPSbHDF0gcY7yETeKm4dw0cm4ACFwgIqkMUojMGYrU82qpCKfVuSl2UNuJnJvscRTHhFkfHkEQhDZy8cUX8/vf/55/+7d/Y/bs2Xz3u9/lrW99K0888QQHH3xw29a1U+JGa82f//mfc88993Dcccfxile8Aq01f/zjH7ngggu48847ufvuuydoqYIwMWQJxZ5tzuekjZBPrIy4ie04BUWKp8zjVDdKwQGwoxrSoT6C1AiYoqrlOTeRVqQ6buTcFDsb4xe2k3PjOA6e5xHHMa7rWnETjrqvIAj7PlprKmm64x0ngA7HGbdJUa1W+eEPf8iPfvQj3vjGNwJw1VVXcffdd3PDDTfw+c9/fiKXul12StzccsstLFmyhP/6r//itNNOa3ntl7/8Je9617u49dZbOe+883brIgVhIskSij3r3KjUTLvXjiaxZdtx4mO8lxRXmSSarFoqcyyVHdWQVgYIEiOYSlTzaqlIK9PELysFL3bmYant5dyACU3FcYznmmtHsTg3grC/UklTDl/yeFuu/cwbX0Gn6+54R0yRQ5IkLf3uAEqlEg8++OBELG/c7FRC8e23386nP/3pEcIG4M1vfjOf+tSn+N73vrfbFicIe4JM3GS3s5OUAdABpBiREudTvlMcGs4NGpIsqTgXN0O49ltXiUp+lxlx09TELwiInJJ5UY/t3EAj78bzbVm4zRMSBEFoF93d3Zx88sl87nOfY+3atSRJwne/+10eeugh1q1b19a17ZRz89hjj/GVr3xlzNcXLFjA17/+9Re9KEHYk1TCGHRD3KhoCAAdKLLsm0S7OIDb5Nyk2IqqMDUVVgU7A6paxtFNzk2Wc6MhSePW8QtOEVLr3IyRc5PtC+C6w3reCIKw39HhODzzxle07do7w7/927/xgQ98gIMPPhjXdXnVq17FOeecwyOPPDJBKxwfOyVutm7dyowZM8Z8fcaMGWzbtu1FL0oQ9iSVeoILKJtzQzJo/g0UqTL5N2nSJG4cK2LslO4oTCjiowoBGkhrFVzr+HRQyXNuQptzk4WlPM8jUkbQqDTBG49zk82ZSlPSNMFxxmcfC4Kw76CUGndoqN0cfvjh3H///ZTLZQYGBpg1axZnn3028+bNa+u6dkqiJUnSUrkxHNd15RulsM9RCZO8UgrAqWfOjYOmbgSLbT/s6yR3brQNL+UVU0UjVHStjpuOknOT0trEz/eJlG0dOI6cGzD3GJjhmbEkFQuCsJfQ2dnJrFmz2LZtG/feey9nnnlmW9ez09VSF1xwAYUx7HNpLCbsi5TDGD+rlHIV2oobAgetIpP0a7sTeyS4yjo3tqtf1uvGKRRIsOLGzToUh03iRpESb2cq+NjiJvtS4WZfLhyXqFYjKJZ2x0cgCIKwS9x7771orXnpS1/KihUruPzyy3npS1/KhRde2NZ17ZS4Of/883e4j1RKCfsazc6NF7gkVStuCi6o0IxesHOljHNjBEUWloptObjKnJt6iNth3ZmmwZmRVsRpUxO/pqngjDPnxrHxcO04RPJlQhCENtPf388VV1zB6tWrmTJlCu9973v5whe+0NJ8tB3slLi5+eabJ2odgtA2KvU4Fzd+wSGtmmop07cmJCRApc3ixua9aCNSIhuWcqyLktZNyXiiXVyVoB0rghJIaDT9832fUDeNXxhHzk2OTAYXBGEv4KyzzuKss85q9zJGsEuzpQRhf6Jcb4Sl/IJHWjV9blTRRTnWubHV3r5OcHJxY7bFWVgqy5kJYxQhsQ5QgOMkdn9FNEzcRNregjoZV85N1lxLOw5RTcSNIAjCaIi4EQ54msNSfsElrRlxQ+DjKOPcNMJSce7cNMJSRrAo69zoMEERkmqTLOw6jU6jdStOHK1wHIcoNc932rlxxLkRBEEYCxE3wgFPa86NQ1qrAqAKTeImSyhOm8JS1u3JRjC4pU5zkjBFqZBE2wonp1FBGFpx4+GSxHEubsabc5OhleTcCIIgjIWIG+GARmtNNUqawlIuadU4IqoQ5OImy7lxdYxn+9zkYaks56bUYTZEKYo6usm50VbD1J2GuIkrQ/k6xjN+oWXdjivOjSAIwhiIuBEOaGqR6TPcXC2lM0ckCHBVaErBs7BU0nBhYqtY8lLwYubcaBQRaebc6LhRMWWrnTztEJUb4gad4gc7dm60DYUhOTeCIAhjIuJGOKAp24ngvhUqfuCiM+cmKOCNCEs1iRt7+8R5WKrLvBBpIESntqswcWO+lG0A6OISDtpOyGmK74LaTtvzTNykdmaVVg6xODeCIAijIuJGOKCp2pBSMXNuCi5p5twUfDwVjci5MaRE9vbJSsHdjkzcgFI0wlIkuXMTu5ngcUgqtiorTfCdphbJozBc3CB9bgRBEMak7eLm+uuvZ968eRSLRU444QQeeOCB7e5fr9f5zGc+w6GHHkqhUODwww/n29/+9h5arbC/kTk3haxaKnCgZsYaqMCIk4gAlQwXN42i7iznxi11m+Mi+0IubuJ8vlRsk5E97RCXbVWWTvHdnRM3WqqlBEEQxmSnmvjtbu644w4uu+wyrr/+ek455RS++c1vsmDBAp544gnmzp076jFnnXUWGzZs4KabbuKII45g48aNMs9K2GUqVpgENizlFVx0GKEwCcVAi3MT5BokJFEBaJX3ucnEDSFoQOnMpWk4N9or2G0uSVaVlab4TqP/zWhk4xdanBvJuREEQRiVtoqba6+9losuuoiLL74YgEWLFnHvvfdyww03cM0114zY/2c/+xn3338/zz77LFOmTAHgsMMO2+416vV6y8yrgYGB3fcGhH2eihUmheacm5oRN7pgbo+6LjTEDZm6iYgIKNFUCt7RCxjnJnVAWefGIwbXnD9pEjdpJk7SFF9tX9xkzk2SmP20kmopQRCEsWhbWCoMQ5YtW8bpp5/esv3000/nV7/61ajH/PjHP+bEE0/kK1/5CgcffDBHHXUUn/zkJ6lWq2Ne55prrqG3tzf/mTNnzm59H8K+TSOh2Dz3Cy7YbfhG3NQokMWg7AxvNBGZX5iFpbyOHsCIm0SpVnFjnZs0z7lxSay4UWmKr7bvPg4XNzgOoTg3giC0mSVLlnDGGWcwe/ZslFLcfffdLa9rrbnqqquYPXs2pVKJU089lT/84Q8Tvq62iZvNmzeTJAkzZsxo2T5jxgzWr18/6jHPPvssDz74IL///e+56667WLRoET/4wQ/48Ic/POZ1rrjiCvr7+/OfF154Ybe+D2HfJksoDmyfGy9woW6Fhg0F1VSxMVvKHpeqmFi1dijOmvipVJGgcGiImyznJsnEjXbQdZPbg07N9HA9dt5NJm7yEKxShOLcCILQZsrlMscddxzXXXfdqK9/5Stf4dprr+W6667j4YcfZubMmbztbW9jMKsWnSDaGpaCxqycDK31iG0ZaZqilOJ73/sevb0mBHDttdfyvve9j3/5l3+hVCqNOKZQKFDYTudX4cBmNOemHmY2jRU3upiHpbLwlSYmsv9No7qdCt70/y/RCpeCPXeUf42IHZtQjMntAVMt5akE4jr4ozfyy8RNFEX5trAm1VKCsD+SNRdtByXfHfNv8GgsWLCABQsWjPqa1ppFixbxmc98hve85z0AfOc732HGjBncdtttfOhDH9otax6NtombadOm4bruCJdm48aNI9ycjFmzZnHwwQfnwgbgmGOOQWvN6tWrOfLIIyd0zcL+R+bcZDeCFzjU61lFks25aZotlcnkhIS6zb/JSsGV76MVKA2xCnJx4xHlYalENcJSOrLOTZZQHJZ3KG7iOMZRilRr6qGIG0HYH6lGCS+78t62XPuJq99OR7B7pMHKlStZv359S/pJoVDgTW96E7/61a8mVNy0LSwVBAEnnHACixcvbtm+ePFiXve61416zCmnnMLatWsZGmp0dn3qqadwHIdDDjlkQtcr7J+UbUKx15RQjE0Qjl3TcK/eXC1lv9EkJLlzkw/OVCqPWyVOCU9Z50ZFjYRix7XXc9DWNVI6xXdSCJs6Fg+jWdy4dl1hGI25vyAIQrvJzIudST/ZXbQ1LLVw4ULOPfdcTjzxRE4++WRuvPFGVq1axSWXXAKYfJk1a9Zw6623AnDOOefwuc99jgsvvJDPfvazbN68mcsvv5wPfOADo4akBGFHVCIjMLymJn4qtI6MZ0UEhTznpkhD3NSznJt6wz7WgUKFmkQV8VSBFPB01NTnxoobXHRsj2t2bsagebaU73lEcUyUOT+CIOxXlHyXJ65+e9uuvbvZmfST3UVbxc3ZZ5/Nli1buPrqq1m3bh3HHnss99xzD4ceeigA69atY9WqVfn+XV1dLF68mI9+9KOceOKJTJ06lbPOOovPf/7z7XoLwj5OVgqe3c6er/ImfLEyxmZrWMqOXCAhzJ2btHHCwAESEhXgU6COdW4ycUND3GDFjcpKwbcjbrI+NwCuFV3N+TeCIOw/KKV2W2ioncycORMwDs6sWbPy7dtLP9ldtP3Tu/TSS7n00ktHfe2WW24Zse3oo48eEcoShF0lSyjOxQ0NFya2s54i/EZCscpESkrdips01SRxius54GfOjofvGHFToJ6HpSIaYSkV6+wE+N72w1KO4+B5HnEc43s2uViaVwqCsBczb948Zs6cyeLFi5k/fz5g2sDcf//9fPnLX57Qa7dd3AhCO6mGCUqDY8NNTtpI0s1yakIdoKw5U7TiJEJTVY3S7ThMWsRNqn0CxyQHB03iJhu26eHmbpDSyQ7DUmBCU3Ec5y5OHCd7xN4VBEEYi6GhIVasWJE/X7lyJcuXL2fKlCnMnTuXyy67jC9+8YsceeSRHHnkkXzxi1+ko6ODc845Z0LXJeJGOKAphwl+84bIdLDWnibBKJpIN/bIwlIhaR6WAlMOXujAhqUg1S6BYxKKi9QbOTejiZs852Zs5wYaoSk3y79RijgK8QNpdSAIQntYunQpp512Wv584cKFAJx//vnccsst/M3f/A3VapVLL72Ubdu28ZrXvIaf//zndHd3T+i6RNwIBzTVMM573KCAyDSW0j7E2gSR4qRxm5RUJm40kdLmGN1UMZU5N6lDwTVJ7iWqo4el0jyuha+2H5aCRlJxJnK0nS8l4kYQhHZx6qmnorfTgFQpxVVXXcVVV1215xbFXjAVXBDaSbme4GdDMwOXpGrFTaBItAlRRWkmbjSBneod0ghbQaPXTR6WSqDomrBUkVpD3GTXws1DXSodf1gKyEvBcVziuvS6EQRBGI6IG+GAplyP87CUHzikVtzgKxKMcIhT65gojauy3je6JSyVVUzlzk2i8DwjbjpUtRGW0o2wVItz46TjFjeOTXTWypHhmYIgCKMg4kY4oKmEce7c+IFLXLHOTUGRYPrIpNa5cVWKazsM11FEaLIh4VmvG2vsoGNwvQ4AOig3Eooz50Y7KCt0TBO/8Ts3mbjBhqXGIk01X/nZn7jk35bxzKbth7wEQRD2JyTnRjigqYQJ05oa+KVVKwICl0Rlzo2LB3gqzZ2bKqOHpZRn9E4aaRy/AFXopNLU58YIExcXpW14aZwJxZm4yaqjtLN95+bu5Wu4/r5nANgwWOOuS0/Z4echCIKwPyDOjXDAkg2ny8NSBZckEzcFlxTTJC9NjAjxSXEds3cNZRKKLXlCsWu26TjF8UxCcQdD6GGl4H6TuFFpYhOKx+fc5DgO0XZybv596Qv540dX9bFio7g3giAcGIi4EQ5Y6nFKqmlJKE6rFfNi4IKqm47CNjfGJ2lybpxW58aGpZxM3EQaN7A5N03VUrF2QGscFIpM3OxcWCp3braTc1MNEx5+bhsA86Z1AvDg05vG87EIgiDs84i4EQ5YynXT4TcrBfcLLmnVCoyCh1ZhS3fiQCe4NqmmiiIEtE26yRKKHcdOFI9SXOvcNDfxS3BwNCgUSmcJOjsXlsrZTs7N79f2k6SaGT0F3vuqgwFysSMIgrC/I+JGOGCp2FBSsVnc1KoAqIIPKiRsmivl6zgXN2UctMrziRs5N44tCQ9T3MCIG5+oJedG2Z4QCg+0xnWUeXmczk3WU0I77pjOzfJVfQAcd8gkXjV3MgCPr+nf4WciCIKwPyDiRjhgyeZKFfJqKYe02hA3ygnNRPBRxE3FVjplIzOznBvXihsdJTiBR6w9HDTKMddKcBqdifEgTQiCwJxkZ8WNGjvnJhMyx82ZxEtnmk6gL2yrUAllHpUgCPs/Im6EA5ayzZPJxI1XcEnrRtwQBDgqJGp2btIYx4qbumv+zcZsxnUblrLl44QpynNItBEurpvJIEWad/P0UGmKVxifuMk7E2fHb6daauVmc64jp3cxtavA1M4ArZGkYkEQDghE3AgHLJmL0RyW0lUjFlQhwHXq1AnynBuvyblJPCNIYlsxFUU2obhZ3PgOqZ1L5ToNxyS1Ygrtm3ybgh2fMM6cmzS1eT1jVEtprXNxkyUTHzXDuDdPbRBxIwjC7mPJkiWcccYZzJ49G6UUd999d8vrd955J29/+9uZNm0aSimWL1++R9Yl4kbYr6gM9PPg92/lyV8/sMN9M+cmyMJSBRddM2JBBQGuqhNSyJ0bLwkb4sY3gqTh3CQQh7jKihgrbnLnxknINE2cV5D7qDQlKJbsMeMLS2XiBuUQj+LcbB4KGarHKAVzp5pGglPsTKwVawZ28KkIgiCMn3K5zHHHHcd111035uunnHIKX/rSl/bouqSJn7Bf8bN/uZaVy5cBUOjo5LDjXjXmvplz01ItZZ0QVSjiqZB6U86NG4d4gREYsR1WGeeDM1OIyjiOPZkNS2nr3HjExI6CRBOnClMF7psGfpm4SUKIQ7Cu0HAycZMkNq9njGqpzLU5eFKJguey/tl+Bv6wDYrw24fXoc84Ji8nFwRhL0RriCrtubbfATvx+2HBggUsWLBgzNfPPfdcAJ577rkXu7KdQsSNsN+wZc0LubABeOSnP96uuCmHmXNjnnuBcW4UgF/AV3XqFBphqaQRWooKHUDdtvmz1VL1IdxsGqZ1bnRqhIqnY1MOnmjiFHBB45sGfsVusmgWUXnc4masJn7PDQtJPX7faiYl5pfVhmrIhucGmDmvd8zPRRCENhNV4Iuz23PtT6+FoLM9196NSFhK2G9Y+ehSALqmTgNg1e9/t93ZS5Wszw1NYamqURmqaJyZ1rBUlB+rC8ZtyboUx3XTp8a1zo0KNcp10DYs5ZHkXYqTPCzl4aaJcW7cHScVjxA3yqE+yvtb3WeSog+Z3EGaalY+tpne1Nzq/U7K849vGfMagiAI+wPi3Aj7DS/84TEAXrXgz3n0pz9hcMsm1q14irnHvnLU/TPnpjksFdaMgFFFIzaaw1JBXuUUUiz2mEfWvY1C02HYs038VARpGkM2UZwI7OiGmIblG+gEv1iEqBOq4bjETRw3HKQwHClu1vcbcTO7t8iWNUNEtYRpRQ+oM+TAc09u5TW8ZMzrCILQZvwO46C069r7ASJuhP0CrTVrn34SgDnHHMu6p//E4JZNrH9mbHFTtTk3bpNzgxU3umCEREWXcqvF1jShVJ2Ogrl1ouYOxfVyk3MDaVpD5c5N3NKlOKOoNV6hCPUuqG7bbsVUs7hxlCkpD2vhiP3W9RvBM2tSiXUrTL+bl8zrpWNzjUqUsOKFAdIkxXHFuBWEvRKl9ovQUDuR327CfkF521ZqgwMo5TB17qHMPPwoADY88/TYx1jnxmtybrAVVMo3oqRCR55zU7DOjaMiOnwzFyrMwlKhDUvZ5ypWxHEFZxRxkyqXVCf5Of1CofGLbBzOTRRFec+bMNqOuLHODcCMeb3MnmRCaf1pQt/G6pjXEQRB2NcR50bYL9i86jkAJs+ajR8UmH6YCbtsfuH5MY/Jcm5c+7xZ3GCFRJViHpbKnRsnoqNgjqpnYam6TSh2G5PCk8pALm58HaPtCIbU8YjTiMB1KaLxC8WdFjcF3yeMIqIoGrHfeituZvYW+d06c74pszqZsbHAik1DDCnNljVDTJkl3wwFQXhxDA0NsWLFivz5ypUrWb58OVOmTGHu3Lls3bqVVatWsXatCbM9+aRx2GfOnMnMmTMnbF3i3Aj7Bc89/RTVQw6ndtDBJEnClIMPAaBvw3rSLAF3GJlzk90EXuCC7TScWmekRjEPSxVt+Eo5MR2BETc11RSWCodQzeKmOoCrjSTyiBrOjeOTaCNKCmBybnZC3MRxnDs3w8XNQC1iyIq2mT0FtlpxM3lWBzN6zJTyIUfnjo4gCMKLYenSpcyfP5/58+cDsHDhQubPn8+VV14JwI9//GPmz5/PO9/5TgD+8i//kvnz5/ONb3xjQtclzo2wz6O15uEnVxB3T2ZDmPDwww/zmpNOwgsKxGGd/o3rmTzr4BHHVcIYR4PTlHOj6rbjsM1HqVJshKUyceMmdAR2DIN1bpI4Ja0N4TigXVAJxJV+XIyg8FWYD89MHY84jvJzGuemy5xoHDk3AF7m4sStwi1zbXpLPqquqVdMM79J01vFTf8mCUsJgvDiOfXUUxsjYUbhggsu4IILLthzC7KIcyPs86xevZpy0x/5pUuXohyHybNMn4ita1ePely5nuSVUgCek5I3GHZt2KmpWqqoMucmbXJuGsfHFSMYbCSKpDKIp42gCHSYl4JnYSmAglI7nXPT/DhFkzRVTzXn22SuTfe0El7gMrOnCBo6UsWW1eLcCIKw/yLiRtjnefppkzTsDvXjOA6bN29m8+bNTJ5tQlPb1q4Z9bhKPSaTC8pREDbcjNAxt0a9qc9Nyd4uytO5uKmrhjqK7ERxCrYqaqgfH5PEG6h6o1pKeSQ6m0jujDsspZTKJ4hnzg2qdXjmOtvjZlZvkYHN5vGk6WYNM3oKnFLzOLnus219hece2zzmtQRBEPZlRNwI+zwrn30WAG9gG7NnzQJg1apVTJo+A4D+TRtHPa5cj1vmSsXlPgC0o4mszRo2Dc4s2tvFcXUelgqbnZuq7RaciZvyAIGy4oZ6U1jKJ7Y5N75yxh2WAnJx41pnSQ+bDL6uKZl4cIt53D3VrGFq4HNivRGJfugnz273WoIgCPsqIm6EfZokSVhjs/ALUY1DDz0UMKGqnoOmAzCweQxxEyYtDfzioa2ACStl4qOugzwsVbLDJ5VP7txEzc5N1nPGVlIl5QEKjhEWBZqcG8clTs2+vnLN4MxxODcwUtygHKJaYwTDpiHz+KDuJnEzxSQ1p2sqBCgqSqPRbH5hiIEtknsjCML+h4gbYZ9m8+bNZkp2kjBp0iQOmTMHgDVr1tAzzYibwTGcm0qYtIxeiMum4Z0uKCJthEGUFvL9S7ZoXHkqd26iJucmsg0AKWbiZpCCY7p9dlBrjF9Qfh6W8nHHHZaChrhxbNhMO26Lc7MlEzddAYNbzfYe69wMPG9coceDGDsBi9V/3Lbd6wmCIOyLiLgR9mk2bjTCxalXmTSj0Tdh8+bN+Yypgc2bRhwXJSlhkrY4N0kWlioo4kzcJI0wToey4iZQTc5N0zlrRrCook32rQxRtK3MS1TzsFSsnNwZ8pRLUOpoEjfjC0tl4oZhYanNQ8YRmtpVyF2Z7qkmqXn9CvP+VnkpVbvudXabIAjC/oSIG2GfZsOGDQC49Sq902fQ29uL53kkSUJqp2vXK2XqlVZHpDLKXKm4PGCeFBwSMufGCBVHpfh2NpTyHTpt6CmkKSxle+Rkc6nSSoXAzZybSh6WipVLkmbixicolZpybrbv3GRVUspWbmnHJa6NdG6mdPiU+4zQ6Z5SpFaOGNhs9lvnppTt9PJNLwxu93qCIAj7IiJuhH2ahnNToXvqQTiOw7RpxrHZNjBAsdsMuBwYFpqqhFm1khEJXuCS5OLGI8WIhCQ1zo2nNJ4yj1XgURolLBWGdvRCyTglaaWM6xtHppOhvENxpBWxDu15/V0KSzXEjUNUb+TcZM5NR6rQqcbxFB09AVvXmvPWA0XdgW12BtbWdRUzOkIQBGE/ou3i5vrrr2fevHkUi0VOOOEEHnjggXEd9z//8z94nsfxxx8/sQsU9mq2bNkCgFOv0TlpMgAHHXQQAJs2baJnmnk8PKm4bMcslJrDUhUbEip5pJiQTpQYp8RXKa6yrkng0WnDUokCbd2bbMyT02FyXNJqFTdoiJvMualrRZza0Q/Kx/ODnRY3Oa5LWDNrrUVJ3p04qBlnpntyEeWovCNx3GVE2YCjUY4RQFk/HEEQhP2FtoqbO+64g8suu4zPfOYzPProo7zhDW9gwYIFrFq1arvH9ff3c9555/GWt7xlD61U2BtJ05S+vj4AnKhO5+QpALlzs2XLllzcDG5u7eky3LnxCw5p2YZoih5amYBTap0bXyV4WVgq8CkFbn6uLDk3imypeIcJRelKDc86N90t4sbJxy+4KiDZto21i25j5b3T2PLb/u12+xwubrTjElYrAGy2IanAdaBs3l+XrZTaYp0bZ7IdCOqYwcMAfRsqY15PEARhX6St4ubaa6/loosu4uKLL+aYY45h0aJFzJkzhxtuuGG7x33oQx/inHPO4eSTT97hNer1OgMDAy0/wv7B4OAgSZKA1qgopMs6N5MmTQKgr6+PzslTASj3bW05NnNuirm48UhtXo4qBqBqRAQo2+Mm0Enu3DgFn8B18GyYKQvqZMnHTmc3AGm1jpc5N6qM9qy4SVWeUOw4AavOP5/+xf9DbVvAxl8nbPnWv475njNxkwkg7TiEtnngljyZOKA6aM7f0WPEzda1xrnpmG5CZmVHk9oSdxE3giDsKkuWLOGMM85g9uzZKKW4++6789eiKOJv//ZvecUrXkFnZyezZ8/mvPPOy4doTiRtEzdhGLJs2TJOP/30lu2nn346v/rVr8Y87uabb+aZZ57h7//+78d1nWuuuYbe3t78Z44tFRb2fbZtM2XMKqqjgA4rbiZPnpy/3mXdnKFtreKm4dyY537BIa02i5s6dYK8O3GgE7wsLFUKUErl7k0mbkI7asHp7gVAV0PcghE3RWpg51XVU5UnFDs6oP70CrxpU5jyUiNANl93HdGa0bsqDxc3OI2wVObcTOsqUOk3jzt7zf7ZLKlJ042rVHUa7lDfRul1IwjCrlEulznuuOO47rrrRrxWqVR45JFH+Lu/+zseeeQR7rzzTp566in+/M//fMLX1bbBmZs3byZJEmbMmNGyfcaMGaxfv37UY55++mk+9alP8cADD+RTkXfEFVdcwcKFC/PnAwMDInD2EzJx44QhjutRssnDmXMzMDBAqfcYYKS4ySaCB00JxamdDaWKBZRTpU4RYituiHBtt2FVMCKmM/AYrMXESoNWRLoEjo/badZBLcQtdKC1ahm/UE8bpeDKDoCYdeUVdP7P+6lt9alsgq233caMyy8f8Z4zcZPYSefGuWkNS03rCqgM2MTingJRmFDpN8+nzzZiq+o1MqHFuRGEvQutNdW4PV86Sl4pL1gYDwsWLGDBggWjvtbb28vixYtbtv3zP/8zJ510EqtWrWLu3Lkvaq3bo+1TwYd/iFrrUT/YJEk455xz+OxnP8tRRx017vMXCgUKhcKOdxT2OXJxE9XpnDQ5/3/T1dWF67okSYIqmj/m5eHOTZZ425RQnFYzcVPEcfoIm4ZmBmncyLkpmf9PWa+bbGxllJag0NUkbmKcgkekCwSqhqeMIIlTh9A6NxDQ+bqT6TrtLfArmHL0EJVNBfp/eCfTP/EJ1DARn5WCN8SNm4elmnvclFdbcdMbMGhLwIOSx4ypxrmpNDs3Gypj3neCIOx5qnGV19z2mrZc+6FzHqLD9ueaCPr7+1FK5V9CJ4q2haWmTZuG67ojXJqNGzeOcHPA5FcsXbqUj3zkI3ieh+d5XH311fzud7/D8zx++ctf7qmlC3sJWTKxiup0Nt0ojuPkN07iGAEylnPT3KFY29lQqlDEycNSJl3YT6NGtVTRODgdttdNbDVBpIsQdLeKm8Ah0UYMeaoxvbtq05CVKjDlwgvBK4Jy6JpVx508iaSvj8rDD494z5lzk6Zp9mapV4Y7N4WGc9Mb5AM0e6YVOajbuE5Dadqo8qon1IYiBEEQJpJarcanPvUpzjnnHHp6eib0Wm1zboIg4IQTTmDx4sW8+93vzrcvXryYM888c8T+PT09PP744y3brr/+en75y1/ygx/8gHnz5k34moW9i0ZYqk7n5ENbXps8eTJbtmyhbhOCqwP9JHGE6xmBkjk3WRM/L3BJbTM8VSjiqTohhTwslYSDeEHm3NjGfH42PNMO2dRmRpTbZXJuqCWowM3FTUBEDVBAzQoLTYHO17/elC4FXaj6AN2vP4m+n/ycwcWL6RyWNJ+Jmzi2Qkk51O26s4TiaV1BI+emp8ALfzLCrndaiSmdVhwBNQU9gUtUTxjcWqPUPazMXBCEtlDySjx0zkNtu/ZEEEURf/mXf0maplx//fUTco1m2hqWWrhwIeeeey4nnngiJ598MjfeeCOrVq3ikksuAUy+zJo1a7j11ltxHIdjjz225fjp06dTLBZHbBcODPr7zSwoJwrzHjcZmXNTrtVwXI80iSn39eWl4UPDxE1Q8qhWQxTgFEp4qk6dYh6WKtZquEVzu5T9lF4azk3Y7NwUuvC6zFpUXaMCl9TOpyromJqrINFU86hQoREOCjqhPkD3yccbcfPf9zHj7/6uJVyUiZsoajgtmbjJnJspJZ91FfP+Wp2bEoHn0Fvy6a9GlJVmsu8YcbOlxvRDJ/ablCAI40MpNaGhoT1NFEWcddZZrFy5kl/+8pcT7tpAm0vBzz77bBYtWsTVV1/N8ccfz5IlS7jnnnvyyc7r1q3bYc8b4cAkTVOGhkx1kYpDOidNaXk9u3kGBwfptNVTzXk3g1bceDYsFRRdtB18GXoKVyXUKeTVUsUsDAScd98l/HLVL+kqWOcmC+9oM0bB65xk1lXX4Olc3ARpmCcVZ7OdNEV0du6CKSHvOGYO+D7xunVEw/7/Z+ImDEM8Oxm8VmstBe+008sdT1Ho8PKxCz0HmW9k07qyXjemkR+QD9kUBEHYnWTC5umnn+YXv/gFU6dO3SPXbXuH4ksvvZTnnnuOer3OsmXLeOMb35i/dsstt3DfffeNeexVV13F8uXLJ36Rwl5HpVLJ805UHI9wbprFTaMcfEv++pAdcullzk3RAytunq2tBqBKCZXl3KiGuHm2vo6P//fH+f1WkxNTtWGpATr4pNrMXz/8WbOuOpz9k3ejU5PnEugwnwxez80YF0Lb2rhg1uyoOqXjXglA+aFWa7rZucmSi0N7fObcdNgKsI4eU7KeOzd2gObULiO2KkqjbdhucIuIG0EQdp6hoSGWL1+e/y1euXIly5cvZ9WqVcRxzPve9z6WLl3K9773PZIkYf369axfvz7/vTVRtF3cCMKuMDhougm7OkWh8+7EGZm4GRgYyF2d5qTiLCyV9Rn2iy5YwbOpakRQWXfkOTfFvGtwjQuOvQhXuaypPAtA1d5FcVrk3rSf31WeAUDFig19z5La4ZsFXQfP7FylEWrS2dgH69xQH6TzJFMpUXnoty3vq9m5aRY6cZKytWJ+WZRsOk5nrxEx5T4jeromG3FzkBU3ZQVJZBKrxbkRBGFXWLp0KfPnz2f+/PmASTeZP38+V155JatXr+bHP/4xq1ev5vjjj2fWrFn5z/b62e0O2l4KLgi7QtZpWsXGbeka5tx0d3fn+3XNMOKmbBOQwTo3Gtw8LOWR1hIcYGrVSJ4ynY2wlA09KV3nJ/8+i1ef8FlWdq9lxVaoWefG0wEfLx7FEa+/BBZ9DIDPv+pv2PjszwErbqxzEzoeiU5wlUtaLuNMmQbFrMpqgI7Xvgauv57yQw+1lGlnbk0cx03iJmZrOURrk5fs27lSHT0BUZhQt/k3nZONqGkOS0V2XxE3giDsCqeeeup2R8Zs77WJRJwbYZ8kc26omz/KHWOEper1OsWeSUBrWGqwHtNcG/Tgc1tQVeNiTKkbATFEZyOh2N6fihqve/rXLF7useJ504Cq0tQe5shNc/n572ag7Z3166UaJzW5LgXqeVgqcgt5l2Jt+9Q0nJsBSscdh/J9ks2biVavzs/fPFsqLwtXig3bzOcxpSOgNpiVgRcobzOujVdwCYpGtGVhqXJTrxsJSwmCsD8h4kbYJ8nEjYpsyfOwhlDNzRuz0u3mhOKhWpQ38EPBR77/KI45FYE2YqRCB8TG2Sjlzk3I+7c9zgWvnUu3rZ6qORrs679dnXLbb18gsRrkd08/T92KpaKu5c5N5AR5l2JtOwxTsCXk9QGcQoHCy0x35WpTXpnnebmLkwsdx2HDNhPamtoV5N2IO3qCRkhqUqMqa5oVN3W/ocrqlZiw1ujDIwiCsC8j4kbYJ3juuef4+te/zre+9S36+voa4iaOKHX35P1rmslCUwRZ7kkjLDVYi/PRC6ECFdaw0SUi24G4SrEpLJUJgRrOhnVcPrPMl97zCqA5ORiOnjuHy956JFiX5F0v7ca3M6cK1MDLwlJ+Phn8qXW2f1NTzg1Ax/HHm3Us/11+fqVULmpc271YO24ubpob+HX2Bgz1ZeKv0aU7C0vVbFDa8x37+dRHfIaCIAj7IiJuhL2eMAz5j//4D7Zu3cqaNWv4z//8z0bOTRSNSCbOyEJTsbI5NFbcaK0Zqse5c1PVKUd0mD/sWmki24HYVEuZnTrsreK4JnTVf9dd9JRstZJqhHdOOmIel731KJySUQ6nzC5wUKdxZIqqhrbDMyM3ILZhqXv/9BOe2vZUS84NQOm448w6ftcQN9BwbFxbCq4dh039JrTVPDRzuHOTkYelrNsU2LWKuBEEYX9BxI2w1/OHP/yBcrmcP1+xYkWjO3E8soFfRiZuIlsyXhnoJ00S6nFKnGoKTc7NuScY50MXILLN+Zr73HRkvWNK5t+Be39OhxUnxifJzmVmWWFDVkm5n8BaOyWqjbCUajg3hVjx8V9+nCHPhpmsc5OJm9qf/pR3T4aR4gbHZdOgESZTm4dm9hZGdW6yaqlB+7l4gTg3giDsX4i4EfZ6nnjiCQDe/OY3c8QRRwCt1VI7EjfVMEI5DmhNuX9bXgaeOTcdnT5h9TEAdBHikhEgNd3oUNxpi8adokfwkpegazXUb38NQL3JuYm0ye9RRVvVVB7EXW0SmYuq0kgodvw852a6O5nVQ6v5x422NLJu3ps3ezbuQdMgjqn94Q/5NfJcIptDox2XzWU7V6pz9JybzhbnxpaTa02IxrFrGhJxIwjCfoKIG2GvJo5jnnvuOQCOOuoojjnGJNlm4wdUPHZYqrvD5LoMDg7S2TsJgEpfX97AL2t2d9jMLlZtMOJB+5B0mtuinjQEQacNbTkBTHqPmYWW/vynZj9FYwilTUam0ybt/umP+ButE6Mqec5N5Ph5WOo1k08E4Iebl/LrYiF3bpRSo+bdjBQ3DlttuXeP75HaxnxjhaU6Cx4l37yf5ung5f6JbaolCIKwpxBxI+zVrF69miiK6OzsZPr06Rx22GEtr6skprN3FOdm67P03P9/ARhY9fu8VHxo29bcuSnZv+ulTkV5m5lOnwaaxOqTMLGDMtGUHPs4UPSc8efgODiPmA7FWmW1UhDZIZmq0wir2lNP4VRtxZVTgSznRnlEqREek1U3f/nSvwTgH6ZOJrE5NzB63k0mbnIcl622jL3Lhs+KnT6u5zScm8mtx2TuTVlpEtuoUMJSgiDsL4i4EfZqstlihx12GI7jMGXKFDo7TV6L6U5MPjuqhXs/Q3f1BQAGqiFdgREY5b6tPL6mD4BSapyPAd1HV5iNB4c0MOInio0g8JyUQBkx4AQu/ozpdL7h9RSTEGVlTWIvG8Z2v0479C5OoDQJgA5nKA9LhY5HrO3YhC0VPjL/I/T4XTwdBNztVPO3sT1xkzXH0o5DX926NXZKREdvQJrq3I1pdm6gUQ5eURCHif1sRNwIgrB/IOJG2KtZv944KrNnzwZMKCab+I1NiO0aNjSTgbXw5E/pwSQJl+mgZOdFlfu28ePl6wAo2rDUpngDnVYcaA+0Z/7Ix6lJCg5UiucY0aJsovCk974XBXTYPjuZuIkS83o6ZJsMKkXhpNcBUHLKTQnFHlFqhMfGrTUc3cGHjn4/AP/SFVCPTQJx8dhjwXWJN2wgWrsWGCluUsel3w4JL8aNkFR1IESnZjhmqae5ZWFrl+Ksg7GIG0EQ9hdE3Ah7NevWGSEyc+bMfFtHh3FFsmnaI5ybP9wFaDrmHIfjKEDhV41I2rZpCw8/Z5r5ZeJmbf0FptnxTvgKnBopisTOhApUgqfMY6dg/u1+y1sIDj+cjsi4LLHtdRPWU8q/eYhwlRFTblcPatJ0s053KBc3sXKJrbjxE8U//3IFf3X0+5kZx2zyXH705H+Y65VKFG2eUeWRR826iybklSRGUpmGgLaxX80mQDdVSnX0BPZzaDCtaXhmapOmy/1hPkhTEARhPCxZsoQzzjiD2bNno5Ti7rvvbnn9qquu4uijj6azs5PJkyfz1re+lYeGDQSeCETcCHsttVotL/meNWtWvt2zzetSx/z3HVEt9ex9ADjH/BldXaYxnrIDK1c8v5bY/gEv2L/j68I1TLHiRnUUQNUIKaCsC1IgaTg3Jfuv63LQRz9CZ2QclshWTFVWb2T1xz4Gdpq40i6xDWl1OBV01sSvybkJcPn2gyt5ftDjgn7j+Hz7ie8Qpzbx+YRXAVB9ZJlZj3VusqnoFceIna6CRzJkjxmjUiojy7mp2S7FSoFONZVBSSoWBGH8lMtljjvuOK677rpRXz/qqKO47rrrePzxx3nwwQc57LDDOP3009m0adOErksGZwp7LVlIqqenJ3droGkQm+PiFUsEpcZrJDE8b0q0Oez19DyxlIGBAbQdOLlp4yY4yLycdSiuezV6qw6gcbq6UE6NWlN34oKO8HNx0xAK3W9/O93/9T0g63UDfb9eSjowgHf4DHhhE1RDYuz4BWr5VPAIP8+56XJ94ljz2f98ghtDhxuThDWVDfx05U854/AzKL3qBPjOrblzk4mbzLmpOkVIsx431q3pbaqUmjyKuMmquQLzGRQ6PGrlmHJfPZ8mLghCe9BaN2bO7WFUqZRXYo6HBQsWsGDBgjFfP+ecc1qeX3vttdx000089thjvOUtb9nlde4IETfCXsuGDRuA1pAUQDW76ZUimDqt9aD1j0E4aOY0zXwF3d1PApDYBnlObQhXGd2SDWyInDodNufGmTwZ19nCECWUdV/8NMazzfmcUim/lFKKyUe+BJ7dlve6iZ0i3W97G86bjqL/0X+BSkREShx7eF4MrjlnpAJC69yU3AIBDg88vRmmdPK/+gf5+pRJ3PbH2zjj8DPoeNV8AOpPPkkyOJiLm6wcvqayad+FPIG4ozdgyxrT+HA052Zatw1L2T6AQcmKm2Hl4El/P5u+9jXigQGmX/YJgkMOHnEuQRB2L7pa5clXndCWa7/0kWWopi+Tu5MwDLnxxhvp7e3lOFssMVFIWErYa9m61eTGTJvWKmCGhobyx273pNaDVi81/845CRw3ny8VuSZ00xmXOWSyESh+Nuk7reKEtlvv1Gm4yjo3WVgqDRthqc7Wm767y5w3tF90nCOO4ZB//jrBNCvIKjGhjkhsWbnrmrCRxqFm+9yo1Od/v+ElAKyp+rx3cIhAefx+y+95bNNjeAcdhD93LmhNdfnvRoibqjbfUaZ2NroTd/Y0JoIPr5QC0+wPoGKrvTw/G1HRmlS85m8vZ9tttzP4n/+PFRe+m7Qu08MFQdg5/vM//5Ouri6KxSL/9E//xOLFi0f8Xt/diHMj7LVk4mbKlNZqqGxoJoAuDvuGsd50GmaW+VaQdSmu+T1AHy4pM7p8nt9axbMjE2Zsq6FsuZPbNQnfqVKllE8ED5IwD0s5tgw9o7tgbqFq5tzYxGO/x9y4qqqpJ5m4qeI5dTRmWMOQPSZNfS497XDufGQ126oFjnBS/r8pr+DHWx7l9j/dzisPeiUd8+fTv2oVlUeWUXy3aSIYhiG+51GLzTWndhWo9Bu3pmOMoZkZmXMzmCaAl3cpbhY31cd/T/m+B/Ln6oVBVt3xeQ477/MjzicIwu5DlUq81ObYtePau5vTTjuN5cuXs3nzZr71rW9x1lln8dBDDzF9+vTdfq0McW6EvZbRxE2tViMMG6GT2B02DXzD782/M83E7sy56fOmU3SM0xE41qWxzs1RA26jlrvYgaciMzTTOjd+VMVTmXPT1XK57mKruMnKqr1M3NQ09bhKEmeVV2HepTgbXKnTAh2Bx6ffeQyDdnzDO4tHA/Cz537G5upmSnlS8aO5c1Or1QiCgJp1bqaUfMKaeSMdvYXtJxRb52YoSUloVEg1j2DY+u+3AVA5KSF83wwABn/4n42cJ0EQJgSlFE5HR1t+dibfZrx0dnZyxBFH8NrXvpabbroJz/O46aabdvt1mhFxI+yVpGmaV0o1i5vMtcn+49aaS5eTGDb+0TweJm62JB10eEbcDJSNOLLpJhy9RaNsLbeyZdZVOnLnxo9q+Q2v7PkyumxpeDbGIKzGaK0Jek3WslNT1GplYhuW8nU9TyouW+co1eaa73zFLIpdkwBY88cBXnnQK4nTmB8+9UM6TjDx9+ry5WQda+r1OsVikaq25em2esz1HYKiO+rohYzJHQFZdXhVQWrziyr2GK01g/f9l1nf62dy1P++0Zz7qRr9q38z4nyCIAjjRWtNvT6xfbVE3Ah7Jf39/aRpiuu6eWgJGuLGs25DuR7mJdFsWQFxDfxOmDwPaISlKolLl2dETX8twtXgWHEx67mNKNsEj4KRDoO6q1EKHmcvpqiOVuemyzo3FSsUtIaonuD2TMr3qZYH85ybAvW8HLyaOyY+ulZFKcXL5s0BYO2GDZww6c8A+Pcn/x112By8mTPR9TqJHaKZJAnFYpGaFTcdtnqqszcgqiVEdft8lGopx1FMsRVTZUcT2ZyjzLmpP/UUetMAqa+Z8oY/pzT3KPScDpRWbPrF7SPOJwjCgcnQ0BDLly9n+fLlAKxcuZLly5ezatUqyuUyn/70p/nNb37D888/zyOPPMLFF1/M6tWr+Yu/+IsJXZeIG2GvJAtJTZ48Gcdp/DfNxI1KItApqdb5hPA8JDXj5WCP6c6dFkXRN3/sB+spRasrNClB3zZUFunyjbjppxciOxNK2541VM108SZ6S0ZYlJUmmzAVVmNUEKCtNVQrl/OwVKHJual5ja7B6aB5D5Ommhh0L0P88IGpTClOZWN1I7984Zd0veEN5vy/+nV+XFAoULWpc34ta9pXYMgmExc6PPzAZTTyLsVKU6+2diku/+p/zLVeqpk6+zRzrtceD0D1tw+Pej5BEA48li5dyvz585k/31R1Lly4kPnz53PllVfiui5/+tOfeO9738tRRx3Fn/3Zn7Fp0yYeeOABXv7yl0/oukTcCHslYyUTZ0JG1Wsom3uT7cumP5l/Z7ws379QKKBsXo5r/8gPRZqC7XGjVR2FRlmHNHGtc0MXyva56bQOj+OMrBTKxE29abp2vRKjlEKX7HTxWi0PSxWo5c5NUujMuxTrbFxDyTQknF2os2ZbREftFABu/+PtdL7RiJvKA0sIArNO3/dz58arVIDWHjej5dtkNHcpDm2uUL0SE4UJg8seBCA+0qen2yRnTzr57ebAJ7eRJJUxzysIwoHDqaeeavryDPu55ZZbKBaL3HnnnaxZs4Z6vc7atWv50Y9+xKtf/eoJX5eIG2GvZEeVUmmljGPnOmW5OWxZYf6demTLMXWbDByVeklwCLWTj17wdR2NRtnnNc8KIDpz5yarj3Lckd17J3UYYVFtysELrQtCyZyrXg/zsFRRhblzEwcdeZfiNCtvL04C4LWzFL6r+NPTx+Lg8sjGR1hz9FTwfaLnVxG45tzacQmtc+MN2b42PduvlMrInRtr7HiBzQXaVqf2+BMABC8/Cscx5+858VSz3zroW/fbMc8rCILQbkTcCHsloyUTQ1NYKg5zcZM7N7m4OTzfvxYlbIvMX2/VM526Y/7YZ2GpYlhF27//2tdUsdPD6cydmy57mygvYji5c6OyAm/yEA9W+IRRkoelSlRz5yb2O/MuxdoKk8y56dFDXLHgGHTcQzhg7NvbV91N1+tfD4Bvk/HC1L43nZLaz2ZHlVIZU61zExbN+yt2mTUOPL8RvaEfrTRdx78239+fPh19UBGlFX2/vXfM8wqCILQbETfCXskOw1JxRMnOmNq2bZvJ5N3yrNlp6hH5/r9duZVBOwBT9cyg7po/6F2pHTRZHiA1xUqkHYqaDbdUdSl3brqVuU0cL2E4k6y4qSnQTTk3AKrDOCNhkubOTYeq5s5NEpSIUiNC0rIN81hxQ62PC085jLNPnEO41UwV/89n/x/e//dms5a+PgDq9n2UCKkNZOIm2G6lVEYWlqrb+VIF+162/cF8jvFMTe/ME1uO8V52KACVP/xuzPMKgiC0GxE3wl5HmqYtCcXN5KXgUURn0fxx3rp1Kwyuh6gMyoVJh+b7L3lqU14qHRcn585NJm68uIIzzVRA6U6HWmrOX6GUOzfdNuzjBCP7u/TYnGCtsJ5Po9cNnQVSpYhS8pybLlXOnZvIKxKmJo8nHbIjJUqTzL/VbSil+Py7j+XkQ04gqc0mSkO+P2UdTm9vnl9Ts+XqJUKqNrTVPBF8TOdm8wqmrrvPXMq1XYptWKrv2TVmfYdqunte2XJY6ehjAUhWrB39vIIgCHsBIm6EvY6hoSHi2CTlTpo0Kd+epmlLWKq3x1RCbdu2Db35abPT5EOhqQrp/qc2UdZ2ArbbSc2Kmw6rU7yogjvbdsns8Am1EQj1uJifo1sZcaNG0QnFeJAiRkhYSUNYM4+crhKR7wOKJDZr6GEwd27qyidMbF+Zik1Wzpybah9oje86XP/+E5iWvBWAm5++He897yGwydT12LhJRSLq5UZ34u2GpTY9BTeeykFP3ArAkF1D1qV4cFO/WdOh3RSC1hbp3a80LpLzQpUo6h95bkEQhL0AETfCXkfm2kyaNAnXbZQxl8vlvDuuiiMm25BVvV6nsu4ps1NTSGptX5WnNw5RsyMyy4mbdzTusDaLH1dwJts+Ol0BCZm4MaLAUSkdjjnGKYxSUl3dxiR7jO0DmDs3Tk83oa1q0olpaT5J9aOzUnDlEmXOTdXm82TiRidQN0Kup+jz/XP+DyqeinYH+ViwjYLt7VPesAmAooqI6sbN6WzKuRk1LHXvFRAOMtUx5x+KrLCygq9SNg+Cw18y4tDOl5lyT2+9YnDr4yPPLQiCsBcg4kbY69hRpZTvKBTQM3lq3sdm67rnzE5N4uaBp80f/oOnm/MMVmokvnFhilYkeXEVZcNbqrtIooxQiRIjSgpOSuAYF8cpji5uepVxTMJsBIPNuXF6e3Nx4yojoCazNR+/UNVuIyxlxybgl8Ar5ufOmD2pi4/M/xAAz/XcxwsHHQZAbfMW836IIa2jHIVfcqkM2gGaw8XNpidhxS9AuUw7zzg3A9pHo0ltGK6emBEQpaNG9qHwZs5Ed3qoVDHwxP+M/DwEQRD2AkTcCHsdOxI3nhUmnZOn5Pts27zR7NRUKXX/U0bcvOrw2fnx5cA4KIVM3Og6WFHidHWgVZkEp6l0O8a3oawsQbiF6jZ6seImSyjO5ktNmpKLG99tFjfmtqtqp5FQXGvK57Hl4M3iBuDC49/H9OJsHG+I30w210xcW+nkJGhdp9TtUxuMQJsuxKWuYbO3lpt5URz1dqbMPQaAWLnUFUShEVj1wiTSgqbz0ONGvF2lFM7hZrRE5QlJKhYEYe+k7eLm+uuvZ968eRSLRU444QQeeOCBMfe98847edvb3sZBBx1ET08PJ598MvfeKyWp+xs7rpQyrkRXk7jZ2m+b4E0x4iZOUh58ejMAbzrWjDRI05SBYeKmY8ZkUluGrbo7cZwyVTpQtlKqqBvixulo5OHkNDk3NRuWqpVNiMmfPLUhbvxes2YaCcXVxGk4N80tdJoqpprxHZ//e/KnzPvrftL8a8/f4cRoXaOjw6HcbzsV9wYoR7Wcgyd/av59xV9Q9F26C7a3jdLUK2YRYdBDNAu6ulr7BWUUjjCfcfTMc6O+LgiC0G7aKm7uuOMOLrvsMj7zmc/w6KOP8oY3vIEFCxawatWqUfdfsmQJb3vb27jnnntYtmwZp512GmeccQaPPvroHl65MJHsyLnRNVNZ1Dl5Sl5Nta1qk2hsWOp3q/sYqMX0lnzmz51KZ6dpxddvS8F927Sv6yWHkA6aUJTT1Y3rVqjQAZEVP7reEDedpZGLrW5jkg1lZcMzG+JmRi5uSsVuIh1QpIotvqKaOoTWudFRU8grTypudW4ATpt7GqceciqRa0RRbJsOllRE6oTwp+Vs+q9fmc9neEhq23Ow+UlTUXa4KSmf1m3eU0VpqgMhCo1WLtWZPXR2HM5olI40Q0n1C31onY66jyAIQjtpq7i59tprueiii7j44os55phjWLRoEXPmzOGGG24Ydf9FixbxN3/zN7z61a/myCOP5Itf/CJHHnkkP/nJT/bwyoWJQmu9Q+cmqRgx0eLc0GNyVXoOBuD+p4xr8/ojp+E6Kh+gWXXMH/Nc3Bx9BHrQiCWnsxvPMeImc25KST3PuVFdrUMzzQkbYalseGZtaBRx09VBPe3AQeO6tgFf4lDNwlJxU/ioqRx8NK48+Uo6SiYvJsBcq0NFpCrCL29h3e0/Mu9tuLhZ8Qvz75zX5NfIuxQ7kKaKAJOUHM48BNcdRcwBXUebCeXu+pR6ff2o+wiCcGCwZMkSzjjjDGbPno1SirvvvnvMfT/0oQ+hlGLRokUTvq62iZswDFm2bBmnn356y/bTTz+dX/3qV+M6R1YaPPyPYDP1ep2BgYGWH2HvpVwuE9oy5+YycGjucROiHIdST0/DuWGSCUnZwZZLbL7Nm440+SFZ4nGozB9sz/7X7z3hWLTtMeN09OCrCmW6cnFTiGsN52YMcZM5N0PKHFO14iaYenBD3HQW80Rdz23MAx+wj9KkKZ+nuRx8FA7qOIhPnPwJsz57zZKK0E5M79GHUi+Yz80f2tR64At24OW8N+SbptrJ4HU7N6tQM4IqmT66awNQPOIoANzNMLT1iTH3EwRh/6dcLnPcccdx3XXXbXe/u+++m4ceeojZs2fvkXV5e+Qqo7B582aSJGHGjBkt22fMmMH69eP7NvjVr36VcrnMWWedNeY+11xzDZ/97Gdf1FqFPUc2dqG3txffb02GbfS4iejsnYTjuLmwHaKT+uQjKADbyiG/W90HwBuPahU3kS7hanBt75quQw5iw5ARU05HJ65KGKI7704chEP4HVbcDBNbQItzM2jDUkmUEoUJwdTZubgJAk1YNw5QSdeo+B5EukncNLkkubjZOubn9KqDX8V/898ExLjEBCRoV+G+8TiIAqhC/Mv/R/3d8yi8ZJ45aK0N385+VX6ead1mfXWnhkqLFIY2Q/EQdO/Y4sadNs1UTJVjhp5ayrTZbx1zX0EQdh6tNXHYnpCvFzgopXa8o2XBggUsWLBgu/usWbOGj3zkI9x77728853vfLFLHBdtEzcZwz9ErfW4Ptjbb7+dq666ih/96EdMnz59zP2uuOIKFi5cmD8fGBhgzpw5u75gYUIZKyQFTQnFUUinFcWlUomSm1JNHLZ1vISZwIMrNqM1vHRGNzN7jaCoeyb8E+liPldKkVIoeVC27fcKZt8+3dtwbsIaqtNOBZ8ydeSCq9voVSaUM9Tkg9aGIrp6GqXgvq4RJ+b8BaqU/V5UlGDToNGU0HGC8lzosNcpbxnzcyqVjBhyFHQ7NZQC7Xn8y++/zOt63ktQhWBwE2s/9SkOu+17qKQKm20voNnz8/PM6DZrqvpQrG2hYJOYU3/se0QphTN3CvqPG6mueAJOHXNXQRB2gThMufHj97fl2v/7a2/CH62n1y6Spinnnnsul19+OS9/+cj2EhNF28JS06ZNw3XdES7Nxo0bR7g5w7njjju46KKL+Pd//3fe+tbtf2ssFAr09PS0/Ah7L2ONXYiiiFrNhE6cOKKz6fUpngkrbfVmAY0S8Dce1eiuu6K2glQrIgI6bL5NwYtBgSobIaMD84d+C1Mbc6WypoGUUcEoDfEqm5mG6dSbJRSDETfK8wiLRtx40RCR7VLcQQ18c+tV8nM66ME+87DTuE2Uh4WVmvA8D8c1301md9gJ4a5HrAeo9BsnylVD1B57jK3f+Q6sewzQJiepu3F/ZeJv0PfprKyjUDdriGuTxrw2gD9vLgDhs89vdz9BEA5svvzlL+N5Hh/72Mf26HXb5twEQcAJJ5zA4sWLefe7351vX7x4MWeeeeaYx91+++184AMf4Pbbb99j9paw59hRpZSjFKQJXZMbr09Ot7CGTrbRg9a6kW9zlHH0Up3ySN8jzMPMScq6ExcKIclAH8r2z6vbBn99NJybbqtXHLcy+oLLm5lqncaKahU3QC5u3NoAcWgng6sq2oqbuNRNnIZ4TkC6dQvO5KnQZZ3I7YgbAOUVIImZUkghNuKmuuk1dIbms7n7VVv54E9h49e+Ttek91KAFtcGGuJmwPHoqj9PIeyz6x974CZA6YhjCFlKumrLuN1WQRDGhxc4/O+vvalt195dLFu2jK997Ws88sgje/x3RFurpRYuXMi//uu/8u1vf5s//vGPfOITn2DVqlVccsklgAkpnXfeefn+t99+O+eddx5f/epXee1rX8v69etZv349/f0y42Z/YUeVUoHtTtw5yb4eVZkSmSGOW+sef1o/yMbBOkXf4cTDjLvz67W/Zk28hpodoNmTxPZcZWobjfOQFjRDqdneT0/u3PTYG9KxYa0WtIbyJqYqs7Zq02TwajnrGWPzdSp9xKFxWDpVBewvkLjQTT01zlOyrc+ct9M6TuXN2/2sUjtKotcOvtSux0zvIHw7Bf2382MefYmCMGTNv/4nWjNS3PQYcbMtdeiuP5c7N5X+mO3RebSZFu6ui4iiscNngiDsPEop/ILblp/dKUIeeOABNm7cyNy5c/E8D8/zeP755/nrv/5rDjvssN12ndFoq7g5++yzWbRoEVdffTXHH388S5Ys4Z577uHQQ81U53Xr1rX0vPnmN79JHMd8+MMfZtasWfnPxz/+8Xa9BWE3M5a4yQSsmxqbpXuaFQBbn2WKDQttHazkrs3JL5lK0Tdi4o4n76Dm1qhp48z02LlMbrKNcNMLAOhuh3JsREpZN6qlejHncP1R/tjXByAJmcwgSgEKrAlEbSgiTVPqvnVuBjehrbjpVpXcuQmDTuqJcYXSTKQ3h6X0yEnkGZE1XrsdG1ZzPd7xEpOvU1aaD738K/zHGZOpe1BfOUjf86UxnZtyoimVN+biZqivns/xGo3SEUcD4G1QDA08PeZ+AGk9Zuu/P8m6L/2WbXc+TRom291fEIT9g3PPPZfHHnuM5cuX5z+zZ8/m8ssvn/AGvG1PKL700ku59NJLR33tlltuaXl+3333TfyChLZRrVapVo2LMTznpq+vDwAVmrybnqk2dLP5aSZjXtu6dStLoyzfxgiE9eX13L/6flInpe4YodFlw1Iq2kp9kxEZusejmmwjwFReqdj8YZ9kq6qcwiiVC9ZZcQtdTPECtpRDIsxNVRsyOUJamfM7Wzeje42LM1n15eKm7nVQT8x7TvtN1RUdVrgldTM8szh6nlhVu3QDHSolxYibzhg2AQOO5of3Vrj23K9x92Mf4Oz/jnhheS89XYfTnCrYXfTpDFwq9YhSfz+k5tW4nhBWYwod/ihXBv/gg9G+QkUwtHIZU6adPOp+Wmu23vYnak+aKriBR56hEq9k9l+8FaXa3iBdEIQXydDQECtWrMifr1y5kuXLlzNlyhTmzp3L1KmthRi+7zNz5kxe+tKXTui65LeLsNewZYsJb3R1dVEotOZ8ZM5NPGRyb7qnWXdjywqmWHEzMDDAspXmHG+y4uY7f/gOqU45adZJYJvSdf7/7d13nFTV3fjxz713+mzvuywsSxcQRFDBBmhEiTGisSYx+NMYexQSjRqfqHmSqIklMdZEIxqNGgtqHhHESFEBBSnS67K07XV6uff8/jizA8vu0txlAc+b17yWmdvOvXd35jvf00Ti1z7eTKxqq/x/mpNoIgMUinmSx83QEjOCu9tJ1foT81l5c8hODIYXSbS7Cftj+P1y/BtHJIJVX4cZleeUrdUlGxSHdAcRK5G58Sdm53Z4wJEYU2cf7W58cRl4OU3ZvkcYNgKN8v+mS6eqOcJbC2wMvvwitueAPayz6S9/abOf/HQXecEGjLiFrkdx2GWw5atvpyouQTMMtB4ZAAQ3fN3heuHVdTKwsWnEzt3M5rFTWJdzI19+fhGxmKpOVpSj3ZIlSxgxYgQjRsis8NSpUxkxYgS/+c1vurVcKrhRjhgtVVJ7R/qwO3NDSGY3ktVSdZtJIYjdkFkChxWiONNNaY6X2lAtb254E4Brj7+WeKJmyY0MVOJWmHilrPbU0r3EE21nInFZVePQTTyJgEh3t5PkbAk8vLnJwfDCWkubmxiBgCyrMxxB1DcTi8r95lGdzNz4hS2ZuTEDe0wwlWx303Fw0xCT52G0BDc2GyG/PP5JQ/PQNZi+bCfH12h8MU6evDV9JoGlS1vtpzDdRS9flbwm+QKPXQYdzbWhDo8NYC+Vo0FHN5e1u1wIQfMcWe1nnBGhjIcQhjxHf3QVS1b9goilpm9QlKPZuHHjEEK0eexd89Ji69at3H777V1eLhXcKEeMlsxNe2PctGRutFgUd1o69pYu1HWb0ICsFBk4pGoRzhyQi6ZpvLDyBSJmhGE5wxhTOIZISH7Au4WseomYUeKJ2cT1jFTQm+WQeIleTV4thjMxXYOe0s6M4HsGNy3TGCQSPCFfNBncuCJhtKYQsZgsYy7VyQbFfmHsbnMT3KONy366gzeHYzQl5qPSYjK4wbARDcn99uudzk3j5DxbO1cv4HpnHfOHyfU33PMLRHx3G6L8tN3BTaxQ4DHlffDVhds9dgtXP5lWNsur210e2xUgttMPNp3K3FcRIkZ22jjcy36LKQyCDXO48rN/saDBv8/jKIqiHCwV3ChHjI4yN0KIZHCjx6KktVRJAdTJut6WNjppWoTxA/NYX7+e19a9BsDNI24mXllJOCQbsjoSv/bhWAyrQWZrjIx0dMMnp15INHhNJYKjJXOT2s48Sy29mVJyyUmRwZY/MdZNsCnaKnOjNUeJxx2YwiAVP8IhAw1f3Ej2lrLCe1R97Se42dkQIpTo/RWLRWiZzyGWCMxSs1z8/Oz+DC5MY5DYTJ5p4pn8XXwucG2tZNe055P7Kkx3UdIsg5tIphu7kOVprtt35sZ7nOwxpe8IE4/72iwPLpNBjxjWSH3jPECndNC9/GroED7WzgXg9Ph0rllVxs5wtM32iqIoh0oFN8oRoyVzs3dwEwgEiCcyDVosSmp24oM/WJ+cosBIkwPTZegRRvfJ5Pdf/B5TmJxTcg6nFp1K86xZBDQZgDgTmZtgLIrVMmlmWiY2w4ePNLSIrCpJMcO4dE9ieWrbAgcSGQtPDtlemblpTvRcCjRGWmVuDJ8MmEJWKg6iGA6ZbQnEdYItDYpje1R97ac7+M6GULJre8yMoJvy/5Ylg7DUbBcOm84T3+9JsSb3YUv7MTPPlw2x6//6FLFdsgt9QZqLEp8cTDOUloOWGPinuXbfmRvPIDlukK1Sw+db12qZsATBFfL6NPeaD0Be3kTebExhowMWRL8LQmM4y3HGtvNwWcU+j6UoinIwVHCjHBGEEB1WS7VkbRyGgYbYnbmp2yx/pvVgV6I9S7HX4r87ZrCsehlum5s7T7oTAN+HM2k2PBgCbIngJm6FsHwyaLKlZuPSfTSSgZaY08UTD+AyvAAYOe1MvZAICEgtIDuRualPjDkTDZv4mmV1izMcQfeBoUeJmjJIctqCyIGSNeqFDCas2B6NqL2J3mD+9qt8djWFCCEDGlOLoFkOEDpaYmLQ1Cx5PfrFZTftzVYh983aSb9Lfs3aYrBF4mx54F4AClKd9PTJ4/jdPRCJBtf7q5Zy9OqFsGnoEQ3/liWtlsV2+rF8MXAL6qL/BSCv4BL+Wi4zRJduycRTJ4diH8MC3qxsoDzUcQNmRVGUg6GCG+WIEAwGiUTkh9vewU1LY2J7ou5ld0+pxPgq2X1ZXScDhDQtxONfPQ7AjcNvpMBbQGzXLoIrVtDo8OK1Wqp+4iDCaM2J6RXSM9E1iwaRCRG5L2eoGYchgwQjv6BtoZtl5oO0IvJSZWDSuMcoxc1NsqrGFQmjmRpemjFjMrhJ2WMgv4ZEkUzTu3vfKYkpEvztTyIrq6USmR5NYODEMGUZ7E59dxfuxGSZNamDicYtnv7Ixlc/PoW4Dta8hTT/978Ux5pwm1FiuoFP70tMJEYtrgvtc6wbzWZDK04HILBueatl4Q2y63d08BZi8QYcjhy+NAdTFY2T67BxmdtLWuUpAJxtLEQAr+xSgwEqitI5VHCjHBFasjZpaWltZgPf3d5GBj/R6e+x6957Ca9YDEAsow+Ld8lqnnjYR32onr7pffnx4B8D0PjOdHx2D3HdRkpiXilNj6IJgdbSltUlA4tK8pOZm9RYSyYhipbeetwdoFVwk5yjydgdDPh9ia7gQu4nJe6DiKzm8go/IjE5XUNiRFCBFxFMjHWT3iNx8jvbvV47GkOYGKAnuoM7nBiJiTndKXu03UkEN8NHn0WvLA87GkKsi13EjFPksbc98Buyd26R+0zJo8nXk5CVIa9r2CQS3PdIxfY+coLN6KbNrV4Pb5TBjT9f9szKyzufVyvkffxRYTapg7JJqT4RTdjIMrfSQ2zntYp6YlbHwZSiKMqBUsGNckTYVzfwlmVmgwyAtKXLaXrrbcr+OIumrW62WIU0mjZMNDSh4Y17+fXoX2PX7Yh4nMa33qLOLQfCy2wZndhp4hIxNKEhdEEoMQllDTloicxNRqLLuGE0o+l7/amY8d1tblKLKMqQ1UFBffcUDP6ADG7chgwQvFEfRjgR3BBAJDI34ZR04lZifJqKRDCTViR/tgRQe9nZEEqULdGOyOXAiMsyOD17jAC8UwYX7pJRPPvjkXgcBl9ttrHg9HOpTgejuh7fv18GoDw1n5pAPj4rD4+eGHRvP93BXf2Pk+XeWpV8zQrHiW5rRiBo4gtZpoxxfNooM1lXFGbh6p+JEffiqRsMwGnGMmpjcRY2qp5TiqJ8cyq4UY4I++oG3rLMisgP2p5X/YSUcePAFOz6IoO1XzUiEAQdMutxVtZZnFRwEgD+efOIV1bSkCODhcxENYvhFHgNmVGx0qAxmpjCgaxk5iarJbixt/MB768CYcnMiTeXTI8dp03+OcWQAU4wnAhuXIk2PNEgrqDMrqTTmMzcxNyZhEz5wW9WJQKmtB67j5MYx2ZPOxtlmey63J/TZccwZXBjaylvc4Ws1tJ0KDiewUVpPPvjkdgNjRWbT+XlCXKgwNDaFQCUpxXQiIO4cOLVZUC5v0bFKcfJ66zvjBKNyobLkc2NYEG8ZxXReC2G4WFBrD+mgKEpbnq7ndhy3BhZLrw1slHy6cZyAP6vpnGfx1MURTkQKrhRjgg1NbLLc25ubptldYllWjSC0+GkeOpUip94jPQ+IRAavd+dQX9rNg12+eF6ZsaZgGykXPvc3wAInToegPREmxvdJfBosvuxyNDxxeUxmkQ6JLqC5yQa7BrOtsFFMqOSWgi6jqZpFCaqpiKawNKjydmyHSkyK+QO+vEEZfVXtlYHzkTmxplKKC4DIbNOZkzw5IBuBwT4WvckCsdManwyMHOSCGhcRjJzA4lu2YkqKXKPA4c87pkDcnnk0uFowsV8z/l8MUBLzl+1LTWfcIoMuJy6LM/+MjfuAbJRsOwxtV6WL9HeJtRnLQCZmafyf7VyLJ/zc9OT27oGZOKtlcFNdnQVbhHgg5omzH2081EURTkQKrhRjgjV1TJjkZeX1+r1WCxGs09+WOvRMFklvQHQ6jdTOKoBd0EcZzzGvXM/JoIcsybYKD9I/f/9L+Gvv0ZzuQicIDMMaVZiLim7iSdRFUSqIIQMjPyxFLTEoLn5emLqhd2zMezm2yO4SWhpdxNMBDcgp5IgKwMATyCE05LVY0VUJMe68etuQmYiuGlItLnRdUhL7HuvqqnyOnl+qS4bRlR2QddtVjJzY8YTDXN3JUYi3muyzAtP6MFfrxwB/lFMO60QPRG/lKfmE3DJ62PTZMamoSrYzsnv5ujVU/aYimr4N8seU+GNjfK8UmRw5c04g08TA/Wdn5uR3NbVPxNHKA9HuAgwGW2soi4WZ5GqmlIU5RtSwY3S7WKxWLJdzd6Zm9ryctA0jHgMzYyTUZBoi1K9Fk0H7Tv5VKS5yGsWfHep/CCurq7GbGyk8n9/B0DWT35CRURmbFJMGVBEtQguU7aF0d1xYno9AghGZIbDY8RIbekGntrO1AvJxsS7g5vCdBlc+HSBlajySktLI5ouq9pc/ig2hxy/pqe2DeGSZWnEuTu48e0xmF1aceJYrRsVl9XKAKg0x4sZkGWztDh6okFx2J/I9Oz8Sv7s0Tq4AfjesCKen3wKtsbvoJkapqFR4c2mKdEgWku0G2qs2HegodlsaD1lY+vg+q+J14Yw68OYzgC+2CoAVmojiQlBf4+TAV5Xcltnv3TQNbyVxwMwwSHnqJpZq+acUpSjxfz587ngggsoKipC0zTefffdVsuvvvpqNE1r9Rg9enSXl0sFN0q3q62VWRO32y0zHXvY/uFMAOyxGBqQkZ8IJqrXADDTyObhy0yCTui/SVYt1VRVsfWaa4lXVWEv6UXOTTeyvUEGPp5E5sYXqccdlyka4bTQbbUESMEKy4AjUwvjsclu20Z6O6mbhnL5M6NX8qWWaql6o3VwE/TKD39Hs4kjLQ9LaORQmwxu6kw7ocQIv6Z/jyqZlkbFTTtaHboluOmV7kZEZHYpGAygoSE0k6bGCrCsPYKbUW3Lj5xc9JkRgwDwF9ixdIOdiR5eMU0Gdg0V/n12Bwdw9JXXILpxU7KXVHTgZsDC6x3ArGYZ9J2Xk95qO91pw9ErNVk11TO2GE1YfFjbtN9jtgjETZ7bXs3dG3bwfnXjAW+nKErnCAQCDB8+nCeffLLDdc477zwqKiqSjxkzZnR5udr5Sqooh9eeVVKatrsbs7AsKpZ+BSUl2BLjx2QWtAQ3axHAG+kN1HgFH11/IpNe3IhumpiGQX15OWnp6RT/9a/oLhfb60PoAuyJRsJhy4crJPdpOly4bDXsIh8tMUVDhhnAa5NZFqMgp22hGxKTRWaWJl9qCW5qDQtTl8FNakoaTfZM8gBbk0AvSCdkZeI16jFcslrMFzNoTswvZYb3+JPMLEkca2urQ5fVygAk32FPjm3T7GsmDYgbISKhMLHKtdjDTWBzQf6QDq+9p2w5zUC8WAZ6DQJMBEEtD7CIhHVCvhietHbm1kpwDx1B9OPlWBtrCPWTVWKBApm1ycway5xKGbhN2Cu4AVk15f54ALrlworX0V8vZ0O4lDWBMENS2pnyYg8VkSiXLNvM5sTgfy/urOW72V5uzg9Qmt6LLFfbxumKcjQQQhCPdM+gljans9X78P5MnDiRiRMn7nMdp9NJQUE7Y4V1IRXcKN2uo/Y2gYULaTAT2ZVoGA3ILEz0IqpezVyPmxpvAxo2fvzjh8g/3yLjhX9QD8QnnkefG2/E3qMHcdNiV2OINEtLZDcsLD2KvVEGMlF3Jk69glry0MKyqiol3IjH0RcAW3GPtoWuTwQ3WbuDm+IsmeGp0gWWIdusuOwetuKlP2D4NZwZcWK1OWDU47U1EzY0NFNQIRJdwSN7ZK6y+iaOtaXVoVsyN5mmhkhURUWiYYRmYtoCWHYHzWs+JRug8AQwWo8btKfQ6uUAaP3cuJrChG0uGnWBEc4izaim2SygoTKwz+AmbcQZNPEitnILX8Va7OTTbCwGE6qdJ9MUN8myG5yY1jYD5hqQiT7bjqd+MP6cpXzftYpHQqV8WNO0z+AmYllcvbKMzaEIhU4752Sn8equWmbUBZhTNoOMpje5bth13DD8BnRNJaiVo0s8EuGJyZd0y7F//tJb2F2u/a94EObOnUteXh4ZGRmMHTuW3//+923e7zub+qtXul1LcLN3e5vG19+gKUN+2zebZXVHdnEvCDcRbdzGnxINdccXXELPtJ44SkroMVxWcUTPOQd7DxmUVDSFiVuCzERPKZvbQrfi2JoSUyV45HG3ix5o4cSkmUEfhmYAJkZhz9YFtqzd2ZQ9Mjd9cmRVTqMhiNtkK12H7qU2GMJKzKxgj9VhmHIsnwyaEW5ZNVWJDOJMKwMRTDTizeojf3YQ3KQELTRhYCQaPpt6hLgtgGV30rg50VOquP0qKZDfDmPrt8lDnTCKbEM2yK7TYiDspOiymq96577b3biHyDYztnqNEKuI5u8gZjZgGCnMicgA7ezsNIx2vg3ae6Sge2x4q+U+hgjZCHp/7W6e3VbDCl+ILLvBuyP6Mdyaj7fmCQBCqecRspfwzIpn+PPSP+9zP4qidK2JEyfy6quv8sknn/Doo4+yePFizjrrrOSI9F1FZW6UbiWEYFdiAseioqLk67GqKpo++YTmiyYBYIRDpOcXyG8UWxbxcnoa2+12iKfxwNifJ7crKipi5cqVyX0CyfY2+XEZy7vSDNKDsgGz5RIEUnIwgJ0UJaulclvGwzEa0Bx7ZT58FWBGQDMgfXfg0yPDjU3XiFsWZiJzYwYNgqEQ0QwDV5WJVbUNjyGnVsiknl2ubPDHCaRmErMi2HUn8W1l2AcN2R3cNO2AeARsThqDUWr9stGxsyFGAI1UbyqNvnpMWwjTFkRYTpq2r5Z/3T1O7PDax6uqoCmM0AWpQ0dzYlN/dq6qp9nyAU7surxub368kb5jCkhztZ8BMlJSoEca7GwmGlgLclw/srJO46N6uY9zsttWSQFouoazXwbeDTIodYRWkYqPVX7YHo7S09U2Y7QzHOXPiTmqfte/mHBoK48sfgSniDPcUc2KaB6pPe8itPk6Xlz1IicXnMzpPU7v8DooypHG5nTy85fe6rZjd6bLL788+f+hQ4cyatQoSkpK+OCDD7j44os79Vh7UpkbpVs1NzcTCATQdZ38/Pzk641vvUXA48a02TB0DS0WIadnbwCqyz/lbxmyS/WotB+T4do9Y3dLgLRncNOS6chNZG7SctxkRmTbkHgm+NPlh3aVlYuWmG6gCJlRsTkDbQvd0t4moycYu78f2AydXlkeXMRBN0FAMDHnVTBTfkiHt6wnW5fnUaBVItxy+1BKHv5YoyzTjkTZU/LA7pWDBTbKDMuaCpld6ZnlJlgls0PZWTITZBoh4vYAlsNJY8t4OR00JgYIrVwpj1cgSM8ewZCeMk3coMkqsvUOWWZvk58f/f0LGgLR9ncEOI6TgZjVuA1/+nIAROppbApGsGkwLqudWdUTXP0zsYezcUZ6AhaTPHKG8VkdZG+eKK8iZFmcku7l+zmp3L/gfuIizlk9z+KVUePxGDrbYy7G9L8dgN8u/C2heOvxenzhGK9/uY2n5mziiy11qiGyckTRNA27y9Utj4Npb3MoCgsLKSkpYePGjV16HBXcKN1q507ZzTkvLy85p5SIx2l88y0a0zMAcBsGGpDTUzawfXz7R4R0nexQKlPGXNFqfwUFBWiahs/nw5cYH2djlaxWyTHlr3t+zwzSw40ARNN1YplyeW08Fy0uAEGJJj/YjRSTNmo3yJ/Z/dosKs3xkpoYI0a3nDQmApBAov1IvGw7BV5ZVVPKVoQn0R3ckY4/LssUr0oEJpq2O3uTmAF9bYU8pwE5KYT9MdAgI0M2nLWcQSw9guVwUR9xyjF49ujNtbfmLz8BINbHICXlOPrnyfY+NQ4ZcJl++Q0uw3SyakcTV7/4JcFo+3NNuQeNlP9paMRvyp5sS6zhAIzJSCHNZnRYDmf/DAA8FUMBONWQIybPrGkb3FRFYrxeKbNud5YW8FH5R6yqW0WqPZV7R99LrtPOtT1kA/AttlMp8BZSEajg9XWvJ/exYHMt4/40l7veWcmfZq3n8r8t4pZ/Levw3BRF6Tx1dXVs376dwsLC/a/8DajgRulW7VVJtUyZ0JT45dcjsmojp1cJy6uX83+iEYB+4Qkc3yOj1f6cTic5OTmt9r2pWgYvqYnMTa8B+WT4ZAYkmpWC4a0lhg1/QGaD0m1Rsu2y+7Ytq52GtFWr5c92eiH1zvGSnugpZcRd+GpktqPJKRvTWjvqSc0bKMuvbUJ4ZUBXITz4YzKoidftkWXIletSLY+5NpG5KU5kVTLzPdgTs4lrngiGzQBdp8rKht5nyACpA8Gv5KB7tuNL0HUbxxXK869weYkhyAxlEdMjIOz0dthZsaOJm19dSjzRyHtPziwZyFguGRympAziwybZKPGc7LQOywBgy3Bhy3WTkpiKISP0BZqwWNjkpyHWOuB4bnsNEUswKs3DyWkunlnxDACTh0wmN9F26oaeeXgNndWBCGcO/CUAz698nuZoM1+V13P1PxZTF4hSmuPl/GGF2HSND1ZWcOMrS4m1c26KonTM7/ezfPlyli9fDkBZWRnLly9n27Zt+P1+fvnLX7Jw4UK2bt3K3LlzueCCC8jJyeGiiy7q0nKp4EbpVjt2yDFc9gxuGl6T37IbB/QHIFZTCUB2aSn3ffZrAC70+Zl4yhXtplBb9tWSFdpQ5cMmoCVMye6RSmpi4slwbj5u504qKUQPyg+2AuEnwyE/KO09204HsTu4Gdpm0YD8FDI1GYzZ4l5iAcDSaRKJAGRXGFdBLiErjVyqIdE5qiZqp7GlWqppjz/LgsQxKmXX6pbgJifxmZ/XO41YkwyQYgTJSUw86jPSCRWc3LbsCVY4THyDvD7eUacAsit7bqoTC41qwyLqKsC0ZIB4Xr8KXHadOetr+PX0VW2qcYRVgLBpxPrJ1z0ZZ7CwSQaV7XUB35urfybuxv7owoMVr2e8eyemgI/rmpPrNMTivLRLjon085J8Zm6dydbmraQ70/nRcT9KrpftsDG5SAa4CyMl9EnvS3O0macW/5MbXllK1LQ4e1AeH952Bk/98ERe/ekpuOw68zbU8OePN+y3rIqi7LZkyRJGjBjBiBFysNCpU6cyYsQIfvOb32AYBitXruTCCy9kwIABTJ48mQEDBrBw4UJSUzuuqu4MKrhRuk08Hk8GNyUlssopsqWMwGefYek6NYmZuLWAD3daOq9VTGeLbxtZpsl36zM5f1T/dvfbq5esitm6dStNwRjVvgjZpuwG7kqx406146yU2ZVATgEu3c8O0RPNL9uaZITqSLXLqh57v76tdy4EVMlql/YyN0OK0pPBjRH3Aho200NjPDFPlU8jFN5MNFKEgUWaowFhaFhCY4slt4sFsxBWyxwQshcRVasIRU3WJ8aMSWuQ1WV5JWn4EgMYx8wI2YkeZJbTTa1R0uG1D339NVpcYKYLMgaOk9dZ0xheLAORerfcf1G9rBraUraZ749dg67BG0u289ScTbsvSdwistGHVZxN+HhZ7jXGaEwBwxITZe6Pc2AmmrDhrZfXdKIjUTW1R7ubaTtrCZgWg70uxmd6klmbq4dcTYqj9eCP1/fMxalrLPWF+M6gKXL7+X5qfBEG5KfwxJUjcNllVdkpfbJ59NITAHh67maWbK3fb3kVRZHGjRuHEKLNY9q0abjdbmbNmkV1dTXRaJTy8nKmTZtGz54997/jb0gFN0q32blzJ/F4HK/Xm6xKanjlFQDiZ59NNBbD0HX0SAh3r3xeWPUCAPfW1kPB6ckPp72Vlsru2Tt27GBZuezO3Csu180q9BLZtRU9BEIX1KTKD94N9EcLyHRIjr8JXTPQCGL03CtAaNoOkSY5G3h22+CqX56XrJbJmmKJ6RtiXgwdrHwZ4DQv/RhHXP5x51GNSEysud1uwxIWlkjDqkhELAWJ4KZuE8vLKolbgsJ0F7EdsqFzdpGXxooQRjwxO3iiobTlclPT0PGM3k2fzgIg2g8yMnY3Oh5enAFAfaa8LropMzGFvr58uHMa3zltGSB45KMNvLtMljG8qRERMTFH5iNcoPvhzSZZpXhBXkaHZdiTq28GmsvAu+sEAHpG5oAQzKn3ETItQqbF8ztk1uaWknw+KPuA7b7tZDoz+eGgH7bZX77TzhUFMkD9NNyDIu1sIk1D0TTBY5edgNfZuqPo+cMKuWRkMULAr6evUtVTinKUU8GN0m3KymSvo969e6NpGmZzM42JeUl8Z8iuux5NoAELrJVYwuI7vhjnBEMcd+r5He43MzOTtLQ0LMviq9Uyw1Ca6Aae3zuNhsUfAhDL12hOkdUe663+6E2yfUz/xCjGdlcdmr7Xn8j2L+XP/KFga9seJxoK4tLiWAL8iVnF7REv+cW90AfJ6rLAiiXka7K/dIm2DStFrteU1RtfTGYNouvkDNuk5oM3D4TFktXytWF5qUQCcWx2nWgojhDg0jIAEI0yE2a6vVSXbe7wGvnmysbExqje2Gy7sx7De8r9lMdloBdLLMsN9MAZ87Cw7g1GnfgpYHHHWytYtKWO0EoZdMROlNfKuVRnUb3MMH3/AIMbzabjHpxNSvWJaMKBGd7KKMd2gqbFpw0+3qispy4Wp9hl57s5Kfztaznb+/8b+v/w2Nub2RRu6pWHocH8Wh/V2ycA4Mr6gqLsdhqJA7/+7nFkeuysr/Ix7fOtB1RuRVGOTCq4UbpNS1fAlkxL/UsvI4JBnP37s92UH0BmnRzPZHN6PV4zlz/UVRLX7GQOPLPD/Wqaltznzm1bAcht6SnVJw3/ks8BCPRMISW9FgFUhHqgxQUGcYbYZNWMIyvWduflC+TPklPbPfa2bbLLdqNws8smt3dEU8nv0w/3UJmFia0to1e2rJ8ewXJEhgySdjpzaYzKAQ1jiXFcAOglJ5n7covMQpUkRhwu7J/BzsQM3AV5MlPib6pHExbC7qR8/bp2yxirrsbaKNsxpY+b0GrZyJJM7IZGVShKg27hzyzFE6gEdG5Oux6A9aEZ9Bs8g5gZ5+aXlhBYVYtlhPFlrQXA86XO0E3rGZ7qpuQAqqRauIflYphuUurktbnYsQiAd6saeGabvC439MxjVtmMZNbm8oGXd7i/EreTi/IysZX5aPAJ7I4gRvaHTFs1rd31M70O7p4og87HP95ARVOo3fUURTnyqeBG6RbNzc3JBr8DBw4k3tBA/bRpAGTeeGMyq0NDLVGbRSwng4nlPXALgdl7LNj3Pe/QgAEDADCad+IQAnei/WtBaTrRlTKbE+/bm1xXBZUUEm+W1RRFsQoKnbLKyNGvncbE+wluysvlhJqVViprHLJqQxdpFPQdStrIsQBoG32kFg8gJpwcp61BZMgqs/Kom6qInNE7snOPapHeZxAUTr6ok1VPuYlpI4oHZbJ9rcz09BvUG4BdVia5mux11RAI4quvbVPGpo9l5irayyKnf+sMmNdpY2SJ7Cm2K1XDxIYnIoOqgvkhfn/67zE0gyrxGQX9/82JERMtYtLc+yssEcTmd+HYonHG8sVcWXBwczu5+mWguW2kbpcNoXtHPsEQcd6rbqQ8HCXLbnBZfnoyazN5yOQOszYtJnlTMLbILNIlZ+SgGRFeW/cataG21wXgkpHFjCzJJBg1uf/91QdVfkVRjhwquFG+OSFg6+cw+z5461r44Bew6m2IdfzNd/16WcVSXFxMamoqNY//GSsQwDloEJt75hCNRjFFFD0cpKFAI7zzp1wqZCNT57D9dyHs378/umHDS4RhZhgNjfQ8N04jhLZZVkU5Bwwh1WhmhTgBo062T+kRrCU90VPKeeIJrXfaXAE1MjtBrzHtHnfr1q2ADG526g5iRggNDd3Wk/SR5yCcYPigeecnhEIDcBAl212DsOuYQudrU5YtEihEhBPXr/fpfGodT1TYKM5wEd8kP6wz8j3U7wqg6xrDThmIQ7cI4iEnQ/ZCML1p7Fi9sk0Z66f/Sy4fnU1KysA2y8/oL89/Z1piROcSOeHd9mAPxlfm8Pi4x3HoDgK2FVzkakQgKM//GADddjYaGmcs/5JzXDva7HtfNJuOd2Q+3tph2MwMRKyGH7iW0lKJ9NPiXGaVvcc23zYynBlcOejKfe7PsgR/m7kBTYCZ48RXVMqwnGGEzTAvrnqx3W10XeP3Fw3FpmvMWl3F7DVV7a53JKnZtpWFb73GjL8+wqxnn2DF7BmEfM3731BRjmEquFG+mcZtMO17MO278PmfYdVbsPh5eOsa+MsJsPRlGfzsZdkyOffRcccdR2DRFzT++98AvHqei6dnyV4wur8eDbD3mEReYx2D9XKEboeB391vsRwOB3qGrKoZqMvsRsnQbKo/fgUtDmauRrNXlmuBOAW9TvaeGmYKNE3DMKoxCopa73Td/8mfxSeDt+1M4Q0NDdTU1KBpGraUbAQadTYZiOzaGMRwOmGYDBQa57xPpnUSAAO09ViJ8XQ2ZhYTivsBJ5Eli+WOcwfxoT4OgFHOMFZckJHvoWab3HfxcVl4jQB9xFYAbDly4L94ajobv1zYqozR7dsxV25DaILM77c/9PmEwXKk6FX+EEFNUEchDtNPzJHGqnv/wqnx3jx7zrMMiw1iWLgn/sy1uFLKiFs2/p5xGc0eL7mNDTTPeKKDu9OxlNGF6MJO+tbxALhMWX2mA5fkOHly2ZMA/GzYz/abtXljyXa+LKvHadeJD85gek0jFw6+WS5b/waVgcp2txtUkMZPz5DX8L73VhGIHJmD+zVVV/Hun37Hy3fcwoI3X2XtZ3NZNecjPn7+af5+8zV8/sY/icfaqVpVlG8BFdwoh0YIWPE6PHMalH8Gdg8M/yGc878w5hZIKwZ/Jbx/K/zrMvDt/ga8a9cudu3ahWEYOO3NbLhVtuWYPUJjumM9RQEZVHjrGtDsTt6ozOUnttkAaIMvBM/+qzuEECwLJNrOuKoRmknvoTk0zZIBin5KCU32LwnhorypFC1qYYgYpztl8OHObecb+4rEKLfHXdDuMdeulVmdkpIS+qbKD8Q1usw7bF1Zh2lapJx5BgCx+asZnCc/wM/hY8x8Wc22zt2bytBWAEJfyaq5pojJh7ETACitkt/I+47IZf1C+eE84KQ8+OIZ+gvZgLiyOYrNZkPYHGxct5ZYZHevqcoX/gxAdBAUDt09Nsye+uenMrRHGqYQbE6FkC9OXq4cs2ZH5ki2X389J1g9+B2/RCDYMuBVAObXnchHkRRmnjoOgMh7i4jF9j0B5t5sOW5cAzPJ2DaeZpHDOzF5jSzgni8epz5cT2l6KVcMumKf+9leH+QPM+T9uGPCQL5TnIUp4PXGXEbknUjEjPC7Rb/rcNqF287uT3Gmm11NYX73wdqDOofDYe2nc3j5zlvZvGQRmqbTd9Rozvjh1Yz+wZXklpQSi4RZ9M4bvHr37VRv3bL/HSrKMUYFN8rBC9bDm1fD9Osh0iwzGTd+Dhc9A6f9HM79Pfx8GUz4HRhO2PgRPHsabJJVF7PnyEAlYKsictNUXL4IZfkw64JCrvBegU3YcGmghwKsdfel0KjnEttn8tgnX3dARVy8tYGvGl2ETSdCj2NmVZObE0V8LtvE6KNOJtO5gc/FmbBLfrsdFCijl1uOa+MR/5Wzf7fY/iXsXAKGA4a3/WAVQrB0qZzRun///qQ3yGq35fY0LGERDsTYvLSa/EnXI2xgK48TD26gOVpCP20jruwQQoe6uJPPo7I6J1iRhwiHeO3LbUQsnb5UYjTmoengSXfiqw/jSrHTt18cFj3DcWzE0DWqq6uTDapD3nTWfjYXgHhNDf53ZgJgv/RkXK6Ohz//wYnFACxzm1gIoi7ZDqkmdwTNNQG23Xgv9k1R/LlLcaXvJC7gP+mycfIHA09DaOBcDWUzf3tA92tPaeeUYMTSeCX0K/xaKr3YiTO8ghU7pgNw10l3Ydfbn8QTIBI3ueVfS/GF44zolcH/O62U3/XvgVvXWNAY4IT+cvt5O+bxny3/aXcfbofBQxcPQ9PgtS+3Jbu9d7doOMSHTz7KjCcfJRoKUjTgOCY/8hST7riXky+8hNMu+xFXPfwEF0y5C096BrXby/nXr6eydMZ7R+38WSIeJ7ZhLZEFnxH5YiHx7Vt3jwOlKB1QwY1ycDZ/As+cCmvelWO9jL8X/t+Hu+dAamFzwKm3wvXzIG8IwWAtM6dfxc9fuIiyjWUgLL7/7mKy/NBQlEr2k4/x+vffxSpLvGntkN82F6cO4/GMtzBEHPqMT/Yc2p8nP9kIQiMlID+Ufa6tbPr7fWgxiPc0WOvZgYnOu+ELMCrk4HnnCR2bbkejHFvDx7KaDeSM3B/eKf8/7DI5oeVeNmzYQG1tLQ6Hg0gkQooVIJ9GTF1nq12e07KPtmHPLkI7VQ4yWP/Pv5MXm4gGjLfNxSqQ2ZtPMkoJxpsRpNDw9n94bp7MyIzGg4ZGaU45K/4re2Udf2YRtg9vg6gfT/FwBg+RIxrHE1254+lZLPrgfcx4nG2//QVa1CLaW9Dr+/fu8/pdflJPsrwOqiIxVrssqnfFyEltQGgGZYMuxt7zu8QdTVQP/ScAixhDtXMEmhWg0fiQxQPk70PoyRn4Gw9u1F9HcSqvj8nkc28fDBHnZ+JxfhR9BIDcnImMLmq/vRNA3LSY+sYKVuxoIt1t569XjsDQNUrcTu4olcHcX3ZG+d7gXwByUs1Vtava3dfp/XO49Sw5ltFd73zNoi11B3Uena1m21ZeuXsKaz6dg6brnHrpj7j8/ofILm49IJqmaQwYfTqTH3mKPiNPxozHmfPS35n+8AMEmxq7p/AHSURjhD6aRe2DL7Hrfz6m6h+11LwvqJkep/Kp7VTe+380PPYSkUULVKCjtKvbg5unn36a0tJSXC4XI0eO5NNPP93n+vPmzWPkyJG4XC769OnDs88+e5hK+i0XrIf3boZ/XgS+CjmA3bWzYewdrWbG3lNjuJGZwXL+p3A4d/uK2bS0iMINcrLJvpu2kNnUjHvC2ZzyzkecNORc3n/vfcLhMEYsiq25njUpx3FL4QpGhT6XgdSE/z2gor63fCfzN9ZyYsQgI1iIPZ5C3Iwxb5eOqeuELx5FinceM8X5+De60CwojNVxvncwAFua57O4vif89wF4+zp46QLYtQyc6TKY20s4HGbmTJkRGTp0KAs+l13NRzUvB+BDdwwLQe12Pyvn7KDwhjsRmsBYWE9RMELQzOBC7T3oLdvdbDAKeScgq0KCK71kBevp6XVS0JiHThSjuZzm2jDeVI0Tmh6Azf8Fmxsu+AtnnHEGmqZRVlZGXl4uaDpVupNl99xMZPZihCZw3TaR1JQB+7yGHoeNm8bJLNY8b1x2C7dy0LCozBjBDo+XncMeJW5vJBrO5B/arQCkNryG07uQ58+qIubScJTDijuu5KstWw8ocyCE4O/ba3g0TQZnkytWUkoZZ6cGOSk9gzWuH3Dfpp1Y7ewrEIlz62vL+GBlBXZD48kfjqA4c3e7nBt65nJuThoRS/BG8HgG9/gBETPC9bOvZ1n1snbLc9vZ/TlrUB7hmMU10xbz0er22+l0pXgsxqK3X+fVe6bQsGsHKVnZXHbfg4y55Ep0o+MJST1p6Uy6438465obMOx2ypYt4aU7bmHL0sVHbBYnvnkDTc/+k4r7Z1L3iYdwUx+E8KIRxmZUYxi1gIlpZRKo7kPNuyZV/zOd5hf/hbnr4BqwK8c2TXTjb/kbb7zBVVddxdNPP81pp53Gc889x/PPP8+aNWuSQ+jvqaysjKFDh3Lddddx/fXX8/nnn3PTTTfx2muv8YMf/OCAjtnc3Ex6ejpNTU2kpe17Qj8FqN0EK/4FX/5dVkGhyaqh7zwAjt0fHOF4mM2Nm9lQt46dq77At3QJ6RsrGbhDUFQPNTk5fDH6FAIpKaQ2NzNp5X8o+M7xeC+bgj97GO/+5wM2bdwAloWnfB2hGIw8IYXvBd+WB5jwO5kJ2o8ZK3dx+7+WMyCsc27Ijo7GwJPDLNryOXGbnWJjE7ljVvMVJ/KPjT/GVh4EBH+OmYyyZ2FpG3mt4VOMhhpOzSnn5Ozt2HQBdi9c8Sr0Hd/qeH6/nzfffJPy8nK8Xi+aEPiDQYyAj+FFeczKO4cPVlZwcthgbNiBpsG4Hw9Cf/NmxMx1WB5ouO1UIj3nMktM5F/rLsG2PYBNi/P7qOA0ezZRtvF/ISdGJIu87CDVdR7A4vzM39PbuVRWlV06DQbJbt0zZszgyy+/xOFwEA+FGLlpEX2WyTf+8CVZDHvgvxiGa7/XMm5aXPbcQpZuayQdncuDdoZpOjXpaygY+SqO1GqiMSf32P9IhVbM5WXruC4rzAPRd1gXKmPsBoub35bfqmtPtPHyqBs5YcQExg7IZXBhGrreel6wTcEwf9hcwYzElAunspNtW+/j3EwfZyWCnY84j7e5nNE5xfxvvx6UepzETIuZqyp5eOY6djSEEoHNiZw7pKDNOfniJleu2MyS5iA2DYqjC/FVTsNBlMlDJjN5yGQyXZmttgnHTK57eQmfbpTdxy8+sQe3nd2fkmzvfq/hNxFsamTtZ/P46oN38dXJ7vilI0Zx3k1T8KTtf76uPdVs28oHf/kjdTtkxq/n4OMZ8d3vU3rCKGz2jqv4Dgdz53bCCxcTXBMkEtw9IriuNePpWYdn9CDsQ0egOWQ5LX8z0aVLCS4uJ1RTiKBlWAgTV9o2vCfm4Rx9MnrGwQ1F0J3C4TBlZWXJL/pHk/nz5/OnP/2Jr776ioqKCqZPn86kSZNarbN27Vp+9atfMW/ePCzLYsiQIfz73/9u93N+X9fiYD6/uzW4OeWUUzjxxBN55plnkq8dd9xxTJo0iQcffLDN+r/61a94//33kw03AW644QZWrFjBwoUL26zfnq4KbmJWjM2Nm1t9IxKIVj93/xBQX4awYiAEwjKhvCrRxkPOy4EAhAUChGXibQgn9i1kY14BomU5oLXsHIE/LEetRVhoaAhhyeVCk/tsKYrYXS6BQENgRSLEI1E0M4wRCaCbIRBgaRpRm5ugp5CY4SBkmURjMYhGIB7HFrNwRTRcMdCEjtA0wnYbfqeLqrRUmtxuYkInYjkZHGvCHWsmip0Gw06tI42A04UQoEUjICwynBFsmkVUM4ilpWE5vLL4iZLGwmGsmIGVOA1LyPmZhLChaXbQZDZJF0GckTqiXmjO1mnQU9gaKMFX7UIPyca+PyHKBZqBho/PHWtp0GwQi2IEfTiJk5PuIKWgD67MPAybDQEEgmEam5uorqvHtCy05H3W0KIRepkhfnT/HwgJOz/8+yJW72pmYtDO0Jgsl3BrFO6aQ2blNuyxZgKXVWEMq+JJ83aWLRmA3iTbAeV7LQY4TYow0cwwwjIRlsBhVGPT69AMHeHwoBkGCIFmCUTMpBobYV0eyxOO4A2HMV2CgNuNbtjQDQ0dHTQdTQOHw4au68nfhZbfXNO0qAuF0YwITkeYNG8jTqdsoGxGPDRtGUOz5cYw/WQEm0CY6CIOdgjqJs6oSU5tSP6GaoJQmo7PayfkcBC12wk5PDS7UtmVlkOdRwYVmmXRr2I5PWvWgAZew8OwzBiRwsT4RJadSn8J1YECagPZbAtkE7Rk1ivDCPPTrNX08QTRBGiAJrRWf38h3c4LPU5icZqszjGsOPmBLWSHduENN5ASdZBl6niFjVTAIXQ0AZ83F7C0KSP5913oitLDEyXbGcdjWHhsFoYm0AEdgcfmRNdaH3v3e8Ne/xOQHgpij4SJ+iOEfAFCTaHk36jT5WDA8QMp6FHQwUzvbd/GXYZGH+/u5Hw8Hmf1V6vZvGYTliXXt9kNsvKyycjKwO1143a5MGw6hs3AMIzdI3Tvsfvdh29djuTrHX2iCIGIRCEYQwTj4NOxQqlg7VnNa6G5yjEGutBOHIbmbDsKeKtdBoNYX6zA2qIhYntWz1lotgo0TwgtVQO3geaxg8sJhi4fuvz9x9AShddIzUjD7jzwgSc7S1gIdmDQu1cvXN1w/G/iw1mzWLBoESNOOIFLf/hD3n79dSZdcAFooLs9bN68mZNPPplrr72WK6+8kvT0dNauXctJJ51EXl7bKv6jPriJRqN4PB7efPPNVlOf33bbbSxfvpx58+a12ebMM89kxIgR/OUvf0m+Nn36dC677DKCwSD2dr6BRCIRIpFI8nlzczM9e/bs9OCmNlTL+H+P3/+K7bDHBa/+qf0h4QE0p52UiU8datG6xELbBlbbth/w+prQcMRLeM5sZ2C8bmIYFqfrWyg1GjptnzZfI8NLe3HutTfgcMvMVlMoxv3vreLdZbs4OWJjTNiGnb0/oARZA2eROXgG72gXMnPdWei7Op4bSmkt3dHE2J6fc17JJzht0f2uL4DFjGY6l7JN633Ax9Eao9g2N6PXRtrcwSPNIHSeb5l2/ggmhKA+UsGu0Ga2+lcRjB/aGD2p9mz6pA6jh6dfcuLbo0U8VcM33ktJUS9cNof8IhvvprZENh2t3QB6/5w90/j33//Fhed9DzBxFGdwxRVXYLfb+ec//3lA++is4Kb9xhKHQW1tLaZpkp+f3+r1/Px8Kivbr9eurKxsd/14PE5tbS2FhW17fzz44IM88MADnVfwDmho5LnzWp60eh1I/rIkn/urwDLRAMMUNO79HqTt/gJkOARp7NmYcY94tMPYVHTw/32vExMGYdPW7jp7/k/Torh1G1rin44MYHb/A5ew4bUcZJpuCs1UtmMnyxCtPhQ0IGTTCNr2av6ltVxGDTSZ0WqzIXt/dwQ00ea1lv3ogAfIswTHRxsYQBU4YpgpacRtdoQQWJaFZVnEYjFM08Q0TSzLTGTKdmfHZEZAYBMCp6GR4fXQt7Q3J5w+joyC1r+H6W47j18xgqkTBvL+4h18tboaW1UEb1jgMSHFjOOJR2hYNRr/hqGM77Ga0wte4fPhfVnl601jwEskbEeYGsJsueV7ZgT2yAy2+57UcrH2/j3Q9vm0o/1o+1+xg/3JF3REMqtiCLBbAqcl8MYFRuIat38P5U+hR0lx+sh0NZLrqaZfejl907eiG1E5WWnMS9vf+UQuao9rMJo1nMID7NSKWKUfxw69Bw3R3kTChfjtOqamYWnIBxpCA8ulw5AMrKiJ1RDF8sexwiYiZiHiu7Oq8iE6/tNrR0rcwmGKxN++zJ4Z7V3GA5SpxbHbKjpcLgDTgpgJptAwLZkBBfm7LvZYr/V/2u7pQE/TFDEiVpCoFSIk/DSbDTRZDUS1qPxTT7Xh5dACEwvBJrGCTYEVuHCRqeXgNdLw6unYdSd2zYVdd6Khy/coTU++e7W8PzsNE0M//N/3dd2Ghhsw5SNuUfNc94yQnXv9ELB/k+a4FmCiafK99IMPPuDOO+/k3HPPZdmyZZSWlnL33Xe3qbrqbN0W3LTYO0IUQuwzamxv/fZeb3H33XczderU5POWzE1ny3Zn89/L/nvoO/hZ55XlcBh0CNucCFzY2QU5ivTM8nDzuQPg3H035m3xky4uj6IoR4ZwOEygrAxHQRoOlwsr2nEmv6vZe6SjOzpuqL7f7XO8OIozAJmQ8Pv9PPTQQ/zud7/j4YcfZubMmVx88cXMmTOHsWPHdlKp2+q24CYnJwfDMNpkaaqrq9tkZ1oUFBS0u77NZiM7O7vdbZxOJ86jrA5TURRF+fbS7DpFv21//rrDcezOYiW66V944YVMmTIFgBNOOIEFCxbw7LPPdmlw021dwR0OByNHjmT27NmtXp89ezanntr+TR0zZkyb9T/66CNGjRrVbnsbRVEURTnaaJqG7jC65XGo7W3ak5OTg81mY/Dgwa1eP+6449i2bVunHac93TrOzdSpU3n++ef5xz/+wdq1a5kyZQrbtm3jhhtuAGSV0k9+sjs5f8MNN1BeXs7UqVNZu3Yt//jHP3jhhRf45S9/2V2noCiKoihKOxwOByeddFJyouQWGzZsoKSkpIOtOke3trm5/PLLqaur47e//S0VFRUMHTqUGTNmJE+6oqKiVXRXWlrKjBkzmDJlCk899RRFRUU88cQTBzzGjaIoiqIoncfv97Np06bk87KyMpYvX05WVha9evXijjvu4PLLL+fMM89k/PjxzJw5k//85z/MnTu3S8vVrePcdAc1iJ+iKIpyJDmaB/GbO3cu48e3HQZl8uTJTJs2DYB//OMfPPjgg+zYsYOBAwfywAMPcOGF7XcvOeq7giuKoiiKcnQbN27cfqfzuOaaa7jmmmsOU4mkbp9bSlEURVEUpTOp4EZRFEVRlGOKCm4URVEURTmmqOBGURRFUZRjigpuFEVRFEU5pqjgRlEURVGUY4oKbhRFURRFOaao4EZRFEVRlGPKt24Qv5bBhpqbm7u5JIqiKIoC0WgUy7IwTRPTNLu7ON3KNE0sy8Lv9xONRlsta/ncPpCJFb51wY3P5wOgZ8+e3VwSRVEURYGSkhKeffZZQqFQdxfliFBbW8v5559PeXl5u8t9Ph/p6en73Me3bm4py7LYtWsXqampnTq1e2dobm6mZ8+ebN++/Zie9+rbcp7w7TlXdZ7HFnWeh1c0GqWqqorevXt32dxSpmny9ddfM2zYMAzD6LT9zp8/n0cffZSlS5dSUVHB22+/3WreKJut/RzKQw89xC9/+cs2r4fDYbZu3Up+fj4Oh6PVMiEEPp+PoqIidH3frWq+dZkbXdcpLi7u7mLsU1pa2jH9htLi23Ke8O05V3WexxZ1nodHOBympqYGwzA6NfBoT2cfIxwOc8IJJ3DNNdfwgx/8AF3XW+2/oqKi1foffvgh1157LZdeemm75TAMA13XSUlJaTfQ21/GpsW3LrhRFEVRlCOZEIJYLNap+zRNk3g8TjQa3WdwY7fbD6pWY+LEiUycOLHD5QUFBa2ev/fee4wfP54+ffoc8DEOhQpuFEVRFOUIEovF+MMf/tAl+545c+Y+l99zzz1tqoM6S1VVFR988AEvvfRSl+x/T6or+BHE6XRy33334XQ6u7soXerbcp7w7TlXdZ7HFnWeSld46aWXSE1N5eKLL+7yY33rGhQriqIoypEkHA5TVlZGaWkpLperS6qlDtTBVkvtSdM0pk+fzqRJk9pdPmjQIM455xz++te/driPva/FoVLVUoqiKIpyBNE0rcuqhrrLp59+yvr163njjTcOy/FUtZSiKIqiKF3qhRdeYOTIkQwfPvywHE9lbhRFURRFOSR+v59NmzYln5eVlbF8+XKysrLo1asXIMcTevPNN3n00UcPW7lUcKMoiqIoyiFZsmQJ48ePTz6fOnUqAJMnT2batGkAvP766wghuPLKKw9buVS1VDfr3bs3mqa1etx111373EYIwf33309RURFut5tx48axevXqw1Tig7d161auvfZaSktLcbvd9O3bl/vuu6/NvCF7u/rqq9tcm9GjRx+mUh+Yp59+OtnwbeTIkXz66af7XH/evHmMHDkSl8tFnz59ePbZZw9TSQ/dgw8+yEknnURqaip5eXlMmjSJ9evX73ObuXPntrl3mqaxbt26w1Tqg3f//fe3Ke/eY3Ts7Wi8n+2952iaxs0339zu+kfTvZw/fz4XXHABRUVFaJrGu+++22r5ob53vv322wwePBin08ngwYOZPn16F53BgfH5fGzcuJEVK1awZMkSGhoaksssy2LHjh2sXr2apUuXsmLFCsrKyvb7fltbW8uSJUvaPCzL2ud248aNQwjR5tES2AD87Gc/IxgMHvAAfJ1BBTdHgN/+9rdUVFQkH/fee+8+1//jH//IY489xpNPPsnixYspKCjgnHPOSc6bdaRZt24dlmXx3HPPsXr1ah5//HGeffZZ7rnnnv1ue95557W6NjNmzDgMJT4wb7zxBrfffju//vWvWbZsGWeccQYTJ05k27Zt7a5fVlbGd7/7Xc444wyWLVvGPffcw89//nPefvvtw1zygzNv3jxuvvlmFi1axOzZs4nH40yYMIFAILDfbdevX9/q/vXv3/8wlPjQDRkypFV5V65c2eG6R+v9XLx4catznD17NgCXXnrpPrc7Gu5lIBBg+PDhPPnkk+0uP5T3zoULF3L55Zdz1VVXsWLFCq666iouu+wyvvjii646jf2yLAuPx5Os9tl7WTAYpLCwkMGDB9O3b1/C4XCrqqOOGIbB8OHDWz32N83BEUso3aqkpEQ8/vjjB7y+ZVmioKBAPPTQQ8nXwuGwSE9PF88++2wXlLBr/PGPfxSlpaX7XGfy5MniwgsvPDwFOgQnn3yyuOGGG1q9NmjQIHHXXXe1u/6dd94pBg0a1Oq166+/XowePbrLytgVqqurBSDmzZvX4Tpz5swRgGhoaDh8BfuG7rvvPjF8+PADXv9YuZ+33Xab6Nu3r7Asq93lR+O9FEIIQEyfPj35/FDfOy+77DJx3nnntXrt3HPPFVdccUWnlTUUCok1a9aIUCh00NsuXrxY1NfX73Mdv98vFi9eLMLhcIfr1NTUiKVLlx708TvbN7kWezpKQ7Jjy8MPP0x2djYnnHACv//97/eZPiwrK6OyspIJEyYkX3M6nYwdO5YFCxYcjuJ2iqamJrKysva73ty5c8nLy2PAgAFcd911VFdXH4bS7V80GuWrr75qdR8AJkyY0OF9WLhwYZv1zz33XJYsWdJtY1ociqamJoADun8jRoygsLCQs88+mzlz5nR10b6xjRs3UlRURGlpKVdccQVbtmzpcN1j4X5Go1FeeeUVrrnmmv2ObXK03cu9Hep7Z0f3+Wh6vzVNE+h4Ess91/v6669ZsWIFGzduJBgMHo7idQkV3HSz2267jddff505c+Zwyy238Oc//5mbbrqpw/UrKysByM/Pb/V6fn5+ctmRbvPmzfz1r3/lhhtu2Od6EydO5NVXX+WTTz7h0UcfZfHixZx11llEIpHDVNKO1dbWYprmQd2HysrKdtePx+PU1tZ2WVk7kxCCqVOncvrppzN06NAO1yssLORvf/sbb7/9Nu+88w4DBw7k7LPPZv78+YextAfnlFNO4eWXX2bWrFn8/e9/p7KyklNPPZW6urp21z8W7ue7775LY2MjV199dYfrHI33sj2H+t7Z0X0+Wt5vW9rgZGVl7XNOKZfLRWlpKf369aNPnz7ous66desIh8OHsbSdR/WW6gL3338/DzzwwD7XWbx4MaNGjWLKlCnJ14YNG0ZmZiaXXHJJMpvTkb2/ZQkhDnlUyUN1MOfZYteuXZx33nlceuml/PSnP93ntpdffnny/0OHDmXUqFGUlJTwwQcfHJbhuw/Ewd6H9tZv7/Uj1S233MLXX3/NZ599ts/1Bg4cyMCBA5PPx4wZw/bt23nkkUc488wzu7qYh2TPyf+OP/54xowZQ9++fXnppZeSPUD2drTfzxdeeIGJEydSVFTU4TpH473cl0N57zwS3m8PhWVZyexjSUnJPtdNSUkhJSWl1fM1a9ZQXV3dbtueI50KbrrALbfcwhVXXLHPdXr37t3u6y29gTZt2tRucNPSe6OyspLCwsLk69XV1W2+XXS1gz3PXbt2MX78eMaMGcPf/va3gz5eYWEhJSUlbNy48aC37Ww5OTkYhtHm29u+7kNBQUG769tstn0GskeKW2+9lffff5/58+dTXFx80NuPHj2aV155pQtK1jW8Xi/HH398h79vR/v9LC8v5+OPP+add9456G2PtnsJh/7e2dF9PtzvtwerJbCJRCIMHDhwn1mb9miahtfrVZkbZbecnBxycnIOadtly5YBtPrj21NpaSkFBQXMnj2bESNGALLefN68eTz88MOHVuBDdDDnuXPnTsaPH8/IkSN58cUXD6kFfl1dHdu3b+/w2hxODoeDkSNHMnv2bC666KLk67Nnz+bCCy9sd5sxY8bwn//8p9VrH330EaNGjcJut3dpeb8JIQS33nor06dPZ+7cuZSWlh7SfpYtW3ZE3LsDFYlEWLt2LWeccUa7y4/W+9nixRdfJC8vj/PPP/+gtz3a7iUc+nvnmDFjmD17dqss+0cffcSpp57a5WU+VHsGNgMGDNhvW5v2CCEIBoN4PJ4uKOFh8M3aNSvfxIIFC8Rjjz0mli1bJrZs2SLeeOMNUVRUJL7//e+3Wm/gwIHinXfeST5/6KGHRHp6unjnnXfEypUrxZVXXikKCwtFc3Pz4T6FA7Jz507Rr18/cdZZZ4kdO3aIioqK5GNPe56nz+cTv/jFL8SCBQtEWVmZmDNnjhgzZozo0aPHEXOer7/+urDb7eKFF14Qa9asEbfffrvwer1i69atQggh7rrrLnHVVVcl19+yZYvweDxiypQpYs2aNeKFF14QdrtdvPXWW911CgfkxhtvFOnp6WLu3Lmt7l0wGEyus/e5Pv7442L69Oliw4YNYtWqVeKuu+4SgHj77be74xQOyC9+8Qsxd+5csWXLFrFo0SLxve99T6Smph5z91MIIUzTFL169RK/+tWv2iw7mu+lz+cTy5YtE8uWLRNA8v21vLxcCHFg751XXXVVqx6Pn3/+uTAMQzz00ENi7dq14qGHHhI2m00sWrSo08p9sD2E4vG4CAQCIhAIiMWLF4vKykoRCAREOBwWlmWJjRs3ihUrVohAICCi0WjyYZpmch9btmwR27dvTz7fuXOnaGxsFOFwWAQCAVFWViaWLFki/H5/p53ngeis3lIquOlGX331lTjllFNEenq6cLlcYuDAgeK+++4TgUCg1XqAePHFF5PPLcsS9913nygoKBBOp1OceeaZYuXKlYe59AfuxRdfFEC7jz3teZ7BYFBMmDBB5ObmCrvdLnr16iUmT54stm3b1g1n0LGnnnpKlJSUCIfDIU488cRW3aMnT54sxo4d22r9uXPnihEjRgiHwyF69+4tnnnmmcNc4oPX0b3b83dy73N9+OGHRd++fYXL5RKZmZni9NNPFx988MHhL/xBuPzyy0VhYaGw2+2iqKhIXHzxxWL16tXJ5cfK/RRCiFmzZglArF+/vs2yo/letnRb3/sxefJkIcSBvXeOHTs2uX6LN998UwwcOFDY7XYxaNCgTg/sDvYDvbm5WSxevLjNY8uWLSIcDre7bPHixa2CuHXr1oktW7Ykn2/btk2sWLFCLFmyRCxbtkysX79e+Hy+Tj3PA9FZwY0mRKIFnKIoiqIoh104HKasrCw52vm3WWddC9UVXFEURVGUQ7K/KS/8fj+33HILxcXFuN1ujjvuOJ555pkuL5cKbhRFURRFOST7m/JiypQpzJw5k1deeYW1a9cyZcoUbr31Vt57770uLZfqLaUoiqIoRxAhBJYV6pZj67r7oMbwmThxYqsxova2cOFCJk+ezLhx4wA5ieZzzz3HkiVLOuxZ2hlUcKMoiqIoRxDLCjF33vHdcuxxY1diGJ3X/fv000/n/fff55prrqGoqIi5c+eyYcMG/vKXv3TaMdqjghtFURRFUbrEE088wXXXXUdxcTE2mw1d13n++ec5/fTTu/S4KrhRFEVRlCOIrrsZN3Zltx27Mz3xxBMsWrSI999/n5KSEubPn89NN91EYWEh3/nOdzr1WHtSwY2iKIqiHEE0TevUqqHuEgqFuOeee5g+fXpyJOxhw4axfPlyHnnkkS4NblRvKUVRFEVROl0sFiMWi7WZbscwDCzL6tJjq8yNoiiKoiiHxO/3s2nTpuTzsrIyli9fTlZWFr169WLs2LHccccduN1uSkpKmDdvHi+//DKPPfZYl5ZLjVCsKIqiKN3oaB6heO7cuYwfP77N65MnT2batGlUVlZy991389FHH1FfX09JSQk/+9nPmDJlSrtdzjvrWqjgRlEURVG60dEc3HQ2Nf2CoiiKoihKO1RwoyiKoijKMUUFN4qiKIqiHFNUcKMoiqIoyjFFBTeKoiiKohxTVHCjKIqiKMoxRQU3iqIoiqIcU1RwoyiKoijKMUUFN4qiKIqiHFNUcKMoiqIoyiGZP38+F1xwAUVFRWiaxrvvvttqeVVVFVdffTVFRUV4PB7OO+88Nm7c2OXlUsGNoiiKoiiHJBAIMHz4cJ588sk2y4QQTJo0iS1btvDee++xbNkySkpK+M53vkMgEOjScqlZwRVFURRFOSQTJ05k4sSJ7S7buHEjixYtYtWqVQwZMgSAp59+mry8PF577TV++tOfdlm5VHCjKIqiKEcQIQRBy+qWY3t0vd3Zug9FJBIBaDUBpmEYOBwOPvvsMxXcKIqiKMq3RdCy6Dt/Zbcce/OZx+M1jE7Z16BBgygpKeHuu+/mueeew+v18thjj1FZWUlFRUWnHKMjqs2NoiiKoiidzm638/bbb7NhwwaysrLweDzMnTuXiRMnYnRSANURlblRFEVRlCOIR9fZfObx3XbszjRy5EiWL19OU1MT0WiU3NxcTjnlFEaNGtWpx9mbCm4URVEU5QiiaVqnVQ0dKdLT0wHZyHjJkiX87//+b5ceTwU3iqIoiqIcEr/fz6ZNm5LPy8rKWL58OVlZWfTq1Ys333yT3NxcevXqxcqVK7ntttuYNGkSEyZM6NJyqeBGURRFUZRDsmTJEsaPH598PnXqVAAmT57MtGnTqKioYOrUqVRVVVFYWMhPfvIT/ud//qfLy6UJIUSXH0VRFEVRlHaFw2HKysooLS1t1W3626izroXqLaUoiqIoyjFFBTeKoiiKohxTVHCjKIqiKMoxRQU3iqIoiqIcU1RwoyiKoijKMUUFN4qiKIqiHFNUcKMoiqIoyjFFBTeKoiiKohxTVHCjKIqiKMoxRQU3iqIoiqIcU1RwoyiKoijKQXvwwQc56aSTSE1NJS8vj0mTJrF+/fpW6wghuP/++ykqKsLtdjNu3DhWr17d5WVTwY2iKIqiKAdt3rx53HzzzSxatIjZs2cTj8eZMGECgUAguc4f//hHHnvsMZ588kkWL15MQUEB55xzDj6fr0vLpibOVBRFUZRudKxMnFlTU0NeXh7z5s3jzDPPRAhBUVERt99+O7/61a8AiEQi5Ofn8/DDD3P99de32UdnXQvbIW+pKIqiKEqnE0IQipndcmy33UDTtEPatqmpCYCsrCwAysrKqKysZMKECcl1nE4nY8eOZcGCBe0GN51FBTeKoiiKcgQJxUwG/2ZWtxx7zW/PxeM4+NBACMHUqVM5/fTTGTp0KACVlZUA5Ofnt1o3Pz+f8vLyb17YfVDBjaIoiqIo38gtt9zC119/zWeffdZm2d6ZICHEIWeHDpQKbhRFURTlCOK2G6z57bndduyDdeutt/L+++8zf/58iouLk68XFBQAMoNTWFiYfL26urpNNqezqeBGURRFUY4gmqYdUtXQ4SaE4NZbb2X69OnMnTuX0tLSVstLS0spKChg9uzZjBgxAoBoNMq8efN4+OGHu7RsR/7VUxRFURTliHPzzTfzr3/9i/fee4/U1NRkG5v09HTcbjeapnH77bfzhz/8gf79+9O/f3/+8Ic/4PF4+OEPf9ilZVPBjaIoiqIoB+2ZZ54BYNy4ca1ef/HFF7n66qsBuPPOOwmFQtx00000NDRwyimn8NFHH5GamtqlZVPj3CiKoihKNzpWxrnpDJ11LdQIxYqiKIqiHFNUcKMoiqIoyjFFBTeKoiiKohxTVHCjKIqiKMoxRQU3iqIoinIEUP17Ou8aqOBGURRFUbqRYchRgaPRaDeXpPu1XIOWa3Ko1Dg3iqIoitKNbDYbHo+Hmpoa7HY7uv7tzDtYlkVNTQ0ejweb7ZuFJ2qcG0VRFEXpZtFolLKyMizL6u6idCtd1yktLcXhcHyj/ajgRlEURVGOAJZlfeurphwOR6dkrlRwoyiKoijKMeXbWbGnKIqiKMoxSwU3iqIoiqIcU1RwoyiKoijKMUUFN4qiKIqiHFNUcKMoiqIoyjFFBTeKoiiKohxTVHCjKIqiKMox5f8DMgLc+BW07TsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(df_scaled).plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e90cffc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final = pd.DataFrame(df_scaled, columns=df_encoded.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "10430e31",
   "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>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Housing_free</th>\n",
       "      <th>Housing_own</th>\n",
       "      <th>Housing_rent</th>\n",
       "      <th>Purpose_business</th>\n",
       "      <th>...</th>\n",
       "      <th>Purpose_domestic appliances</th>\n",
       "      <th>Purpose_education</th>\n",
       "      <th>Purpose_furniture/equipment</th>\n",
       "      <th>Purpose_radio/TV</th>\n",
       "      <th>Purpose_repairs</th>\n",
       "      <th>Purpose_vacation/others</th>\n",
       "      <th>Saving accounts_little</th>\n",
       "      <th>Saving accounts_moderate</th>\n",
       "      <th>Saving accounts_quite rich</th>\n",
       "      <th>Saving accounts_rich</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.271006</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>-0.933901</td>\n",
       "      <td>-1.864869</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>1.603567</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.446152</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>1.163046</td>\n",
