FinallyFinal Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loan Approval System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## @authors: Shouvit & Debaleen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # for numpy functions and ndarray\n",
    "import pandas as pd # for handling data\n",
    "import matplotlib.pyplot as plt # for plotting graphs i.e. data visualisation\n",
    "import seaborn as sns # for plotting graphs i.e. data visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Loading data and getting data descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Application_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>99</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>94</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "      <td>69</td>\n",
       "      <td>60</td>\n",
       "      <td>77</td>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1160.470000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4122.83000</td>\n",
       "      <td>1700.550000</td>\n",
       "      <td>134.221053</td>\n",
       "      <td>341.684211</td>\n",
       "      <td>0.836957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.622212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2258.89434</td>\n",
       "      <td>1947.668891</td>\n",
       "      <td>63.456163</td>\n",
       "      <td>61.309342</td>\n",
       "      <td>0.371429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1002.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1062.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2636.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.500000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1153.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3598.00000</td>\n",
       "      <td>1558.500000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1253.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4710.00000</td>\n",
       "      <td>2394.500000</td>\n",
       "      <td>154.500000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1343.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12841.00000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Application_ID Gender Married Dependents Education Self_Employed  \\\n",
       "count       100.000000     99     100        100       100            94   \n",
       "unique             NaN      2       2          4         2             2   \n",
       "top                NaN      M     Yes          0  Graduate            No   \n",
       "freq               NaN     84      69         60        77            81   \n",
       "mean       1160.470000    NaN     NaN        NaN       NaN           NaN   \n",
       "std         104.622212    NaN     NaN        NaN       NaN           NaN   \n",
       "min        1002.000000    NaN     NaN        NaN       NaN           NaN   \n",
       "25%        1062.500000    NaN     NaN        NaN       NaN           NaN   \n",
       "50%        1153.000000    NaN     NaN        NaN       NaN           NaN   \n",
       "75%        1253.500000    NaN     NaN        NaN       NaN           NaN   \n",
       "max        1343.000000    NaN     NaN        NaN       NaN           NaN   \n",
       "\n",
       "        ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count         100.00000         100.000000   95.000000         95.000000   \n",
       "unique              NaN                NaN         NaN               NaN   \n",
       "top                 NaN                NaN         NaN               NaN   \n",
       "freq                NaN                NaN         NaN               NaN   \n",
       "mean         4122.83000        1700.550000  134.221053        341.684211   \n",
       "std          2258.89434        1947.668891   63.456163         61.309342   \n",
       "min          1000.00000           0.000000   17.000000         60.000000   \n",
       "25%          2636.00000           0.000000   99.500000        360.000000   \n",
       "50%          3598.00000        1558.500000  120.000000        360.000000   \n",
       "75%          4710.00000        2394.500000  154.500000        360.000000   \n",
       "max         12841.00000       10968.000000  349.000000        480.000000   \n",
       "\n",
       "        Credit_History Property_Area Loan_Status  \n",
       "count        92.000000           100         100  \n",
       "unique             NaN             3           2  \n",
       "top                NaN         Urban           Y  \n",
       "freq               NaN            50          64  \n",
       "mean          0.836957           NaN         NaN  \n",
       "std           0.371429           NaN         NaN  \n",
       "min           0.000000           NaN         NaN  \n",
       "25%           1.000000           NaN         NaN  \n",
       "50%           1.000000           NaN         NaN  \n",
       "75%           1.000000           NaN         NaN  \n",
       "max           1.000000           NaN         NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading training dataset\n",
    "Data = pd.read_csv(\"train.csv\")\n",
    "\n",
    "# making a copy of the main Data to manipulate\n",
    "X = Data.copy()\n",
    "\n",
    "# dataset description\n",
    "X.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the data description, we can see that there are null values in columns 'Gender', 'Self_Employed', 'LoanAmount', 'Loan_Amount_Term', and 'Credit_History'. Also there are non-numeric columns.\n",
    "So we need to pre-process the data before using it to train any model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pre-Processing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Changing non-numeric values to numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Application_ID     Gender     Married  Dependents   Education  \\\n",
      "count      100.000000  99.000000  100.000000  100.000000  100.000000   \n",
      "mean      1160.470000   0.151515    0.690000    0.730000    0.770000   \n",
      "std        104.622212   0.360375    0.464823    1.013594    0.422953   \n",
      "min       1002.000000   0.000000    0.000000    0.000000    0.000000   \n",
      "25%       1062.500000   0.000000    0.000000    0.000000    1.000000   \n",
      "50%       1153.000000   0.000000    1.000000    0.000000    1.000000   \n",
      "75%       1253.500000   0.000000    1.000000    1.250000    1.000000   \n",
      "max       1343.000000   1.000000    1.000000    3.000000    1.000000   \n",
      "\n",
      "       Self_Employed  ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
      "count      94.000000        100.00000         100.000000   95.000000   \n",
      "mean        0.138298       4122.83000        1700.550000  134.221053   \n",
      "std         0.347063       2258.89434        1947.668891   63.456163   \n",
      "min         0.000000       1000.00000           0.000000   17.000000   \n",
      "25%         0.000000       2636.00000           0.000000   99.500000   \n",
      "50%         0.000000       3598.00000        1558.500000  120.000000   \n",
      "75%         0.000000       4710.00000        2394.500000  154.500000   \n",
      "max         1.000000      12841.00000       10968.000000  349.000000   \n",
      "\n",
      "       Loan_Amount_Term  Credit_History  Property_Area  Loan_Status  \n",
      "count         95.000000       92.000000     100.000000   100.000000  \n",
      "mean         341.684211        0.836957       1.410000     0.640000  \n",
      "std           61.309342        0.371429       0.652811     0.482418  \n",
      "min           60.000000        0.000000       0.000000     0.000000  \n",
      "25%          360.000000        1.000000       1.000000     0.000000  \n",
      "50%          360.000000        1.000000       1.500000     1.000000  \n",
      "75%          360.000000        1.000000       2.000000     1.000000  \n",
      "max          480.000000        1.000000       2.000000     1.000000  \n",
      "Application_ID         int64\n",
      "Gender               float64\n",
      "Married                int64\n",
      "Dependents             int64\n",
      "Education              int64\n",
      "Self_Employed        float64\n",
      "ApplicantIncome        int64\n",
      "CoapplicantIncome      int64\n",
      "LoanAmount           float64\n",
      "Loan_Amount_Term     float64\n",
      "Credit_History       float64\n",
      "Property_Area          int64\n",
      "Loan_Status            int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "X.Married.replace(['No', 'Yes'], [0, 1], inplace=True) # replacing 'No' with 0 and 'Yes' with 1 in 'Married' \n",
    "                                                       # column\n",
    "    \n",
    "X.Gender.replace(['M', 'F'], [0, 1], inplace = True) # replacing 'M' with 0 and 'F' with 1 in 'Gender' column\n",
    "\n",
    "X.Dependents.replace(['0', '1', '2', '3+'], [0, 1, 2, 3], inplace=True) # replacing '0' with 0, '1' with 1, '2' \n",
    "                                                                        # with 2 and '3+' with 3 in\n",
    "                                                                        # 'Dependents' column\n",
    "        \n",
    "X.Education.replace(['Not Graduate', 'Graduate'], [0, 1], inplace=True) # replacing 'Not Graduate' with 0 \n",
    "                                                                        # and 'Graduate' with 1 in 'Education'\n",
    "                                                                        # column\n",
    "        \n",
    "X.Self_Employed.replace(['No', 'Yes'], [0, 1], inplace=True) # replacing 'No' with 0 and 'Yes' with 1 in \n",
    "                                                             # 'Self_Employed' column\n",
    "\n",
    "X.Property_Area.replace(['Rural','Semiurban', 'Urban'], [0, 1, 2], inplace=True) # replacing 'Rural' with 0, \n",
    "                                                                                 # 'Semiurban' with 1 and \n",
    "                                                                                 # 'Urban' with 2 in \n",
    "                                                                                 # 'Property_Area' column\n",
    "\n",
    "X.Loan_Status.replace(['N', 'Y'], [0, 1], inplace = True) # replacing 'N' with 0 and 'Y' with 1 in \n",
    "                                                          # 'Loan_Status' column\n",
    "\n",
    "print(X.describe(include=\"all\"))\n",
    "print(X.dtypes)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "                                                                Non-Numeric to Numeric Change table\n",
    "                                                               -------------------------------------\n",
    "\n",
    "                                                Columns              Initial Value              Replaced Value\n",
    "                                                --------            ---------------            ----------------\n",
    "                                                Married             No                         0\n",
    "                                                                    Yes                        1\n",
    "\n",
    "                                                Gender              M                          0\n",
    "                                                                    F                          1\n",
    "\n",
    "                                                Dependents          0                          0    Note: Changed type from str to int\n",
    "                                                                    1                          1    Note: Changed type from str to int\n",
    "                                                                    2                          2    Note: Changed type from str to int\n",
    "                                                                    3+                         3\n",
    "\n",
    "                                                Education           Not Graduate               0\n",
    "                                                                    Graduate                   1\n",
    "\n",
    "                                                Self_Employed       No                         0\n",
    "                                                                    Yes                        1\n",
    "\n",
    "                                                Property_Area       Rural                      0\n",
    "                                                                    Semiurban                  1\n",
    "                                                                    Urban                      2\n",
    "\n",
    "                                                Loan_Status         N                          0\n",
    "                                                                    Y                          1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Searching for Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null values count:\n",
      " Application_ID       0\n",
      "Gender               1\n",
      "Married              0\n",
      "Dependents           0\n",
      "Education            0\n",
      "Self_Employed        6\n",
      "ApplicantIncome      0\n",
      "CoapplicantIncome    0\n",
      "LoanAmount           5\n",
      "Loan_Amount_Term     5\n",
      "Credit_History       8\n",
      "Property_Area        0\n",
      "Loan_Status          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Null values count:\\n\", X.isnull().sum())  # sum of Null values in a column"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can see that the dataset has null values. Let us visualise the null values using heatmap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Visualising Null values using heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a0db995780>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X.isnull(),yticklabels=False,cbar=True,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Handling Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "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>Application_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1160.470000</td>\n",
       "      <td>0.15000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>4122.83000</td>\n",
       "      <td>1700.550000</td>\n",
       "      <td>135.480000</td>\n",
       "      <td>341.830000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>1.410000</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.622212</td>\n",
       "      <td>0.35887</td>\n",
       "      <td>0.464823</td>\n",
       "      <td>1.013594</td>\n",
       "      <td>0.422953</td>\n",
       "      <td>0.337998</td>\n",
       "      <td>2258.89434</td>\n",
       "      <td>1947.668891</td>\n",
       "      <td>62.427409</td>\n",
       "      <td>60.023237</td>\n",
       "      <td>0.422953</td>\n",
       "      <td>0.652811</td>\n",
       "      <td>0.482418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1002.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1062.500000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2636.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1153.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3598.00000</td>\n",
       "      <td>1558.500000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1253.500000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4710.00000</td>\n",
       "      <td>2394.500000</td>\n",
       "      <td>159.750000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12841.00000</td>\n",
       "      <td>10968.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Application_ID     Gender     Married  Dependents   Education  \\\n",
       "count      100.000000  100.00000  100.000000  100.000000  100.000000   \n",
       "mean      1160.470000    0.15000    0.690000    0.730000    0.770000   \n",
       "std        104.622212    0.35887    0.464823    1.013594    0.422953   \n",
       "min       1002.000000    0.00000    0.000000    0.000000    0.000000   \n",
       "25%       1062.500000    0.00000    0.000000    0.000000    1.000000   \n",
       "50%       1153.000000    0.00000    1.000000    0.000000    1.000000   \n",
       "75%       1253.500000    0.00000    1.000000    1.250000    1.000000   \n",
       "max       1343.000000    1.00000    1.000000    3.000000    1.000000   \n",
       "\n",
       "       Self_Employed  ApplicantIncome  CoapplicantIncome  LoanAmount  \\\n",
       "count     100.000000        100.00000         100.000000  100.000000   \n",
       "mean        0.130000       4122.83000        1700.550000  135.480000   \n",
       "std         0.337998       2258.89434        1947.668891   62.427409   \n",