       "      <td>1.708369</td>\n",
       "      <td>1.491914</td>\n",
       "      <td>-1.491914</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>1.603567</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.226696</td>\n",
       "      <td>-1.383771</td>\n",
       "      <td>-0.181559</td>\n",
       "      <td>-0.673790</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>3.993639</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.942455</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>1.525148</td>\n",
       "      <td>1.478913</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>2.873893</td>\n",
       "      <td>-1.576173</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>2.127172</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.488620</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>0.904743</td>\n",
       "      <td>0.517289</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>2.873893</td>\n",
       "      <td>-1.576173</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.103614</td>\n",
       "      <td>-1.383771</td>\n",
       "      <td>1.703911</td>\n",
       "      <td>1.214026</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>2.873893</td>\n",
       "      <td>-1.576173</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>3.993639</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.488620</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>0.207588</td>\n",
       "      <td>0.517289</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>2.127172</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-1.91648</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>3.856555</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.103614</td>\n",
       "      <td>1.677670</td>\n",
       "      <td>1.362630</td>\n",
       "      <td>1.214026</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>-1.576173</td>\n",
       "      <td>2.141633</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.957856</td>\n",
       "      <td>-1.383771</td>\n",
       "      <td>0.305575</td>\n",
       "      <td>-0.673790</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>1.603567</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-1.91648</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>4.453463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.641198</td>\n",
       "      <td>1.677670</td>\n",
       "      <td>0.997621</td>\n",
       "      <td>0.900731</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age       Job  Credit amount  Duration  Sex_female  Sex_male  \\\n",
       "0  2.271006  0.146949      -0.933901 -1.864869   -0.670280  0.670280   \n",
       "1 -1.446152  0.146949       1.163046  1.708369    1.491914 -1.491914   \n",
       "2  1.226696 -1.383771      -0.181559 -0.673790   -0.670280  0.670280   \n",
       "3  0.942455  0.146949       1.525148  1.478913   -0.670280  0.670280   \n",
       "4  1.488620  0.146949       0.904743  0.517289   -0.670280  0.670280   \n",
       "5  0.103614 -1.383771       1.703911  1.214026   -0.670280  0.670280   \n",
       "6  1.488620  0.146949       0.207588  0.517289   -0.670280  0.670280   \n",
       "7  0.103614  1.677670       1.362630  1.214026   -0.670280  0.670280   \n",
       "8  1.957856 -1.383771       0.305575 -0.673790   -0.670280  0.670280   \n",
       "9 -0.641198  1.677670       0.997621  0.900731   -0.670280  0.670280   \n",
       "\n",
       "   Housing_free  Housing_own  Housing_rent  Purpose_business  ...  \\\n",
       "0     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "1     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "2     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "3      2.873893    -1.576173     -0.466933         -0.327749  ...   \n",
       "4      2.873893    -1.576173     -0.466933         -0.327749  ...   \n",
       "5      2.873893    -1.576173     -0.466933         -0.327749  ...   \n",
       "6     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "7     -0.347960    -1.576173      2.141633         -0.327749  ...   \n",
       "8     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "9     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "\n",
       "   Purpose_domestic appliances  Purpose_education  \\\n",
       "0                    -0.110208          -0.250398   \n",
       "1                    -0.110208          -0.250398   \n",
       "2                    -0.110208           3.993639   \n",
       "3                    -0.110208          -0.250398   \n",
       "4                    -0.110208          -0.250398   \n",
       "5                    -0.110208           3.993639   \n",
       "6                    -0.110208          -0.250398   \n",
       "7                    -0.110208          -0.250398   \n",
       "8                    -0.110208          -0.250398   \n",
       "9                    -0.110208          -0.250398   \n",
       "\n",
       "   Purpose_furniture/equipment  Purpose_radio/TV  Purpose_repairs  \\\n",
       "0                    -0.470108          1.603567        -0.149983   \n",
       "1                    -0.470108          1.603567        -0.149983   \n",
       "2                    -0.470108         -0.623610        -0.149983   \n",
       "3                     2.127172         -0.623610        -0.149983   \n",
       "4                    -0.470108         -0.623610        -0.149983   \n",
       "5                    -0.470108         -0.623610        -0.149983   \n",
       "6                     2.127172         -0.623610        -0.149983   \n",
       "7                    -0.470108         -0.623610        -0.149983   \n",
       "8                    -0.470108          1.603567        -0.149983   \n",
       "9                    -0.470108         -0.623610        -0.149983   \n",
       "\n",
       "   Purpose_vacation/others  Saving accounts_little  Saving accounts_moderate  \\\n",
       "0                -0.110208                 0.52179                 -0.338862   \n",
       "1                -0.110208                 0.52179                 -0.338862   \n",
       "2                -0.110208                 0.52179                 -0.338862   \n",
       "3                -0.110208                 0.52179                 -0.338862   \n",
       "4                -0.110208                 0.52179                 -0.338862   \n",
       "5                -0.110208                 0.52179                 -0.338862   \n",
       "6                -0.110208                -1.91648                 -0.338862   \n",
       "7                -0.110208                 0.52179                 -0.338862   \n",
       "8                -0.110208                -1.91648                 -0.338862   \n",
       "9                -0.110208                 0.52179                 -0.338862   \n",
       "\n",
       "   Saving accounts_quite rich  Saving accounts_rich  \n",
       "0                   -0.259299             -0.224544  \n",
       "1                   -0.259299             -0.224544  \n",
       "2                   -0.259299             -0.224544  \n",
       "3                   -0.259299             -0.224544  \n",
       "4                   -0.259299             -0.224544  \n",
       "5                   -0.259299             -0.224544  \n",
       "6                    3.856555             -0.224544  \n",
       "7                   -0.259299             -0.224544  \n",
       "8                   -0.259299              4.453463  \n",
       "9                   -0.259299             -0.224544  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "22936595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                            float64\n",
       "Job                            float64\n",
       "Credit amount                  float64\n",
       "Duration                       float64\n",
       "Sex_female                     float64\n",
       "Sex_male                       float64\n",
       "Housing_free                   float64\n",
       "Housing_own                    float64\n",
       "Housing_rent                   float64\n",
       "Purpose_business               float64\n",
       "Purpose_car                    float64\n",
       "Purpose_domestic appliances    float64\n",
       "Purpose_education              float64\n",
       "Purpose_furniture/equipment    float64\n",
       "Purpose_radio/TV               float64\n",
       "Purpose_repairs                float64\n",
       "Purpose_vacation/others        float64\n",
       "Saving accounts_little         float64\n",
       "Saving accounts_moderate       float64\n",
       "Saving accounts_quite rich     float64\n",
       "Saving accounts_rich           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8722e5bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                            0\n",
       "Job                            0\n",
       "Credit amount                  0\n",
       "Duration                       0\n",
       "Sex_female                     0\n",
       "Sex_male                       0\n",
       "Housing_free                   0\n",
       "Housing_own                    0\n",
       "Housing_rent                   0\n",
       "Purpose_business               0\n",
       "Purpose_car                    0\n",
       "Purpose_domestic appliances    0\n",
       "Purpose_education              0\n",
       "Purpose_furniture/equipment    0\n",
       "Purpose_radio/TV               0\n",
       "Purpose_repairs                0\n",
       "Purpose_vacation/others        0\n",
       "Saving accounts_little         0\n",
       "Saving accounts_moderate       0\n",
       "Saving accounts_quite rich     0\n",
       "Saving accounts_rich           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053e172f",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "<li> We can notice that all of the features in the data are now numerical. </li>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9b0a54",
   "metadata": {},
   "source": [
    "### K-Means Clustering using Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2cc821c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_numeric = pd.DataFrame(data_final, columns=['Age', 'Job', 'Credit amount', 'Duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0c19758e",
   "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>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.271006</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>-0.933901</td>\n",
       "      <td>-1.864869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.446152</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>1.163046</td>\n",
       "      <td>1.708369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.226696</td>\n",
       "      <td>-1.383771</td>\n",
       "      <td>-0.181559</td>\n",
       "      <td>-0.673790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.942455</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>1.525148</td>\n",