       "min         0.000000       1000.00000           0.000000   17.000000   \n",
       "25%         0.000000       2636.00000           0.000000  100.000000   \n",
       "50%         0.000000       3598.00000        1558.500000  122.000000   \n",
       "75%         0.000000       4710.00000        2394.500000  159.750000   \n",
       "max         1.000000      12841.00000       10968.000000  349.000000   \n",
       "\n",
       "       Loan_Amount_Term  Credit_History  Property_Area  Loan_Status  \n",
       "count        100.000000      100.000000     100.000000   100.000000  \n",
       "mean         341.830000        0.770000       1.410000     0.640000  \n",
       "std           60.023237        0.422953       0.652811     0.482418  \n",
       "min           60.000000        0.000000       0.000000     0.000000  \n",
       "25%          360.000000        1.000000       1.000000     0.000000  \n",
       "50%          360.000000        1.000000       1.500000     1.000000  \n",
       "75%          360.000000        1.000000       2.000000     1.000000  \n",
       "max          480.000000        1.000000       2.000000     1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filling Null values in 'Credit_History' with 0 (as stated in the dataset description from source)\n",
    "X.Credit_History.fillna(0, inplace=True)\n",
    "\n",
    "#filling Null values in binary columns with mode value\n",
    "X.Self_Employed.fillna(X.Self_Employed.mode()[0], inplace=True)\n",
    "X.Gender.fillna(X.Gender.mode()[0], inplace=True)\n",
    "\n",
    "# filling Null values with mean +- std in non-binary columns\n",
    "avg = X.LoanAmount.mean()\n",
    "std = X.LoanAmount.std()\n",
    "count = X.LoanAmount.isnull().sum()\n",
    "random = np.random.randint(avg-std,avg+std,size=count)\n",
    "X['LoanAmount'][np.isnan(X['LoanAmount'])]=random\n",
    "\n",
    "avg = X.Loan_Amount_Term.mean()\n",
    "std = X.Loan_Amount_Term.std()\n",
    "count = X.Loan_Amount_Term.isnull().sum()\n",
    "random = np.random.randint(avg-std,avg+std,size=count)\n",
    "X['Loan_Amount_Term'][np.isnan(X['Loan_Amount_Term'])]=random\n",
    "\n",
    "\n",
    "sns.heatmap(X.isnull(),yticklabels=False,cbar=True,cmap='viridis')\n",
    "X.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now we have successfully handled Null values and converted non-numeric values to Numeric values. We didn't drop the rows with null values as we have a small dataset(only 100 entries).\n",
    "So we are moving on to find if there are any outliers in our data and find the correlations of different attributes to our target i.e. 'Loan_Status' column in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a copy of the data after removing Null values\n",
    "old = X.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checking correlation of every attribute with our target attribute i.e. 'Loan_Status'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Correlation of each attribute with Loan Status: \n",
      "\n",
      "Gender : -0.09335200560186736\n",
      "Married : 0.12792979689405484\n",
      "Dependents : 0.0057840889995565475\n",
      "Education : -0.013861413315217981\n",
      "Self_Employed : 0.04212465653604544\n",
      "ApplicantIncome : -0.1196673096146476\n",
      "CoapplicantIncome : -0.03110315854904195\n",
      "LoanAmount : -0.14446441796336063\n",
      "Loan_Amount_Term : -0.09806487882763729\n",
      "Credit_History : 0.5306941097826289\n",
      "Property_Area : 0.24889474150088223\n"
     ]
    }
   ],
   "source": [
    "f = X.columns[1:-1] # selecting all columns except 'Application_ID' since that is not a determining factor\n",
    "print(\"\\n\\nCorrelation of each attribute with Loan Status: \\n\")\n",
    "for i in f:\n",
    "    corr = X[i].corr(X['Loan_Status'])\n",
    "    print(i, \":\", corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Checking for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x3600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting distribution plot and boxplot for \n",
    "\n",
    "f, axes = plt.subplots(3, 2, figsize=(20, 50))\n",
    "\n",
    "sns.distplot(old[\"LoanAmount\"], ax=axes[0][0])\n",
    "plt.grid()\n",
    "sns.boxplot( y=\"LoanAmount\", data=old, palette = \"winter\", ax = axes[0][1])\n",
    "plt.grid()\n",
    "\n",
    "sns.distplot(old[\"ApplicantIncome\"], ax=axes[1][0])\n",
    "plt.grid()\n",
    "sns.boxplot( y=\"ApplicantIncome\", data=old, palette = \"winter\", ax = axes[1][1])\n",
    "plt.grid()\n",
    "\n",
    "sns.distplot(old[\"CoapplicantIncome\"], ax=axes[2][0])\n",
    "plt.grid()\n",
    "sns.boxplot( y=\"CoapplicantIncome\", data=old, palette = \"winter\", ax = axes[2][1])\n",
    "plt.grid()\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the distribution plots and boxplots we can see that  there are possible outliers in the columns 'LoanAmount', 'ApplicantIncome' and 'CoapplicantIncome'.\n",
    "\n",
    "We have not checked for outliers in the columns viz. 'Gender', 'Married', 'Education', 'Self_Employed', and \n",
    "'Credit_History' as they are columns having values in binaries and also initially they had non-numeric values. We have not checked for outliers in the columns viz. 'Dependents' and 'Property_Area' as they are columns having values that were initially non-numeric. We have not checked for outliers in the column 'Loan_Amount_Term' as the mean and MAD values of this column are in the range 3*mean. So this method won't have any affect on the data of the column 'Loan_Amount_Term'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Handling possible outliers using non-parametric statistics method\n",
    "##### We used Median Absolute Deviation or MAD to handle the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ApplicantIncome MAD :\n",
      " 3597.0\n",
      "CoapplicantIncome MAD :\n",
      " 1542.25\n",
      "LoanAmount MAD :\n",
      " 122.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Median Absolute Deviation(MAD) , based on the median, is a robust non-parametric statistics\n",
    "# exclude data outside 3 * mad and replace those values with median of the data.\n",
    "\n",
    "# handling outliers with MAD\n",
    "\n",
    "# Handling 'ApplicantIncome' column\n",
    "mad=1.4826*np.median(np.abs(X.ApplicantIncome-X.ApplicantIncome.median()))\n",
    "size_outlr_mad=X.ApplicantIncome\n",
    "size_outlr_mad[((X.ApplicantIncome-X.ApplicantIncome.median()).abs()>3*mad)]=X.ApplicantIncome.median()\n",
    "print(\"ApplicantIncome MAD :\\n\",size_outlr_mad.median())\n",
    "\n",
    "# Handling 'CoapplicantIncome' column\n",
    "mad=1.4826*np.median(np.abs(X.CoapplicantIncome-X.CoapplicantIncome.median()))\n",
    "size_outlr_mad=X.CoapplicantIncome\n",
    "size_outlr_mad[((X.CoapplicantIncome-X.CoapplicantIncome.median()).abs()>3*mad)]=X.CoapplicantIncome.median()\n",
    "print(\"CoapplicantIncome MAD :\\n\",size_outlr_mad.median())\n",
    "\n",
    "# Handling 'LoanAmount' column\n",
    "mad=1.4826*np.median(np.abs(X.LoanAmount-X.LoanAmount.median()))\n",
    "size_outlr_mad=X.LoanAmount\n",
    "size_outlr_mad[((X.LoanAmount-X.LoanAmount.median()).abs()>3*mad)]=X.LoanAmount.median()\n",
    "print(\"LoanAmount MAD :\\n\",size_outlr_mad.median())\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now we have successfully handled the possible outliers using non-parametric statistics method. We have used Median Absolute Deviation to handle the outliers. Let us visualise the data after handling the outliers and compare it to our old data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAR8CAYAAADLpHOHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xt0nHd97/v3VyONNJKsu3yVbcmxncR2Qi7GgYRyKCmQtBRzupNNwqaEln1yVg/pvauFs1fTtWmzT1mnm7TdXLpzCCWlhRBSumuoIezukLa54MRxAontOJZt2ZIlWff7dTTf88c848iyZEvyjOb2ea3ltWaeeZ5nfgPO+DO/y/dn7o6IiIiIZIeCdDdARERERBZP4U1EREQkiyi8iYiIiGQRhTcRERGRLKLwJiIiIpJFFN5EREREsojCm+QEM/s1MztnZiNmVpvu9oiIXI6ZRczsu2Y2aGbfTnd7JHsovEnGMLMWMxsPAli/mf2TmW1cxHVFwOeB97t7ubv3pr61IiKLZ2bPBN9rxbMO3wWsAWrd/W4z+4SZPZumJkoWUXiTTPOL7l4OrAPOAf9tEdesAUqAw0t9M4vTfwcikjJm1gj8DODAh2a9tBl4092jSXqfwmTcRzKf/tGSjOTuE8CTwA4AMys2sz8zszPB8OhfBUMO24FjwWUDZvZ0cP6tZvZSMBzxkpndmrh38Av4ITN7DhgDtphZpZk9amYdZnbWzP7EzEIr+6lFJEd9HPgx8DXgPgAz+8/Ag8BHgtGGTwF/BbwzeD4QnDfvd1/w2nvMrM3M/sDMOoG/TsNnkzRQSpeMZGalwEeIf+EBfA7YAtwATAPfAB5098+Y2U7gFFDl7lEzqwH+CfgN4JvA3cA/mdnWWUOqvwzcSTz4GfBt4j19W4Ey4HtAK/DfU/1ZRSTnfZz41I4DwI/NbI27/5GZObDV3T8GYGajwH9093fNunbe7z7gM8Hra4Ea4r146pDJE/o/WjLN/wh+cQ4B7wP+XzMz4P8Aftvd+9x9GPgvwD0L3OMXgOPu/nV3j7r7N4E3gF+cdc7X3P1wMFxRQzzI/Za7j7p7F/DwJe4vIrIoZvYu4sHqCXd/GTgBfHSR1y7muy8G/JG7T7r7eHJbL5lKPW+SaT7s7v8cDFnuBf6F+C/OUuDl+HcZEO8tW2hYcz1wes6x08CGWc9bZz3eDBQBHbPuXzDnHBGR5bgP+KG79wTPvxEce3gR19Zz+e++7mCaieQRhTfJSO4+A3zHzP478A5gHNjp7mcXcXk78UA22ybgB7PfYtbjVmASqEvWxGERkWBu2r8HQsGcNIBioMrM3jbPJT7neQ+X/+6be43kAQ2bSkYKVoHuBaqJryL9/4CHzWx18PoGM/vAApfvB7ab2UfNrNDMPkJ84cP35jvZ3TuAHwL/1cwqzKzAzK4ys/8t2Z9LRPLKh4EZ4t8/NwR/rgX+jfg8uLnOAQ1mFgZw9xhL++6TPKHwJpnmu2Y2QnzO20PAfe5+GPgDoJn4ZN8h4J+Bq+e7QbAo4YPA7wK9wO8DH5w1bDGfjwNh4AjQT3yl67qkfCIRyVf3AX/t7mfcvTPxB/gC8B+4ePTraeI/VjvNLPF9tejvPskf5q4eVxEREZFsoZ43ERERkSyi8CYiIiKSRRTeRERERLKIwpuIiIhIFlF4ExEREckiOV2kt66uzhsbG9PdDBFZQS+//HKPu9enux3JoO8wkfyy2O+vnA5vjY2NHDx4MN3NEJEVZGZzt0bLWvoOE8kvi/3+0rCpiIiISBZReBMRERHJIgpvIiIiIllE4U1EREQkiyi8iYiIiGQRhTcRERGRLKLwJiIiIpJFFN5EREREsojCm4iIiEgWUXgTERERySIKbyIiIiJZROFNREREJIsovImIiIhkkcJ0N0Ay2zcOnFmR9/noLZtW5H1EFsPM7gD+AggBX3H3P53zejHwN8DNQC/wEXdvMbNa4Eng7cDX3P2BWdeEgS8A7wFiwH9y979fgY8jWWa537v6Hs0fCm8iIrOYWQj4IvA+oA14ycz2ufuRWad9Euh3961mdg/wOeAjwATwh8Cu4M9s/wnocvftZlYA1KT4o4hIjtKwqYjIhfYAze5+0t2ngMeBvXPO2Qs8Fjx+ErjdzMzdR939WeIhbq5fBf4fAHePuXtPapovIrlO4U1E5EIbgNZZz9uCY/Oe4+5RYBCoXeiGZlYVPPxjMztkZt82szULnHu/mR00s4Pd3d3L/QwiksMU3kRELmTzHPNlnDNbIdAAPOfuNwEvAH8234nu/oi773b33fX19Ytpr4jkGYU3EZELtQEbZz1vANoXOsfMCoFKoO8S9+wFxoB/CJ5/G7gpGY0Vkfyj8CYicqGXgG1m1hSsEL0H2DfnnH3AfcHju4Cn3X3Bnrfgte8SX2kKcDtwZKHzRUQuRatNRURmcfeomT0APEW8VMhX3f2wmX0WOOju+4BHga+bWTPxHrd7EtebWQtQAYTN7MPA+4OVqn8QXPPnQDfwKyv5uUQkdyi8iYjM4e77gf1zjj046/EEcPcC1zYucPw08O7ktVJE8pWGTUVERESyiMKbiIiISBZReBMRERHJIgpvIiIiIllE4U1EREQkiyi8iYiIiGQRhTcRERGRLKLwJiIiIpJFFN5EREREsojCm4iIiEgWUXgTERERySIKbyIiIiJZROFNREREJIsovImIiIhkkZSENzO7w8yOmVmzmX16nteLzexbwesHzKwxOF5rZj8ysxEz+8Kca242s9eCa/7SzCwVbRcRERHJZEkPb2YWAr4I3AnsAO41sx1zTvsk0O/uW4GHgc8FxyeAPwR+b55bfxm4H9gW/Lkj2W0XERERyXSp6HnbAzS7+0l3nwIeB/bOOWcv8Fjw+EngdjMzdx9192eJh7jzzGwdUOHuL7i7A38DfDgFbRcRERHJaKkIbxuA1lnP24Jj857j7lFgEKi9zD3bLnNPERERkZyXivA231w0X8Y5yzrfzO43s4NmdrC7u/sStxQRERHJPqkIb23AxlnPG4D2hc4xs0KgEui7zD0bLnNPANz9EXff7e676+vrl9h0ERERkcyWivD2ErDNzJrMLAzcA+ybc84+4L7g8V3A08Fctnm5ewcwbGbvCFaZfhz4x+Q3XURERCSzFSb7hu4eNbMHgKeAEPBVdz9sZp8FDrr7PuBR4Otm1ky8x+2exPVm1gJUAGEz+zDwfnc/Avwa8DUgAnw/+CMiIiKSV5Ie3gDcfT+wf86xB2c9ngDuXuDaxgWOHwR2Ja+VIiIiItlHOyyIiIiIZBGFNxEREZEsovAmIiIikkUU3kRERESyiMKbiIiISBZReBMRmcPM7jCzY2bWbGafnuf1YjP7VvD6ATNrDI7XmtmPzGzEzL6wwL33mdnrqf0EIpLLFN5ERGYxsxDwReBOYAdwr5ntmHPaJ4F+d98KPAx8Ljg+Afwh8HsL3PuXgJFUtFtE8ofCm4jIhfYAze5+0t2ngMeBvXPO2Qs8Fjx+ErjdzMzdR939WeIh7gJmVg78DvAnqWu6iOQDhTdJio7BcZ442Mqffv8o54Yu+ndLJJtsAFpnPW8Ljs17jrtHgUGg9jL3/WPgvwJjyWmmiOQrhTe5Yl1DE3zxR80caR9iMhrjf7xyltjCW9WKZDqb59jcv9CLOeetk81uALa6+z9c9s3N7jezg2Z2sLu7+3Kni0geUniTK3agpQ/D+O33beeD16/jdN8YL7f0p7tZIsvVBmyc9bwBaF/oHDMrBCqJ79O8kHcCNwd7Nz8LbDezZ+Y70d0fcffd7r67vr5+WR9ARHKbwptckemZGK+c6WfnhgoqI0XctKmaproyfnC4k+hMLN3NE1mOl4BtZtZkZmHgHmDfnHP2AfcFj+8CnnZfuLvZ3b/s7uuDvZvfBbzp7u9JestFJC8ovMkVea1tkInpGHsaawAwM267qo7x6RlO92lqj2SfYA7bA8BTwFHgCXc/bGafNbMPBac9CtSaWTPxRQjny4kEvWufBz5hZm3zrFQVEbkihelugGS3F1v6qCsvpqmu7Pyxq+rLKDA4fm6Yq+rL09g6keVx9/3A/jnHHpz1eAK4e4FrGy9z7xZg1xU3UkTylnreZNnGJqOc6Rvjho1VmL01f7u4KMTm2jKOd6mclYiISLIpvMmynQmGRRvrSi96bfuaVXQMTjA0Pr3SzRIREclpCm+ybKf7xigwaKi6OLxtWx0fLlXvm4jI8rk7Tx3u5K/+5QRf//Fpzg6Mp7tJkgEU3mTZTveOsb4qQrjw4r9G6ypLWFVcyJvnhtPQMhGR3PBq6wD/8mY3MzHndO8o33rpjFbyi8KbLE80FqOtf4zNNRf3ukF81WlTfdn5oVUREVmawfFpvvvTdjbXlPJr77mKf797Iz0jUzzX3JPupkmaKbzJsnQMTBCNOZtqyxY8Z0NVhMHxaUYnoyvYMhGR3PDDw53MxJx/d3MDBWZsX7OKHesqePpYF4OaT5zXFN5kWRI13BbqeQNYVxkBoGNQe52KiCzFVDTG4fYhbthYTV158fnjd+5ay/SM82rrQBpbJ+mm8CbLcqZ3lKrSIioiRQues7ayBIhvWi8iIot37NwwUzMxrm+ovOB4bXkx66tKONoxlKaWSSZQeJNlaR+coKEqcslzyosLqSgpVM+biMgS/bRtgFXFhRcUQE+4dl0FrX1jDE9o6DRfKbzJkk3PxOgfnWJ1Rcllz11XGVHPm4jIEkxMz3Csc5hdDZUUzCqAnrBjXQUOvNGp1fz5SuFNlqx7eBIH1iwmvFWV0D08ybSWtouILMrRjiGiMedtGyrnfX1tRQlVpUUaOs1jCm+yZF3D8WHQ1auKL3NmvOct5tA1NJnqZomI5IQT3SOUhkM0XKIU07XrKmjuGmEqqh/G+UjhTZasa2iSAoPa8vBlz12vRQsiIktyqmeUprqyeYdME7avLicac9r6VUszHym8yZKdG56ktryYwoLL//WpLgsTLiygXYsWREQuq31gnP6xaRovUUMToKE63ivX1q8fxvlI4U2WrGtoYlFDpgAFZqxeVUzPsIZNRUQu56WWPoB5V5nOVlZcSE1ZmFb1vOUlhTdZkumZGH2jU4tarJBQV15Mz4jCm4jI5Rw41UdxYcH5OpmX0lAdoVVbEOYlhTdZkp6R+ErTxfa8QXxu3OD4tFaciohcxkun+thcW3rJ+W4JG6tLGZqIaqusPKTwJkuSWDW6mBpvCXVlxTjQOzqVolaJiGS/3pFJjneN0HSZ+W4JG6vjhdK1aCH/KLzJkpwbnqDAoK7s8itNExL78mnem4jIwg6die9XunmR4W1dVYQCg9Y+LVrINwpvsiS9I1NUl4YpDC3+r06ipIh63kREFvb62UEKDNZfZuvBhKJQAesqI+p5y0MKb7IkfaNT1Cyh1w2gpChEeXGhFi2IiFzC4fZBttSXEy5c/D/NDdURzg6M4+4pbJlkGoU3WZLe0cklhzeAuvIwvQpvIiILOtw+xK71FUu6Zk1FCZPRmBYt5BmFN1m08akZJqZjywxvxfSMaNhURGQ+PSOTdAxOsHP9/PuZLiRRtumctiDMKwpvsmi9o/Evh9plhLfa8mJGJqNMTM8ku1kiIlnvcHt8k/mdG5bY8xaUbUrsOS35QeFNFq0vWHBQvcxhU4gveBARkQsdbh8EWHLPW2lxIeXFhep5yzMKb7JoifC23GFTQIsWRETmcfjsEBtrIlRGipZ87ZqKYvW85RmFN1m0vtEpyosLKS4MLfnaROBLDL2KiMhbDrcPsmuJvW4JqytK6BqaJBbTitN8ofAmi9a7jDIhCUWhAlaVFNI/phVRIiKzDU9M09I7xs4lrjRNWLOqhKmZGGcHVKw3Xyi8yaL1X0F4A6guDdM/pjlvIiKzvXluGIBr1i4zvFXEp6Uc7xpOWpsksym8yaJEZ+J1hK4svBXRr10WJAuY2R1mdszMms3s0/O8Xmxm3wpeP2BmjcHxWjP7kZmNmNkXZp1famb/ZGZvmNlhM/vTlfs0kunePDcCwPY1q5Z1/epVJRfcR3KfwpssSv/YNM7yFiskVJeGGRyfZkbzMiSDmVkI+CJwJ7ADuNfMdsw57ZNAv7tvBR4GPhccnwD+EPi9eW79Z+5+DXAjcJuZ3ZmK9kv2efPcMJGiEA3Vi9sWa65IOERFSeH5HjzJfQpvsiiJlabLqfGWUF0aJuYwNKF5b5LR9gDN7n7S3aeAx4G9c87ZCzwWPH4SuN3MzN1H3f1Z4iHuPHcfc/cfBY+ngENAQyo/hGSP4+dG2LamnIICW/Y9Vq8q4USXet7yhcKbLErf2PJrvCUkrtW8N8lwG4DWWc/bgmPznuPuUWAQqF3Mzc2sCvhF4H8t8Pr9ZnbQzA52d3cvsemSjd48N8y21csbMk2oLQ/T0qsN6vNFysLbcueMBK99Jjh+zMw+MOv4bwfzRV43s2+aWUmq2i8XGhibIlRglBcXLvse1aXx+kX9o+p5k4w2X/fH3LH+xZxz8Y3NCoFvAn/p7ifnO8fdH3H33e6+u76+/rKNlew2MDZF1/Ak29eUX9F9asuLGRyf1rziPJGS8HYlc0aC8+4BdgJ3AF8ys5CZbQB+A9jt7ruAUHCerICBsWmqIkUU2PK79StLizDU8yYZrw3YOOt5A9C+0DlBIKsE+hZx70eA4+7+50lop+SA84sV1l5hz1swsnGqd/SK2ySZL1U9b8ueMxIcf9zdJ939FNAc3A+gEIgEX5alXPyFKikyMDZFZenSK3/PVlhQQEVEK04l470EbDOzJjMLE/+RuG/OOfuA+4LHdwFPu/sle97M7E+Ih7zfSnJ7JYslFhksd6VpQm2wBWFLj8JbPkhVeLuSOSPzXuvuZ4E/A84AHcCgu/9w7htrvkhqDI5PUx1Z/ny3hOrSIvW8SUYLvo8eAJ4CjgJPuPthM/usmX0oOO1RoNbMmoHfAc5PDTGzFuDzwCfMrM3MdphZA/CfiI9EHDKzV83sP67cp5JMdfzcMOXFhayvvLJZQDWlYQoMzXvLE8ufwHRpVzJnZN7jZlZNvFeuCRgAvm1mH3P3v73gRPdHiA9NsHv3btWkSIJoLMbwRJSqK+x5g/iK05P6ZSgZzt33A/vnHHtw1uMJ4O4Frm1c4LbLn3MgOevNcyNsXV2OXcGUFIDCUAHrqyLqecsTqep5u5I5Iwtd+3PAKXfvdvdp4DvArSlpvVxgaDyKQ1LCW1VpmKHxaaKx2JU3TEQkyx3vGrnixQoJTXVltGjOW15IVXi7kjkj+4B7gtWoTcA24EXiw6XvCCqVG3A78SENSbHEMGdV6ZUPm9aUFeHAoPY4FZE8NzQxTc/IJFvqkxPeGmvLONUzymWmX0oOSMmwqbtHzSwxZyQEfDUxZwQ46O77iM8Z+XowZ6SPYOVocN4TwBEgCnzK3WeAA2b2JPHillHgFYLhUUmtRNCqiiSn5w3iOzbUlhdf8f1ERLJVYoizsbYsKffbXFvK8ESU/rEr28pQMl+q5rxd6ZyRh4CH5jn+R8AfJbelcjn94/Get8okhLfqILwNaNGCiOS5U0F421KfnPDWVFd2/r4Kb7lNOyzIZQ2OTbOquJDC0JX/dakoKcSAgXENm4pIfmvpGcMMNtWUJuV+jUF406KF3KfwJpc1MDadlMUKEF8RVV5SqDlvIpL3TvWMsL4yQklRKCn321hdGpQLUXjLdQpvcln9Y1NJWayQUBUpYmBcw6Yikt9O9Y6dH+pMhnBhAesqI7T2qdZbrlN4k0tydwbHp5OyWCGhqjTMgHreRCSPuTunukdorEvOkGnCxpoIbf3jSb2nZB6FN7mkkcko0ZgnbdgU4j1vg+PTxLScXUTyVP/YNEMTUZrqklMmJKGhulThLQ8ovMklDQYLC5I5bFpZWkQ05oxORpN2TxGRbJJYadqU5J63huoI54YnmIzOJPW+klkU3uSSEuEtGWVCEqqCPVIHteJURPLUqSTXeEtoqC7FHdoHJpJ6X8ksCm9ySYmAVZHUOW/xe2nem4jkq5aeUUIFxsYklQlJaKiOANDWr0ULuUzhTS5paHyaUIFRFk7OUnaYFd7U8yYieepU7ygbqyMUJaF+5mxvhTfNe8tlCm9ySYPj01RGiohvJ5sckaIQ4VCBdlkQkbx1qnv0fFHdZFpbUUKowNTzluMU3uSSBsejVJQkb8gUwMyoLC3SsKmI5CV3p6V3NKk13hIKQwWsrypRz1uOU3iTSxqamKYykvwtcBPlQkRE8k3X8CRjUzMpCW8ADVWlKtSb4xTeZEGJAr3JXGmaEC/Uq2FTEck/qVppmtBQrUK9uU7hTRbUNzrFTMyTutI0oaq0iNGpGaZnYkm/t4hIJms5X+MtVeGtlK7hSSamVestVym8yYI6BuN1glLS8xZRuRARyU+nekYJhwpYXxVJyf0TK07bB9T7lqsU3mRBnSkMb5Xny4Vo6FRE8supnlE215YSKkjeKv7ZVC4k9ym8yYI6huLhLRXDptWJXRbU8yYieaalNzVlQhIagsK/Cm+5S+FNFtQ5OE6BQXlx8lebVkSKMFSoV0TySyzmtPSOpWy+G8RrvRWq1ltOU3iTBXUMTlBRUkRBEgv0JoQKjFUlhZrzJiJ5pX1wnKloLKXhLVRgrK+K0Kqet5yl8CYL6hiYSMmQaYLKhYhIvkl1mZCEeLkQ9bzlKoU3WVDn0ERKFiskVEaKNGwqInkl1WVCElTrLbcpvMm83J2OwfGUhrfq0vguCzH3lL2HiEgmOdUzRqQoxJqK4pS+T0N1Kd2q9ZazFN5kXoPj00xMx1I6bFpZGmYm5oxORlP2HiIimeRUzwiNdWVYCuYSz5YoF3JWtd5yksKbzCuVBXoTVKhXRPJNfKVpacrfp6Fa5UJymcKbzOt8gd6S5JcJSag6X6hX4U1Ect/0TIzWvtSWCUnYWBPvedMG9blJ4U3mleh5S+lq06BQr1acSqYxszvM7JiZNZvZp+d5vdjMvhW8fsDMGoPjtWb2IzMbMbMvzLnmZjN7LbjmLy3V42aScdr6x4nGPOUrTQFWryqhKGTqectRCm8yr0SB3lUlqQtvJUUFFBcWqOdNMoqZhYAvAncCO4B7zWzHnNM+CfS7+1bgYeBzwfEJ4A+B35vn1l8G7ge2BX/uSH7rJZMlVppuqU99eEvUelO5kNyk8Cbz6hicoH5Vccr23gMwMyojRdoiSzLNHqDZ3U+6+xTwOLB3zjl7gceCx08Ct5uZufuouz9LPMSdZ2brgAp3f8HdHfgb4MMp/RSScU6uUI23BJULyV0KbzKvzqEJ1lZGUv4+VaVFGjaVTLMBaJ31vC04Nu857h4FBoHay9yz7TL3BMDM7jezg2Z2sLu7e4lNl0zW0jPKqpJCasrCK/J+DVWlCm85KnWz0SWrdQxOsG11ecrfpyoS1peLZJr5upvnFiNczDnLOt/dHwEeAdi9e7eKIGaxbxw4c8HzF072Uhkp4psvti5wRXLfr3d0ip6RSR57voWi0MJ9NR+9ZVNK2iOpo543mVfn4ARrK0tS/j5VpUWMTc0wNqVab5Ix2oCNs543AO0LnWNmhUAl0HeZezZc5p6S43pGJqldoV43iBdCB+gf1ehGrlF4k4sMT0wzMhll3QqFN4D2gYnLnCmyYl4CtplZk5mFgXuAfXPO2QfcFzy+C3g6mMs2L3fvAIbN7B3BKtOPA/+Y/KZLppqeiTE4Nk1teWp3VpitujRY0a9FYTlH4U0ukqjxthJz3iqDciHtqgIuGSKYw/YA8BRwFHjC3Q+b2WfN7EPBaY8CtWbWDPwOcL6ciJm1AJ8HPmFmbbNWqv4a8BWgGTgBfH8lPo9khr7RKRyoW8Hwlvhx3K95xTlHc97kIokab+sqSzg+MZLS90p8uWgLF8kk7r4f2D/n2IOzHk8Ady9wbeMCxw8Cu5LXSskmvSOTANSVr9ywaUWkiALTLja5SD1vcpHzPW8VqR82rSgpwlDPm4jktp6ReO9XbdnK9bwVmFER0Yr+XKTwJhdJ9LytWYHwFiqIf7mo501Eclnv6CRl4RCRcGhF37cqElbPWw5SeJOLdA6NU1deTLhwZf56VEWK1PMmIjmtZ2RqRRcrJFSXFmnBQg5SeJOLdAxOrMhK04TK0iKtNhWRnNY7MrmiixUSqkqLGBqfZiamkoG5ROFNLtI5OLEiQ6YJVZEwHYPjxPTlIiI5aDI6w9BEdEUXKyRUlYZxYFC9bzlF4U0u0j4wzvqqFQxvpUVMzzjdwWosEZFc0ptYrJCmnjdAixZyjMKbXGB0MsrQRHRFdldIULkQEcllvcEOB+noeasOamlq0UJuUXiTC3QOvVXjbaVUqVCviOSwnmBUYSXLhCRUJgr1jqvnLZcovMkFOgYS4S31uyskvLVFlsKbiOSe3pFJKkoKV2wF/2xFoQLKiwvV85ZjFN7kAh2D8QC1kj1vJUUhVhUXcrZf4U1Eck+6yoQkVJWqUG+uUXiTC6xkgd7ZNlRHOKtyISKSg3pGJtMy3y2hqlSFenONwptcoGNwgtqyMCVFK1sFfH1VRMOmIpJzxqdmGJuaSct8t4TqSLxQb8xVjilXpCS8mdkdZnbMzJrN7NPzvF5sZt8KXj9gZo2zXvtMcPyYmX1g1vEqM3vSzN4ws6Nm9s5UtD3fdQ6Or+hK04T1VSW0Dyq8iUhu6Tm/IX16h01nYs7IZDRtbZDkSnp4M7MQ8EXgTmAHcK+Z7Zhz2ieBfnffCjwMfC64dgdwD7ATuAP4UnA/gL8AfuDu1wBvA44mu+2S2F1h5RYrJKyvijAwNs2ovlxEJIf0jgYrTdM8bAoqF5JLUtHztgdodveT7j4FPA7snXPOXuCx4PGTwO1mZsHxx9190t1PAc3AHjOrAN4NPArg7lPuPpCCtue9ld4aK2FDVTwwauhURHJJz8gUBtSWpTO8qVBvrklFeNsAtM563hYcm/ccd48Cg0DtJa7dAnQDf21mr5jZV8ysLAVtz2tjU1EGx6fTMmyaCG8q1CsiuaRnZJKq0iIKQ+mbYl6tnreck4q/TTbPsbmzJBc6Z6HjhcBNwJfd/UZgFLhoLh2Amd1vZgfN7GB3d/fiWy3nV5qu5NZYCevP97xpxamYzPUzAAAgAElEQVSI5I7eNJcJgXg5ppKiAgZUqDdnpCK8tQEbZz1vANoXOsfMCoFKoO8S17YBbe5+IDj+JPEwdxF3f8Tdd7v77vr6+iv8KPmlMwhvaytWfs7b6lXFhApMw6YikjPcnd7R9JYJSaiKqFxILklFeHsJ2GZmTWYWJr4AYd+cc/YB9wWP7wKedncPjt8TrEZtArYBL7p7J9BqZlcH19wOHElB2/NaouctHXPeCkMFrK0oUXgTkZwxOjXDxHQsrWVCEuKFehXeckVhsm/o7lEzewB4CggBX3X3w2b2WeCgu+8jvvDg62bWTLzH7Z7g2sNm9gTxYBYFPuXuM8Gtfx34uyAQngR+Jdltz3cdQXBKx5w3iM97a1N4E5Ec0Xu+TEgG9LyVhjnVM5ruZkiSJD28Abj7fmD/nGMPzno8Ady9wLUPAQ/Nc/xVYHdyWyqzdQxNUJOGAr0J66tKOHi6Py3vLSKSbJlQ4y2hurSIyWiM8akZIuH0fMdL8miHBTmvY2CctSu8LdZs66sidA5OMBNTFXARyX49I1MU2Ft11tLpfK03LVrICQpvcl7H4ERaVpomrK+KEI053cOTaWuDiEiy9I5MUl0aJlQwXyGFlVUVSdR607y3XKDwJud1Dk2kbb4bzK71Npa2NoiIJEvPyFRGDJnCW4V6+1WoNycovAkQ3zx5YGw6LVtjJWyoToQ31XoTkewWi2VOmRCA8uJCCgtMPW85QuFNAOgINoVPR5mQhMR7n+3XilMRyW6dQxNMzzh1qzKj583MgnIh6nnLBQpvAswq0JvG8LaqpIiq0iLa+jVsKiLZ7WR3vCxHpgybQnzRwsC4et5ygcKbANCe2BorjcOmAJtryzjdq/AmItntZM8IAPWZFN4iRfRr2DQnKLwJAJ2D6S3Qm7C5ppSWXhWSlPQyszvM7JiZNZvZRfsoB7vAfCt4/YCZNc567TPB8WNm9oFZx3/bzA6b2etm9k0zS+9/bJJSJ7pGCBcWsKokJeVUl6WqNMzoZJTpmVi6myJXSOFNgHjPW3VpUdoK9CY01pbSPjDOVFRfLpIeZhYCvgjcCewA7jWzHXNO+yTQ7+5bgYeBzwXX7iC+Y8xO4A7gS2YWMrMNwG8Au919F/HdZ+5Zic8j6XGyZ5T68mLM0l8mJKG6VOVCcoXCmwDxOW/pXGmasLm2jJijeW+STnuAZnc/6e5TwOPA3jnn7AUeCx4/Cdxu8X+l9wKPu/uku58CmoP7QXxHm4iZFQKlQHuKP4ek0cnuUeozZLFCwvlCvVq0kPUU3gSIF+hN50rThMa6UgDNe5N02gC0znreFhyb9xx3jwKDQO1C17r7WeDPgDNABzDo7j9MSesl7canZjg7MJ4xZUISqtTzljMU3gSIlwpJ93w3gE01ZQCc1rw3SZ/5xrnm7tm20DnzHjezauK9ck3AeqDMzD4275ub3W9mB83sYHd39xKaLZkisQF8Jq00BagoKaLAoF9bZGU9hTc5X6B3fVX6h03rysOUhUO0qOdN0qcN2DjreQMXD3GePycYBq0E+i5x7c8Bp9y9292nge8At8735u7+iLvvdvfd9fX1Sfg4stLOrzTNsGHTUIFRUVKknrccoPAmdA4FNd7SuCl9gpkF5ULU8yZp8xKwzcyazCxMfGHBvjnn7APuCx7fBTzt7h4cvydYjdoEbANeJD5c+g4zKw3mxt0OHF2BzyJpkKjxVluWWeENUKHeHJE5a5glbToGgt0V0rgp/Wyba0s51jmc7mZInnL3qJk9ADxFfFXoV939sJl9Fjjo7vuAR4Gvm1kz8R63e4JrD5vZE8ARIAp8yt1ngANm9iRwKDj+CvDISn82WRknukfYUBUhXJh5/SNVpWFaevTjONspvAkdQYHeTFhtCvEVp/989BwzMSdUkDnL7CV/uPt+YP+cYw/OejwB3L3AtQ8BD81z/I+AP0puSyUTneweZUt9WbqbMa+q0iKGJqb1/ZrlMu9ngay4xL6mmTBsCvFab9MzTvuA9jgVkezi7pzsHuGq+vJ0N2Ve1ZEwMYehCc17y2YKb8LZgQlqysJEwukt0JuwuTax4lSLFkQku3QNTzI6NZPRPW+gciHZTuFNODswzoYMWGma0FQX/9JLrNgSEckWJ7rj31tb6jKz502FenODwptwtn8so8LbmopiVhUX0tyl8CYi2SWx0jTje97G1fOWzRTe8py7x3veqjMnvJkZW9eUc/ycwpuIZJcT3SNEikIZM4d4rqJQAWXFhfSPquctmym85bm+0SkmpmMZ1fMGsLW+nOPqeRORLJNYaVqQwSs5q0tVqDfbKbzlufaBeJmQTNhdYbZta8rpGZnUvAwRySone0bYkqErTRNqysL06bs1qym85bmzA/EVnQ0ZNGwKsG31KgDNexORrDExPUNb/zhb6jJzvltCTWmYgbEpZmJzt+yVbKHwlufa+uO11DJu2HR1/Jerhk5FJFuc7h3DPXMXKyRUlwW13rRoIWspvOW5swPjlIZD51cgZYoNVRFKigq0aEFEssbJoExIphboTagpi5cL0dBp9lJ4y3PtQY23+F7ZmaOgwNi6upzmboU3EckOiRpvTVkwbApoxWkWU3jLc2cHxjNusULCttWraD6nDepFJDuc7B5lXWUJZcWZvW14RaSIAotXG5DspPCW5872Z1aNt9m2ri6nfXCCkcloupsiInJZb3YNn5+vm8lCBUZVqVacZjOFtzw2NhWlf2w64xYrJGxfE19xeqxzKM0tERG5tFjMae4aOf+9lelqSsMaNs1iCm95rH0gvtI008qEJOzaUAHA62cV3kQks7X2jzExHWP7mszveQOoLivSsGkWU3jLY4kyIZk6521tRQl15WFeOzuY7qaIiFzSm8HK+K2rs6fnbXRqhsnoTLqbIsug8JbHWoPwtqmmNM0tmZ+ZsXN9Ja8rvIlIhjveFV9ctS1ret4SK05V6y0bKbzlsda+McKFBdSXF6e7KQu6bkMlx7tGmJjWr0MRyVzHz42wrrKEipLMqpm5kEStt34tWshKCm957EzvGBurIxm9gfKuDRXMxJyjHZr3JiKZ681zw2zLksUK8Fatt17Ne8tKCm95rLV/LGOHTBN2bagE4PV2hTcRyUwzwUrTbVlQJiQhEg5RUlRA78hkupsiy6DwlqfcPd7zluHhbUNVhOrSIl5v07w3EclMrX1jTEazZ6UpxOcU15YVa8VpllJ4y1OD49MMT0YzvufNzNi1oZLX2xXeRCQzvXkusVghe4ZNAWrLw/So5y0rKbzlqda+RI23zA5vANc3VHKsc5ixKe20ICKZ53hXvExINg2bAtSWFTMwNs1UNJbupsgSKbzlqTN9Y0DmlgmZbU9TLdGYc+j0QLqbIiJykaMdQzRUR1iVJStNE+rKwzjx+c+SXRTe8lQivG2sycwCvbPdvLmaUIHx45O96W6KiMhFjnYMce26inQ3Y8lqg3IhLT2jaW6JLJXCW55q7R+jurQoK34plhcXct2GSoU3Eck4E9MznOoZ5dq12TXfDaA2qPF5SuEt6xSmuwGSHq19mV8mZLZ3bKnl0WdPMjYVpTQc/2v7jQNnlnWvj96yKZlNE5E8dvzcCDGHa7Kw5600KBdyulfDptlGPW956kzfGA1ZFd5qmJ7RvDcRySxHO+M1KLNx2DRRLqSlVz1v2UbhLQ/NxJyz/eNZ1fO2u7FG895EJOMc7RgiUhTKqu/T2WrLwwpvWUjhLQ+1D4wTjTkbs6BMSEJi3ttzJ3rS3RTJA2Z2h5kdM7NmM/v0PK8Xm9m3gtcPmFnjrNc+Exw/ZmYfmHW8ysyeNLM3zOyomb1zZT6NpNIbHcNsX7uKUAZvM3gptWXFnO0fV7mQLKPwlocSv7Ka6srS3JKlee81q3m1dYCuoYl0N0VymJmFgC8CdwI7gHvNbMec0z4J9Lv7VuBh4HPBtTuAe4CdwB3Al4L7AfwF8AN3vwZ4G3A01Z9FUsvdeaNziB3rsm+xQkJdeZiYv1WBQLJDysJbKn65Bq+FzOwVM/teqtqe6xLLwrMtvN2xay3u8NSRc+luiuS2PUCzu5909yngcWDvnHP2Ao8Fj58EbjczC44/7u6T7n4KaAb2mFkF8G7gUQB3n3J3TeDMcl3Dk/SPTXPN2uyb75agFafZKSXhLYW/XAF+E/1ivSIne0aJFIVYU1Gc7qYsybbV5WypL+Op1zvT3RTJbRuA1lnP24Jj857j7lFgEKi9xLVbgG7gr4Mfn18xs3l/PZnZ/WZ20MwOdnd3J+PzSIocac/exQoJ9UF4O9E9kuaWyFKkquct6b9cAcysAfgF4CspandeaOkZpbGujPj/3NnDzLhj51peONlLvzZTltSZ7z8MX+Q5Cx0vBG4CvuzuNwKjwEUjEgDu/oi773b33fX19Ytvtay4184OYgY71mdveIuEQ9SvKqa5S+Etm6QqvKXilyvAnwO/Dyw4s1K/Wi/vVM8oTXXZs1hhtjt2rWUm5vzzUQ2dSsq0ARtnPW8A2hc6x8wKgUqg7xLXtgFt7n4gOP4k8TAnWey1s4M01ZVRXpzdJVO31pcrvGWZVIW3pP9yNbMPAl3u/vKl3li/Wi9teiZGa/941s13S7huQyUN1RH+4ZWz6W6K5K6XgG1m1mRmYeLTOPbNOWcfcF/w+C7gaXf34Pg9wZzeJmAb8KK7dwKtZnZ1cM3twJFUfxBJrdfPDnLdhsp0N+OKbV1dzomuEeJ/hSUbpCq8peKX623Ah8yshfgw7HvN7G9T0fhc1tY/zkzMaazNzvBmZty7ZxPPn+ila1irTiX5gpGAB4CniM+vfcLdD5vZZ83sQ8FpjwK1ZtYM/A7BEKi7HwaeIB7MfgB8yt1ngmt+Hfg7M/spcAPwX1bqM0ny9YxM0jE4wa712R/erqovY3gyStfwZLqbIouUqvCWil+un3H3BndvDO73tLt/LEXtz1mneuJd41vqszO8AXzk7RspChkvnupLd1MkR7n7fnff7u5XuftDwbEH3X1f8HjC3e92963uvsfdT8669qHguqvd/fuzjr8ajApc7+4fdvf+lf9kkiyvnx0EYFdO9LzFS52c0NBp1khJeEvhL1e5Qqd64rV8srXnDaCuvJg7d63j0Jl+FZYUkbRIhLedG7J3sULC1tXlADRrxWnWSNksS3ffD+yfc+zBWY8ngLsXuPYh4KFL3PsZ4JlktDPfnOoZYVVJITVl4XQ35Yp87B2b2feTdg6d6ecdW2rT3RwRyTOJxQoVJUXpbsoVW1NRTHlxoRYtZBHtsJBnWnrG2JKFZULmentjNRurI/zLm91EY+p9E5GV9frZIXZmcYmQ2cyMq1ZrxWk2UXjLM6eCGm/Zzsy4/do1DI5P8/JpTR0SkZXTOzLJ2YHxnFhpmqByIdlF4S2PjE5GOTswzlX15eluSlJsW13OpppSnjnWTXRGvW8isjJebY3vbHbjpuo0tyR5rlpdRtfwJIPj0+luiiyCwlseSWx/sn1NboQ3M+Pngt63A1p5KiIr5NCZfgoLLKd63q5ZG19xeqxzOM0tkcVQeMsjb56Lh7fEsvBcsHV1OVvry/nRsS4mprUoWURS75UzA1y7roJIOHT5k7NEYn/Wox1DaW6JLIbCWx453jVMOFRAY212bo21kA/sXMvY1Az/+qa2QxOR1JqJOT9pHeDGTVXpbkpSra0ooaq0iDc6Fd6ygcJbHjl+boQt9WUUhnLr//YN1RGub6jkuRM9mq8hIin15rlhRqdmci68mRnXrq3gSIeGTbNBbv0rLpd0vGv4fDHGXPP+HWuJxeB/acN6EUmhQ2fiq9tvyqHFCgnXrFvFsc4hZmLa4zTTKbzlibGpKK1942xfkzvz3WarKQtzy5YaXj7dz7kh7XkqIqnxypkBasrCbKrJreknEJ/3NjEdo6V3NN1NkctQeMsTJ7ri/zFuy9GeN4D3XL2acGEBPzyi3jcRSY1DZ/q5cWNV1hc6n88OLVrIGgpveeJ4V3wew7Yc7XkDKC8u5F1b6zjaMUTnoHrfRCS5uocnOdk9ytubatLdlJTYurqcUIEpvGUBhbc88ea5EYpCxuYcW2k61zuvqiUcKuBfj2vlqYgk14tBPclbcjS8lRSFuKq+jKNatJDxUrYxvWSW4+eGaaoroyjHVprOVRouZE9TDc+f6OF9O9ZQXRpOd5NEJEN848CZZV330Vs2AXDgVC+l4RC7cqg471zXrqvgwEkVPc90uf0vuZx3tGPofBHGXHfb1joM49nmnnQ3RURyyIGTfdy8uTqnfwRf31BF59CEpp5kuNz9Gyjn9Y9O0T44wc71+RHeKiNFXNdQyStn+pmKas9TEblyfaNTHDs3zDu21Ka7KSmVqF/3amt/mlsil6LwlgcOt8cnn+5cn7td/XPtbqxmYjrG6+2D6W6KiOSAXJ/vlrBzfQXhUAGvtA6kuylyCQpveeBwEGDypecNoKm2jLryMC+1aO6GiFy5A6d6KSkq4PqG3NpZYa7iwhDXrq/glTMKb5lM4S0PHG4fYkNVhKo8mrxvZuzeXMPp3jEV7RWRK/Z8cy83b64mXJj7/2zeuLGK19oGic5o2kmm0mrTPHC4fZCKSNGyV1plq5s2V/PDI50cOtPPnbvWpbs5IpKlOgcnOHZumF+66Zp0N2VF3Lipiq8938Kxc8N5Nd0mm+T+T4g8NzYV5WTPKOsrS9LdlBVXXlzI1tXlvH52EHft1Sciy/NvQd3Id2+vT3NLVsaNG+P7tr6qeW8ZS+Etxx3tGMYd1ldF0t2UtLhuQyX9Y9OcHRhPd1NEJEv96/Ee6lcVc83a3N2hZraNNRFqy8IcOq3wlqkU3nLckWCxwro87HkD2LGukpAZr7Vp1amILF3MnWePd/Mz2+pycj/T+ZgZuxurOXCqV6MWGUrhLcf9tG2QmrIwlZGidDclLSLhEFtXl/Nau4ZORWTp2gfG6R+b5t3b8mPINOFdW+to6x/ndO9Yupsi89CChRx36Ew/N26syptfjPO5bkMlxw4N09Y/zsaa3N7bVUSS63jXCADnhibyatHXbVvrAHi2uYfGurKk3/9KtyrLd+p5y2EDY1Oc6B7lps3V6W5KWl2zbhUGvNE5lO6miEiWeaNjiIbqCKtK8mv0oqmujA1VEZ49rm0GM5HCWw5LFFlMbHeSr0rDhWyqLeXYueF0N0VEssjwxDSt/eN5sy/0bGbGbVtref5EDzMxTTnJNApvOezQmX5CBcbbcrwi+GJcvWYV7QMTDE1Mp7spIpIl3uiI/+C7dm3+hTeAd22rZ2giymtnteAr0yi85bBDZ/q5Zu0qyoo1tfHqYIn/m53qfZPLM7M7zOyYmTWb2afneb3YzL4VvH7AzBpnvfaZ4PgxM/vAnOtCZvaKmX0v9Z9CrtTRziGqS4tYU1Gc7qakxa1X1QLwr292p7klMpfCW46aiTmvnhngpk35Pd8tYW1FCZWRIg2dymWZWQj4InAnsAO418x2zDntk0C/u28FHgY+F1y7A7gH2AncAXwpuF/CbwJHU/sJJBmmojGau0a4Zl1F3i74qisv5qZNVex/rSPdTZE5FN5y1JvnhhmdmuGmzRoyhfj8je1rVtHcNcJUVPv1ySXtAZrd/aS7TwGPA3vnnLMXeCx4/CRwu8X/hd8LPO7uk+5+CmgO7oeZNQC/AHxlBT6DXKHmrmGiMc/bIdOEvTds4I3OYY5p1CKjKLzlqIMtfQDcvKkmzS3JHFevKWcyGtOWL3I5G4DWWc/bgmPznuPuUWAQqL3MtX8O/D5wyV8PZna/mR00s4Pd3RquSpfXzg5SGg7RlIIyGdnk569bR4HBvp+cTXdTZBaFtxz1XHMvG6oibKzJz22x5tNUV44Rr1skcgnzjZHNXW630DnzHjezDwJd7v7y5d7c3R9x993uvru+Pr8Kw2aK6ZkYRzuH2bm+glBBfg6ZJtSvKua2rXV89ycdKnSeQRTectBMzHnhZC+3ba3N27ka84mEQ2yojvC8wptcWhuwcdbzBqB9oXPMrBCoBPouce1twIfMrIX4MOx7zexvU9F4uXJvnhtmKhrjug2adgLwi29bz5m+MQ6dSd6oRd/oFCe6RzjSPkjP8CQxBcMl0TLEHHS4fZDB8enzFbLlLVfVl/Nccw+jk1GtwpWFvARsM7Mm4CzxBQgfnXPOPuA+4AXgLuBpd3cz2wd8w8w+D6wHtgEvuvsLwGcAzOw9wO+5+8dW4sPI0mnI9EJ37lrLH3/3CF999hQ3X0HR98noDI+/2Mq3XmrlSMeFRdNXlRTynu31vL2xhsKQ+pUuR/965aDnmnsBeGewzFveclV9Of/yZjcvnurjZ69Zne7mSAZy96iZPQA8BYSAr7r7YTP7LHDQ3fcBjwJfN7Nm4j1u9wTXHjazJ4AjQBT4lLvPpOWDyLJMz8R4o2OYGzZW5f2QacKqkiI+futmvvTMCZq7htm6etWS7/H0G+f4z989wuneMa5vqOQXrlvHusoSwoUFdA5OcOjMAN/9aQcvtvTx8Xc2Ul0aTsEnyR0Kbzno+RM9XL1mFatXlaS7KRlnc20p4cICnmvuUXiTBbn7fmD/nGMPzno8Ady9wLUPAQ9d4t7PAM8ko52SfEc7hpiaiXFdQ2W6m5JRfvW2Jr76bAtf+tEJPv+RG4DF7U86FY2x//UOXjzVx+pVxfzKrY1sW3Nh+GuoLuXmzdW80TnMt19u5a+eOcF9tzayvkpztheivskcMzE9w4un+rh1q3rd5lMUKuDmTdU8d6I33U0RkQz0ypkBKiNFGjKdo7a8mP9wyyb+8SftvL7IHRc6Bsf50jPNvHiqj5/ZVscD7916UXBLMDOuXVfB//nuqygoMB57voXBce2IsxCFtxzz4qk+JqMx3qX5bgu69apajnYMMTA2le6miEgGGZmMcrxrmLc1VFGgxV4X+b9+dit15WEe+MYhhi+x1aC78/yJHr78zAnGp2b4ldsauXPXOgoLLh851lSU8IlbG5maifH1H7eoLucCFN5yzP88co5IUUiLFS7hli3xXsmXWvrT3BIRySQ/bRsg5nDDJq0ynU9NWZj/du9NtPaP87tP/ITpmYuDVf/YFH/zwmm+99MOrqov59dv38a2Jc6RW1NRwkfevpGOgQm+/7p2d5iP5rzlEHfnn4+e42e21VFSFLr8BXnq+oZKwoUFHDjZy/t2rEl3c0QkQ7zaOsC6yhLWVmi+8EL2NNXwf//8tfzx947wypkBPrBzDWsqSugfm+ZoxxAvtvRhwAevX8c7tyy/XNU1ayu49apanjvRy/UNVRrGnkPhLYe8fnaIjsEJfvf9V6e7KUu2mImvyVJSFOLGjVW8GOxCISLSOThBW/84P3/dunQ3JeN98l1NbFtdzq9/8xX+dtZ3d4HBjRuruf3a1VQlYbXo+3as5UjHEN851MZv3L6NIpUQOU/hLYf88EgnBQbv1SrKy7qlqYYv/KiZkcko5ar3JpL3XmrpI1Rg3LRRQ6aL8e7t9fz2z22nfWCc3tFJyooLaaotoziJoz7hwgL+9xsb+Opzp3jhRC/v3q4dRxIUY3PI/zxyjt2NNdSUqT7O5expqiXmb+0BKyL5ayoa45XWfnatr6BUP+YWLVxYQGNdGTdvruGatRVJDW4JW1eXc/WaVTzzZhdjU9Gk3z9bKbzliOauYd7oHOYDO9emuylZ4abNVRQWGC+eUngTyXevnx1kYjrG25tq0t0UmccHdq1lcjrGM8e6092UjKHwliO+c+gsBQa/+DbN11iM0nAh1zVUckDhTSTvvdjSR115mKZaTYrPRGsrSrhpUzUvnOzl3NBEupuTERTeckAs5vzjq+38zLZ67aqwBLc01fLTtgHGp7R7kUi+6hya4EzfGG9vrFn2ykhJvZ+9ZjXuzlf+7WS6m5IRUhLezOwOMztmZs1m9ul5Xi82s28Frx8ws8ZZr30mOH7MzD4QHNtoZj8ys6NmdtjMfjMV7c5WL7b0cXZgnF+6aUO6m5JVbmmqYXrGeaVV9d5E8tX5hQqblr/huqReTVmY6xuq+LsDZ+gfVYH1pIc3MwsBXwTuBHYA95rZjjmnfRLod/etwMPA54JrdxDf4HkncAfwpeB+UeB33f1a4B3Ap+a5Z976h0NnKQuHeP8OzXdbipsbqykwOHBSQ6ci+Wh6JsYrZ/rZub6CMi1UyHjv3l7P2NQMj73Qku6mpF0qet72AM3uftLdp4DHgb1zztkLPBY8fhK43eL91XuBx9190t1PAc3AHnfvcPdDAO4+DBwF1M0EDE9M872ftnPndeuIhFWYdykqSorYsb5CixZE8lRiocKeRi1UyAZrK0r4uWtX89jzLUxM5/d0l1SEtw1A66znbVwctM6f4+5RYBCoXcy1wRDrjcCBJLY5a33n0FlGp2b45XdsTndTstKexloOnelnMprfXwQi+Sa+/2YvdeXFqt6fRX71XU30j02z79X2dDclrVIR3uab8emLPOeS15pZOfD3wG+5+9C8b252v5kdNLOD3d25vazY3fn6j0/ztoZK3qbCksuyp6mGyWiM19oG090UEVlBp3vHODswzq1XLX8LJ1l579xSy9VrVvG151twnxst8kcqBvnbgI2znjcAcyNy4pw2MysEKoG+S11rZkXEg9vfuft3Fnpzd38EeARg9+7dOfn/bGIrqRPdIzR3jXDXTQ0rur1ULtkT1HU6cKqP3Ro6Eckbz53oIVIU0kKFLGNmfOK2Rj7zndd4qaX//Hd4vklFz9tLwDYzazKzMPEFCPvmnLMPuC94fBfwtMcj9D7gnmA1ahOwDXgxmA/3KHDU3T+fgjZnpedP9BIpCnFdQ2W6m5K1asrCbF9TrnpvInmkb3SKI+1D7GmqIVyoilnZ5sM3bKAyUsRjz7ekuylpk/S/tcEctgeAp4gvLHjC3Q+b2WfN7EPBaY8CtWbWDPwO8Ong2sPAE1F2WfsAACAASURBVMAR4AfAp9x9BrgN+GXgvWb2avDn55Pd9mzSNTTB0Y4h3nlVrTbrvUJ7mmp4uaWP6Ews3U0RkRXwb8e7KTDjHVtq090UWYZIOMS/u6mBHx7ppHdkMt3NSYuU/Kvv7vvdfbu7X+XuDwXHHnT3fcHjCXe/2923uvsedz8569qHguuudvfvB8eedXdz9+vd/Ybgz/5UtD1b/NvxHopCxjv15XPFbmmqZXRqhiMd806jFJEcMjQ+zcun+7lpcxWVkaJ0N0eW6d49G5mecf7+UFu6m5IW6rLJQoPj0/z/7N15fFzleff/z6XRLmu1ZFm2bMsrxixmMWbfQyA0qdMGgiElNEvTppC9TcPztGmePOX5NU2TtGQjBGhSGsqWzQUSEkLYEryxGbwiy5tsWZIta19Hc//+mDOOEJI1lmd05sx836+XXp45c86Z68jS0TX3ct2v7mtnxbwK1SZKgKPj3lTvTSTtPf9mKxHnuHTJDL9DkROwuLqYs+eV8+CGfRk5cUHJWwA992YrDsdFiyr9DiUtVJfkUze9UOPeRNJc90CY9bvbWF5bRkVRrt/hyAlafc4cGlp72LA781bJUfIWMAc7+tmwq42z5pZTrptPwpw7fzobdrcRiWTeJziRTPH0thaGI47LTlKrWzr4o9NrKM7L5sH1mVdtQclbwHznmXoiznG5bj4JtXJ+BR19Q2xv7vI7FBFJgl2Heli/6zAr6iqoKs7zOxxJgMLcbP74jFk8/noTHb1DfoczpTRgKkD2t/fx4Pp9nD2vQq1uCXbugui4t/W72ji5psTnaEQk0f7ll9vIzsriyqX64Btko2ualhXmMhCO8Pc/e53zF44/lOimc+cmO7QppZa3APm3X+8A4PKTqnyOJP3Ulhcyu6yAdbsO+x2KiCTYczta+cUbB7l4SSXF+Zphmk5mlxUwqyyfDbuPZNTEBSVvAbGjuYsfv9zIB8+fR1mhWt2SYeX8CtbvasuoG4BIuusZCHP7T15nQVURlyzWB990dE5dBQc7+9nf3ud3KFNGyVtA/Msvt1OUm82tly/yO5S0de78Cg51D9JwqMfvUEQkQb765Hb2t/fxlfedroLmaWp5bRk5IcuoWaf6SQ6AjbvbeGprM3956QKNdUsi1XsTSS+Pb2riB7/fzZ9fUMc5Wrs4beXnhDh1VimbGtsZDGfGSjlK3lKcc46v/HIbVcV5fPii+X6Hk9bmVxZROS2P9Rr3lvHM7Boz225m9Wb2hTFezzOzh7zX15lZ3YjXbve2bzezq71tc8zst2a21cw2m9mnpu5qMtP2g1387aOvcdbcMm6/dqnf4UiSrairYCAc4Y39HX6HMiWUvKW432xtYcPuI3zqysUU5mpycDKZGecuqGCdxr1lNDMLAd8G3gUsA240s2WjdvsIcMQ5twj4BvAV79hlwGrgFOAa4Dve+cLA55xzJwPnAbeOcU5JkDebu7j53nUU5WXz3T87m7zskN8hSZLVTS+kclouG/ZkRs+JkrcUNhxx/MuT25hfWcQN58zxO5yMcO78Cpo6+tnXljkDX+VtVgL1zrkG59wg8CCwatQ+q4Afeo8fBa40M/O2P+icG3DO7QLqgZXOuSbn3MsAzrkuYCswewquJeO8tKeN93/vRQAe+Oi5VJfk+xyRTAUz4+x5Few53EtrV/ovVq/kLYX99JX97Gju5m/eeZIG2k6RCxZOB+CF+kM+RyI+mg3sG/G8kbcnWkf3cc6FgQ5gejzHel2sZwLrEhhzxusbHOarT27j+rteZFp+Ng//5fksri72OyyZQmfNLSPLogl8ulM/XIrqHxrm67/azum1pVx72ky/w8kYC6umUVOaz3M7WtOuqKPEzcbYNroffbx9jnmsmU0Dfgx82jnXOeabm30M+BjA3Ln6GTwW5xw7W7t5fNNBfvjibtp6BrlhxRz+/t0nq55bBirOz+GkmSW8tLedq5bNJJQ11q9jelDylqL+a+0eDnT089XrlxPtjZGpYGZcsriKJ95oIjwcIVstnpmoERg5TqEWODDOPo1mlg2UAm3HOtbMcogmbj9yzv1kvDd3zt0N3A2wYsUKDb4cZSA8zPpdbfxmawtPb2thb1svEC1efuvli1ihWaUZbcW8crY2dbL9YCfLZpX6HU7SKHlLQR19Q3zrt/VcvLiSCxeNv9yHJMclS6p4aOM+Xmts5+x5+kOQgTYAi81sPrCf6ASEm0btswa4BXgRuA542jnnzGwN8ICZfR2YBSwG1nvj4e4Ftjrnvj5F15HyRi91NJ6Ic+xo7uLVfe1sO9jFYDhCXnYWFy6q5GOXLOCKpTOYVVaQ5GjTV7z/D0GwpLqY4vxsNu45ouRNptZdz+6kvXeIv7tG09v9cOGi6WQZPLfjkJK3DOScC5vZbcCTQAi4zzm32cy+DGx0zq0hmojdb2b1RFvcVnvHbjazh4EtRGeY3uqcGzazi4CbgdfN7FXvrf6Xc+6Jqb26YAlHImzcfYQX6g/R1jNIQU6I02eX8leXLuTCRZUU5GoWqbxVKMs4a245z+1opaNviNKC9Ow+V/KWYpo6+rjvhV2894xZnDo7fT81pLKywlxOry3juTdb+cxVS/wOR3zgJVVPjNr2xRGP+4Hrxzn2DuCOUdteYOzxcDKOHc1dPLapiUPdA8wpL+DqU+Zyck0x2VlZvGNZtd/hSQpbMa+cZ3e08sreI1x20gy/w0kKJW8+G91c/ZOXGwlHHItnFKdVU3Yqief7WlGUy2+3tfD95xooysue1OSFyfz/TXaSxFS+l0gyDQwN8/jrTWzcc4TpRbl88Px5nFRdrLG/Erfp0/KYX1nExj1HuGRJFVlp+LOj0dgppLmzn5f2HOG8+RVaBstnJ9eU4IBtB7v8DkUkYzR19PGt39bz0p4jXLqkik9duZilM0uUuMlxWzGvnLaeQXan6VrVSt5SyK82HyQ3Oyttm3mDZFZpPmUFOWw5kBlLrYj47bV97dz17E4GhyN89OIFXH3KTM32lkk7dXYp+TlZbNidnjXf9JuRInYf6mHrwS4uXVJFUZ56s/1mZpw8q4Q3W7ozZqFjET8453hqazMPbdzH7LICbrt8EfMri/wOSwIuJ5TFGXPKeeNAJ90DYb/DSTglbynAOccv3miiJD+bCxaqNEiqWFZTQjgSLVEgIok3NBzhwQ37eHpbC2fPK+fDF81XcV1JmPPmVzAccWxMw9Y3NfGkgC1Nnew70sefnDmb3Gzl06mibnoRBTkhtjSNWQhfRE5AS1c/9zzfQOORPq45ZSYXL67U2DZJqBkl+SysKmLdrra0K7qePlcSUMMRx5Obm6kqzuOsueV+hyMjhLKMZTUlbG3qpHcw/ZrdRfzyemMHq771Ow529vOBc+dyyZIqJW6SFOctmE5H3xC/2dbidygJpeTNZy/tOcKh7gGuTvN12ILqzHllDIQj/PKNg36HIpIW1rx2gOvu+j1ZZvzVpQvTugq++G/pzBLKCnK474VdfoeSUErefNQ7GOY3W5uZV1HIyTXFfocjY5g/vYiKolwe2djodygigRaJOL765DY++d+vsLy2jJ/fdiE1pVrSSpIrlGVcsHA663a1samx3e9wEkZj3nx07/O76BoIc9O5c9VlkKLMjLPmlvHU1hb2tfUyp6LQ75BEUs5ERaL7h4Z5eOM+th3s4py6ct69vIZfbW5O2vuJjLSiroLn3zzE95/fxTdvPNPvcBJCyZtPWrr6+d5zDSyrKWHedE2LT2VnzS3nN9taeOSlRj6bwOWyegbCvNbYzr62Xlq6Bog4x0Mb9lJXWcTJNSWct2A6p84qmdQg28FwhI6+IXoHw2SZUZAboqIoNy0rjUtqO9w9wP1r93Coe4D3LJ/FefMr9GFVplR+Toibzp3LPS/s4vNXn5QWH8KVvPnkK7/YzmA4wjWnzvQ7FJlAWWEul580g/9au4e/unQBhbkn9mvT1T/Er7c088q+doYjjtKCHGaW5BPKMkoKcti4+wg/f/UAANPysjmnrpzzF07nvAXTWVJdTH7OWxfjbu0aYEdzFwfa+zjQ0U9Tex+Hewbf9r45IaNuehHLa8u0bq5MiZ2t3UdbyT504XwWVk3zOSLJVH9+YR33/W4X33tuJ//03tP8DueEKXnzwct7j/Djlxv5+GULqZyW53c4EodbL1/E+777ex5Yt5ePXrxg0ud5aU8bj7/exFDYsaKunHMXTGdmSf7R12PrjbZ2DbC24TBrGw7zYsNhfru99eg+VcV5FOWGiDho6xl8SwHK8sIcZpUVcObcMsoLcynKy8a5aMLY1NnPtqZOHn25kV9uPsiwc9x83jyVp5GEc86xdlcbj286QOW0PG4+bx7Tda8TH9WUFnD9ijk8vKGRWy9fFPjxlkreplgk4vjSms1Ul+Rx2+WLjrawSGo7e145Fyyczveea+DPzpv3ttaviYQjER7b1MT6XW3MryzivWfMpqp4/D9mVcV