       "      <td>1.478913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.488620</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>0.904743</td>\n",
       "      <td>0.517289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>-0.301466</td>\n",
       "      <td>-1.383771</td>\n",
       "      <td>-0.424376</td>\n",
       "      <td>-0.673790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.549317</td>\n",
       "      <td>1.677670</td>\n",
       "      <td>0.604255</td>\n",
       "      <td>0.900731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.378110</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>-1.416199</td>\n",
       "      <td>-0.673790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>-1.297780</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>-0.345911</td>\n",
       "      <td>1.597468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>-0.762586</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>0.824508</td>\n",
       "      <td>1.597468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age       Job  Credit amount  Duration\n",
       "0    2.271006  0.146949      -0.933901 -1.864869\n",
       "1   -1.446152  0.146949       1.163046  1.708369\n",
       "2    1.226696 -1.383771      -0.181559 -0.673790\n",
       "3    0.942455  0.146949       1.525148  1.478913\n",
       "4    1.488620  0.146949       0.904743  0.517289\n",
       "..        ...       ...            ...       ...\n",
       "995 -0.301466 -1.383771      -0.424376 -0.673790\n",
       "996  0.549317  1.677670       0.604255  0.900731\n",
       "997  0.378110  0.146949      -1.416199 -0.673790\n",
       "998 -1.297780  0.146949      -0.345911  1.597468\n",
       "999 -0.762586  0.146949       0.824508  1.597468\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dc8fb8e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = []\n",
    "for i in range(2, 16):\n",
    "    clustering = KMeans(n_clusters=i, random_state=5)\n",
    "    clustering.fit(data_numeric)\n",
    "    wcss.append(clustering.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "42d7daf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2805.159602249922,\n",
       " 2336.553915984457,\n",
       " 2015.2554513095804,\n",
       " 1790.6454058818751,\n",
       " 1632.1549764209335,\n",
       " 1486.6155002077235,\n",
       " 1367.3140346587293,\n",
       " 1278.5577607553946,\n",
       " 1203.8138928831831,\n",
       " 1130.7912379993572,\n",
       " 1078.923929024219,\n",
       " 1021.9576803848007,\n",
       " 995.0199484900256,\n",
       " 937.5041780162371]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1675e6e",
   "metadata": {},
   "source": [
    "#### An elbow plot has been used to find out the optimal number of clusters. Over x-axis we have number of clusters and over y-axis we have wcss(within cluster sum of square)\n",
    "#### The optimal number of clusters in k-means is the point where graph forms an elbow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9c4a223c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(range(1,15), wcss, marker='X')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.title('The Elbow Method')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458bad29",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "In the above graph, the elbow point comes at around number of clusters(n)=6 which will be the optimal number of clusters for the above data. After n=6, as we keep on increasing the number of clusters, the wcss decreases slightly.\n",
    "Hence, the optimal number of clusters=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "15e573cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=6)\n",
    "y_kmeans = kmeans.fit(data_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "eebcbd70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of the squared error: 14279.365767435436\n",
      "Centroids: [[-1.97473178e-01 -1.02023452e-01 -2.85563295e-01 -1.85819244e-01\n",
      "   1.49191369e+00 -1.49191369e+00 -3.47960103e-01  6.34448220e-01\n",
      "  -4.66933396e-01 -8.34944918e-02 -1.90427177e-01  5.57677790e-02\n",
      "   5.63996751e-02  9.31578601e-02  1.67977447e-01 -2.67768040e-02\n",
      "  -1.10207754e-01  3.60217709e-01 -3.38861626e-01 -2.59298784e-01\n",
      "   8.54440875e-02]\n",
      " [ 1.25199134e-01 -5.93181384e-02  2.18542109e-04 -7.64958442e-03\n",
      "  -6.70280063e-01  6.70280063e-01 -3.47960103e-01  6.34448220e-01\n",
      "  -4.66933396e-01  6.36551751e-02 -1.77598212e-02 -2.33616121e-02\n",
      "  -7.98340521e-02 -9.59704149e-03  5.03352536e-02  2.73018622e-02\n",
      "  -1.10207754e-01  3.71919795e-01 -3.38861626e-01 -2.59298784e-01\n",
      "   6.29927431e-02]\n",
      " [ 7.11385050e-01  4.25262022e-01  5.50224314e-01  4.49797635e-01\n",
      "  -3.42674949e-01  3.42674949e-01  2.87389270e+00 -1.57617276e+00\n",
      "  -4.66933396e-01 -1.91229851e-01  4.41001082e-01 -1.10207754e-01\n",
      "   3.92637745e-01 -1.81521009e-01 -3.53648714e-01  5.66051382e-02\n",
      "  -1.10207754e-01  2.50870917e-01 -2.39167120e-01 -9.85310433e-03\n",
      "  -1.30039156e-01]\n",
      " [-7.27191716e-02 -1.87088077e-03 -1.18129076e-01 -1.58444539e-02\n",
      "  -1.44746859e-01  1.44746859e-01 -2.36090214e-01  2.04605253e-01\n",
      "  -5.02873645e-02  1.65001020e-01 -7.75710582e-03  8.11251521e-02\n",
      "  -7.35633116e-02 -2.35631008e-01  1.34249281e-01  3.93894636e-02\n",
      "  -1.10207754e-01 -1.91648042e+00  1.78587754e+00  1.19839940e+00\n",
      "  -2.24544357e-01]\n",
      " [ 4.24908902e-01  9.12309495e-01  1.10005199e+00  8.20904845e-01\n",
      "  -1.29731625e-01  1.29731625e-01  7.25990832e-01 -1.02425439e-01\n",
      "  -4.66933396e-01 -3.27749465e-01 -7.12948544e-01 -1.10207754e-01\n",
      "  -2.50398195e-01 -4.70107670e-01 -6.23609564e-01 -1.49982957e-01\n",
      "   9.07377173e+00  1.15411459e-01  2.09458157e-01 -2.59298784e-01\n",
      "  -2.24544357e-01]\n",
      " [-5.46366359e-01 -6.89216776e-02 -2.14819252e-02 -1.15496499e-01\n",
      "   5.90999625e-01 -5.90999625e-01 -3.47960103e-01 -1.57617276e+00\n",
      "   2.14163306e+00 -8.94964837e-02  3.29270156e-02  7.53557291e-03\n",
      "  -5.54989481e-03  2.95756929e-01 -1.66752741e-01 -1.06281630e-01\n",
      "  -1.10207754e-01  2.09191084e-01 -2.33415514e-01 -1.00996718e-01\n",
      "   4.53406874e-02]]\n"
     ]
    }
   ],
   "source": [
    "print('Sum of the squared error: {}'.format(kmeans.inertia_))\n",
    "print('Centroids: {}'.format(kmeans.cluster_centers_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c4d367",
   "metadata": {},
   "source": [
    "### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "52a9fa57",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=6)\n",
    "german_data_pca_6 = pca.fit_transform(data_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bef44f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11833021, 0.10440294, 0.08923184, 0.07720514, 0.06569601,\n",
       "       0.06264465])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4103d624",
   "metadata": {},
   "source": [
    "#### Apply Label Encoder to convert the target variable from categorical to a binary feature so that it can be used for further classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "beacc734",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "data['Risk'] = le.fit_transform(data['Risk'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "830d14b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "5    1\n",
       "6    1\n",
       "7    1\n",
       "8    1\n",
       "9    0\n",
       "Name: Risk, dtype: int32"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Risk'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73be772",
   "metadata": {},
   "source": [
    "#### Since all the 6 principal components cannot be visualized over a graph, the first 2 principal components have been chosen and visualized over a graph.\n",
    "#### Since we have only 2 possible values for target variable ('good', 'bad'), we can see 2 different colours in the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bdb21dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(german_data_pca_6[:,0], german_data_pca_6[:,1], c=data['Risk'])\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9ce475",
   "metadata": {},
   "source": [
    "#### Split the data into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "31770bb1",
   "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>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>Credit amount</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Housing_free</th>\n",
       "      <th>Housing_own</th>\n",
       "      <th>Housing_rent</th>\n",
       "      <th>Purpose_business</th>\n",
       "      <th>...</th>\n",
       "      <th>Purpose_domestic appliances</th>\n",
       "      <th>Purpose_education</th>\n",
       "      <th>Purpose_furniture/equipment</th>\n",
       "      <th>Purpose_radio/TV</th>\n",
       "      <th>Purpose_repairs</th>\n",
       "      <th>Purpose_vacation/others</th>\n",
       "      <th>Saving accounts_little</th>\n",
       "      <th>Saving accounts_moderate</th>\n",
       "      <th>Saving accounts_quite rich</th>\n",
       "      <th>Saving accounts_rich</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.271006</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>-0.933901</td>\n",
       "      <td>-1.864869</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>1.603567</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.446152</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>1.163046</td>\n",
       "      <td>1.708369</td>\n",
       "      <td>1.491914</td>\n",
       "      <td>-1.491914</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>1.603567</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.226696</td>\n",
       "      <td>-1.383771</td>\n",
       "      <td>-0.181559</td>\n",
       "      <td>-0.673790</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>3.993639</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.942455</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>1.525148</td>\n",
       "      <td>1.478913</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>2.873893</td>\n",
       "      <td>-1.576173</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>2.127172</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.488620</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>0.904743</td>\n",
       "      <td>0.517289</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>2.873893</td>\n",
       "      <td>-1.576173</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.103614</td>\n",
       "      <td>-1.383771</td>\n",
       "      <td>1.703911</td>\n",
       "      <td>1.214026</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>2.873893</td>\n",
       "      <td>-1.576173</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>3.993639</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.488620</td>\n",
       "      <td>0.146949</td>\n",
       "      <td>0.207588</td>\n",
       "      <td>0.517289</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>2.127172</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-1.91648</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>3.856555</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.103614</td>\n",
       "      <td>1.677670</td>\n",
       "      <td>1.362630</td>\n",
       "      <td>1.214026</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>-1.576173</td>\n",
       "      <td>2.141633</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.957856</td>\n",
       "      <td>-1.383771</td>\n",
       "      <td>0.305575</td>\n",
       "      <td>-0.673790</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>1.603567</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-1.91648</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>4.453463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.641198</td>\n",
       "      <td>1.677670</td>\n",
       "      <td>0.997621</td>\n",
       "      <td>0.900731</td>\n",
       "      <td>-0.670280</td>\n",
       "      <td>0.670280</td>\n",
       "      <td>-0.347960</td>\n",
       "      <td>0.634448</td>\n",
       "      <td>-0.466933</td>\n",
       "      <td>-0.327749</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>-0.250398</td>\n",
       "      <td>-0.470108</td>\n",
       "      <td>-0.623610</td>\n",
       "      <td>-0.149983</td>\n",
       "      <td>-0.110208</td>\n",
       "      <td>0.52179</td>\n",
       "      <td>-0.338862</td>\n",
       "      <td>-0.259299</td>\n",
       "      <td>-0.224544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age       Job  Credit amount  Duration  Sex_female  Sex_male  \\\n",
       "0  2.271006  0.146949      -0.933901 -1.864869   -0.670280  0.670280   \n",
       "1 -1.446152  0.146949       1.163046  1.708369    1.491914 -1.491914   \n",
       "2  1.226696 -1.383771      -0.181559 -0.673790   -0.670280  0.670280   \n",
       "3  0.942455  0.146949       1.525148  1.478913   -0.670280  0.670280   \n",
       "4  1.488620  0.146949       0.904743  0.517289   -0.670280  0.670280   \n",
       "5  0.103614 -1.383771       1.703911  1.214026   -0.670280  0.670280   \n",
       "6  1.488620  0.146949       0.207588  0.517289   -0.670280  0.670280   \n",
       "7  0.103614  1.677670       1.362630  1.214026   -0.670280  0.670280   \n",
       "8  1.957856 -1.383771       0.305575 -0.673790   -0.670280  0.670280   \n",
       "9 -0.641198  1.677670       0.997621  0.900731   -0.670280  0.670280   \n",
       "\n",
       "   Housing_free  Housing_own  Housing_rent  Purpose_business  ...  \\\n",
       "0     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "1     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "2     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "3      2.873893    -1.576173     -0.466933         -0.327749  ...   \n",
       "4      2.873893    -1.576173     -0.466933         -0.327749  ...   \n",
       "5      2.873893    -1.576173     -0.466933         -0.327749  ...   \n",
       "6     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "7     -0.347960    -1.576173      2.141633         -0.327749  ...   \n",
       "8     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "9     -0.347960     0.634448     -0.466933         -0.327749  ...   \n",
       "\n",
       "   Purpose_domestic appliances  Purpose_education  \\\n",
       "0                    -0.110208          -0.250398   \n",
       "1                    -0.110208          -0.250398   \n",
       "2                    -0.110208           3.993639   \n",
       "3                    -0.110208          -0.250398   \n",
       "4                    -0.110208          -0.250398   \n",
       "5                    -0.110208           3.993639   \n",
       "6                    -0.110208          -0.250398   \n",
       "7                    -0.110208          -0.250398   \n",
       "8                    -0.110208          -0.250398   \n",
       "9                    -0.110208          -0.250398   \n",
       "\n",
       "   Purpose_furniture/equipment  Purpose_radio/TV  Purpose_repairs  \\\n",
       "0                    -0.470108          1.603567        -0.149983   \n",
       "1                    -0.470108          1.603567        -0.149983   \n",
       "2                    -0.470108         -0.623610        -0.149983   \n",
       "3                     2.127172         -0.623610        -0.149983   \n",
       "4                    -0.470108         -0.623610        -0.149983   \n",
       "5                    -0.470108         -0.623610        -0.149983   \n",
       "6                     2.127172         -0.623610        -0.149983   \n",
       "7                    -0.470108         -0.623610        -0.149983   \n",
       "8                    -0.470108          1.603567        -0.149983   \n",
       "9                    -0.470108         -0.623610        -0.149983   \n",
       "\n",
       "   Purpose_vacation/others  Saving accounts_little  Saving accounts_moderate  \\\n",
       "0                -0.110208                 0.52179                 -0.338862   \n",
       "1                -0.110208                 0.52179                 -0.338862   \n",
       "2                -0.110208                 0.52179                 -0.338862   \n",
       "3                -0.110208                 0.52179                 -0.338862   \n",
       "4                -0.110208                 0.52179                 -0.338862   \n",
       "5                -0.110208                 0.52179                 -0.338862   \n",
       "6                -0.110208                -1.91648                 -0.338862   \n",
       "7                -0.110208                 0.52179                 -0.338862   \n",
       "8                -0.110208                -1.91648                 -0.338862   \n",
       "9                -0.110208                 0.52179                 -0.338862   \n",
       "\n",
       "   Saving accounts_quite rich  Saving accounts_rich  \n",
       "0                   -0.259299             -0.224544  \n",
       "1                   -0.259299             -0.224544  \n",
       "2                   -0.259299             -0.224544  \n",
       "3                   -0.259299             -0.224544  \n",
       "4                   -0.259299             -0.224544  \n",
       "5                   -0.259299             -0.224544  \n",
       "6                    3.856555             -0.224544  \n",
       "7                   -0.259299             -0.224544  \n",
       "8                   -0.259299              4.453463  \n",
       "9                   -0.259299             -0.224544  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9a1a856c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "5    1\n",
       "6    1\n",
       "7    1\n",
       "8    1\n",
       "9    0\n",
       "Name: Risk, dtype: int32"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Risk'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7c4532de",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(data_final, data['Risk'], test_size=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "503d6bfd",
   "metadata": {},
   "source": [
    "### 5 Fold CV using Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e00dfbcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_classifier = RandomForestClassifier()\n",
    "scores = cross_val_score(rf_classifier, data_final, data['Risk'], cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "63ae115e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.685, 0.695, 0.7  , 0.72 , 0.69 ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006c6ea0",
   "metadata": {},
   "source": [
    "### SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "553ed126",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(results):\n",
    "    print('BEST PARAMS: {}\\n'.format(results.best_params_))\n",
    "\n",
    "    means = results.cv_results_['mean_test_score']\n",
    "    stds = results.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, results.cv_results_['params']):\n",
    "        print('{} (+/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2d6c30b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEST PARAMS: {'C': 0.1, 'kernel': 'linear'}\n",
      "\n",
      "0.7 (+/-0.0) for {'C': 0.1, 'kernel': 'linear'}\n",
      "0.7 (+/-0.0) for {'C': 0.1, 'kernel': 'rbf'}\n",
      "0.7 (+/-0.0) for {'C': 1, 'kernel': 'linear'}\n",
      "0.694 (+/-0.028) for {'C': 1, 'kernel': 'rbf'}\n",
      "0.7 (+/-0.0) for {'C': 10, 'kernel': 'linear'}\n",
      "0.663 (+/-0.044) for {'C': 10, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "svc = SVC()\n",
    "parameters = {\n",
    "    'kernel': ['linear', 'rbf'],\n",
    "    'C': [0.1, 1, 10]\n",
    "}\n",
    "\n",
    "cv = GridSearchCV(svc, parameters, cv=5)\n",
    "model = cv.fit(data_final, data['Risk'].values.ravel())\n",
    "\n",
    "print_results(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f274b3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.1, kernel='linear')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bf00741f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(name, model, features, labels):\n",
    "    start = time()\n",
    "    pred = model.predict(features)\n",
    "    end = time()\n",
    "    accuracy = round(accuracy_score(labels, pred), 3)\n",
    "    precision = round(precision_score(labels, pred), 3)\n",
    "    recall = round(recall_score(labels, pred), 3)\n",
    "    print('{} -- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}ms'.format(name,\n",
    "                                                                                   accuracy,\n",
    "                                                                                   precision,\n",
    "                                                                                   recall,\n",
    "                                                                                   round((end - start)*1000, 1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "436d0bf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC -- Accuracy: 0.758 / Precision: 0.758 / Recall: 1.0 / Latency: 7.9ms\n"
     ]
    }
   ],
   "source": [
    "evaluate_model('SVC', cv.best_estimator_, X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b84ddd3",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "<li> After using all the possible combinations of Support Vector Machine parameter using hyperparameter tuning, the best performance of the model is obtained at C=0.1 and kernel='linear'</li>\n",
    "<li> The accuracy of the model is 0.758, precision is 0.758 and recall of 1 with a total latency of 7.9ms </li>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}