5vGf5LN6zfBYQXfd2w+42Glp7aDzSy4C36kNFUS615YU0dfRRU1JAQe6x43r3aTXsOtzD09ta+L+PbeHB9Xv55/edxtnzKo7rekTGE45E+J/Xmtiwu42lM4t5/4o5x/37IpIMf33ZQh7ZuI/vPrOTL6861e9wToiStyn2yEv72NTYwb/dcIaWwQqY265YxE3fX8cPf7+bv7x0YdzHdfUP8cD6vew53Msli6t45ynVxz32rLokn3efPmvc1+MdwG1mLKicxoKLpjGzNI+//+kbXHfXi3ziisV86srFKlcjJ6RnIMyP1u1l9+EeLltSxTuWHf/Pukiy1JYXct3ZtTy4fh9/delCZpUFt/VN/SVTqKNviH/55XZWzCtn1Rnj/yGW1HT+gum84+RqvvbrHdS3xLdkVuORXr7923oOtPdxwzlzuObUmSnzx+yKpdX86rOX8qdn1nLnb97kA/espbmz3++wJKBauvr57rM7aTzSyw0r5vDOU1LnZ10k5tbLF4HBv/5qu9+hnBAlb1Po67/aTlvvIF/641M02yqAzIz/96enUpQb4rMPv8bQ8LEXrH957xHufq6BrCzjLy9ZyPLasimKNH7T8rL52vuX89XrTue1fR1c++/P89yO1okPFBmhvqWbu57dyUA4wl9cvIDlc1LvZ10Eoq1vH7qwjp+8vJ839nf4Hc6kKXmbIht3t/Gfa/fwwfPmaaZfgM0ozueOPzmNTY0d/OX9L425bFZH7xCffvAVHn2pkbkVhdx62aKUb56/fsUc1tx2IdOn5XLLf6zna7/aznDE+R2WBMCD6/fyg9/voiQ/h7++dGFalGGQ9Hbr5YuoKMrlnx7fgnPBvM8peZsC/UPDfP7Hm5hVWsDnr1nqdzhygq49rYZ/eu+pPLO9hevvepHHNh2gpaufHc1dfOPXO7jia8/w2KYmrlw6gw9dOD8wYxsXVxfz81sv4rqzavnm0/V84J61tKgbVcYRiTj+vye28oWfvM7Cqmn81aULKS/K9TsskQmV5OfwmauWsLahjZ+9ut/vcCYlGH9VAu5fn9xOQ2sP939kZWD+kMux/dl586guyecffvYGtz3wylteu2LpDD7zjiW8HsAm+YLcEF+9fjnnLpjO3//sda698wXuXH0GFyyq9Ds0SSF9g8N8+qFXeHJzMzefN48l1cWa7CKBctPKufz4pUb+72NbuXTJDCoC9sFDmUSSPbO9hXte2MUt58/j4sVVfocjCXTVsmquWDqDtQ2HebO5i4ppeZw2u5T5ldEVM4KYvMVcd3Ytp9eW8tc/epkP3LuOT125mE9codmoEp2Y8Bc/3Mim/R188d3L+NCFdfz3+n1+hyVyXEJZxj+/7zTefecLfPl/NvNvq4O1bJaStyRq7uznbx55jaUzi7n92pP9DkeSIJRlXLiokgsT1DI1lWs2xvNeHzh3LmtePcC/PfUmP36pkR98eKWq5Gew7Qe7+PAPNtDWM8jdN6/gqmXVfockMmlLZ5Zw6+WL+PffvMnFi6t439m1focUN415S5L+oWE+dv9L9A4Oc+eNZ6pIpQRSXnaI686u5YYVczjUPci1//489zzfQESTGTLOcztaue67v2doOMLDf3m+EjdJC5+4YhEr51fwDz9/g/qWbr/DiZuStyRwznH7T17ntX3tfP39Z7CkutjvkEQmzcxYPqeMT71jMRctquSfHt/K+7/3ItsOdvodmkyRB9bt5UM/2MDs8gJ+duuFnFarGfOSHrJDWdy5OtrA8hf/uZG2MdaFTkVK3hLMOcf/fWwrP31lP5+9aokWnpe0UZKfwz23rOBr1y9nZ2s3f3TnC/y/J7bSM/D2cimSHnoHw/zNI6/xv376OhcvruTRj1+Q8mVvRI7XzNJ87r75bPa39/GRH26gb3DY75AmpOQtgZxzfOWX27nvd7v48wvq+MQVi/wOSSShzIz3nV3L05+7jOvPruXu5xp4x9ef5eev7ldXaprZ0dzFqm/9jh+/3Mgnr1jEPR9cwTTNlpc0taKugjtXn8Gr+9r58A820NU/5HdIx6TkLUEGwxE+9/Br3PXsTj5w7lz+8T3LtIqCpK3yolz++X2n8+OPn095YS6fevBVVn37d/yu/pDfockJikQc97+4mz/+1gsc6R3i/g+fy2ffeRLZIf25kPR2zak1fOP9Z7B+dxs3fX9dSi8XqN/GBNhzuIcb7n6Rn7yyn89dtYR/eu+pStwkI5w9r4LHPnERX3//ctp6BvnAPeu4+d51/H7nocBWLs9kb+zv4Lq7fs8//Hwz59RV8MSnLuKixarxJ5njvWfO5u6bz6a+pZtr//15nk3R5QLVBn4C+oeGuf/FPXzjqR1kZxnfvuks/uj0Gr/DEplSWVnGn55Vy7Wn1fBfa/fw3Wd2ctP313HKrBI+evF83n36LHLUapPSthzo5NvP1PP4piYqinL5+vuX8ydnztaHUMlIV55czZrbLuS2B17hlvvW88fLZ3H7tUupKU2d8Z5Ju6Oa2TVmtt3M6s3sC2O8nmdmD3mvrzOzuhGv3e5t325mV8d7zqnS3jvIPc83cOXXnuWOJ7Zy7vwKnvzMJUrcJKPl54T46MUL+N0XruCf//Q0BsIRPvPQa6y84yn+7tFNPLO9hcFwxO8w45LO96+Yrv4hHtm4j9V3v8i1dz7Pb7e18IkrFvHM317Gn55Vq8RNMtri6mJ+duuFfPKKRfxy80Eu/Zdn+NtHXuO1fe0p0auQlJY3MwsB3wauAhqBDWa2xjm3ZcRuHwGOOOcWmdlq4CvADWa2DFgNnALMAp4ysyXeMROdMyl6BsJsb+7ilb3tPLO9hXUNbQwORzinrpyvXne6lg4SGSE/J8TqlXN5/4o5PPtmKz9/ZT+Pv97EQxv3UZyfzdnzylleW8YZc8pYNquEqml5ZKXQyg3pdv8CCA9H2H24l+0Hu9h+sJN1u9p4ee8RhoYdcyoK+MK7lnLjOXMpLcyZinBEAqEgN8Rn33kS16+Yw/efb+CRjY088lIjcyoKuHRJFefUVbCkupi66UUU5E5tLddkdZuuBOqdcw0AZvYgsAoYeaNaBXzJe/wo8C2LftRbBTzonBsAdplZvXc+4jjnpAyGI3zz6TfpGxymd2iY/sFhOvuHONDez4GOPtp7/zDrZEFlEbdcMI8/PauWk2tKTvStRdJWVpZx+UkzuPykGQyEh3nhzUP8ekszr+xt59kdbxL78JoTMqpL8plVWkBZYQ7T8rIpysumMDdEdsgIZWXx15ctnMpC14G6fwGsee0Amw90MBiOMBCOMDAUob13kEM9gxzuHqCla+Boq2eWwck1JXz04gW84+RqzppbplY2kWOYU1HIl1edyueuOokntxzkF6838dOX9/Nfa/+wSs3MknxmlxdQWpBDcX42xfnZFOSEyA5l8Y6Tqzl7XnlCY0pW8jYbGLnYXSNw7nj7OOfCZtYBTPe2rx117Gzv8UTnxMw+BnzMe9ptZtsneQ1j2gP8Fvj7+HavBNJp+l3GXs8HkhxIgiT9/ydZ34f6sTcfvZ7PHd/p5p1gOL7dvyD59zCAXcATb98ctN9vxZs8QYoV4og32ffwPfHvOlGscd2/kpW8jfUxbnQn8Xj7jLd9rPF5b+t4ds7dDdw9UYBTwcw2OudW+B1Houh6UpuuJ3FvPca2Kbl/gX/3sKD9/Cje5AlSrBCseBMVa7ImLDQCc0Y8rwUOjLePmWUDpUDbMY6N55wiIidK9y8RSWnJSt42AIvNbL6Z5RIdwLtm1D5rgFu8x9cBT7voFI41wGpvNtd8YDGwPs5zioicKN2/RCSlJaXb1BsDchvwJBAC7nPObTazLwMbnXNrgHuB+70BvW1Eb2Z4+z1MdCBvGLjVOTcMMNY5kxF/AqVE920C6XpSm64nATL4/hW0nx/FmzxBihWCFW9CYrVUqFciIiIiIvFR2XMRERGRAFHyJiIiIhIgSt6SINWWwYmXmd1nZi1m9saIbRVm9msze9P7t9zbbmZ2p3eNm8zsLP8ifzszm2NmvzWzrWa22cw+5W0P6vXkm9l6M3vNu57/422f7y3P9Ka3XFOut33c5ZtSiZmFzOwVM3vMex7o6wmqVL9nmdluM3vdzF41s43etjF/l32KL1D3znHi/ZKZ7fe+x6+a2bUjXhtzybcpijVQ9/JjxJvY769zTl8J/CI6GHknsADIBV4DlvkdV5yxXwKcBbwxYtu/AF/wHn8B+Ir3+FrgF0TrWp0HrPM7/lHXUgOc5T0uBnYAywJ8PQZM8x7nAOu8OB8GVnvb7wI+7j3+a+Au7/Fq4CG/r2Gc6/os8ADwmPc80NcTxK8g3LOA3UDlqG1j/i77FF+g7p3jxPsl4G/G2HeZ9zORB8z3flZCUxhroO7lx4g3od9ftbwl3tGldZxzg0BsGZyU55x7jujMuZFWAT/0Hv8QeO+I7f/potYCZWZWMzWRTsw51+Sce9l73AVsJVrpPqjX45xz3d7THO/LAVcQXZ4J3n49set8FLjSLLXWQDKzWuCPgHu850aAryfAgnrPGu93ecoF7d45TrzjObrkm3NuF9EFUVZOcEzCBO1efox4xzOp76+St8Qba2mdY/3Hpbpq51wTRH8ogRne9sBcp9fFdibR1qrAXo/Xxfgq0AL8mugntHbnXNjbZWTMb1m+CYgt35RK/g34PBDxnk8n2NcTVCn/s0/0g8qvzOwliy4fBuP/LqeKIN5rbvO6Gu8b0Q2dMvEG7V4+Kl5I4PdXyVvixbO0TjoIxHWa2TTgx8CnnXOdx9p1jG0pdT3OuWHn3BlEq/OvBE4eazfv35S+HjN7N9DinHtp5OYxdg3E9QRcEL63FzrnzgLeBdxqZpf4HdAJSNXv93eBhcAZQBPwNW97SsQbtHv5GPEm9Pur5C3x0m0ZnOZYk7P3b4u3PeWv08xyiP7y/Mg59xNvc2CvJ8Y51w48Q3Q8R5lFl2eCt8Y83vJNqeJC4I/NbDfRbroriLbEBfV6gizlf/adcwe8f1uAnxL98DLe73KqCNS9xjnX7H1AjADf5w9dd77HG7R7+VjxJvr7q+Qt8dJtGZyRywDdAvx8xPYPejN7zgM6Yk3YqcAbD3UvsNU59/URLwX1eqrMrMx7XAC8g+hYit8SXZ4J3n49Yy3flBKcc7c752qdc3VEf0eeds59gIBeT8Cl9D3LzIrMrDj2GHgn8Abj/y6nikDda0aNC/sTot9jGH/Jt6mKK1D38vHiTfj3N1kzLjL5i+hslx1ExyT9b7/jOY64/5toc+4Q0U8DHyE6rug3wJvevxXevgZ827vG14EVfsc/6louItr0vAl41fu6NsDXczrwinc9bwBf9LYv8H7R64FHgDxve773vN57fYHf13CMa7uMP8w2Dfz1BPErle9Z3s/Ea97X5lh84/0u+xRjoO6d48R7vxfPJqIJRc2I/f+3F+924F1THGug7uXHiDeh318tjyUiIiISIOo2FREREQkQJW8iIiIiAaLkTURERCRAlLyJiIiIBIiSNxEREZEAUfImSWNm3RPvdcLv8Sdm5sxsabLfa4I4Pm1mhX7GICKJpXuYpColbxJ0NwIvEC0s6qdPA7rxicjx0j1MjpuSN5lSZjbPzH7jLc77GzOb621/j5mtM7NXzOwpM6v2tn/JW8T3GTNrMLNPjjjXNKLLLH2EETc+M7vMzJ41s4fNbIeZ/bOZfcDM1pvZ62a2cIJYfmBm1404X/eI8z5jZo+a2TYz+5FXxfuTwCzgt2b226R/E0XEN7qHSSpQ8iZT7VvAfzrnTgd+BNzpbX8BOM85dybRtS4/P+KYpcDVRNeC+0eLrhsH8F7gl865HUCbmZ014pjlwKeA04CbgSXOuZXAPcAnJojlWM4k+gl1GdHK7xc65+4kuhbd5c65y+P7NohIQOkeJr5T8iZT7XzgAe/x/USXEoHoYrxPmtnrwN8Cp4w45nHn3IBz7hDRxYerve03Er1J4v1744hjNjjnmpxzA0SXHfmVt/11oG6CWI5lvXOu0UUXF351xLlEJDPoHia+y/Y7AMl4sfXZvgl83Tm3xswuA740Yp+BEY+HgWwzmw5cAZxqZg4IAc7MPj/GMZERzyOM/3MfiyWM98HGW2Q491ixHOviRCTt6R4mU04tbzLVfs8fxnZ8gGhXA0ApsN97fEsc57mOaHfBPOdcnXNuDrCL+D55ThTLbuBs7/EqIIeJdQHFx/HeIhJMuoeJ75S8STIVmlnjiK/PAp8EPmRmm4iO4/iUt++XgEfM7HngUBznvhH46ahtPwZuOo74xovl+8ClZrYeOBfoieNcdwO/0GBfkbSie5ikJHPOTbyXiIiIiKQEtbyJiIiIBIiSNxEREZEAUfImIiIiEiBK3kREREQCRMmbiIiISIAoeRMREREJECVvIiIiIgGi5E1EREQkQJS8iYiIiASIkjcRERGRAFHyJiIiIhIgSt5EREREAkTJN/MpAgAAIABJREFUm4iIiEiAKHkTERERCRAlbyIiIiIBouRNREREJECUvImIiIgEiJI3ERERkQBR8iZpwcw+bmbNZtZtZtP9jkdEZCJmVmBm/2NmHWb2iN/xSHAoeZOUYWa7zazPS8COmNnjZjYnjuNygK8D73TOTXPOHU5+tCIi8TOzZ7z7Wt6IzdcB1cB059z1ZvbnZvaCTyFKgCh5k1TzHufcNKAGaAa+Gccx1UA+sPl438yi9HsgIkljZnXAxYAD/njES/OAHc65cILeJzsR55HUpz9akpKcc/3Ao8AyADPLM7N/NbO9XvfoXV6XwxJgu3dYu5k97e1/gZlt8LojNpjZBbFze5+A7zCz3wG9wAIzKzWze82sycz2m9k/mVloaq9aRNLUB4G1wA+AWwDM7P8AXwRu8HobbgXuAs73nrd7+4157/Neu8zMGs3s78zsIPAfPlyb+EBZuqQkMysEbiB6wwP4CrAAOAMYAh4Avuicu93MTgF2AWXOubCZVQCPA58E/hu4HnjczBaN6FK9GXgX0cTPgEeItvQtAoqAx4B9wPeSfa0ikvY+SHRoxzpgrZlVO+f+0cwcsMg592cAZtYDfNQ5d9GIY8e89wG3e6/PBCqItuKpQSZD6D9aUs3PvE+cncBVwFfNzIC/AD7jnGtzznUB/w9YPc45/gh40zl3v3Mu7Jz7b2Ab8J4R+/zAObfZ666oIJrIfdo51+OcawG+cYzzi4jExcwuIppYPeycewnYCdwU57Hx3PsiwD865wacc32JjV5SlVreJNW81zn3lNdluQp4lugnzkLgpei9DIi2lo3XrTkL2DNq2x5g9ojn+0Y8ngfkAE0jzp81ah8Rkcm4BfiVc+6Q9/wBb9s34ji2ionvfa3eMBPJIEreJCU554aBn5jZ94DzgD7gFOfc/jgOP0A0IRtpLvDLkW8x4vE+YACoTNTAYRERb2za+4GQNyYNIA8oM7PlYxziRj0/xMT3vtHHSAZQt6mkJG8W6CqgnOgs0u8D3zCzGd7rs83s6nEOfwJYYmY3mVm2md1AdOLDY2Pt7JxrAn4FfM3MSswsy8wWmtmlib4uEcko7wWGid5/zvC+TgaeJzoObrRmoNbMcgGccxGO794nGULJm6Sa/zGzbqJj3u4AbnHObQb+DqgnOti3E3gKOGmsE3iTEt4NfA44DHweePeIbouxfBDIBbYAR4jOdK1JyBWJSKa6BfgP59xe59zB2BfwLeADvL3362miH1YPmlnsfhX3vU8yhzmnFlcRERGRoFDLm4iIiEiAKHkTERERCRAlbyIiIiIBouRNREREJEDSus5bZWWlq6ur8zsMEZlCL7300iHnXJXfcSSC7mEimSXe+1daJ291dXVs3LjR7zBEZAqZ2ejVNQJL9zCRzBLv/UvdpiIiIiIBouRNREREJECUvImIiIgEiJI3ERERkQBR8iYiIiISIEreRERERAJEyZuIiIhIgCh5ExEREQkQJW8iIiIiAaLkTURERCRAlLyJiIiIBIiSNxEREZEAUfImIiIiEiBK3kREREQCJNvvACS5Hli397j2v+ncuUmKREQkGI73vjma7qOSbGp5ExEREQkQJW8iIiIiAaLkTURERCRAlLyJiIiIBIiSNxEREZEA0WzTDNfRN8ThngGys7KYW1HodzgiIiIyASVvGWzLgQ5+tG4vDjDgY5cs8DskERERmYC6TTPU0HCEx15vYkZJHn9+QR0lBTn89JX9DIYjfocmIiIix6DkLUM9/+Yh2nuHePfps1hSXcyq5bNo6Rrge8/u9Ds0EREROQYlbxmodzDMsztaOGVWCQurpgGwtKaEk2tKuOeFXYSH1fomIiKSqpS8ZaBtB7sYGnZcsrjqLdvPnFNGR98QG/cc8SkyERERmYiStwy0tamTkvxsZpcXvGX74upp5IayeGpLs0+RiYiIyEQ02zTDDA1HeLO5mzPmlpFl9pbX8rJD1FUW8pNX9jO/sggb9fp4tAiziIjI1FHLW4bZ2dLN4HCEZTUlY75+ck0JbT2DtHQNTHFkIiIiEg8lbxlmS1MnedlZLKgsGvP1pTOjSd3Wps6pDEtERETipOQtgzjn2H6wiyXVxWSHxv6vLy3IoaY0n4bWnimOTkREROKh5C2DHOkdomsgzIKqsVvdYmrLC2ls78U5N0WRiYiISLyUvGWQvW29AMwpP/YaprXlBfQPRWjrGZyKsEREROQ4KHnLIPvaeskNZVFdkn/M/WaXRUuINLb3TUVYIiIichyUvGWQfUd6mV1eQCjr2CVAqkvyyc4y9h9R8iYiIpJq4krezOwaM9tuZvVm9oUxXs8zs4e819eZWd2I1273tm83s6snOqeZ/cjb/oaZ3WdmOd72y8ysw8xe9b6+eCIXnmmGhiMcaO9jbsWxu0wBQllGTWk+jUreREREUs6EyZuZhYBvA+8ClgE3mtmyUbt9BDjinFsEfAP4infsMmA1cApwDfAdMwtNcM4fAUuB04AC4KMj3ud559wZ3teXJ3PBmepAex8RR1zJG8Ds8kLvGE1aEBERSSXxtLytBOqdcw3OuUHgQWDVqH1WAT/0Hj8KXGnR8vyrgAedcwPOuV1AvXe+cc/pnHvCeYD1QO2JXaLAiMkKcSZvteUFDA5HaFWxXhERkZQST/I2G9g34nmjt23MfZxzYaADmH6MYyc8p9ddejPwyxGbzzez18zsF2Z2yljBmtnHzGyjmW1sbW2N4/Iyw762XiqKcpmWF9+KaLFJCxr3JiIiklriSd7GGt0+ui9tvH2Od/tI3wGec8497z1/GZjnnFsOfBP42VjBOufuds6tcM6tqKqqGmuXjHSgo59ZZQUT7+ipKs4jO8s42NmfxKhERETkeMWTvDUCc0Y8rwUOjLePmWUDpUDbMY495jnN7B+BKuCzsW3OuU7nXLf3+Akgx8wq44g/4w2GIxzpGWRmSV7cx2SZUVWcp25TERGRFBNP8rYBWGxm880sl+gEhDWj9lkD3OI9vg542huztgZY7c1GnQ8sJjqObdxzmtlHgauBG51zkdgbmNlMbxwdZrbSi/3wZC460zR39uNgwvpuo1VOy6O1W8mbiIhIKplwAJRzLmxmtwFPAiHgPufcZjP7MrDRObcGuBe438zqiba4rfaO3WxmDwNbgDBwq3NuGGCsc3pveRewB3jRy9V+4s0svQ74uJmFgT5gtdP6TXFp9ro+Zx5n8lZVnMcb+zsYGo6QM85aqCIiIjK14hq97nVTPjFq2xdHPO4Hrh/n2DuAO+I5p7d9zJicc98CvhVPvPJWBzv7yQkZ5UW5x3XcjOI8HHCoe4Ca0vjHy4mIiEjyqDklAxzs7Ke6JJ8sO/bKCqNVFUfHyGncm6SrqSxAPuL1b5pZd7KuSUTSn5K3DNDc0X/c490gOubNUPIm6cmHAuSY2QqgLKkXJiJpT8lbmuvqH6JncPi4x7sB5ISyKCvM0aQFSVdTWoDcS+y+Cnw+ydclImlOyVuaa+6MJl6TaXkDmFGcr5Y3SVdTXYD8NmCNc64pQfGLSIZS8pbmYkV2Z5ZOLnmL1XrTGqeShqasALmZzSI6qeubEwalVWJEZAJK3tJcS2c/hbmhuJfFGq2qOI9wxNHeO5TgyER8N5UFyM8EFgH1ZrYbKPRKK72NVokRkYkoeUtzh7oHjs4anYyqaZpxKmlrygqQO+ced87NdM7VOefqgF5vEoSIyHGbXHOMBMah7kFOmlk86eMrpkVrw7X1DiYqJJGU4EMBchGRhFDylsY6+4foHggfbT2bjOK8bHJCxpEeJW+SfqayAPmofaZNJl4REVC3aVrb1doDQOW041tZYSQzo7wwlzYlbyIiIilByVsaazgULeJeeQItbwAVRUreREREUoWStzS2q7UHI5p8nYiKolzaegdxKhciIiLiOyVvaWznoR7Ki3LJDp3Yf3NFUS6D4Qg9g8MJikxEREQmS8lbGtvV2nNC491iKgq9GafqOhUREfGdkrc0FYk4dh3qOeHxbgDlRUreREREUoWStzTV3NVP39BwQpK3CiVvIiIiKUPJW5pqOFom5MSTt5xQFiX52ar1JiIikgKUvKWpXYdOvMbbSOXejFMRERHxl5K3NLWvrZfcUBYlBTkJOV+FCvWKiIikBCVvaWrP4V5qKwrIMkvI+SqKcunsGyI8HEnI+URERGRylLylqb1tvcyrKEzY+cqLcnFAe+9Qws4pIiIix0/JWxpyzrGvrZe5CUzeygqj3a/tfUreRERE/KTkLQ0d6R2iayDMnEQmbwXRiQ/tmrQgIiLiq2y/A5Dj88C6vRPus6+tF4iOezu5piQh71takIOhljcRERG/qeUtDcVKepSf4IL0I4WyjOL8bI15ExER8ZmStzQUK+kRW5M0UcoKc9VtKiIi4jMlb2morWeQ4rxscrMT+99bVpijblMRERGfKXlLQ209gwntMo0pK8ilo2+IiHMJP7eIiIjER8lbGmrrGWR6MpK3whyGI47u/nDCzy0iIiLxUfKWZsLDETr7hpLU8qZabyIiIn5T8pZm2nuHcESXs0q0skLVehMREfGbkrc0c7RMSIJnmsKIVRZULkRERMQ3St7STCyxKvcSrUTKzwmRn5NFe59a3kRERPyi5C3NHOkdJMugpCDxyRtEZ5yq5U1ERMQ/St7STHvvIKUFOWSZJeX8ZYU5St5ERER8pOQtzRzpHTo6sSAZSgty1G0qIiLiIyVvaaa9dzApkxViygpy6B+KMBAeTtp7iIiIyPiUvKWR8HCErv7w0VmhyVDqnbtDXaciIiK+UPKWRjr6ojXektnyVloQPXdHv5I3ERERPyh5SyNHvNawpLa8FajlTURExE9K3tJIexIL9MaU5GcD0VY+ERERmXpK3tLIkd4hjD+0jiVDdiiLaXnZSt5ERER8ouQtjbT3DlJSkEMoKzk13mJKC3KUvImIiPhEyVsaOdI7lJRlsUaL1npT8iYiIuIHJW9ppL13MKkFemNKC3PoVPImIhmuf2iYFxsO8/r+DvVGyJTK9jsASYzhiKOjbyipM01jSvNzGAhH6B8aJj8nlPT3ExFJNW09g/zni7tp6RoAIJRlfPjC+cyvLPI3MMkIanlLE90DYRzJnawQEyvUq65TEclE3QNhvvtMPV39Yf78gjr++rKFlBfm8MD6vUdn/YskU1zJm5ldY2bbzazezL4wxut5ZvaQ9/o6M6sb8drt3vbtZnb1ROc0sx95298ws/vMLMfbbmZ2p7f/JjM760QuPN10eDeMsilI3spU601EMtivtxykb2iYv7h4AUuqi6ktL+TPzp1HeDjCgxv24ZzzO0RJcxMmb2YWAr4NvAtYBtxoZstG7fYR4IhzbhHwDeAr3rHLgNXAKcA1wHfMLDTBOX8ELAVOAwqAj3rb3wUs9r4+Bnx3Mhecrjr6wwCUTEXLWyx5U8ubiGSYA+19bNx9hPMXTGdmaf7R7TNK8rnm1Jnsbetlw+4jPkYomSCelreVQL1zrsE5Nwg8CKwatc8q4Ife40eBK83MvO0POucGnHO7gHrvfOOe0zn3hPMA64HaEe/xn95La4EyM6uZ5HWnnVgiNRXdpsX5ORjQ0afuARHJLL94o4mC3BBXLK1+22tnzimnICfEfS/s8iEyySTxJG+zgX0jnjd628bcxzkXBjqA6cc4dsJzet2lNwO/PI44MLOPmdlGM9vY2toax+Wlh86+IXJCRsEUTCAIZRnF+dl09IWT/l4iIqmiubOfna09XLK4ioLct99rc7OzWDm/gl9tOci+tl4fIpRMEU/yNlbF19Ed+uPtc7zbR/oO8Jxz7vnjiAPn3N3OuRXOuRVVVVVjHJKeOvqGKMnPIdrgmXzRQr1qeRORzLFxdxshM86aVz7uPuctmI6Zcf/aPVMYmWSaeJK3RmDOiOe1wIHx9jGzbKAUaDvGscc8p5n9I1AFfPY448hYHX1DU9JlGqNVFkQkkwwNR3h5bzvLZpUwLW/8KlulBTlcsXQGj712QBMXJGniSd42AIvNbL6Z5RKdgLBm1D5rgFu8x9cBT3tj1tYAq73ZqPOJTjZYf6xzmtlHgauBG51zkVHv8UFv1ul5QIdzrmkS15yWOn1K3nRzEpFMsPlAB31Dw6ycXzHhvledXM2Bjn62NHVOQWSSiSYs0uucC5vZbcCTQAi4zzm32cy+DGx0zq0B7gXuN7N6oi1uq71jN5vZw8AWIAzc6pwbBhjrnN5b3gXsAV70ugB/4pz7MvAEcC3RSQ+9wIcS8Q1IBxHn6OwfmpKZpjGlhbkMDTv6Boen7D1FRPzy8p52Kopy4yrCe/nSGZjBU1taOGVW6RREJ5kmrhUWnHNPEE2eRm774ojH/cD14xx7B3BHPOf0to8Zk9eSd2s88Waa7oEwETc1M01jYu+lQr0iku56B8M0HOrm4sVVZMUxrriqOI8z55Txm23NfOodi6cgQsk0WmEhDcSK5U5l8hYr1Ks1TkUk3W1r6iLi4JRZJXEfc+XJ1Wxq7OBgR38SI5NMpeQtDUxljbeYErW8iUiG2NzUSWlBDrPLCuI+5qpl0Tpwv9nWnKywJIMpeUsDnf3RBGoqx7wV52eTZVplQUTS22A4wpvNXSyrKTmuUkyLZ0yjpjSf3+88nMToJFMpeUsDHX1DhLKMojGKRiZLlhkl+SoXIiLpbUdzF+GIO64uUwAz45y6CjbsatOsfEk4JW9pIFbjbaoK9Mao1puIpLttBzspyAkxb/rEs0xHO2d+BS1dA+zVaguSYEre0kCnt7rCVCstVPImIunLOcebzd0smjGNUNbxfzg+16sJt35XW6JDkwyn5C0NRFve4qr6klAq1Csi6exgZz9dA2GWVBdP6vhFVdMoK8xR8iYJp+Qt4KIFesNTOtM0prQgh+GI43CP1jgVkfTzZnM3EJ18MBlZWcaKeRVs2K3kTRJLyVvA9Q4OMxxxviVvAE3tqmMkIulnR3MXM0vyT2gm/8r55ew+3EtLl+6TkjhK3gLOjwK9MbH3PNDRN+XvLSKSTAPhYfYc7mVx9eRa3WLOqYuOe9u4+0giwhIBlLwFXmzCwFTWeIv5Q8ubkjcRSS+7WnsYdo7FMyY33i1m2awSckLGpsaOBEUmouQt8Dr6/Wt5K8rLJpRlNGn5FxFJMztbu8nOMuZNLzyh8+Rlh1g6s4TX97cnKDIRJW+B19k3RJZFE6mpFi3Um63kTQLLzK4xs+1mVm9mXxjj9Twze8h7fZ2Z1Y147XZv+3Yzu3qic5rZvWb2mpltMrNHzezE+uMkqRoO9TC3opCc0In/mTyttpRNjR2amS8Jo+Qt4Dr6higpyCFrigv0xpQW5NKkMW8SQGYWAr4NvAtYBtxoZstG7fYR4IhzbhHwDeAr3rHLgNXAKcA1wHfMLDTBOT/jnFvunDsd2AvcltQLlEnrHQxzsKOfBVXHX5h3LKfPLqWrP8yewyrWK4mh5C3gOvqGKPWhQG9MWWEOBzTbVIJpJVDvnGtwzg0CDwKrRu2zCvih9/hR4EqLLmWyCnjQOTfgnNsF1HvnG/eczrlOAO/4AkDNMClq16EeHLCgMjGNo6fVlgKwab/GvUliKHkLuE6v5c0vpQU5NHf2MxzR3yEJnNnAvhHPG71tY+7jnAsDHcD0Yxx7zHOa2X8AB4GlwDfHCsrMPmZmG81sY2tr6/FflZywhtYeckJGbUVBQs63pLqY3Ows3lDyJgmi5C3AnHNH1zX1S2lBDuGI41D3gG8xiEzSWGMNRn8KGW+f490efeDch4BZwFbghrGCcs7d7Zxb4ZxbUVVVNdYukmQNh7qZN72I7KzE/InMCWWxrKaETY2atCCJoeQtwPoGhwn7VKA35mitN5ULkeBpBOaMeF4LHBhvHzPLBkqBtmMcO+E5nXPDwEPA+074CiThDncP0Nw5wILKxIx3izltdilv7O8kol4KSQAlbwHW3udfmZCY2Hsf1IxTCZ4NwGIzm29muUQnIKwZtc8a4Bbv8XXA0y46ZXANsNqbjTofWAysH++cFrUIjo55ew+wLcnXJ5OwzluHdEFVYicDn1ZbSvdAmF2HexJ6XslMU19fQhKmM4WStwNK3iRgnHNhM7sNeBIIAfc55zab2ZeBjc65NcC9wP1mVk+0xW21d+xmM3sY2AKEgVu9FjXGOWcW8EMzKyHatfoa8PGpvF6Jz4s7D5MbymJ2WWLGu8UsqykBYFtTFwsTnBhK5lHyFmCxAr1+TlgozA2Rl52lVRYkkJxzTwBPjNr2xRGP+4Hrxzn2DuCOOM8ZAS5MQMiSZC82HKauspBQVmLLLy2aMY1QlrG1qZM/Or0moeeWzKNu0wDr8Ar0Fuf7l4ObGbPKClSoV0QCr6Wrn/qW7oSVCBkpPyfEgsoith3sTPi5JfMoeQuwzr4hivP9K9AbU1Oar8XpRSTw1jbExrsldrJCzMk1JWxt6krKuSWzKHkLsI6+IUp8bHWLqSktoEmFekUk4F7ceZjivGxqShM73i1maU0x+9v76PDGK4tMlpK3AOvoC/s6WSGmpjSflq5+wsMRv0MREZm0dQ2HWTm/IuHj3WJO9iYtbD+o1jc5Mf4328ikOOfo7BvipGr/Zy3VlOUTcdDSNcCsBM/QEhGZCs2d/TQc6uHGlXNP+FwPrNs75vZYi9v9L+6mvqV73ONvOvfEY5D0ppa3gOofijA4HEmJlrdZXheDFqgXkaBa23AYgPMXTk/ae5TkZ1OYG+Jgp4aZyIlR8hZQsU9wfpYJiakpywfQAvUiElhrG9oozs8+2rWZDGbGzJJ8zc6XE6bkLaA6UqBAb0yNWt5EJODWNRxmZV3yxrvF1JTm09zZT8RpmSyZPCVvAZUKqyvElORnU5QbUsubiARSbLzbeQuS12UaM6Mkn6FhR3uvZpzK5Cl5C6iO/iEMKM73P3kzM2aW5mt9UxEJpNh4tylJ3orzAGjRuDc5AUreAqqjb4hp+dlJb+KPV3SVBXWbikjwrG1oozgvm2WzkjfeLWZGcXSMcHPXQNLfS9KXkreA6uwbSoku05joKgv6JCkiwZPs+m4jFeSGKMnPVsubnBAlbwEVXV0hlZK3Ag51DzAYVqFeEQmOlikc7xYzoySfFrW8yQlQ8hZQHSnW8jarLB/nogN/RUSCYu2u6HqmU5m8VRfn0dKlGacyeUreAqh/aJiBcGoU6I35Q7kQJW8iEhxrGw5P2Xi3mBnFmnEqJ0bJWwClUo23mJrS6CBcTVoQkSBZ23CYc6ZovFvMjBLNOJUTo+QtgDpTaHWFmBpvTVPVehORoGjp7KehtYfzFlRM6fvGZpxq3JtMlpK3AErFlrdpedkU52er5U1EAsOP8W7whxmnGiMsk6XkLYA6+r2Wt/xsnyN5q1mlBWp5E5HAODreLYnrmY5nRrFmnMrkKXkLoM6+IYrysskOpdZ/X01ZvlreRCQwYuPd/LiXzijRjFOZvNT66y9xiZYJSa1WN4hOWtASWSISBH6Nd4up9macdmjGqUyCkrcA6uwLU5pCBXpjakoLONwzSP/QsN+hiIgck1/j3WJiM06bu/SBV46fkrcA6ugbSqmZpjGxciFqfRORVLeu4TDTfBrvBiNmnHZq3JscPyVvATMYjtA3NExZCiZvs8uj5UL2t2vcm4iktrUNhzmnrty3scMFuSGK87NpUcubTIKSt4DpSMEabzFzygsB2NfW63MkIiLja+nqZ2fr1K5nOpbq4nya1fImk6DkLWBSscZbTE1pPqEsY98RJW8ikrrWNfg73i2mqiSP1q4BzTiV4xZX8mZm15jZdjOrN7MvjPF6npk95L2+zszqRrx2u7d9u5ldPdE5zew2b5szs8oR2y8zsw4ze9X7+uJkLzrIOlM4ecsOZVFTmk/jEXWbikjq+v3OQxTnZ3PKFK5nOpbq4nwGhyOacSrHbcJ6E2YWAr4NXAU0AhvMbI1zbsuI3T4CHHHOLTKz1cBXgBvMbBmwGjgFmAU8ZWZLvGPGO+fvgMeAZ8YI53nn3LsncZ1p42iB3hRM3iDadapuUxFJZS/UH+L8BdN9r5VZPWLGaXlRrq+xSLDE85O7Eqh3zjU45waBB4FVo/ZZBfzQe/wocKWZmbf9QefcgHNuF1DvnW/cczrnXnHO7T7B60pbHX1DFOaGyEmxAr0xcyoK2KeWNxFJUXsP97KvrY+LFldOvHOSacapTFY8GcBsYN+I543etjH3cc6FgQ5g+jGOjeecYznfzF4zs1+Y2Slj7WBmHzOzjWa2sbW1NY5TBktn31BKdpnG1JYX0to1oFpvIpKSXqg/BMCFi/xP3jTjVCYrnuTNxtg2enTlePsc7/ZjeRmY55xbDnwT+NlYOznn7nbOrXDOraiqqprglMHT0TdESQoW6I2ZUxEtF6JxbyKSil6ob6WmNJ8FlUV+hwLAjOI8rXEqxy2e5K0RmDPieS1wYLx9zCwbKAXajnFsPOd8C+dcp3Ou23v8BJAzckJDpuhI8Za3WLmQRs04FZEUMxxx/H7nYS5cVEl0ZI//YgvUO804leMQT/K2AVhsZvPNLJfoBIQ1o/ZZA9ziPb4OeNpFfxLXAKu92ajzgcXA+jjP+RZmNtMbR4eZrfRiPxzPRaaL/qFhegeHKS1M3eStNlbrTS1vIpJithzopL13iItSoMs0ZkZJHoPhyNEyUCLxmHC2qXMubGa3AU8CIeA+59xmM/sysNE5twa4F7jfzOqJtrit9o7dbGYPA1uAMHCrc24YoiVBRp/T2/5J4PPATGCTmT3hnPso0aTw42YWBvqA1S7DPqrElp1KxXVNY2YU55GbnUWjZpyKSIqJjXe7YJG/9d1GOjppoWuAskLNOJX4TJi8wdFuyidGbfviiMf9wPXjHHsHcEc85/S23wncOcb2bwHfiifedNXkJW+pWiYEICvLqC0r0Jg3EUk5v6s/xEnVxUcTplRQVRwtF9LSNcCS6mKfo5E+NO6nAAAgAElEQVSgiCt5k9RwsDOaEKXymDeA2opCrbIgIr55YN3et20bGo6wtuEw586vGPN1v0zLy6YwN0RLp2acSvxSs1iYjOkPLW+pnXPXlheoUK+IpJQ9h3sJRxwLZ0zzO5S3iU1aEImXkrcAOdjRT35OFnnZIb9DOaa5FYUc6R2is18DcEUkNexs7SbLYP701CgRMtKMkjxauvo141TipuQtQJo6+lO+yxSgzrs57jmk1jcRSQ31Ld3MqSgkLyf1PvzOKM6jfyhC10DY71AkIJS8BcjBoCRvldFyIbsP9/gciYgI9A6GOdDex6Kq1OsyhT/MOG1V16nESclbgDR19Kf06gox8yqiLW+7Dyl5ExH/NbT24IBFKTjeDaLdpoAmLUjclLwFxGA4wqHugZQu0BtTkBtiZkk+uw+r21RE/Fff2k1edtbRIuKppjgvm/ycLE1akLgpeQuI5s7UL9A7Ul1lobpNRSQl7GzpZn5lEaGs1FgSazQz04xTOS5K3gIiViYkCGPeAOZXFrFHyZuI+OxIzyCHewZTtss0ZkZxnrpNJW5K3gKiqSNaoDeVV1cYad70Ig51D9KlciEi4qOdrd0ALEzRyQoxM4rz6BkcplszTiUOSt4C4mDAWt5i5UJ2q1yIiPiovrWb4vxsZnjLUKWqGSWacSrxU/IWEE0d/UzLyyY/BWsUjUXlQkTEbxHn2NnSzaKqaZil5ni3mBlH1zhV16lMTMlbQBzs6GdmaeospjwRlQsREb81d/bTMzickktijVZakENudhYtnWp5k4kpeQuIps5+agKUvKlciIj4rb4lGOPdIDbjNE8tbxIXJW8BcbCjj5klwUneQOVCRMRfO1u7qSrOC8xY4WjyppY3mZiStwAYGo7Q0jVATVmB36Ecl4VV06hv6dZiyyIy5cLDEXYd6knZJbHGMqM4n67+MB19mqUvx6bkLQBaugZwjkB1mwIsnjGNjr4hDnUP+h2KiGSYvUd6GRp2KV/fbaTYpIX6li6fI5FUp+QtAA56Nd6CNGEBYHF1MQBvNutGJPL/s3fn8XXVdf7HX5/c7PvedEmbLmlLW6ClpUBZZJFVh+qIUlDBbRgVdUZ/8xtlfjPq+NPfDC7DuOCCIoIDAiJiEWQHoaUUWqD7lu5p0mZp1mZPvr8/7glcQtLepEnOXd7PxyOP3px7zvd8zr03337u+W4yvnbXtJJgwQnDo8UEr2vM9sOqM+X4lLxFgf7VFaLxzhvALq/TsIjIeKmoaWVKXnrUTK8EkJueREpiAjuUvMkJKHmLAv0T9E7Mjq4+b0VZKWSnJrJLTQAiMo46unupbGiPilGmocyMCdmpuvMmJ6TkLQpUN3WQlhQgOy3R71CGxcwon5DFriO68yYi42dP7TEcRFV/t34TslPZcbhFA73kuJS8RYHDTcE53iJ9hvDBlBdnvjXXkojIeKiobSUpYJTmR1drBUBJdgpN7d0c0WS9chxK3qJAdVN71A1W6DerOJP6Y13Ut6oiEpHxsbumlemFGSQmRN9/cRNy+gctNPsciUSy6Ptkx6FoWxorVP+IU919k0hkZleY2Q4zqzCzrw3yfIqZPeA9v9bMykKeu8XbvsPMLj9RmWZ2r7d9s5n92syiY+bYKFPd1E5ta2dUze8Wqn8ydg1akONR8hbhevscR1o6mZQTfbf/QSNOJXKZWQC4HbgSmAdcZ2bzBuz2aaDBOTcLuA241Tt2HrACmA9cAfzUzAInKPNeYC5wKpAGfGYMLy9ura6oB4iK9UwHk56cyITsFCVvclxK3iJcbUsnvX0uau+8TcxJJSM5oLneJBItBSqcc3ucc13A/cDyAfssB+72Hj8EXGLBzqfLgfudc53Oub1AhVfekGU65x53HuBVYMoYX19cWl1RR0ZK4ltzpkWjOSXZGnEqx6XkLcJVexP0Rtscb/3MjDklWWyrVkUkEWcycDDk90pv26D7OOd6gCag4DjHnrBMr7n048ATJ30F8g7OOVZV1DGzKIOEKBzg1W9uSRYVNa309Pb5HYpEKCVvEa5/jrdovfMGsGByDlurm+nr09B3iSiD/e8+8EM61D7D3R7qp8CLzrmXBg3K7CYzW2dm62prawfbRYZQUdNKbUv09nfrN2dCFl3e2qwig1HyFuHeXl0hOvu8AcyflE1rZw/7j7b5HYpIqEqgNOT3KUDVUPuYWSKQAxw9zrHHLdPMvgEUAV8ZKijn3B3OuSXOuSVFRUXDvKT4trqiDiDqJucdaE5JcKCXmk5lKEreItzh5g6SExPIS4/egWnzJ+UAsKWqyedIRN7hNaDczKabWTLBAQgrB+yzErjRe3wN8JzXZ20lsMIbjTodKCfYj23IMs3sM8DlwHXOObWHjYFVFfVMzU8nLyPZ71BOyqziTAIJpkELMqTomrI/DlVH8QS9/WZPyCIpYGw+1Mz7T5vEfWsPDLuM68+aOgaRSTxzzvWY2ReAJ4EA8Gvn3BYz+xawzjm3ErgT+K2ZVRC847bCO3aLmT0IbAV6gJudc70Ag5XpnfLnwH5gjff3/LBz7lvjdLkxr6e3j7V76nn/6RP9DuWkpSYFKCtI1503GZKStwh3uKn9rXl/olVyYgLlxVm68yYRxzn3OPD4gG1fD3ncAXx4iGO/A3wnnDK97apvx9CmQ020dPZw7qxCmtt7/A7npM0tyWbjoUa/w5AIpWbTCFfV2BG1I01DLZiczZaqZq3XJyJjor+/2zkzCnyOZHTMKcni4NF2WjujPxGV0afkLYL19jmONHcwMTd6Byv0mz8ph6PHujjc3OF3KCISg1ZX1HPKxGwKMlP8DmVUzPUGLezUHJkyCCVvEayutZOePsekGEjeFkzOBmDzIa3XJyKjq72rl/X7GzhvVmzcdYNgsylomSwZnJK3CHaoMThB76QYaDY9ZWI2CQYbK9WHQ0RG17r9R+nq7WPZrEK/Qxk1U/LSSE8OKHmTQSl5i2DVjcEmxli485aenMgpE7NZv7/B71BEJMasrqgnMcFYWpbvdyijJiHBmD0hi+2H1Voh76bkLYJV9d95i4HkDWDJtDzePNhIr1ZaEJFRtLqijjOm5pGRElsDek+ZGFxaUAO9ZCAlbxHsUGM7GckBslNjo0JaXJZPW1evBi2IyKhpbOtic1UTy2Kov1u/+ZNyaGrvprKh3e9QJMIoeYtg1U3tTMpNi+oJekMtmZYHwP56rdcnIqPjlT31OAfnxlB/t34LJvevTqOmU3knJW8RrKoxNqYJ6TcpN42JOansr9capyIyOlZV1JGRHGBhaa7foYy6uSVZBBJME5zLuyh5i2DVTe1Mzo3+kaahFk/L44AWqBeRUfJyRT1Lp+eTFIi9/85SkwLMKspk8yElb/JOsfdpjxEd3b3UtXYxKSd27rxBsOm0qb2bxrYuv0MRkShX1djOnrpjMdlk2m/+5Gw2q9lUBlDyFqEONwU79cdSsynAmdODQ/n31qnfm4icnP4lsWI5eVswKYfalk5qNNBLQih5i1BvTxMSW82mp5Rkk54coKKm1e9QRCTKvby7noKMZOZMyPI7lDGjQQsyGCVvEarKu/MWa82mCQnGzKJMKmpbNXeRiIyYc47VFXWcM7OAhITYGJE/mHmT+pcWVL83eZuStwjVf+etJAaWxhqovDiTlo4ejrR0+h2KiESpippWalo6OS+Gm0wBMlMSmVGYwSYlbxIirOTNzK4wsx1mVmFmXxvk+RQze8B7fq2ZlYU8d4u3fYeZXX6iMs3sC942Z2aFIdvNzH7kPbfRzM4Y6UVHg+qmdgozk0lNCvgdyqibVZwJoKZTERmxeOjv1u+0KTlsrFTyJm87YfJmZgHgduBKYB5wnZnNG7Dbp4EG59ws4DbgVu/YecAKYD5wBfBTMwucoMzVwHuB/QPOcSVQ7v3cBPxseJcaXQ41dsTMslgD5aYnU5iZTEWNFlwWkZFZvbue0vw0SvPT/Q5lzJ1emsvh5o63BrKJhHPnbSlQ4Zzb45zrAu4Hlg/YZzlwt/f4IeASCy4LsBy43znX6ZzbC1R45Q1ZpnPuDefcvkHiWA7c44JeAXLNbOJwLjaaVDW2x1x/t1CzijPZW3eMnt4+v0MRkSjT09vHK7vrY77JtN/p3gTEbx5s9DkSiRThJG+TgYMhv1d62wbdxznXAzQBBcc5NpwyRxIHZnaTma0zs3W1tbUnKDIyOeeobmxnYoyNNA01e0IW3b2OPZoyRESGadOhJlo6e1g2Mz6St3kTs0lMMDZUKnmToHCSt8GG8QwcJjjUPsPdfrJx4Jy7wzm3xDm3pKio6ARFRqbm9h6OdfUyOUabTQFmFmWSFDC2VWv4u4gMz8u76wFYNjP2FqMfTGpSgFMmZrNBd97EE07yVgmUhvw+Bagaah8zSwRygKPHOTacMkcSR0yoagqONJ0Yw82mSYEEyouz2FbdrClDRGRYVu2qY25JFgWZKX6HMm4WluaysbKJvj7VlxJe8vYaUG5m080smeAAhJUD9lkJ3Og9vgZ4zgX/R14JrPBGo04nONjg1TDLHGglcIM36vRsoMk5Vx1G/FEnVifoHWjexGyaO3o45F2viMiJdHT3sv5AQ9z0d+t3emkurZ097K7VKH0JI3nz+rB9AXgS2AY86JzbYmbfMrOrvd3uBArMrAL4CvA179gtwIPAVuAJ4GbnXO9QZQKY2ZfMrJLgnbWNZvYr7xyPA3sIDnr4JfD5k776CPXWBL0x3GwKMKckCwM1nYpI2Nbta6Crpy8upggJtbA0uNLCG2o6FSAxnJ2cc48TTJ5Ct3095HEH8OEhjv0O8J1wyvS2/wj40SDbHXBzOPFGu6rGdpICRlGMNwlkpCQyrSCDrdXNXDqvxO9wRCQKrKqoIzHBWOqtkxwvZhRmkpWayBsHGvnIktITHyAxLazkTcZXdWM7JTmpUbPky31rD4z42PmTsnlsUzV1LZ0UZsV2sioiJ+/l3XUsmppLRkp8/feVkGAsnpbHun1H/Q5FIoCWx4pAVY0dMT1YIdT8/nX7qjR7uIgcX1NbN5sONcXNFCEDnVmWz66aVhqOdfkdivhMyVsEOtTYHtPThITKTU+mNC9Niy6LyAmt2VOHc/GxJNZglkzLA2D9/gafIxG/KXmLML19jiPNHUyMwQXph3Lq5Byqmjqob9VC9SIytNUV9aQnB1jorTgQb04vzSUpYLy2X02n8U7JW4Spbemkp8/F/EjTUPMnB0dRba7SqFMRGdrqijrOmp5PcmJ8/teVmhTg1Mk5rNunO2/xLj7/AiJY/wS9sT7HW6i89GSmqOlURI6jqrGdPXXH4rbJtN+ZZflsqmyio7vX71DER0reIszbE/TGz503gAWTcjjU2M5RdcQVkUGsrqgD4re/W7/F0/Lo6u1jY6W+7MYzJW8RproxPiboHWhBf9Op7r6JyCBWV9RRmJnMnAlZfofiq6XT8zGDNd76rhKflLxFmEON7WSmJJKdmuR3KOMqPyOZyblpmjJERN7FOcfq3fUsm1kYNfNfjpXc9GTmT8pm9e46v0MRHyl5izCVDW1MyYuvu279FkzOobKhnYY2NZ2KyNt21bRS29LJubMK/A4lIpw7s5A3DjTQ1tXjdyjiEyVvEaayoZ0peel+h+GLBd6EvVvUdCoiIVbtUn+3UMtmFdLd63hNo07jlpK3COKc4+DR+L3zVpCZwqScVDYpeROREKsr6igrSI/bL7YDnVmWR1LAeLlCTafxSslbBGls6+ZYVy+l+fFbQS2YnMPBhnYa1XQqIkB3bx+v7KnXXbcQ6cmJLJqap35vcSy+VvaNMAMXdD/UEJwmZG9t60kt9h7NFkzK4amtR9hS1azKWkTYWNnIsa5e1QcDnDuzkP9+dicNx7rIy0j2OxwZZ7rzFkH6O+rnpsfvH2JhVgol2amaMkREAFi1qx4zOGeGBiuEumB2Ic7Bi7tq/Q5FfKA7bxGkP3nLi+PkDYJNp89sO0JTezc5afE1ZYqIvLNV4o9vVDIpJ42/bD7sY0SR5/QpuRRkJPPsthqWL5zsdzgyznTnLYI0tHWRmpRAWnLA71B8tWBycNTp1mqtdSoSzzp7ejlwtI2ZRZl+hxJxEhKMi+YW88KOGnp6+/wOR8aZkrcI0nCsO+7vugEUZ6VSkJHMdiVvInFtX10bfQ5mFSt5G8wlc4tp7uhh/X5NGRJvlLxFkIa2LiVvnlMmZrOn7hidWnxZJG7trm0lMcGYVhC/I/CP57zyQpICxnPba/wORcaZkrcI4Zzzkjf18QKYOzGL3j7HrppWv0MREZ9U1LQytSCdpID+qxpMVmoSZ00v4JltR/wORcaZ/iIixLGuXrp7XVyPNA01LT+DtKQA2w+r6VQkHrV29nC4uYNZ6u92XJfNn8Du2mPsONzidygyjpS8RYj+SWnzNV8PAIEEY05JFtsPt9DnnN/hiMg4210bvOuu/m7Hd+WCiSQY/Hljld+hyDhS8hYhjh7rn+NNzab95pZk0dbVS+XRNr9DEZFxtrumldSkBCblxudygeEqykph2cxCHt1QhdMX3bih5C1CNHjJW76aTd8yqzgTA3aq35tIXHHOUVHbyozCTBLM/A4n4r3/tInsq29j8yF1M4kXSt4iRP2xLjJSEklJiu853kKlJycyJS+NXUfUl0MkntS3dtHY1q0m0zBdsaCExATjUTWdxg2tsBAhjh7rIl9Npu9SPiGL57fX0NTWTY5eH5G4sLMm+IWtPE6Tt5GsbT2rOJPfvXqA0rx0Pn7OtHE/f6jrz5p6UsfLienOW4Q4eqyLgswUv8OIOOXFmThgVUWd36GIyDjZdaSV/Ixk1YnDcGZZPi0dPWzT5OZxQclbBOjp7aOpvVsjTQcxJS+d1KQEXtypxZdF4kFnTy976lrj9q7bSM2ekEVOWhKv7TvqdygyDpS8RYDGtm4cmiZkMIEEY2ZRJi/uqtVIKpE4sH5/A929jtkTsvwOJaoEEowl0/LYVdPKgXqN0I91St4iQL030rRAydugyouzqG7qoEKjTkVi3os760gwmF6Y4XcoUWdJWT4G/M/a/X6HImNMyVsEOHqsE9Cdt6GUTwg2n/xVTaciMe/FnbVMzc8gVSPvhy0nLYlTp+Rw7yv735p+SmKTkrcIcPRYF0kBIzNFg38Hk5eezIyiDF7cpUELIrGstqWTrdXNzJ6g/m4jdeGcYo519XLX6r1+hyJjSMlbBKg/1kV+RjKmySiHdEF5EWv31NPR3et3KCIyRl7aFby7Xl6s/m4jVZKdyhXzS7jr5X00d3T7HY6MESVvEeDosS7yMzQk/njeM7uIzp4+Xt2rkVQiserFnbUUZCQzMTfV71Ci2hcunkVLRw8/e2G336HIGFHy5jPnHA1tXRqscAJnzcgnOaApQ0RiVV+f46VddZxXXqglsU7Sgsk5XLN4Cr96aQ8VNVqhJhYpefNZS0cP3b1OgxVOID05kTOn5/HiLiVvMnrM7Aoz22FmFWb2tUGeTzGzB7zn15pZWchzt3jbd5jZ5Scq08y+4G1zZlY41tcWbbZWN1N/rIsLyov8DiUmfO3KuaQlBfi3R7ZomqUYpOTNZ3UaaRq282YVsfNIKzXNHX6HIjHAzALA7cCVwDzgOjObN2C3TwMNzrlZwG3Ard6x84AVwHzgCuCnZhY4QZmrgfcCmsdhEP2jyc8vV147GgozU/jnK+ayZk89v169z+9wZJQpefNZfUtwOHeRloE5of5KXUtlyShZClQ45/Y457qA+4HlA/ZZDtztPX4IuMSCI4uWA/c75zqdc3uBCq+8Ict0zr3hnNs31hcVrf66o5ZTJmZTnK3+bqPl+qVTuXTeBP7f49tYu6fe73BkFCl581ldayeJCaZF18Mwb2I2+RnJrNKUITI6JgMHQ36v9LYNuo9zrgdoAgqOc2w4ZR6Xmd1kZuvMbF1tbXx0E2hq62b9gQYunqsm09GUkGD84COnMy0/nc/f+zrbD2vd01ih5M1nta2dFGQmq4NuGBISjGUzC1hVUac+HDIaBvujG/jBGmqf4W4Pm3PuDufcEufckqKi+Ehm/rqrlt4+x8Vzi/0OJeZkpybxyxuXkBgwrv3FK7x+oMHvkGQUKHnzWV1rF4VqMg3b+eWF1LR0svOIlsqSk1YJlIb8PgWoGmofM0sEcoCjxzk2nDJlgOe315CXnsTC0jy/Q4lJM4syeeizy8hNT+LaX6zhh8/soru3z++w5CQoefNRb5/j6LFOJW/DcJ43Eu0ljTqVk/caUG5m080smeAAhJUD9lkJ3Og9vgZ4zgVv+64EVnijUacD5cCrYZYpIXr7HC/sqOHCOcUEEtQCMVZK89N55PPncuWCidz2zE4u/sEL3P3yPlo0kW9UUvLmo8a2LvocSt6GYXJuGjMKMzRoQU6a14ftC8CTwDbgQefcFjP7lpld7e12J1BgZhXAV4CvecduAR4EtgJPADc753qHKhPAzL5kZpUE78ZtNLNfjde1RrI3DzbQ0NbNRWoyHXN5Gcn86LpF3PWJMynKTOEbK7ew+NvP8Hf3rONPbx6itbPH7xAlTFpM00e1rcFpQgozNU3IcJxXXsjv11XS1dNHcqK+f8jIOeceBx4fsO3rIY87gA8Pcex3gO+EU6a3/UfAj04y5Jjz3PYaAgnGezS/27i5aG4xF80t5o0DDazcUMXjm6p5eusRUhITuGhOMYWZycydmK2+2BFMyZuP6lo1TchInDerkHvW7Of1Aw2cPaPA73BE5CQ8t72WxdPyNOLeB4um5rFoah7/9r55rD/QwGMbq3lsUzW1LZ3BNVIXlDB7gtaZjUS6beGjutZO0pICpKcohx6Os2cWEEgwTRkiEuWqGtvZVt2sUaY+S0gwzizL55tXz+eVWy7hI0tK6e7t4zcv72PlhkMa3BCBlLz5qK61U02mI5CdmsTpU3J4Sf3eRKLa8ztqALhEyVvECCQYC0tz+dIl5Zw7s4BX9hzl16v20tHd63doEiKs5G2c1/+b7pWxyysz2dv+CTOrNbM3vZ/PnMyFR4K6Fo00HanzyovYVNlIU5tGSolEq+e31zAlL41ZxZl+hyIDJAUSeN9pk7hu6VQONrRx56q9tHcpgYsUJ0zefFj/71bgNudcOdDgld3vAefcQu8nqkdqtXR009zRQ1GWkreROL+8kD4HL+/W3TeRaNTR3cvqinounluMqWN8xDp1cg4fO3sah5s7+N2rB+jt0wTpkSCcO2/jtv6fd8zFXhl4ZX5g5JcXuXbVBCeZnaB1/EZkYWkuWSmJby1mLSLRZc3uetq7ezVFSBSYW5LNBxZOoqK2lSc2V/sdjhBe8jae6/8VAI1eGYOd60NmttHMHjKz0FnM3xIt6wJWeCsEFOvO24gkBRI4f3Yhz++o0VJZIlHoyS2HyUxJZNlMjRiPBoun5XPOzAJW765nS1WT3+HEvXCSt/Fc/+9453oUKHPOnQY8w9t3+t65c5SsC7jzSAuJCUZehgYsjNSFc4o50tzJtuoWv0MRkWHo7XM8tfUIF80tJiUx4Hc4EqYrF5QwMSeVR96s4pgm9PVVOMnbeK7/VwfkemW841zOuXrnXKe3/ZfA4jBij1g7a1opzkrRJIgn4cLZweT8hZ01PkciIsPx2r6jHD3WxRXzS/wORYYhMSGBDy8upaOrl5UbtGSvn8JJ3sZt/T/vmOe9MvDK/BOAmU0MOd/VBJeeiVq7jrRQrP5uJ6U4O5UFk7N5YXvkNo+LyLs9ueUwyYkJXDgncltHZHAlOalcNLeYTYea2HlErR5+OWHyNt7r/wFfBb7ilVXglQ3wJTPbYmYbgC8Bnzi5S/dPc0c31U0dTFB/t5N24exi1h9o0JQhIlHCOceTmw9zQXkRGZqgPCpdUF5IQUYyf95YTU+fJvD1Q1h/OeO8/t8egqNRB26/BbglnHgj3a7+wQq683bSLppbzE+er+CFnTUsXzhwHI2IRJpNh5qoaurgy5fO9jsUGaHEQALvO20i96zZz5rd9ZyvdWnHnVZY8MEu71azpgk5eYtKcynKSuHJLYf9DkVEwvDklsMEEoz3njLB71DkJMwtyWb2hEye31GjyXt9oOTNBzuPtJKWFCBXCzGftIQE47J5E3hhR62WbxGJAk9sPszZM/I10j4GXDavhI7uPl6qUL/j8abkzQe7alqYVZypkaaj5IoFJbR19fKSFqoXiWgVNS3srj3G5RplGhMm5aZx6uQcXq6op1VTh4wrJW/jzDnHtupm5pRk+R1KzDh7RgHZqYlqOhWJcE9sDv6NXjZPyVusuOSUYrp7+/jrDk3ZNJ6UvI2zmpZO6lq7mD8p2+9QYkZSIIH3njKBZ7YdobtXI59EItUTWw6zaGouJTnq7xsrirNSWTQ1j7V7j9LUrlH/40XJ2zjrX1Zk/qQcnyOJLVedOpHGtm5e1FqnIhFpT20rmw81c9WCiSfeWaLKJXOLcQ6e3667b+NFyds421rVDMApE9VsOpreM6eI/IxkHn7jkN+hiMggHnmzCjP4m9Mn+R2KjLK8jGSWlOWxbv9R6ls7T3yAnDQlb+NsS1UzZQXpZKVqpOloSgokcPXpk3h66xHduheJMM45/vTmIc6ZUaAm0xh10dxiEsx4Xn3fxoWSt3G2paqZeervNiY+uGgyXT19/GVTtd+hiEiINw82sr++jQ9oIu2YlZ2axFnT83njQCN76475HU7M09ok46i5o5sDR9u49sxSv0OJSadNyWFmUQYPra9kxdKpfocjErfuW3vgHb8/uqGKxASjtbPnXc9J7LhgdhGv7jvKj5/dxX9du9DvcGKa7ryNo/7+brrzNjbMjGvPLGXd/oa3BoaIiL96+xwbDzUxtySL1KSA3+HIGMpKTeKs6QU88uYhdte2+h1OTFPyNo76kzdNEzJ2rl0ylbSkAL9Zvc/vUEQE2F3byrHOHhaW5vodioyDC2YXkZIY4EfP7ta9wG8AACAASURBVPI7lJim5G0cbT7URFFWCsVZ6rA7VnLSk/jQ4sn8aUOVRj2JRIA3DzaSmpTA7AkaYR8PMlMSueGcaazcUEVFTYvf4cQsJW/j6PUDDSzSt88x94ll0+nq6eNe9a0R8VVXTx9bq5o5dXIOiQH9dxMvbrpgBmlJAX74bIXfocQs/TWNk/rWTvbVt3HGtDy/Q4l5s4ozuXhuMb96aQ9NbZo2RMQvW6ub6ert43R9aY0rBZkp3LisjD9vrGJbdbPf4cQkJW/j5I0DjQCcMVXJ23j4p8vm0NLZw89f3O13KCJx6/UDDeSmJVFWkOF3KDLO/v6CGWSlJPK9J3f4HUpMUvI2Tl4/0EBignHaFC2LNR7mTcpm+emTuGv1Xo40d/gdjkjcOXqsi4qaVhZPyyPBzO9wZJzlpifz+Ytm8dz2Gl7ZU+93ODFHyds4ef1AA/MmZWuo/Dj6X5fNobfP8a0/b/U7FJG4s37/UQxYrK4icesTy8ooyU7lP/6ynb4+53c4MUXJ2zjo6e1jw8EmNZmOs9L8dP7hknIe21jNoxuq/A5HJG709jnW729g9oQsctOT/Q5HfJKaFOCfLp/DhoON/FHrTo8qJW/jYPvhFtq7e1k0VZ12x9tn3zOT06fk8G9/2szhJjWfioyHnUdaaO7oYUmZvrDGu79dNJnTS3P5zye209rZ43c4MUPLY42D1w80ABqs4IfEQAI/+MhCrv7JKj71m9d48LPnkJmij73IWHplTz3ZqYnMLdGE5H6IpCXIEhKMf796Ph+4fTU/enYX/3LVKX6HFBN0520crK6oY3JuGlPy0vwOJS7NKs7kpx89gx1HWvjc/6yno7vX75BEYtbu2lZ21bSydHoBgQQNVBBYWJrLijNLuXPVXjZVaunC0aDkbYz19jnW7K7nvFmFmEZc+ebCOcX8xwdP5aVddXzsV2s5eqzL75BEYtI9L+8jkGAsnZ7vdygSQW656hQKM5P53w9toKunz+9wop6StzG2+VATzR09nFte6Hcoce8jZ5byk+sXsfFQE8tvX8Vr+476HZJITGnp6Oah9ZWcNjlH3RPkHXLSkvj2B05l++EWfvyc1j09WfrrGmOrKuoAWDazwOdIotto9uH41LIyHlh3kI/8fA1Lp+dz0dxislOThl3O9WdNHbWY/DKS1zUWrlvGxn1rD3Csq5dzVN/JIC6dN4FrFk/hJ89XcPaMAs6dpZsaI6U7b2Ps5d11zC3JojAzxe9QxDO1IIMvXVLOWTMKeG3fUX7w1A7+sqlaI6FETkJnTy93rtrLspkFTMlL9zsciVDfWj6fmUWZ/MP9b2oC9ZOg5G0MdXT38tq+Bs7Tt4uIk5IY4OrTJ/Hl985mwaQcVlXU8f2ndvDE5mpaOrQeqshwPfLGIWpaOvnse2b6HYpEsPTkRH760TNo6+rh03e/xjF9aR4RJW9jaO3eo3T19Km/WwQryEzhw0tK+YdLyplbksVLu+r43pM7eOTNQxrUIBKm3j7HL17cw/xJ2Zyv+k5OYPaELG6//gy2VjXzxd+9QXevBjAMl5K3MfTE5sNkJAc4Z4b6f0S64uxUVpw5la9cOptFU3NZv7+B/3p6B39685DuxImcwKMbqthTe4zPXzhLo+olLBfNLeZbyxfw3PYavnjfGxqBOkxK3sZIb5/j6a2HuWhusdYzjSIFmSl8cNEU/umyOZxZlu/1idvJM9uO0Kn54UTepae3j/9+ZidzS7K4ckGJ3+FIFPnY2dP4xt/M44kth/ns/6xXE+owaLTpGFm37yh1rV1cuWCi36HICOSkJbF84WTOnVnIU1sP89z2Gl7be5QrFpSwsDRXdxdEPA+/foh99W3c8fHFJGhSXhmmT547nZTEAP/6yCY+9LOX+eUNS3hpV91JlRkPI+J1522MPLHlMMmJCVw4p8jvUOQkFGalcP1Z0/jse2aSk57E79dX8osX93Cosd3v0ER8197Vy38/s5PTpuRw6bwJfocjUer6s6Zy96eWUtXYzlU/eok3Dzb6HVLEU/I2BpxzPLn5MBeUF5GhiSpjwtT8dD77npn87aLJ1Ld28tPnK7jl4U0a1CBx7Y4X91DV1MG/XHWK7kbLSTm/vIg/f/F8Zk/I4sF1B/ntmn3UtXb6HVbEUvI2BtbsrqeqqYP3n6Ym01iSYMaSsny+cukcls0s4MF1B7no+y9wz5p99Gi0lMSZ6qZ2fv7X3Vy5oISzNShLRsHUgnQeuOlsrphfwu66Y/zwmV08trGK9i71Nx5It4XGwL2vHiAnLYkr1Hk3JqUlB3jfaZP4xtXz+ebKLXz9T1u4b+0Bvnn1/Kj7T6zPORrbuqlp7uBISyc1zR20dPTQ0dNLwIy05AATslOZmp9OeXEmiQF935Og7zy2jV7n+JerTvE7FIkhiYEELphdxKKpuTy99Qgv767n9QONnDurgLNnFJCerLQFlLyNurrWTp7acpiPn12mUaYxbvaELO79zFk8sfkw335sGyvueIVL503gpgtmsGRanu/NSM45jnX10nCsi8a2bhrauqhqbGf/0TYO1Lexr/4YO4+00N3r3jomOzWR3PRk0pICbyV2O4+00OcgLSnAGVNzuXTeBIqytGJIPHt66xH+vLGaL793NqX5Wk1BRl9WahJ/e8YUzp5RwNNbj/DMthpe3FnH0un5nDurkJy04S9pGEuUvI2y36+rpLvXcf1ZpX6HIuPAzLjy1IlcOKeYO17cw69X7+XprUc4vTSXz5w3ncvmTyAlceyS+LauHjYfambDwUYOHG2juqmDI83Bn4a2rnckZv2SAsaUvHSm5qeTl55McVZK8Cc7ddAvHN29feytO8b6/Q2s2VPPhd97npsvnsVN58/Qnbg41NTezb8+som5JVl87kKtpiBja1JuGjcuK6O6qZ2XdtXx8u461uyuZ+HUXC4oL4rbL5JK3kZRd28f967dz9KyfGYVZ/kdjoyjtOQA//Decv7ugun8YX0ld67ayxd/9wZZKYlcfEoxVy4o4XBTJ8mJI092evscNS0dVDa0c/BoG5UN7Rxp7qA/PUtLCpCTlkR2WiKl+enMLckmPTng/SSSnhx8Pic9iYRh3BVMCiQwe0IWsydkUdvSyZaqJr77xA6e3HKE/752IdMLM0Z8TRJdnHP82yObqW3p5Jc3LDmpz7PIcEzMSeMjS0p57ykTWFVRy7p9Dby+v4F5k7J5z+yiuFtPV8nbKHpofSWVDe383+UL/A5FfJKenMjHzynj+rOm8dKuWv6y6TBPbT3Mn96sImDGxNxUpuSlU5qXRlFWCtmpSWSkJBIYMD9WW2cPNS2d1LYG+6EdauygqrGdLm9gRFpSgNL8NOZNKmZKXhpT8tLJHIeRzUVZKdxxwxIe3VDFvz6ymat/soofXbeIi+YUj/m5xX/3v3aQlRuq+KfLZnPalFy/w5E4lJ+RzNWnT+biuRNYs7uONXvq2VLVzIyiDN4zu4hZRZl+hzgulLyNko7uXn707C4WTc3V3G5CIMG4cE4xF84p5ju9C3h171F+/tc9HGxo4/X9Dbyyp/6tfQ1IT0kkYNDngp+lnr63mzsTE4yJOaksLsuj1Ev88jOSfe1T9zenT2LR1Fz+7p71fOo3r/HVK+by9xfM8L2fn4ydTZVNfHPlFs4vL+TzF87yOxyJc5kpiVw6r4QLyot4dd9RVlfUcdfqfUzKTSU3PZkrFpS860txLFHyNkp+9+oBqps6+P6HT9d/YPIOiYEEls0qZF99GxAc4VnT3ElDWxfNHd00t/fQ2tmDcw4zSE0KkJWSSFFWCkVZqeQOs5lzvEzJS+cPnzuH//3QRv7zL9vZVt3MrR86TQN1YlBlQxufuvs1CjNTuO3ahVpJQY7rvrUHxu1cKUkBzi8v4pwZBbx5sJG/7qzl5vteZ3phBp+7cCYfXDSZpBjsm6vkbRRUNbbzX0/t5NxZBSybGV1TRcj4SzCjJCeVkpxUv0M5aenJifzkukXMm5jN95/awZ7aY9xxw2Im5qT5HZqMkrrWTj5512t0dPdy32fOojAzPjuIS2RLDCSwpCyfM6blkZ+RzO3PV/DPD23kh8/s4rMXzuTDi6fE1BfL2EtHx5lzjq/+YSO9zvEfHzxNd90k7pgZN180izs+voQ9ta1c/ZPVvH6gwe+wZBQcae7g2l+s4WBDG7+8YQnlEzQQSyJbghlXnTqRP3/xPO76xJkUZ6fwb49s5oLvPs+vXtpDW1eP3yGOCiVvJ+nOVXt5aVcdt1x1ClML4mu0i0ioS+dN4I83n0taUoAVv3iF36876HdIchK2VTfzoZ+9zOGmDu7+5NKom4Ba4puZcdHcYh7+3DLu+8xZzCzK5NuPbeO8W5/n9ucraO7o9jvEk6Lk7ST86c1DfPuxbVw+fwIfO2uq3+GI+G72hCz+dPO5LCnL438/tJEvP/AmLVFeScYb5xx/fKOSv/3py8Hpj/7ubM5S4iZRysxYNquQ3910Ng999hxOm5LD957cwbn/+Rzff3IH1U3tfoc4IurzNkIPv17JV/+wkbOm5/PDFYvUXCriyctI5p5PLeX253fzw2d38sqeer559Xwun6/l4iJdZUMb3/jTFp7dXsOZZXnc/tEzKM6K/r6ZIgBLyvL5zSeXsqmyiZ88v4vbX6jgZ3/dzaWnTOBjZ0/j3FkFUfN/uZK3YWrt7OG7T2znnjX7OWt6Pr+8cUlMdYIUGQ2JgQT+4b3lnD+7kH95eBN//9v1nDurgK9cOpvF0/L9Dk8GOHi0jTtX7eW+tQdISIB/fd8pfPLc6TE91YLEr1On5PCLjy/hQH0b9766nwdfO8gTWw4zOTeNKxeUcNVpE1lUmhvRiVxYyZuZXQH8EAgAv3LO/eeA51OAe4DFQD1wrXNun/fcLcCngV7gS865J49XpplNB+4H8oHXgY8757qOd47x0NTWzcNvVHL787upa+3k0+dN55Yr52p5IJHjOGNqHo9+8Tx+u2Y/P32hgg/9bA0LS3NZcWYpl80vIT8j2df4IqFuG+trHEpdayfPbavhz5uqeWlXLQlmfGTJFL54cTmTcjVaWGLf1IJ0brnyFL783tk8sfkwKzdUcfeaffxq1V4m5aSybFYhS6fnc9b0fKbmp0dUMnfC5M3MAsDtwKVAJfCama10zm0N2e3TQINzbpaZrQBuBa41s3nACmA+MAl4xsxme8cMVeatwG3OufvN7Ode2T8b6hwn+wIMpqO7l7rWTvbVtbG5qolX9x5lVUUdXT19LC3L55c3LGbR1LyxOLVIzEkKJPCp86azYmkp9796kN+9eoCvPbyJW/64idOn5HLG1DxOmZjFlLx0puSlUZKTOi7zMkVQ3TZmenr7qG3t5LC35u2Bo21sP9zChoON7K49BsDk3DS+eHE51y0t1RQvEpdSkwJ8YNFkPrBoMk3t3Tyz9QhPbT3Ms9uO8ND6SgAKMpKZVZzJrOJMyoszmZyX7s3FmUJBRjIpiQnjmtyFc+dtKVDhnNsDYGb3A8uB0ApuOfBN7/FDwE8seBXLgfudc53AXjOr8MpjsDLNbBtwMXC9t8/dXrk/G+oczrl3r7w9TB3dvbz/x6toau+mqb2brp6+dzw/oyiD65dO5ZrFU5g/KTuism+RaJGenMinzpvOJ88tY/OhZp7ZdoSXd9dx36v76eh++2/ODDKSE0lNCpCWnEBaUoCHPreM7NSk0Q4pUuq2k/bdJ7bz/I5aOrp76ejupd37N/R17VeclcKCyTlcs7iU88sLVaeJhMhJS+JDi6fwocVT6OtzVNS2snbvUTZVNlJR08qjG6po7nj3dCOBBCMtKUBaciD4b1KApEQjYMbfXTCD9582aVTjDCd5mwyEjvmvBM4aah/nXI+ZNQEF3vZXBhw72Xs8WJkFQKNzrmeQ/Yc6R11oIGZ2E3CT92urme0I4xqPaz/wPPDvQ+9SODAOn0VaPBCDMX10FAPxRMVrNAbXfUI5X3nrYTiv0bQwi42Uuu0dxqIOC7UfeA0K74q8z9pwROLfynBEe/wQwdcQZh01bvGvHN7uYdVf4SRvg30lG3i3a6h9hto+WJvI8fYPNw6cc3cAdwyy75gxs3XOuSXjec7jibR4QDGFI9LigciLaZTjiZS67Z0bx6EOi7T3dbgUv/+i/RqiPf5wOpZUAqUhv08Bqobax8wSgRzg6HGOHWp7HZDrlTHwXEOdQ0RkJCKlbhMRGZZwkrfXgHIzm25myQQ76Q68C7gSuNF7fA3wnNcXbSWwwsxSvJFW5cCrQ5XpHfO8VwZemX86wTlEREYiUuo2EZFhOWGzqdfP4wvAkwSHvv/aObfFzL4FrHPOrQTuBH7rddo9SrDCwtvvQYIdgHuAm51zvQCDlemd8qvA/Wb2beANr2yGOkeEGNdm2jBEWjygmMIRafFA5MU0avFEUN3mh0h7X4dL8fsv2q8hquM33bwSERERiR6aYVZEREQkiih5ExEREYkiSt5OgpldYWY7zKzCzL42hucpNbPnzWybmW0xs3/wtn/TzA6Z2Zvez1Uhx9zixbXDzC4fi5jNbJ+ZbfLOvc7blm9mT5vZLu/fPG+7mdmPvPNuNLMzQsq50dt/l5ndONT5wohnTshr8aaZNZvZP47362RmvzazGjPbHLJt1F4XM1vsve4V3rHHnWF1iHi+Z2bbvXP+0cxyve1lZtYe8lr9/ETnHeraRhDTqL1PFhwwsNaL6QELDh4Qxq/eGi4bup7zrU4ZwTUEzOwNM/uz9/ugn0MLDnR5wIt9rZmVhZQx6Gd9nOLPNbOHvLphm5mdE2Wv/5e9z85mM/udmaVG23sQNuecfkbwQ7Az8m5gBpAMbADmjdG5JgJneI+zgJ3APIIztP/TIPvP8+JJAaZ7cQZGO2ZgH1A4YNt3ga95j78G3Oo9vgr4C8H5rs4G1nrb84E93r953uO8UXp/DhOc8HBcXyfgAuAMYPNYvC4ERzWe4x3zF+DKEcRzGZDoPb41JJ6y0P0GlDPoeYe6thHENGrvE/AgsMJ7/HPgc2PxtxltPyf72R7j2Iaq5yKiTgnzGr4C3Af82ft90M8h8Hng597jFcAD3uNBP+vj+B7cDXzGe5wM5EbL609w0uu9QFrIa/+JaHsPwv3RnbeRe2tpHRdcXLp/aZ1R55yrds697j1uAbYxxOzsnreW7nHO7QX6l+4Zj5iXE6wA8P79QMj2e1zQKwTnvJoIXA487Zw76pxrAJ4GrhiFOC4Bdjvn9p8g1lF/nZxzL/LuOQhH5XXxnst2zq1xwZrmnpCywo7HOfeUe3u2/1cIzjs2pBOcd6hrG1ZMxzGs98m7I3gxweWswo4pToxbvTVcx6nnIqVOOS4zmwK8D/iV9/vxPoeh1/QQcIm3/1Cf9TFnZtkEv1TdCeCc63LONRIlr78nEUiz4HyK6UA1UfQeDIeSt5EbbGmd4yVUo8K7tbsIWOtt+oJ3y/rX9nZz1VCxjXbMDnjKzNZbcEkfgAnOuWoIVsZA8TjH1G8F8LuQ3/18nWD0XpfJ3uPRjO1TBL9B95vuNf381czOD4lzqPMOdW0jMRrvU9hLUcUhX+qt4RpQz0VKnXIi/w38M9C/oGzYyz0Cocuu+fX+zABqgbu8v/9fmVkGUfL6O+cOAd8HDhBM2pqA9UTXexA2JW8jF/ZyN6N2QrNM4A/APzrnmgkuaj0TWEjww/qDE8Q22jGf65w7A7gSuNnMLjjOvuMVE16fhquB33ub/H6djme4MYxqbGb2fwjOU3avt6kamOqcW4TXBOR9Ix+P12S03qdx/9uMIhH/2gxSzw256yDbfHn/zez9QI1zbn3o5uPEETGxh0gk2JXhZ97f/zGCzaRDiahr8L7sLSfY1DkJyCD4f9NQsURU/MOl5G3kwllaZ9SYWRLBCu1e59zDAM65I865XudcH/BL3r61O9yle0bEOVfl/VsD/NE7/xHv1nl/U1vNeMbkuRJ43Tl3xIvP19fJM1qvSyXvbOIccWxeR+L3Ax/1mkLxmgrqvcfrCfb3mH2C8w51bcMyiu+TlqIa2rjWW8M1WD1HZNQpJ3IucLWZ7SPYFH0xwTtxw13u0c/3pxKodM71t+o8RDCZi4bXH+C9wF7nXK1zrht4GFhGdL0HYVPyNnLhLK0zKrx2+DuBbc65/wrZPjFktw8C/SP3hrV0zwhjyjCzrP7HBDvAb+adywkNXN7sBm+E0tlAk3cL/kngMjPL8745XeZtOxnXEdJk6ufrFGJUXhfvuRYzO9v7XNzACJZZMrMrCM74f7Vzri1ke5GZBbzHMwi+JntOcN6hrm24MY3K++QlolqKanDjVm8N11D1HJFRpxyXc+4W59wU51wZwdf0OefcRxn+co9DfdbHnHPuMHDQzOZ4my4huIJIxL/+ngPA2WaW7n2W+uOPmvdgWFwEjJqI1h+Co212Erw78X/G8DznEbxtuxF40/u5CvgtsMnbvhKYGHLM//Hi2kHIaMTRiplg/4gN3s+W/rII9hl4Ftjl/ZvvbTfgdu+8m4AlIWV9imCn0Argkyf5WqUD9UBOyLZxfZ0IJo7VQDfBb3GfHs3XBVhCMLHZDfwEb6WUYcZTQbBfR//nqX/U1Ye893MD8DrwNyc671DXNoKYRu198j6fr3rX+XsgZaz+PqPt52Q+22Mc11D1nK91ygiu40LeHm066OcQSPV+r/CenxFy/KCf9XGKfSGwznsPHiE4WjRqXn/g34HtXj31W4IjRqPqPQj3R8tjiYiIiEQRNZuKiIiIRBElbyIiIiJRRMmbiIiISBRR8iYiIiISRZS8iYiIiEQRJW8yImb2QTNzZjb3JMr4jZld4z3+lZnNG70Iwcz+ZcDvraNZvohEL9VhEs2UvMlIXQesIjgh5Ulzzn3GObd1NMoK8S8n3kVE4pTqMIlaSt5k2Ly1B88lOLHqCm/bhWb2opn90cy2mtnPzSzBe67VzH5gZq+b2bNmVjRImS+Y2RLv8RXevhvM7Flv21Ize9mCCya/3D8LuJl9wsweNrMnzGyXmX3X2/6fQJqZvWlm9w4414Xe+R4ys+1mdq83IzdmdqZX/gYze9XMssws1czuMrNN3vkvCjn3I2b2qJntNbMvmNlXvH1eMbN8b7+ZXnzrzeylk/mmLyInT3WY6rCo5/cswfqJvh/gY8Cd3uOXCa5/dyHQQXA26wDwNHCNt48juH4mwNeBn3iPfxOyzwsEZ/AvIjjz/3Rve/9s3tlAovf4vcAfvMefAPYQXJcuFdgPlHrPtQ6Iu9X790KgieCadQnAGoKzuyd7ZZ0Zek7gfwF3edvmElyGJdU7dwWQ5cXdBHzW2+82ggtrQ3BW8nLv8VkEl2Hx/X3Uj37i9Ud1mOqwaP/pX6xVZDiuI7joMgQXYb4OeAx41Tm3B8DMfkewMnkI6AMe8Pb/H4ILBg/lbOBF59xeAOfcUW97DnC3mZUTrEiTQo551jnX5J13KzCNYOV5PK865yq9Y94EyghWXNXOude8czd7z58H/Njbtt3M9hNcrB3geedcC8F1P5uAR73tm4DTvG/4y4Dfe1+MIbhki4j4R3WY6rCopuRNhsXMCoCLgQVm5gh+Q3XA496/oYZae+14a7LZEM//X4KVzAfNrIzgt9x+nSGPewnvcz3YMUOd2wbZNlg5fSG/93llJgCNzrmFYcQkImNMddhxy1EdFiXU502G6xrgHufcNOdcmXOuFNhL8BvqUjOb7vUTuZZgZ2AIfs6u8R5fH7J9MGuA95jZdID+PhcEv7Ue8h5/IsxYu80s6cS7vWU7MMnMzvTOnWVmicCLwEe9bbOBqQQXLD4h75vvXjP7sHe8mdnpw4hJREaX6jDVYVFPyZsM13XAHwds+wPBCm0N8J/AZoKVYf9+x4D5Zrae4Dfebw1VuHOuFrgJeNjMNvB2U8V3gf8ws9UEvymH4w5g48DOvsc5dxfBCvvH3rmfJtgv5KdAwMw2efF8wjnXOXRJ7/JR4NNemVuA5cM4VkRGl+ow1WFRz5w73t1fkfCY2YXAPznn3j/Ic63Ouczxj0pEJDyqwySa6M6biIiISBTRnTcRERGRKKI7byIiIiJRRMmbiIiISBRR8iYiIiISRZS8iYiIiEQRJW8iIiIiUUTJm4iIiEgUUfImIiIiEkWUvImIiIhEESVvIiIiIlFEyZuIiIhIFFHyJiIiIhJFlLyJiIiIRBElbyIiIiJRRMmbiIiISBRR8iYiIiISRZS8iYiIiEQRJW8SE8zsc2Z2xMxazazA73hERE7EzNLM7FEzazKz3/sdj0QPJW8SMcxsn5m1ewlYg5k9ZmalYRyXBPwXcJlzLtM5Vz/20YqIhM/MXvDqtZSQzdcAE4AC59yHzewTZrbKpxAliih5k0jzN865TGAicAT4cRjHTABSgS3DPZkF6e9ARMaMmZUB5wMOuDrkqWnATudczyidJ3E0ypHIp/+0JCI55zqAh4B5AGaWYmbfN7MDXvPoz70mh9nADu+wRjN7ztt/mZm95jVHvGZmy/rL9r4Bf8fMVgNtwAwzyzGzO82s2swOmdm3zSwwvlctIjHqBuAV4DfAjQBm9u/A14FrvdaGm4GfA+d4vzd6+w1a93nPXWhmlWb2VTM7DNzlw7WJD5SlS0Qys3TgWoIVHsCtwAxgIdAN3Ad83Tl3i5nNB/YCuc65HjPLBx4DvgT8Dvgw8JiZzQppUv04cCXBxM+A3xO80zcLyAD+DBwEfjHW1yoiMe8Ggl071gKvmNkE59w3zMwBs5xzHwMws2PAZ5xz54UcO2jdB9ziPV8C5BO8i6cbMnFCb7REmke8b5zNwKXA98zMgL8DvuycO+qcawH+H7BiiDLeB+xyzv3WOdfjnPsdsB34m5B9fuOc2+I1V+QTTOT+0Tl3zDlXA9x2nPJFRMJiZucRTKwedM6tB3YD14d5bDh1Xx/wDedcp3OufXSjl0ilO28SaT7gNWBCSAAAIABJREFUnHvGa7JcDvyV4DfOdGB9sC4DgnfLhmrWnATsH7BtPzA55PeDIY+nAUlAdUj5CQP2EREZiRuBp5xzdd7v93nbbgvj2CJOXPfVet1MJI4oeZOI5JzrBR42s18AZwPtwHzn3KEwDq8imJCFmgo8EXqKkMcHgU6gcLQ6DouIeH3TPgIEvD5pAClArpmdPsghbsDvdZy47ht4jMQBNZtKRPJGgS4H8giOIv0lcJuZFXvPTzazy4c4/HFgtpldb2aJZnYtwYEPfx5sZ+dcNfAU8AMzyzazBDObaWbvGe3rEpG48gGgl2D9s9D7OQV4iWA/uIGOAFPMLBnAOdfH8Oo+iRNK3iTSPGpmrQT7vH0HuNE5twX4KlBBsLNvM/AMMGewArxBCe8H/hdQD/wz8P6QZovB3AAkA1uBBoIjXSeOyhWJSLy6EbjLOXfAOXe4/wf4CfBR3t369RzBL6uHzay/vgq77pP4Yc7pjquIiIhItNCdNxEREZEoouRNREREJIooeRMRERGJIkreRERERKJITM/zVlhY6MrKyvwOQ0TG0fr16+ucc0V+xzEaVIeJxJdw66+YTt7KyspYt26d32GIyDgys4Gra0Qt1WEi8SXc+kvNpiIiIiJRRMmbiIiISBRR8iYiIiISRZS8iYiIiEQRJW8iIiIiUUTJm4iIiEgUUfImIiIiEkWUvImIiIhEESVvIiIiIlFEyZuIiIhIFFHyJiIiIhJFlLyJiIiIRBElbyIiIiJRJNHvAOSd7lt7YMTHXn/W1FGMRERk9JxM3dZPdZxIkO68iYiIiEQRJW8iIiIiUUTJm4iIiEgUUfImIiIiEkXCSt7M7Aoz22FmFWb2tUGeTzGzB7zn15pZWchzt3jbd5jZ5cMo88dm1hrOOURERETixQmTNzMLALcDVwLzgOvMbN6A3T4NNDjnZgG3Abd6x84DVgDzgSuAn5pZ4ERlmtkSIDecc4iIiIjEk3DuvC0FKpxze5xzXcD9wPIB+ywH7vYePwRcYmbmbb/fOdfpnNsLVHjlDVmml9h9D/jnMM8hIiIiEjfCSd4mAwdDfq/0tg26j3OuB2gCCo5z7PHK/AKw0jlXHeY5REREROJGOJP0DnZ3y4W5z1DbB0sanZlNAj4MXDjCODCzm4CbAKZO1YSOIiIiElvCufNWCZSG/D4FqBpqHzNLBHKAo8c5dqjti4BZQIWZ7QPSzaziBOd4B+fcHc65Jc65JUVFRWFcnoiIiEj0CCd5ew0oN7PpZpZMcADCygH7rARu9B5fAzznnHPe9hXeSNHpQDnw6lBlOucec86VOOfKnHNlQJs3QOF45xARERGJGydsNnXO9ZjZF4AngQDwa+fcFjP7FrDOObcSuBP4rXeX7CjBZAxvvweBrUAPcLNzrhdgsDJPEMqg5xARERGJJ2EtTO+cexx4fMC2r4c87iDYV22wY78DfCecMgfZJzOcc4iIiIjEC62wICIiIhJFlLyJiIiIRBElbyIiIiJRRMmbiIiISBRR8iYiIiISRZS8iYiIiEQRJW8iIiIiUUTJm4iIiEgUUfImIiIiEkWUvImIiIhEESVvIiIiIlFEyZuIiIhIFFHyJiIiIhJFlLyJSNwysyvMbIeZVZjZ1wZ5PsXMHvCeX2tmZSHP3eJt32Fmlw+jzB+bWetYXZOIxD4lbyISl8wsANwOXAnMA64zs3kDdvs00OCcmwXcBtzqHTsPWAHMB64AfmpmgROVaWZLgNwxvTARiXlK3kQkXi0FKpxze5xzXcD9wPIB+ywH7vYePwRcYmbmbb/fOdfpnNsLVHjlDVmml9h9D/jnMb4uEYlx/5+9Ow+vu7zvvP/+nqN93y1LsizZljFewIAxexZIWNpp3AQymLQpaaFMUmjaZtpnwrPQebiGzjDLwyRp0gwJSUmGhG2mjdsQSAJNWIJtDNjGK5Yl2Vosa9/3o/v54/zkCEXL0Xq2z+u6dF3n3Of+3ef7s63j77lXJW8iEq9KgfpJzxu8smnrOOfGgG4gf5ZrZ2vzAWCPc+7cEsUvInEqIdwBiIiEiU1T5kKsM1P5dF+InZmVAJ8GPjJnUGb3AfcBlJeXz1VdROKQet5EJF41AGsmPS8DmmaqY2YJQDbQMcu1M5VfBmwAqs2sDkgzs+rpgnLOPe6c2+Gc21FYWLiwOxORmKbkTUTi1VtAlZlVmlkSwQUIe6bU2QPc7T2+A3jFOee88t3eatRKoArYP1ObzrkfO+eKnXMVzrkKYMBbBCEiMm8aNhWRuOScGzOzB4CXAD/wHefcUTN7GDjgnNsDPAF83+sl6yCYjOHVexY4BowB9zvnAgDTtbnS9yYisU3Jm4jELefcC8ALU8oemvR4iOBctemufQR4JJQ2p6mTsZB4RURAw6YiIiIiUUXJm4iIiEgUUfImIiIiEkWUvImIiIhEESVvIiIiIlFEyZuIiIhIFAkpeTOzW83spJlVm9mXp3k92cye8V7fZ2YVk1570Cs/aWa3zNWmmT1hZofM7LCZPW9mGV7558ys1cwOej/3LubGRURERKLRnMmbmfmBrwO3AZuBu8xs85Rq9wCd3o7hjwGPetduJrip5RbgVuAbZuafo82/cM5d6py7BDhL8DDnCc8457Z7P99e2C2LiIiIRK9Qet52AtXOuRrn3AjwNLBrSp1dwJPe4+eBm8zMvPKnnXPDzrlaoNprb8Y2nXM9AN71qfzmQdEiIiIicSuU5K0UqJ/0vMErm7aOc24M6AbyZ7l21jbN7LtAM7AJ+NqkerdPGk6dfPiziIiISFwIJXmzacqm9obNVGe+5cEHzv0hUAIcB+70iv8JqPCGU3/Or3v6PhiI2X1mdsDMDrS2tk5XRURERCRqhZK8NQCTe7nKgKaZ6phZApBN8BDnma6ds03vkOdngNu95+3OuWHv5W8BV0wXrHPucefcDufcjsLCwhBuT0RERCR6hJK8vQVUmVmlmSURXICwZ0qdPcDd3uM7gFecc84r3+2tRq0EqoD9M7VpQRvgwpy33wFOeM9XT3q/TxDslRMRERGJKwlzVXDOjZnZA8BLgB/4jnPuqJk9DBxwzu0BngC+b2bVBHvcdnvXHjWzZ4FjwBhwv9ejxgxt+oAnzSyL4NDqIeALXihfNLNPeO10AJ9bkj8BERERkSgyZ/IG4Jx7AXhhStlDkx4PAZ+e4dpHgEdCbHMcuG6Gdh4EHgwlXhEREZFYpRMWRERERKKIkjcRERGRKKLkTURERCSKKHkTERERiSJK3kRERESiiJI3ERERkSii5E1EREQkiih5ExEREYkiSt5EREREooiSNxEREZEoouRNREREJIooeRMRERGJIkreRERERKKIkjcRERGRKKLkTURERCSKKHkTERERiSJK3kRERESiiJI3ERERkSii5E1EREQkiih5ExEREYkiSt5EREREooiSNxEREZEoouRNREREJIooeRMRERGJIkreRERERKKIkjcRERGRKKLkTURERCSKKHkTERERiSIhJW9mdquZnTSzajP78jSvJ5vZM97r+8ysYtJrD3rlJ83slrnaNLMnzOyQmR02s+fNLGOu9xARERGJF3Mmb2bmB74O3AZsBu4ys81Tqt0DdDrnNgCPAY96124GdgNbgFuBb5iZf442/8I5d6lz7hLgLPDAbO8hIiIiEk9C6XnbCVQ752qccyPA08CuKXV2AU96j58HbjIz88qfds4NO+dqgWqvvRnbdM71AHjXpwJujvcQERERiRuhJG+lQP2k5w1e2bR1nHNjQDeQP8u1s7ZpZt8FmoFNwNfmeA8RERGRuBFK8jZd75YLsc58y4MPnPtDoAQ4Dtw5jzgws/vM7ICZHWhtbZ3mEhEREZHoFUry1gCsmfS8DGiaqY6ZJQDZQMcs187ZpnMuADwD3D7HezDlusedczucczsKCwtDuD0RERGR6BFK8vYWUGVmlWaWRHABwp4pdfYAd3uP7wBecc45r3y3t1K0EqgC9s/UpgVtgAtz3n4HODHHe4iIiIjEjYS5KjjnxszsAeAlwA98xzl31MweBg445/YATwDfN7Nqgr1hu71rj5rZs8AxYAy43+tRY4Y2fcCTZpZFcJj0EPAFL5Rp30NEREQknsyZvAE4514AXphS9tCkx0PAp2e49hHgkRDbHAeum6GdGd9DREREJF7ohAURERGRKKLkTURERCSKKHkTERERiSJK3kRERESiiJI3ERERkSii5E1EREQkiih5ExEREYkiSt5EREREooiSNxGJW2Z2q5mdNLNqM/vyNK8nm9kz3uv7zKxi0msPeuUnzeyWudo0syfM7JCZHTaz580sY7nvT0Rik5I3EYlLZuYHvg7cBmwG7jKzzVOq3QN0Ouc2AI8Bj3rXbiZ4RN8W4FbgG2bmn6PNv3DOXeqcuwQ4CzywrDcoIjFLyZuIxKudQLVzrsY5NwI8DeyaUmcX8KT3+HngJjMzr/xp59ywc64WqPbam7FN51wPgHd9KuCW9e5EJGYpeROReFUK1E963uCVTVvHOTcGdAP5s1w7a5tm9l2gGdgEfG26oMzsPjM7YGYHWltb539XIhLzlLyJSLyyacqm9obNVGe+5cEHzv0hUAIcB+6cLijn3OPOuR3OuR2FhYXTVRGROKfkTUTiVQOwZtLzMqBppjpmlgBkAx2zXDtnm865APAMcPui70BE4pKSNxGJV28BVWZWaWZJBBcg7JlSZw9wt/f4DuAV55zzynd7q1ErgSpg/0xtWtAGuDDn7XeAE8t8fyISoxLCHYCISDg458bM7AHgJcAPfMc5d9TMHgYOOOf2AE8A3zezaoI9bru9a4+a2bPAMWAMuN/rUWOGNn3Ak2aWRXBo9RDwhZW8XxGJHUreRCRuOedeAF6YUvbQpMdDwKdnuPYR4JEQ2xwHrluCkEVENGwqIiIiEk2UvImIiIhEESVvIiIiIlFEyZuIiIhIFFHyJiIiIhJFlLyJiIiIRBElbyIiIiJRRMmbiIiISBRR8iYiIiISRZS8iYiIiEQRJW8iIiIiUSSk5M3MbjWzk2ZWbWZfnub1ZDN7xnt9n5lVTHrtQa/8pJndMlebZvaUV37EzL5jZole+UfMrNvMDno/DyEiIiISZ+ZM3szMD3wduA3YDNxlZpunVLsH6HTObQAeAx71rt0M7Aa2ALcC3zAz/xxtPgVsArYBqcC9k97nNefcdu/n4YXcsIiIiEg0C6XnbSdQ7Zyrcc6NAE8Du6bU2QU86T1+HrjJzMwrf9o5N+ycqwWqvfZmbNM594LzAPuBssXdooiIiEjsCCV5KwXqJz1v8MqmreOcGwO6gfxZrp2zTW+49LPAi5OKrzGzQ2b2EzPbEkLsIiIiIjElIYQ6Nk2ZC7HOTOXTJY1T2/wG8Kpz7jXv+TvAWudcn5n9FvCPQNVvBGt2H3AfQHl5+TRvIyIiIhK9Qul5awDWTHpeBjTNVMfMEoBsoGOWa2dt08z+GigEvjRR5pzrcc71eY9fABLNrGBqsM65x51zO5xzOwoLC0O4PREREZHoEUry9hZQZWaVZpZEcAHCnil19gB3e4/vAF7x5qztAXZ7q1ErCfaU7Z+tTTO7F7gFuMs5Nz7xBmZW7M2jw8x2erG3L+SmRURERKLVnMOmzrkxM3sAeAnwA99xzh01s4eBA865PcATwPfNrJpgj9tu79qjZvYscAwYA+53zgUApmvTe8tvAmeAN71c7X97K0vvAL5gZmPAILDbSxBFRERE4kYoc94mhilfmFL20KTHQ8CnZ7j2EeCRUNr0yqeNyTn3t8DfhhKviIiISKzSCQsiIiIiUUTJm4iIiEgUUfImIiIiEkWUvImIiIhEESVvIiIiIlFEyZuIiIhIFFHyJiIiIhJFQtrnTVZWYNyxt6adwLjj+qoCfDbdEbEiIiISj5S8RZiO/hF+uP8sjV2DAJxu7ePOHWtIS9ZflYiIiGjYNOL8+HATbX3D7L5yDZ/cXkpNWz/Pvd0Q7rBEREQkQqg7J4Kcae/nRHMvH7moiEvKcgAYGA3w0tFm6tr6qShID3OEIiIiEm7qeYsgf/+rOnxmXLUu70LZNevyyUxJ4KWjzTjnwhidiIiIRAL1vEWI3qFRnjvQwLaybLJSEi+UJyX4uHFTET862MSplj42rsoMY5QiItHtB/vOLur6z1xVvkSRiCycet4ixItHmukbHuOadfm/8dqOtXlkJiewr6Y9DJGJiIhIJFHyFiHePN1OfnoSZbmpv/Ga32dcVp7LyfO99A6NhiE6ERERiRRK3iKAc8F93a5el4/NsKfbFWtzGXfw7tmuFY5ORGRp9A+PcaSxm1+cbKGtdzjc4YhELc15iwD1HYM0dQ/xhUkLFaYqzExmbX4aB850ckNVwYxJnohIJOocGOHr/1LNwEgAgF++38qnLi9jW2l2mCMTiT7qeYsAe725bFdPM99tsh1rc2nrG6a+Y2AlwhIRWRLDYwF+uP8sgXHHvddX8qWPb6QoM5kf7j/L8XM94Q5PJOooeYsAb9a0U5CRxIaijFnrbS3JJsFnHGzoXqHIREQW77/99H0aOge544oy1hVmUJCRzB/fsI7irBR+dLCRodFAuEMUiSpK3sJsYr7bVbPMd5uQnOjn4tVZvNfQRWBce76JSOTrGRrl+2+e4bI1OWwp+fUQaYLfxycvK6V3aIwXjzaHMUKR6KPkLcwaOgc51z3E1ZUzz3eb7NKyHPpHApxu7VvmyEREFu8f321kcDTANet/c1rImrw0rl2fz/7aDpp7hsIQnUh0UvIWZkebgkOgE8dhzWXjqgxSEn0cqteqUxGJbM45ntp7lm2l2ZTlpk1b56MXFZHoN159v3WFoxOJXkrewuxEcy9mhHxyQoLfx9aSbI6e62FkbHyZoxMRWbi3z3Ry8nwvvzfLqQRpyQnsrMjjcEMXHf0jKxidSPRS8hZmJ871UpmfTmqSP+RrLl2Tw8jYOCeatUpLRCLXP7zbSHqSn09sL5m13vVVhRjGa6fU+yYSCiVvYXaiuYdNq+d3XmllQTpZKQkc0qpTEYlQzjl++X4r124oIC1p9i1Fs1MT2V6ewztnOxkc0cpTkbkoeQuj/uExznQMcNGqrHld5zPjkrIc3m/u1QediESk2rZ+GjoH+dDGwpDqX7Mun9GA452zncscmUj0U/IWRu+f78U55t3zBsGh04BzHGlS75uIRJ6JBQgfrgoteSvJSaU8L429Ne2MO22FJDIbJW9hdKK5F4CLi+fX8wZQkp1CQUYyB7XqVEQi0C/fb6UiP43y/OlXmU7nqso82vtHqGntX8bIRKJfSMmbmd1qZifNrNrMvjzN68lm9oz3+j4zq5j02oNe+Ukzu2WuNs3sKa/8iJl9x8wSvXIzs6969Q+b2eWLufFIcLK5l/QkP2W5qfO+1sy4vDyH2rZ+WrQ/kohEkOGxAHtrOkIeMp2wtTSbtCT/hSMDRWR6cyZvZuYHvg7cBmwG7jKzzVOq3QN0Ouc2AI8Bj3rXbgZ2A1uAW4FvmJl/jjafAjYB24BU4F6v/Dagyvu5D/i7hdxwJDl+roeLijPx+RZ2yPyOijz8PmNvrT7oRCRyvF3XyeBogA+FOGQ6IdHvY8faPI6f66F7cHSZohOJfqH0vO0Eqp1zNc65EeBpYNeUOruAJ73HzwM3WfCsp13A0865YedcLVDttTdjm865F5wH2A+UTXqP73kv7QVyzGz1Au877JxznGju5aIFDJlOyEhO4JLSbN4526WzAUUkYrxV14kZ7FwX2skxk+30TpvZX9ux1GGJxIzZ128HlQL1k543AFfNVMc5N2Zm3UC+V753yrWl3uNZ2/SGSz8L/NkscZQC50K4h4jT0T9C9+DonIfRz+Wa9fm8W9/FO2c7SdkX+l5xS+kzs2zAKSLx52B9JxsKM8hKSZz3tXnpSWxclcmBug4+uqmQBJ+mZotMFcpvxXRjelOXAs1UZ77lk30DeNU599o84sDM7jOzA2Z2oLU1cjd8rGsPTsitLAh9Mu90ynLTKM9L49X3Wxkem1/vW9/wGGc7BhgL6KQGEVkazjkO1nexfU1oR/5N5+p1efQOj3GsSRuRi0wnlJ63BmDNpOdlQNMMdRrMLAHIBjrmuHbGNs3sr4FC4N/MMw6cc48DjwPs2LEjYteb17YNAFCRn77otn5razHffLWGX55s5eYtxbPWdc5xuLGbV4630No3DEBSgo9tpdn8ziUlJCXoW66ILNzZjgE6B0bZXr7w5K1qVSa5aYnsq+0I+dxnkXgSyv/UbwFVZlZpZkkEFyDsmVJnD3C39/gO4BVvztoeYLe3GrWS4GKD/bO1aWb3ArcAdznnxqe8xx94q06vBrqdc1E5ZApQ19aP32esyVtczxtAeX4629fk8Hp126xnA3b0j/Ct12p55q16EvzGbVuLuWtneXDe3JlOvvurWs2dE5FFmdi+aDE9bz4zrqrMp7atn/NaTS/yG+ZM3pxzY8ADwEvAceBZ59xRM3vYzD7hVXsCyDezauBLwJe9a48CzwLHgBeB+51zgZna9Nr6JrAKeNPMDprZQ175C0ANwUUP3wL+ZHG3Hl617f2U5aaS6F+anq5btxTj8xlPvllH79AHV2k553j7TCdfe+UU57oH+eT2Uu7/6AZuqCpkW2k2n7q8jDuvXEN9xwDfe/OMNsgUkQV792wXqYl+Llo1/83HJ7tibS4JPmOfVtOL/IZQhk1xzr1AMHmaXPbQpMdDwKdnuPYR4JFQ2vTKp43J68m7P5R4o0FdW/+SDJlOyEpN5A+uWcuTv6rj26/XctuWYkpzU2nsHOTVU63UtQ+wNj+Nf33FGnLTk37j+kvKgofd/+93G3m7rpMrK+e/SkxE5GB9F9tKs0lY5BfT9OQEtpVm8+7ZLm7ZXExyYngWZIlEIk1wCgPnHHVt/VQWLF3yBrCuIIO7r62gb2iM7+09w3/8yQm+t/cMLb3DfPKyUv74hnXTJm4TrlibS0V+Oi8ebaZveGxJYxOR2Dc8FuBYU8+i5rtNdvW6fIbHxnmrTtuGiEwWUs+bLK3WvmH6RwJUzOPYmFCtK8jgwds2UdvWz7nuIcpyU1mTlxbS8KyZsWt7CV975RQ/P36e391eOuc1IiIT3m/uYyQwzqVLtMhgTV4a6wvTefVUGzsr85ekzbHxcZwDv8/w2cI2SBcJN/W8hUHdxErTJe55m5Dg91G1KpMPbSxkXWHGvObVrcpK4Yq1ubxzppN+9b5JjIuEo/9iyfHm4NYem0sWvvn4VDduWkXf8Bj7F9H75pzjJ++d43Pf3c+/33OUv95zlP/3n47yo4ONtHur7kWiiZK3MKhrm9jjbXmSt8W6dn0BY+NOQxUS0yLo6L+YceJcL6mJfsqXYBX9hMqC9GDv2/utC/pCWd8xwGef2M8XnnqHU+f7uG59AbdsKWZbaTYHznTy1VdOcep875LFK7ISlLyFQW17Pwk+ozRn/gfSr4RVWSlsKMpgb007gXGtPJWYFSlH/8WME809bCzOxL/A85pn8vHNxfQPj/FfXjo5r+teOXGe3/rKaxys7+I//O5WXvs/Pspt21bz4Y2F3HHFGv7y5osoyEjme2+e4Uhj95LGLLKclLyFQV1bP+V5aYtejbWcrlufT8/QGEea9IEmMWumI/emreNtcTT56L/prp2zzUlH/7246DuIIM45jp/r4eLixW0RMp3yvDSuWpfPk2/W8faZuUcEnHN84xfV3PPkAcrz03jxz2/g969ei29KUpmdmsi916+jNDeV596up7VXQ6gSHSI3e4hhZ7xtOyJZ1apMclITefdsZ7hDEVkukXL03wffMEqO+JuqtXeYzoFRNi1D8gZwy+ZVlGSn8pfPHaZtlnlqAyNj/OkP3+U/v3iSf3VJCc9//lrKcmf+vE1N8nPXznISfD6ePVDP2LiOC5TIp+QtDBq7BinNjcwh0wk+My4py6a6pU8LFyRWzefoP0I8+m/WNicd/felmYJyzj3unNvhnNtRWFg4z1sKn+PNwXljm1Yv3WKFyZIT/fz33ds51z3I731r37QLDQ7Wd/Gvvvo6P37vHP/u1k18dfd2UpPm3h8uOzWR2y8vpbFrkF+cjJ6EWeKXkrcV1jc8RvfgKKU5kd3zBsGNe8cdGjqVWBUpR//FhOPngitNLy5enuQN4MqKPJ64+0rq2vu57Suv8firpzlQ18HLx8/zJ0+9ze1/9yuGRgM8dc9VfOEj67F5bAWyuSSbbaXZvHaqlZ4pp9SIRBrt87bCmroGASK+5w1gdXYKhRnJHG7o5qol2mNJJFI458bMbOKYPj/wnYmj/4ADzrk9BI/++7539F8HwWQMr97E0X9jeEf/AUzXpveW3wTOEDz6D+B/O+ceXqHbXXYnzvVQkp1Cdtry7oBy3YYCnr7vav7rT0/yNy+cuFCek5bIvddXcv+NG8hKWVgMN29exbGmHl453sLvXqZ9LiVyKXlbYY2dXvKWkxLmSOZmZlyyJptXjrfQPThKdmrMbUslcS4Sjv6LFSeae5dtyHSqy8pzeereqznR3ENr7zCJfh/b1+SQssgjtPIzktlZmce+2nau21BAYWbyEkUssrQ0bLrCGid63qJg2BTgktIcHL8eEhERmWo0MM7p1j4uWqbFCjPZVJzFDVWFXL0uf9GJ24SPbirC7zNeO6W5bxK5lLytsMauQRJ8FjXf6Aozk8lPT+JEs5I3EZnemfYBRgOOqqKMcIeyaBnJCVxWnsvB+i6d8SwRS8nbCmvqGmR1TsqSb2K5nDYVZ1LT2s/IWMzNsRaRJVDd0gfAhhhI3gCu806Z2VfTHu5QRKal5G2FNXYOUpId+YsVJtu0OouxcXfhA1pEZLLTrcHPhvWFsZG8FWYms6k4k7017YwG9KVVIo+StxXWFAV7vE21Nj+N5ASfhk5FZFrVLX2UZKeQnhw7azKuXV9A/0iAo0363JPnjxB7AAAgAElEQVTIo+RtBY0GxmnuGYrYM01nkuDzUbUqk5Pnexl3OutURD6ouqWP9TEyZDphXWE6uWmJHKib+zgukZWm5G0Fne8ZYtwRdckbwKZVmfQOjdHcPRTuUEQkgoyPO0639sXMkOkEnxlXrM2jpq1/2tMcRMJJydsKmtjjrSQKk7eJb9UTc1tERADO9QwxMBKImcUKk12xNhcD3tYZzxJhlLytoKbu6DldYars1EQKMpKVvInIB8TaStPJslMTqVqVwTtnOjVlRCKKkrcVdKHnLcpWm05YX5hOXdsAY+NafSUiQbGcvAFcXp5Lz9AYtW394Q5F5ILYWRoUBRq7hshPTyI1aWl2Al9p6wsz2FfbQUPHIBUF6eEOR0QiQHVLHzlpieSnJy37e/1g39llf4+pNhVnkZTg41B9V8zN65PopZ63FdTYNRiV890mrCtMx9C8NxH5tdMtfWwozMAsejYen4+kBB9bVmdxpKmbMe35JhFCydsKauoajMqVphPSkhIoyUlV8iYiF9S09bOuMLZ74i8py2FodJz3z+uzTyKDkrcV4pwLnq4QxckbBOe91XcM6qgsEaF3aJS2vmEqC2J7OHFDUQZpSX4ONXSFOxQRQMnbiukaGGVwNBCVK00nW1+YQcA56to1eVck3tW1DQBQWZAW5kiWl99nbC3N5kRzD4MjgXCHI6LkbaU0dnnbhOSkhDmSxVmbn47fZ5zWOacica/W+xIX6z1vAFtLshkNOH75fmu4QxFR8rZSfp28Rfc31KQEH+V5aZr3JiLUtgaTt7X50f25ForKgnRSE/28dLQ53KGIhJa8mdmtZnbSzKrN7MvTvJ5sZs94r+8zs4pJrz3olZ80s1vmatPMHvDKnJkVTCr/iJl1m9lB7+ehhd50OPz6dIXo7nmD4Ly3c91DDAyPhTsUEQmjuvZ+SnNSSUmMzu2P5sPvMzavzuLnx89rzq+E3ZzJm5n5ga8DtwGbgbvMbPOUavcAnc65DcBjwKPetZuB3cAW4FbgG2bmn6PNN4CPAWemCec159x27+fh+d1qeDV1DZKS6CNvBfZCWm7rCzNwwGltWikS12ra+qmI8fluk20pyaJ3aIw3TreFOxSJc6H0vO0Eqp1zNc65EeBpYNeUOruAJ73HzwM3WXDTn13A0865YedcLVDttTdjm865d51zdYu8r4jT6G0TEgt7IZXlppGU4NPQqUgcc85R29pHZRxt2L2hKIOM5ARefE9DpxJeoSRvpUD9pOcNXtm0dZxzY0A3kD/LtaG0OZ1rzOyQmf3EzLaEUD9iNEX5Br2T+X1GZX46Na3qeROJV50Do/QMjVGRHz/JW4Lfx42bivjZ8fPasFfCKpTkbbquoqkn9M5UZ77ls3kHWOucuxT4GvCP01Uys/vM7ICZHWhtjZxVQY1dg5RF+TYhk60rTKetb5ieodFwhyIiYTBx1mesb9A71W1bi+noH2F/XUe4Q5E4Fkry1gCsmfS8DGiaqY6ZJQDZQMcs14bS5gc453qcc33e4xeAxMkLGibVe9w5t8M5t6OwsHDuu1sBQ6MB2vpGovZA+ulMDJXosGaR+DTxux9PPW8AH76okJREHy8e0dCphE8oydtbQJWZVZpZEsEFCHum1NkD3O09vgN4xTnnvPLd3mrUSqAK2B9imx9gZsXePDrMbKcXe3soNxluTRPbhMRQz9vq7FSSE3wXtgoQkfhS19aP32esyYufBQsQPCbwwxsLefFIM+Pjcw0YiSyPOZM3bw7bA8BLwHHgWefcUTN72Mw+4VV7Asg3s2rgS8CXvWuPAs8Cx4AXgfudc4GZ2gQwsy+aWQPB3rjDZvZt7z3uAI6Y2SHgq8BuL0GMeE1dQwAxM+cNgvPeKvLTqVHPm0hcqm3rZ01uKon++Nsu9Latq2npHebd+s5whyJxKiGUSt4w5QtTyh6a9HgI+PQM1z4CPBJKm175VwkmZ1PL/xb421DijTSNXcEjZKL5UPrprCtM5+T5Xlp6hijKiv7960QkdLVt/XG10nSyGy8uItFvvHikmSvW5oU7HIlD8feVKQwaOwfxGRRnx1aCM/HBvbdWE3dF4onzzjeuiNPkLSslkWvXF/DTY+eJkgEgiTFK3lZAY9cQq7JSYm54YWLe296aqJh6KCJLpKV3mIGRAOviNHkDuHnLKs60D3DyfG+4Q5E4FFvZRIRq7BqIqfluEybmvSl5E4kvF1aaxnHy9vHNqzCDnx49H+5QJA4peVsBTV1DMTffbcK6wuBmvS09Q+EORURWyETyFq9z3gCKMlO4vDxXB9VLWCh5W2bj445z3bFzusJUmvcmEn/q2vpJSvDF1N6VC3Hz5lUcbeqhoXMg3KFInFHytsxa+4YZDbiY2uNtstXZqWQkJ7BPQ6cicaOmrZ+K/DR8vug/q3kxbtlSDGjoVFaekrdl1tDpbdCbE1srTSf4fcaVFbma9yYSR+ra+uPuZIXpVBSkc9GqTA2dyopT8rbMLpyukBO7u5BfvS6f0639tPRq3ptIrAuMO860D1AZZ2eazuTmLat4q66Djv6RcIcicUTJ2zJr9JK3khjteYNg8gawr0bz3kRiXVPXICOBcSrV8wYEh07HHfz8uIZOZeUoeVtmTV2DZKUkkJmSGO5Qls2WkiwykhM0dCoSB7TS9IO2lGRRmpPKTzV0KitIydsya+yM3ZWmExL8Ps17E4kTde1K3iYzMz6+eRWvnmqjf3gs3OFInFDytswauwYpi9GVppNp3ptIfKhp7Sc9yU9hZnK4Q4kYt2wpZmRsnFffbw13KBInlLwts8au2O95A817E4kXE2eamsX3NiGTXVmRS25aoladyopR8raMeoZG6R0ai9nTFSbTvDeR+FDb1q8h0ykS/D5uungVL59oYTQwHu5wJA4khDuAWHZhm5A4GDbVvDeR2DcyNk5D5yC7Li0Jdyhh84N9Z6ctT0300zs0xt+8cJyqosxZ2/jMVeXLEZrEEfW8LaOmC9uExH7yBpPmvemcU5GYVN85QGDcxfWB9DPZUJRBot841tQT7lAkDih5W0aN3ukKZXGSvF2zPjjv7Ven1fsmEotqW7XSdCaJfh8bV2Vy7FwP486FOxyJcUrellFj1xBJfh8FGfGxKmtLSTY5aYm8dqot3KGIyDLQNiGz21qSTe/QGGfbdVC9LC8lb8uosWuQ1TkpcXN4s99nXLe+gDeq23D65ikSc2ra+slNSyQnLSncoUSkTcWZJPiM9xq7wx2KxDglb8uoqWuQkuz4GDKdcH1VAc09Q5xu7Qt3KCKyxOra+jXfbRbJiX4uKs7kSFO3hk5lWSl5W0aNnYNxsdJ0sus3FABo6FQkBmmbkLltLQ0OnZ7R0KksIyVvy2Q0MM753qG4WWk6YU1eGmvz03hdyZtITBkcCXCue4h1St5mpaFTWQlK3pZJc/cQzsXPStPJrt9QwN6adm1WKRJDJhYraNh0dskJwaHToxo6lWWk5G2ZNMbZHm+T3VBVQP9IgIP1XeEORUSWSG2bVpqGapuGTmWZKXlbJhN7vMXbnDeAa9YX4DPNexOJJRPJW0W+kre5XFScSaJfQ6eyfJS8LZOJ0xVWZ6eEOZKVl52ayCVlObx+qjXcoYjIEqlt62dVVjLpyTpVcS7JCX42rsrkaKOGTmV5KHlbJo1dgxRkJJOS6A93KGFx/YYCDjV00zM0Gu5QRGQJ1Lb1q9dtHraVZtM7PHZhrqDIUlLytkwauwYpzYm/XrcJ11cVEBh37NVRWSIxoa6tn3WFSt5Ctak4i0S/cbhBQ6ey9EJK3szsVjM7aWbVZvblaV5PNrNnvNf3mVnFpNce9MpPmtktc7VpZg94Zc7MCiaVm5l91XvtsJldvtCbXgmNXfG3x9tkl5fnkpbk5/VqzXsTiXbdA6O094+o520ekhJ8bF6dxXsN3Yxp5b0ssTmTNzPzA18HbgM2A3eZ2eYp1e4BOp1zG4DHgEe9azcDu4EtwK3AN8zMP0ebbwAfA85MeY/bgCrv5z7g7+Z3qyvHOReXpytMlpTg46rKPH75fquOyhKJcrU603RBLivPZXA0wMnzveEORWJMKD1vO4Fq51yNc24EeBrYNaXOLuBJ7/HzwE1mZl750865YedcLVDttTdjm865d51zddPEsQv4ngvaC+SY2er53OxKae8fYWh0PK573gBu3FTEmfYBato050MkmtV5v8MaNp2f9YUZZCYn8O5ZbZskSyuU5K0UqJ/0vMErm7aOc24M6AbyZ7k2lDYXEkdEaPC2CVmTmxbmSMLrxotXAfDy8fNhjkREFqOmrR+fBU9QkdD5fcala3I42dzLwPBYuMORGBJK8mbTlE0dB5upznzLFxsHZnafmR0wswOtreHZqqK+I7gxY1lefPe8leakcvHqLH5+vCXcoYjIItS19VOam0pyQnyunl+M7WtyCDjHYe35JksolOStAVgz6XkZ0DRTHTNLALKBjlmuDaXNhcSBc+5x59wO59yOwsLCOZpcHhM9b2Vx3vMGcNOmIt4+00nXwEi4QxGRBdI2IQu3OjuF4qwU3j3bGe5QJIaEkry9BVSZWaWZJRFcgLBnSp09wN3e4zuAV1xwlvoeYLe3GrWS4GKD/SG2OdUe4A+8VadXA93OuXMhxL/i6jsHyE1LJEObWXLTxUUExh2/fF8b9krkiYSV9JHOOUdtW78OpF8gM2P7mhzqOwdp6xsOdzgSI+ZM3rw5bA8ALwHHgWedc0fN7GEz+4RX7Qkg38yqgS8BX/auPQo8CxwDXgTud84FZmoTwMy+aGYNBHvWDpvZt733eAGoIbjo4VvAnyz67pdJfceA5oZ4Li3LoSAjSUOnEnEiaCV9RGvtG6ZveEwH0i/CpWtyMNDCBVkyIXUNOedeIJg8TS57aNLjIeDTM1z7CPBIKG165V8FvjpNuQPuDyXecGvsHGTT6sxwhxERfD7joxcV8eLRZkYD4yT6tS+0RIwLq94BzGxi1fuxSXV2Af/ee/w88LdTV9IDtd4X151evWnbdM6965Ut600tteqWPgA2FGWEOZLolZ2ayPrCDA7Wd3LTxUXhDkdigP4nXWLj446GzsG4X2k62U0Xr6J3aIwDdZrzIRElUlbSf0AkLLqa7LSStyVxWXkOnQOj1GrrJFkCSt6WWGvfMCOBccrifI+3yW6oKiDJ79OWIRJpImUl/QcrR8Ciq8lOt/aTnuSnOCt+j/tbCltLs0lJ9LG/tiPcoUgM0Iz6JfbrbULip+ftB/vOzllnbX4a//BuI+sKP/jt/TNXlS9XWCJzmc9K+oYQV9ITQptRpbqlj/VFGVE33BtpEv0+LivPZX9NB219wxRkJIc7JIli6nlbYr/eoFc9b5NtWp1Fe/8Ibb1abSURI1JW0ke06pY+NhRqyHQp7KzII+Acz7/dEO5QJMopeVtiF3reNOftAzYVBxdwHDvXE+ZIRIIiaCV9xOobHqO5Z4j1mu+2JFZlpVCRn8YP959lfFxnPsvCadh0iTV0DlKQkUxKonYinyw3LYnSnFTea+zmQxvDP49HBCJjJX0km1issF49b0tmZ2Uezx5o4I3TbdxQpc9CWRj1vC2x+s4B1sT5sVgzuaQsm8auQdq1UaVIVNA2IUtvS0k2uWmJIc0VFpmJkrclVt85oCHTGWwrzQbgPZ3xJxIVTrf2keAz1ubrM22pJPp93HFFGT87dp6W3qFwhyNRSsnbEhoZG6exc5AKfdBNKyctifK8NA43KHkTiQbVLX2szU/T5tpL7K6d5YyNO547oIULsjD6jVxCDZ0DjDtYqwOcZ7StNJvmniF94xSJAtWtfRoyXQbrCjO4dn0+T+09w2hgPNzhSBRS8raEzrQHV5pWFqjnbSbbSrMx4D31volEtKHRAGfaB9i4Skf9LYc/uq6Spu4hXnjvXLhDkSik5G0J1bUHjz1Rz9vMslITWZufzuHGboLbZYlIJDrd2kdg3HFRsZK35XDjpiLWFabz7ddq9Vko86bkbQnVtfWTkZxAfnpSuEOJaJeUZdPaO8z5Hq06FYlU75/vBeAi9bwtC5/PuOf6St5r7NaRWTJvSt6WUF37AGvz03SMzBy2lGRhwOHGrnCHIiIzONHcS5LfR0WBRhKWy+2Xl5Gblsi3XqsNdygSZZS8LaEz7f36oAtBZkoi6wrTea9BQ6ciker95l7WFaZrpekySkn089mr1/LyifPUtPaFOxyJIvqtXCKjgXEatE1IyC4ty6G9f4SD9ep9E4lEJ5t7LxxrJ8vns9dUkOj38cTr6n2T0Cl5WyJNXYOMjTstVgjR1tJsEv2mA5pFIlDP0ChN3UNcVJwV7lBiXmFmMp/cXsrzbzfQ0T8S7nAkSih5WyK1bcGVphVK3kKSkuhnS0k2ew41MTQaCHc4IjLJ+83eYoVi7fG2Eu69oZLhsXH+/g31vklolLwtkYk93iq0x1vILi/PpXdojJ8dOx/uUERkkpMTK03V87YiqlZlctvWYr77Rh3dA6PhDkeigJK3JVLX3k9akp/CjORwhxI11hWmU5qTynMaOhWJKCebe8lMTqAkOyXcocSNL95URe/wGE+o901CoORtidS29bM2P13bhMyDz4zbryjjtVOt1HcMhDscEfEcP9fDRcWZ+jxbQRevzgr2vr1eq943mZOStyVS3dJHlc4AnLfdV67BgKffOhvuUEQEGB93HG3qYWtpdrhDiTsXet9erwl3KBLhlLwtgYGRMRo6B5W8LUBJTio3birimbcadECzSASobe9nYCTAlhLNd1tpF6/O4re2Bee+dQ1o5anMTMnbEjjdElxpWrVKydtC/N5Va2nrG9bCBZEIcKSxG0A9b2Ey0fv2He37JrNQ8rYETrUEV2ZtKNKGlgvxoY2FlOak8r0368IdikjcO9rUQ1KCjw0aSQiLTcXB3rfvvFFHe5/Of5bpKXlbAqda+kjwGWt1usKC+H3GZ69Zy96aDo419YQ7HJG4dqSxm03FmToWK4y+9PGNDI4G+MrLp8IdikQo/XYugVPn+6gs0BmAi7H7yjWkJvr5rpbJi4SNc44jjd1sKdGQaThtKMrk964q56l9Zznl7bknMpmyjSVQ3dKr+W6LlJOWxO1XlPKjg020aahAJCwaOgfpGRpja6kWK4Tbn91URVqSn7954Xi4Q5EIFFLyZma3mtlJM6s2sy9P83qymT3jvb7PzComvfagV37SzG6Zq00zq/TaOOW1meSVf87MWs3soPdz72JufKkMjQY42zGg+W5L4HPXVjISGOd7b54JdygicenCYgX1vIVdfkYyf3rjBv7lZCuvvt8a7nAkwsyZvJmZH/g6cBuwGbjLzDZPqXYP0Omc2wA8BjzqXbsZ2A1sAW4FvmFm/jnafBR4zDlXBXR6bU94xjm33fv59oLueInVtvUz7tA2IUtgQ1EGH7t4FU/+qo7+4bFwhyMSd95r7CbBZ1xUrC+jkeDuaytYk5fKIz8+TmDchTsciSCh9LztBKqdczXOuRHgaWDXlDq7gCe9x88DN1lwa+5dwNPOuWHnXC1Q7bU3bZveNTd6beC1+bsLv73ld6qlD9A2IUvlTz66nu7BUX64X5v2iqy0d852srkki5REf7hDESA5wc+Dt13MyfO9PPNWfbjDkQgSSvJWCkz+V9PglU1bxzk3BnQD+bNcO1N5PtDltTHde91uZofN7HkzWxNC7Mvu/eZe/D6jsiA93KHEhMvLc7lmXT6Pv1rD8Fgg3OGIxI3RwDiH6ru5vDw33KHIJLdtLWZnRR6PvniC1l7NB5aghBDqTHe43dT+25nqzFQ+XdI4W32AfwJ+6JwbNrPPE+yVu/E3gjW7D7gPoLy8fJrmltbRpm6qijJITtA31YX4wb7f7GG7eHUWb9a081fPHebqdfkzXvuZq5b/71ckXpw418vgaIDL1yp5iyRmxt98aiu/9ZXXefifj/G1uy4Ld0gSAULpeWsAJvdylQFNM9UxswQgG+iY5dqZytuAHK+ND7yXc67dOTfxteNbwBXTBeuce9w5t8M5t6OwsDCE21ucI009Wla/xNYXprM2P41fnGzRkVkiK+Sds50AXKHkLeJsKMrkgRs38E+Hmnj5uE6ikdCSt7eAKm8VaBLBBQh7ptTZA9ztPb4DeMU557zy3d5q1EqgCtg/U5veNf/itYHX5o8AzGz1pPf7BBD29dMtPUO09g5rWf0SMzNu3lxMz9AYe2vawx2OSFx4+0wnxVkplGSnhDsUmcbnP7yejasy+L//8Qh9WtAV9+ZM3rz5Zw8ALxFMmJ51zh01s4fN7BNetSeAfDOrBr4EfNm79ijwLHAMeBG43zkXmKlNr61/B3zJayvfaxvgi2Z21MwOAV8EPre4W1+8I006A3C5VBakU1WUwS/fb2VoVHPfRJbbO2c7uXxtDsF1YxJpkhJ8/KfbL6G5Z4j//OKJcIcjYRbKnDeccy8AL0wpe2jS4yHg0zNc+wjwSChteuU1BFejTi1/EHgwlHhXypHGHsyCc7Rk6d28uZiv/6KaX5xs5datxeEORyRmtfQM0dA5yOeurQh3KDKLy8tzufuaCp58s47f3raaq2aZEyyxTScsLMKRxm4qC9LJSA4pB5Z5Ks1N5fLyHN443aYDmkWW0YEzwfluWqwQ+f7qlosoz0vjS88eontwNNzhSJgo61iEo009mty7zG7eXMyRxh5+cqSZ3796bbjDEYlJb1S3kZ7kZ5umgKyI6VbZz8dvbV3Nt16r4f/8h/f427su01B3HFLP2wJ19I/Q2DWoxQrLLCs1kY9cVMixcz2caO4JdzgiMelXp9u5el0+iX79lxAN1uSl8Rcf38iPD5/jqUUmghKd9Ju6QBNnAGqbkOV3fVUBRZnJ/OhgE8NavCCypBo6B6ht6+e6DQXhDkXm4QsfXs9HLirk4X86xsH6rnCHIytMydsCHajrwGdw6ZqccIcS8xJ8Pj51WSk9g6P89Jj2OBJZSr+qDm7Hc32Vkrdo4vMZ//3O7RRlJfOF//k2LT1D4Q5JVpCStwXaV9vB1tJsLVZYIeX56Vy9Lp83a9o51dIb7nBEYsYbp9sozEymqkjnM0ebnLQk/sdnr6B7cJQ//t4BbasUR5S8LcDwWIB367vYWZEX7lDiyq1biynMTOb5txvo1yaVIovmnOON6nauW5+vSe9RaktJNl/ZfRmHG7v586cPEhifenqlxCIlbwtwuKGbkbFxdlYqeVtJiX4fd+5Yw8BwgOffbmBcH1Iii3K0qYe2vmHNd4tyH9+8iv/ntzfz4tFm/q9/eI/gYUUSy5S8LcD+2g4ArlTP24oryUnlty9Zzcnzvfz3l0+FOxyRqPbTo834DG7cVBTuUGSR/uj6Sv70xg08/VY9/+HHx5XAxThN2FqAfbUdXLQqk9z0pHCHEpeuqsyjoXOQr758iouLM7lt2+q5LxKR3/DS0fPsrMwjPyM53KHIEvjSxzfSNzzGE6/XEhh3/PXvbNZweIxS8jZPY4Fx3q7r4FOXl4U7lLhlZuzaXoLD8WfPHKQgM1m9oCLzVNPax8nzvfz172wOdyiyRMyMh/7VZvxmfPv1WvqGx/iPn9qm/ftikP5G5+nd+i76RwJctU7JQjgl+n08cfeVlOWkcu+TBzh+Thv4iszHS0eD2+7cskXnBscSM+P/+u2L+fOPVfH82w3c8+QBeoZ0jFasUfI2Tz87dp5Ev/GhjYXhDiXu5aUn8eQf7SQtyc9nvrVXCZzIPLx4tJlLy7IpyUkNdyiyxMyMP//YRh69fRu/qm7jE197nWNN+nyMJUre5sE5x8+OnefqdflkpSSGOxwheEzMD//4alIS/dz1rb28e7Yz3CGJRLzTrX0cqu/SfNEYd+eV5Tx939UMjgb45Dfe4LkD9eEOSZaIkrd5ON3aR21bPzdvXhXuUGSSioJ0nr7varJSErnrW3t5+bhOYRCZzTNv1ZPgMz51eWm4Q5FltqMijx9/8QauWJvLXz1/mL987hDdgxpGjXZK3uZh4mimjyl5izhr89P5X1+4lqqiTO793gG+8vNT2gdOZBojY+P8r7cbuOniIooyU8IdjqyAgoxkvn/PVfzpjRv4h3cb+dj/90tePHIu3GHJIih5m4efHTvPttJsVmdrjkgkKsxM5tl/cw2f3F7KYz9/nz968i06+0fCHZZIRPn58fO094+we2d5uEORFeT3Gf/25ov40f3XUZiRzOf/5zt8/vtvc657MNyhyQJoq5AQ1bT28e7ZLv7qlovCHYp4frDv7LTlV6zNJeAc/3z4HB/9b7/gzh1rWJuffuH1z1yl/7Qkfj217wwl2Sl8qEqLrqLZTJ9/obhrZzmvV7fx8+PnefnEea7bUMCHqgpJSfTPq52l+CxdzH0sZRzRRj1vIfrBvrMk+IxP79D+bpHOzLiqMp/7blgHwOOv1vBPh5oYHtOhzRLf3j7TyRvV7dx9bQV+nzZvjVd+n/HhjYX8xcc2cvHqLH5xspX/9tOT7K1pZywwHu7wJATqeQvB0GiA595u4JYtxZojEkXW5KXxZzdW8dKx8+ytaef4uR52bdcEbYlfX3n5FHnpSXz2mrXhDkUiQG56EruvLOf6DQP85Egzew418YuTLXxoYyE71uaRlKD+nUilv5kQ/PjwOboHR/m9OOyajXbJiX4+cWkJ931oHYkJPp58s47PPrGPQ/Vd4Q5NZEW9e7aTV99v5Y9vWEdakr63y6+V5aZx7/WV/OF1FeSlJ/PPh8/xX356kn852ULf8Fi4w5Np6Dd4DuPjju+8Ucu6gnSuWZ8f7nBkgdbmp/OnH93A3pp23qxpZ9fX3+CWLav4c2/YQCSWjY87HvnxcfLSk/gD9brJNMyMqqJMqooyqW3r5xcnW/jZsfO8cqKFS0qzuXpdPmW5qTorNUIoeZvDjw41crSph8fuvFT/aKNcgt/H9VWFPHrHJXzn9Tq+/VoNLyiH8yoAABYrSURBVB19jasq87hrZzkf37yK9GT9SkjseWr/WQ6c6eS/fvpS/RuXOVUWpFNZUElLzxB7a9t552wX79Z3UZiZzKVl2VxalkN+RnK4w4xr+i2exdBogP/y4km2lmax61LNlYoVmSmJ/NnHqrj72rU881Y93997hj9/5iApiT6uWZfP9VWFbF+Tw6biTP1HJ1HvXPcgj/7kBNdvKOB2bcor81CUlcInLi3l5s3FHGro4lB9Fz8/3sLPj7ewOjuFuvZ+rlmXz5WVeWTos3JF6U97Ft/85Wmauof4r//6UnxamRVzctKS+DcfXs8f37COt8928s+Hmnj1VBv/cvLYhToZyQkUZSZTmJlMenICjZ2DTHTABsYdAecIjDvGvcc+MxL9PhL9RpLfR1ZqIrlpSeSmJZKXkUxRZjK+BfTgLmYp/GKW4sfjEvxYMjQa4PPff5tx53jkk1s1eiALkpLo56rKfK6qzKdrYIT3Grs5fq6Xv3+jjsdfrcHvMzYUZrC+KJ0NhRlUFqaTm5ZEdmoi2amJpCb9egsS54KfnSOBcUYD4zR2Dl74HA3+jBMYd4yNO8adI8HnIyXRT3KCj9REP1mpiVpIgZK3Gb1R3cZXXz7Fru0lXLu+INzhyDLy+YwrK/K4siIPCPZUHGns4VRLLy09w7T2DtPSO0Rr7zC9w96xMi54nd9n+M1ISvThM8PhGA04BkYCdI6O8v75PkYmLb1P8vsoyUllTW4qFQXprCtMJzlhfnsriYRifNzxb587xOHGbv7H71/xgb0ORRYqJy2JG6oKuaGqkE9eVsrbZzrZW9POieYejp/r5cUjzSz34TapiX6yUxPJS09iVVYyGSkJXLQqk/WF6ST44yOxU/I2jfqOAf70h++yrjCDv/nktnCHIytsdXYqq7NT+fg0x6DNtxfLOcfgSIDOgVFaeodo6BqkoWOAN2vaea26Db8ZawvS2FiUycZVmazKSlbviCza8FiAv3zuMD8+fI4Hb9vEzVuKwx2SxKDUJD/XVxVwfdWvOziGxwI0dA7SNTBKz+Ao3YOjDI4GmPypljBpdOJXp9uDX4K9L8IXHnvPR8fHGRodZ2QswMBIgJ7BUbq8dlt6hzl+rod/OdkKQFqSn0vKsrmsPJfL1uRwWXkuhZmxOTdPydsUx8/18Lnv7mc0MM43f/8KzXmSRTEz0pITSEtOoDQ3lcvKcwEYC4xzpmOA98/3cup8Hy8ebebFo81kpSSwoSiTqqIMNhRlLOu/v3Hn6Ogb4XzvEJ39I3QOjNI5MMLASIDAuGM0MM63X6shMyWB3PQk8tKSKMxMZn1RxoX4MlMSly0+WZiGzgG+9Mwh9td18OBtm7jvQ+vCHZLEkeQEP+sLM0Ku3zkwuqj3Gw2Mc2VFHieaezhUH1xY8a1Xaxjzuv/KclO5Ym0uV6zN5fLyXDYVZ8ZE71xI/zOY2a3AVwA/8G3n3H+a8noy8D3gCqAduNM5V+e99iBwDxAAvuice2m2Ns2sEngayAPeAT7rnBuZ7T2WQmDc8cP9Z3n0JydIT07guc9fw4ai0P8BisxHgt/H+sIM1hdmcNtW6B4c5dT5Xk619HH8XA/vnO3EgJKcVKqKMlidncLW0uwFf4scHAnQ0jtEc88Q57qHONc1yPme4Q8M6SYn+MhNSyIt2U9qop8Ev1FZkE7f8Bid/SOcbu0LXjP262tKc1LZVprNtrJstpRksa00O6pWoUXCZ9tS3Uvf8Bj/c+8ZvvryKQC+snu7NqWWmJfo97G5JIvNJVl86vLgCUhDowGONnXzzpku3q3v5M3T7fzoYBMQ7J27tCwnmMytzeHy8lxy0pLCeQsLMmfyZmZ+4OvAx4EG4C0z2+OcOzap2j1Ap3Nug5ntBh4F7jSzzcBuYAtQAvzczDZ618zU5qPAY865p83sm17bfzfTeyz2DwCgpXeIP/r7tzjS2MNVlXk8dud2SnJ0+LysnOzURHZU5LGjIo9x5/j/2zv34Liq+45/vpZkyZK1tiUZvzAIDJgxtOHlUofHODySQJOYTJiBllLatGXaQAl0mJSWmZYknU4T0mkmJYUBQkMobxJahklaIOGRFLCxjd822MZPWbYxtiU/ZVn+9Y9zZF/Ju6uVtKvdlX+fmTs6e/bcc777u/f+dO65555fy64DrN4eOnNvrf6YNz4MjwUmpKqZMSnF5LGjmJCqYWKqhvqaY5fxEYOd+zrYvqeD7e0dbGk7wOJNu2k/eGyhzZqqEUwaM4oLm8cxeUwNE1I1NNSNZFRVxXGPbHu/sNB1xNi0cz+rt+/lw217WNnazrKWNv5n+dajZSaPqeGcKWP4rSljOHdKiukTU0xK1QzopZ+uI8a6HXs546T6fu/bFyXk2wbND19fw0NvrmXPwcNccfZJfPu6c5niPsw5QampquDCUxu48NQwj9nMaNl9gAUbdvH+xt0s2LCLB99cS1ccnWturGX6xHqmT0xx9sR6mhvrOKWxdtBv0JoZm3YeYHRNJQ11+e0g5qLsd4A1ZvYRgKRngDlA0sHNAe6L6ReABxT+C8wBnjGzDmCdpDWxPtLVKWklcAXwB7HM47HeBzO1YWaDnhrZWFfNxFQNt14+jS/+9iSfc+QUlRESUxtqmdpQyxVnT6Cjs4sZk1MsbWljWUsbH2zby6JNu7M+bhghaBxdzaQxNUwbP5qTUjVMqK9mQqqGsbVVAz7HK0aI5qY6mpvqeswJbDvQyYotoSO3tKWNZVvaeG3lNrqvzpEVIzi5YRSnNtQyvr6ahrpqGuqqSNVUUTFCjIhzXQ52dtF2oJMtuw/w0Y59vL9xN/sOHWbxP3yWVP4f0ZaKbxs0HZ1dXHZmE7dePo3zpo7NR5WOM2yQxMnjajl5XO3R0ej9hw6zeFMbCzfuCn516x5eXbGtx8sWDXUjmTJ2FOPrq2msG0lTfTWpmipqR4anE9VV4fHr4a7wpuy+Q4fZ1t7BtvaDbG07yKqt7eza38m355zDzbOa8/qbcum8TQE2JT5vBi7OVMbMDktqAxpj/ru99u0ex09XZyOw28wOpymfqY0dOfyGrFSMEI/eMnOw1ThOQaiuquDi0xu5+PSeET4Odnbx8Z4O9nYcPrp8iRDjaqtoHF19NPD4YJYKyZUxo6qYNa2xRxSSvR2HWd7SxtqP97Fh5z427NjPxp37WdHazs59h+jsynzfNbq6klMba7nu/MnMbG6gsjBL9ZSKbxs0d119lt90Ok4/qB1ZeZzPOtjZxZrte9nwyX427Qr+qmXXAba1H2TFlnY+2deR1W8BVFWIk+prmJCq5uoZE/jU1LFcdsb4vOvPpfOWziP0Vp+pTKb8dLMFs5XPVQeSbgVujR/3SvogzX7dNJGHzl+eKUVNUJq6BqTppgIISVAQOw1S84A1FdNWy4GfD6zeXOM/lYpv69lg/3xYJobN9VpgXFPuHKerwP4hF5qAHSWgI0nTmsEdv5z8Vy6dt83A1MTnk4EtGcpsllQJjAF29rFvuvwdwFhJlfEONVk+Uxs9MLOHgYdz+F1Imm9mF+VSdqgoRU1QmrpcU26UoiYoCV2l4tt60B8flokSsO1xuKbcKEVNUJq6TmRNubwv+x5wpqTTJI0kTNJ9qVeZl4BbYvp64FdxLtpLwI2SquObVmcC8zLVGfd5PdZBrPO/+2jDcRxnIJSKb3Mcx+kXfY68xXketwP/S3j1/TEzWy7pW8B8M3sJ+BHwRJy0u5PgsIjlniNMAD4M3GZmXQDp6oxN/g3wjKR/BN6PdZOpDcdxnIFQQr7NcRynX+hEHrySdGt8RFEylKImKE1drik3SlETlK6u4UAp2tY15UYpaoLS1HUiazqhO2+O4ziO4zjlRvnHiHAcx3EcxzmBGLadN0n3S1olaYmkFyWNjfnNkg5IWhS3hxL7XChpqaQ1kn4QF+NEUoOkVyWtjn/HFUjz5yV9ENu/pxBtJNqaKul1SSslLZf09Zh/n6SWhH2uTezzt1HbB5I+VwjdktbHY7BI0vyYl9b+CvwgtrtE0gWJem6J5VdLuiVTeznomZ6wxSJJ7ZLuLIadJD0mabukZYm8vNkm0/k/AE1ld+2VE5nsG7/r17mn8GLF3GjfZxVesiiE5iHxbcrs14rmQxL1VUh6X9LL8XNa2yu8BPNs1DRXUnOijrTHdxCaxkp6IZ5PKyXNKratJN0Vj90ySU9LqhlqW6kEfe1xmNmw3IDPApUx/R3gOzHdDCzLsM88YBZhTaZfANfE/O8C98T0Pd115VlvBbAWOB0YCSwGZhTQPpOAC2K6HvgQmEFY9f3uNOVnRE3VwGlRa0W+dQPrgaZeeWntD1wbj5OA3wXmxvwG4KP4d1xMj8vTMdpKWIdnyO0EXA5ckDx/82mbTOf/ADSV1bVXblsW+/b73AOeA26M6YeAvyyA3iHzbWT2a0X3IcBfA08BL2ezPfA14KGYvhF4NtvxHaSmx4E/i+mRwNhi2oqwcPU6YFTCRn881LaiBH1t723YjryZ2St2bDXzdwnrKmVE0iQgZWbvWLDuT4Dr4tdzCCc58e91aaoYLEdD9VgIVt0dqqcgmFmrmS2M6T3ASrKv+H40HJCZrQO6wwENhe5M9p8D/MQC7xLW0ZoEfA541cx2mtku4FXg83nQcSWw1sw29KG1IHYys7c4fm3DvNimj/O/X5rK8NorK7LYt1/nXrzbv4IQ9guGgW/L4teK6kMknQz8HvBo/JzN9kmtLwBXxvKZju9ANaUInZQfAZjZITPbTfH9bSUwSmFdxVqglSG2VSn62t4M285bL75K6N12c1ocvn5T0mUxbwph4c1ukuFrJphZKwTnAJxUAI3pQvXkLXxONuJQ8/nA3Jh1exz+fUzHHlNl0pdv3Qa8ImmBwkrzkNn+Q6WpmxuBpxOfi2mnbvJlm2zn/2Aoh2uvnEnat7/HvKAhuxIUxbf18mvF9iHfB74BHImfcw4FCSRDsuVT0+nAx8B/xGvyUUl1FNFWZtYCfA/YSOi0tQELKL6toMR8bVl33iS9Fp+L997mJMrcS1iH6cmY1QqcYmbnE4ex4x1IzuFrCkRR2pc0GvgpcKeZtRMCZU8DziPY6l/60Jdv3ZeY2QXANcBtki7PUnaoNBHnWHwJeD5mFdtOfdFfHYWwWblceyXHAH1b0Y95Bob8+KbxaxmLpsnLq20kfQHYbmYLcmh3SDRFKgmPBh+M1+Q+wuPATAyFrcYRRrJOAyYDdYT/BZnqL/a5PRANedGWS3isksXMrsr2fZwg+AXgyjg8iZl1AB0xvUDSWuAsQu83+XgnGb5mm6RJZtYahzy35/eXALmF6skrkqoIDu5JM/sZgJltS3z/CPByDvryptvMtsS/2yW9SBjqzmT/TJo2A7N75b8xUE2Ra4CF3fYptp0S5Ms22c7/flNm117JMRDfRgFDdg2SIfVt6fwaxfUhlwBfUnipqQZIEUbi+hsKMt923AxsNrPuJy4vEDpvxbTVVcA6M/sYQNLPgE9TfFtBqflaK8Ck0VLYCM/cVwDje+WPJ05cJAwbtwAN8fN7hAmH3ZMIr43599NzouJ3C6C3kjCh8TSOTeo9p4D2EeFZ+/d75U9KpO8izBsAOIeeE0A/IkxEzptuwl1WfSL9djyOae1PmEOSnCg6L+Y3ECa9jovbuu5jPAh7PQP8SbHtRK9J//m0TabzfwCayuraK7cti337fe4RRpKTE8G/VgC9Q+bbyOzXiu5DYr2zOfbCQlrbA7fRcxL+c9mO7yD1/BqYHtP3RTsVzVbAxcBywlw3EeaW/VUxbEUJ+toe+gpxAZXCRpiguAlYFLfuA/yVeHIsBhYCX0zscxGwjPBmygMcW8S4EfglsDr+HfRFnEHztYS3o9YC9xbYPpcShmqXJGx0LfAEsDTmv0TPTsq9UdsHJN6OyZduwj/0xXFb3l1XJvvHE/+Hsd2lwEWJur4az4E1JDpdA9RVC3wCjEnkDbmdCPPtWoFOwt3bn+bTNpnO/wFoKrtrr5y2TPYdyLkXr7l5sc7ngeoCaR4S30Zmv1ZUH5KoczbHOm9pbU8YnXs+5s8DTu/r+A5Cz3nA/Giv/yJ0Mortb78JrIr+4AlCB2xIbUUJ+trem0dYcBzHcRzHKSPK+oUFx3Ecx3GcEw3vvDmO4ziO45QR3nlzHMdxHMcpI7zz5jiO4ziOU0Z4581xHMdxHKeM8M6bkxVJEyU9I2mtpBWSfi7prCFo9z5Jd8f0tyRlXbR0APXfKak28Xm9pKZ8tuE4TnFx/+UMV7zz5mQkBvh9EXjDzKaZ2Qzg74AJQ6nDzP7ezF7Lc7V3EtZvcxxnGOL+yxnOeOfNycZngE4ze6g7w8wWAb+RdH+MtbhU0g0Q4glK+qWkhTF/TsxvlrRK0uMxkPsL3XeN8Y7xO5Lmxe2M3iIk/VjS9TE9U9LbkhbH8vWx/l/HdhdK+nQsO1vSG7G9VZKeVOAOQty81yW93qutZkkrJT0iabmkVySNit+dEWNOLo7tTIv1pbPFbIXg689J+lDSP0u6KWpeKmlaLDde0k8lvRe3S/J9EB3nBMX9l/uv4UuhVrr2rfw34A7gX9PkfwV4lRB6ZwKwEZhECIOTimWaCKtKixBmxAhB5wEeA+6O6fUci6TwRxxbffy+RJkfA9cTQut8BMyM+anYZi1QE/POBObH9GygjRA7bgTwDnBpot2mxG9aHzU3E4J9nxfznwP+MKbnAl+O6ZrYbiZbzAZ2x3Q1IRTUN+O+XyeG7wGeSmg6BVhZ7OPum2/DYXP/5f5rOG8+8uYMhEuBp82sy0KA9jeBmQRH90+SlgCvAVM49ohik5n9X0z/Z6yjm6cTf2dlaXc60Gpm7wGYWbuFQMVVwCOSlhJCpcxI7DPPzDab2RFCqJzmHH7fOgt36AALgGZJ9cAUM3sxtn3QzPZnsQXAe2bWaiEg+1rglZi/NKHjKuABSYsIYbZSsS3HcQqD+y/3X2VPZbEFOCXNcsIdY2+UofxNhODjF5pZp6T1hDs8CHeuSSyHdLp2031/F7AN+BThDvVg4ruORLqL3M753vuMIvNvzpTfu54jic9HEjpGALPM7EAOuhzHyR33X+6/hi0+8uZk41dAtaQ/786QNBPYBdwgqULSeOByQlDgMcD26Pg+A5yaqOsUSd13pb8P/Cbx3Q2Jv+9k0bMKmBw1EOeLVMZ2W+Pd6c2ERwB9sQfI+Q7RzNqBzZKui21Xx3kvb5HeFrnyCnB79wdJ5/VjX8dxMuP+K+L+a/jhnTcnI2ZmwJeBqxVetV9OmMvxFLAEWExwkN8ws63Ak8BFkuYT7mJXJapbCdwSH0k0AA8mvquWNJcwl+KuLHoOERzkv0laTJirUQP8e6z7XeAsYF8OP+9h4Be9J/z2wc3AHfE3vA1MJLzNls4WuXIHwWZLJK0A/qIf+zqOkwH3X8fh/msYoXB+O07hkNRMmMh7bprv1gMXmdmOIZblOI7TJ+6/nFLER94cx3Ecx3HKCB95cxzHcRzHKSN85M1xHMdxHKeM8M6b4ziO4zhOGeGdN8dxHMdxnDLCO2+O4ziO4zhlhHfeHMdxHMdxygjvvDmO4ziO45QR/w/mg9Gwbsd8cQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing distribution plots of dataset before and after handling outliers\n",
    "\n",
    "f, axes = plt.subplots(3, 2, figsize=(10, 20))\n",
    "\n",
    "sns.distplot(old[\"LoanAmount\"], ax=axes[0][0]).set_title(\"Before\")\n",
    "plt.grid()\n",
    "sns.distplot(X[\"LoanAmount\"], ax=axes[0][1]).set_title(\"After\")\n",
    "plt.grid()\n",
    "\n",
    "sns.distplot(old[\"ApplicantIncome\"], ax=axes[1][0]).set_title(\"Before\")\n",
    "plt.grid()\n",
    "sns.distplot(X[\"ApplicantIncome\"], ax=axes[1][1]).set_title(\"After\")\n",
    "plt.grid()\n",
    "\n",
    "sns.distplot(old[\"CoapplicantIncome\"], ax=axes[2][0]).set_title(\"Before\")\n",
    "plt.grid()\n",
    "sns.distplot(X[\"CoapplicantIncome\"], ax=axes[2][1]).set_title(\"After\")\n",
    "plt.grid()\n",
    "f.savefig('oldnewcompa.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing boxplots plots of dataset before and after handling outliers\n",
    "\n",
    "f, axes = plt.subplots(3, 2, figsize=(10, 20))\n",
    "\n",
    "sns.boxplot( y=\"LoanAmount\", data=old, palette = \"winter\", ax = axes[0][0]).set_title(\"Before\")\n",
    "plt.grid()\n",
    "sns.boxplot( y=\"LoanAmount\", data=X, palette = \"winter\", ax = axes[0][1]).set_title(\"After\")\n",
    "plt.grid()\n",
    "\n",
    "sns.boxplot( y=\"ApplicantIncome\", data=old, palette = \"winter\", ax = axes[1][0]).set_title(\"Before\")\n",
    "plt.grid()\n",
    "sns.boxplot( y=\"ApplicantIncome\", data=X, palette = \"winter\", ax = axes[1][1]).set_title(\"After\")\n",
    "plt.grid()\n",
    "\n",
    "sns.boxplot( y=\"CoapplicantIncome\", data=old, palette = \"winter\", ax = axes[2][0]).set_title(\"Before\")\n",
    "plt.grid()\n",
    "sns.boxplot( y=\"CoapplicantIncome\", data=X, palette = \"winter\", ax = axes[2][1]).set_title(\"After\")\n",
    "plt.grid()\n",
    "f.savefig(\"boxafterbefore.png\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the distribution plots and boxplots shown above, we can see that a lot of outliers have been handled. Now we will use this data to train a Random Forest Classifier model. We will be training a Random Forest Classifier model to determine the principal attributes in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Visualising other attributes with respect to our target column i.e. 'Loan_Status'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We have used barplot to visualise other attributes against the data in 'LoanAmount' column and using our target attribute i.e. 'Loan_Status' as hue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a0dd445550>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x2160 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(7, 1, figsize=(10, 30)) # preparing the canvas\n",
    "\n",
    "sns.barplot(x = X.Gender, y = X.LoanAmount, hue = X.Loan_Status, ax = axes[0])\n",
    "\n",
    "sns.barplot(x = X.Education, y = X.LoanAmount, hue = X.Loan_Status, ax = axes[1])\n",
    "\n",
    "sns.barplot(x = X.Married, y = X.LoanAmount, hue = X.Loan_Status, ax = axes[2])\n",
    "\n",
    "sns.barplot(x = X.Self_Employed, y = X.LoanAmount, hue = X.Loan_Status, ax = axes[3])\n",
    "\n",
    "sns.barplot(x = X.Property_Area, y = X.LoanAmount, hue = X.Loan_Status, ax = axes[4])\n",
    "\n",
    "sns.barplot(x = X.Credit_History, y = X.LoanAmount, hue = X.Loan_Status, ax = axes[5])\n",
    "\n",
    "sns.barplot(x = X.Dependents, y = X.LoanAmount, hue = X.Loan_Status, ax = axes[6])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the above graphs one interesting thing that can be noted is that no loans are approved i.e. 'Loan_Status' = 1 for 'Property_Area' value 'Rural' i.e. 'Property_Area' = 0. All the loan requests for 'Property_Area' value 'Rural' i.e. 'Property_Area' = 0 are denied.\n",
    "\n",
    "We will now train a Random Forest Classifier Model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training a Random Forest Classifier to determine the principal attributes."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We have used Random Forest Classifier to determine the principal attributes as this classifier has a built-in feature importance function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required module\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now we will split the dataset into train and test parts for training the model. We use 75% of the data to train and 25% for testing purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no. of observations for the training dataframes: 76\n",
      "no. of observations for the test dataframes: 24\n"
     ]
    }
   ],
   "source": [
    "X[\"is_train\"] = np.random.uniform(0,1,len(X)) <= 0.75  # making a new column with random boolean values \n",
    "                                                       # indicating which data belongs to train and which\n",
    "                                                       # which data belongs to test.\n",
    "train, test = X[X[\"is_train\"]==True], X[X[\"is_train\"]==False]  # splitting train and test dataframe\n",
    "print(\"no. of observations for the training dataframes:\",len(train))\n",
    "print(\"no. of observations for the test dataframes:\",len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed',\n",
      "       'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
      "       'Loan_Amount_Term', 'Credit_History', 'Property_Area'],\n",
      "      dtype='object')\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=2,\n",
      "            oob_score=False, random_state=0, verbose=1, warm_start=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 1 0 0 1 0 0 0 0 1 0 1 1 1 1 0 0 0 1 1 1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "f = X.columns[1:12]  # selecting feature attributes\n",
    "print(f)\n",
    "y = train[\"Loan_Status\"] # selecting target attribute\n",
    "\n",
    "cif = RandomForestClassifier(n_jobs=2, n_estimators=100, random_state=0, verbose=1) # Random Forest Classifier \n",
    "                                                                                    # Object\n",
    "print(cif) # printing details of the Random Forest Classifier Object\n",
    "cif.fit(train[f], y) # training the model\n",
    "preds = cif.predict(test[f])  # predicting the target attribute for test dataset\n",
    "print(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Predicted Loan_Status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Loan_Status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Loan_Status  0   1\n",
       "Actual Loan_Status          \n",
       "0                      7   1\n",
       "1                      4  12"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a confusion matrix to view the actual and predicted results\n",
    "pd.crosstab(test[\"Loan_Status\"], preds, rownames=[\"Actual Loan_Status\"], colnames=[\"Predicted Loan_Status\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7916666666666666"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = test[\"Loan_Status\"]\n",
    "cif.score(test[f], z) # accuracy of the Random Forest Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Gender', 0.022998752001938204),\n",
       " ('Married', 0.029356323610496546),\n",
       " ('Dependents', 0.04632544519905084),\n",
       " ('Education', 0.01632747444018825),\n",
       " ('Self_Employed', 0.01291837290989398),\n",
       " ('ApplicantIncome', 0.1908564421569762),\n",
       " ('CoapplicantIncome', 0.10614248164617252),\n",
       " ('LoanAmount', 0.1759079613657668),\n",
       " ('Loan_Amount_Term', 0.03874584019674761),\n",
       " ('Credit_History', 0.21857887517056812),\n",
       " ('Property_Area', 0.1418420313022009)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view a list of the features & their importance scores\n",
    "list(zip(train[f], cif.feature_importances_))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "As we can see from the calculated feature importance values, the attributes viz. 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Credit_History' and 'Property_Area' have higher values compared to other attributes. So we will select these 5 attributes as our principal attribute to train our required models.\n",
    "One interesting thing to notice here is that although 'Married' attribute had a high correlation value with our target attribute, 'Loan_Status', the feature importance of 'Married' attribute is lower."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "NOTE: These values will change with each run. But these 5 attributes will always have the highest feature importance if trained as above.\n",
    "\n",
    "Sl.No.    Principal Attributes selected        Feature importance\n",
    "-------  -------------------------------      ---------------------\n",
    "1.        ApplicantIncome                      0.1746432652177679\n",
    "2.        CoapplicantIncome                    0.11756790122516193\n",
    "3.        LoanAmount                           0.12842666389187288\n",
    "4.        Credit_History                       0.22613376236476954\n",
    "5.        Property_Area                        0.14291201191868688"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now we will modify the dataset to include only the selected principal attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3636.720000</td>\n",
       "      <td>1606.455000</td>\n",
       "      <td>121.520000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>1.410000</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1325.607679</td>\n",
       "      <td>1707.964583</td>\n",
       "      <td>39.314962</td>\n",
       "      <td>0.422953</td>\n",
       "      <td>0.652811</td>\n",
       "      <td>0.482418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2636.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3597.000000</td>\n",
       "      <td>1542.250000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4197.500000</td>\n",
       "      <td>2341.500000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7660.000000</td>\n",
       "      <td>8106.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Credit_History  \\\n",
       "count       100.000000         100.000000  100.000000      100.000000   \n",
       "mean       3636.720000        1606.455000  121.520000        0.770000   \n",
       "std        1325.607679        1707.964583   39.314962        0.422953   \n",
       "min        1000.000000           0.000000   17.000000        0.000000   \n",
       "25%        2636.000000           0.000000  100.000000        1.000000   \n",
       "50%        3597.000000        1542.250000  122.000000        1.000000   \n",
       "75%        4197.500000        2341.500000  138.000000        1.000000   \n",
       "max        7660.000000        8106.000000  210.000000        1.000000   \n",
       "\n",
       "       Property_Area  Loan_Status  \n",
       "count     100.000000   100.000000  \n",
       "mean        1.410000     0.640000  \n",
       "std         0.652811     0.482418  \n",
       "min         0.000000     0.000000  \n",
       "25%         1.000000     0.000000  \n",
       "50%         1.500000     1.000000  \n",
       "75%         2.000000     1.000000  \n",
       "max         2.000000     1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_Dataset = X.copy() # making a new copy of the modified dataset\n",
    "final_Dataset = final_Dataset.drop([\"Application_ID\", \"Education\",\"Gender\", \"Married\", \\\n",
    "                        \"Dependents\", \"Self_Employed\", \"Loan_Amount_Term\", \"is_train\"],axis=1) # keeping only\n",
    "                                                                                    # the principal attributes\n",
    "                                                                                    # and the target attribute\n",
    "final_Dataset.describe(include=\"all\") # describing the final dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Saving the modified dataset containing only the principal attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_Dataset.to_csv(r'finalTrain.csv', index = None, header = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training our required models"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Our project goal requires us to train specific 4 classifier models viz.\n",
    "1. KNN classifier\n",
    "2. Naive Bayes Classifier\n",
    "3. Decision Tree Classifier\n",
    "and \n",
    "4. Linear Regression Classifier (As the target attribute is binary, we will be using Logistic Regression Classifier)\n",
    "\n",
    "We will be using the dataset obtained after pre-processing the given train dataset to train our required models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required modules\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, auc, mean_squared_error, accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no. of observations for the training dataframes: 76\n",
      "no. of observations for the test dataframes: 24\n",
      "Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Credit_History',\n",
      "       'Property_Area'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "final_Dataset[\"is_train\"] = np.random.uniform(0,1,len(final_Dataset)) <= 0.75\n",
    "train, test = final_Dataset[final_Dataset[\"is_train\"]==True], final_Dataset[final_Dataset[\"is_train\"]==False]\n",
    "print(\"no. of observations for the training dataframes:\",len(train))\n",
    "print(\"no. of observations for the test dataframes:\",len(test))\n",
    "f = final_Dataset.columns[:-2]\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required modules\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 8 candidates, totalling 16 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:   13.7s finished\n"
     ]
    }
   ],
   "source": [
    "# First Find the value of K :\n",
    "            # standardizer = StandardScaler()\n",
    "            # standardize features\n",
    "            # X_std = standardizer.fit_transform(final_Dataset[f])\n",
    "# fit a k-neearest neighbor classifier\n",
    "# fit a KNN with 5 neighbors\n",
    "y = final_Dataset[\"Loan_Status\"]\n",
    "knn = KNeighborsClassifier(n_neighbors=5, metric=\"euclidean\",\n",
    "                           n_jobs=-1).fit(final_Dataset[f], y)\n",
    "# create search space of possible values of k\n",
    "# create a pipeline\n",
    "            # pipe = Pipeline([(\"standardizer\", standardizer), (\"knn\", knn)])\n",
    "pipe = Pipeline([(\"knn\", knn)])\n",
    "# create space of candidate values\n",
    "search_space = [{\"knn__n_neighbors\":[2,3,4,5,6,7,8,9]}]\n",
    "# search over possible values of k\n",
    "# create grid search GridSearchCV impliments a \"fit\" and \"score\" method.\n",
    "# the parameters of the estimator used to apply these methods are \n",
    "# optimized by cross-validated grid-search over a parameter grid\n",
    "clf = GridSearchCV(pipe, search_space, cv = 2, verbose = 1).fit(final_Dataset[f], y)\n",
    "# view k for best performing model\n",
    "# best neighborhood size (k)\n",
    "k = clf.best_estimator_.get_params()[\"knn__n_neighbors\"]\n",
    "print(\"k =\", k)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We didn't standardize the data. The reason is that with standardized data, the value of k came 6 and with k = 6, the accuracy of KNN classifier becomes very less (0.37).But without standardizing the data the value of k comes 3. And accuracy of KNN with k = 3, is higher than accuracy of KNN with k = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5833333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "# Training the K-NN with K as 3\n",
    "# convert the data into np.arrays\n",
    "# the scikit-learn library requires data to be formatted as numpy array.\n",
    "X = train[f].as_matrix(columns=[\"ApplicantIncome\", \"CoapplicantIncome\", \n",
    "                                \"LoanAmount\", \"Credit_History\",\"Property_Area\"])\n",
    "y = np.array(train[\"Loan_Status\"])\n",
    "# train the learner\n",
    "KNNmodel = neighbors.KNeighborsClassifier(k, weights=\"uniform\") # k neighbors with same\n",
    "                                                          # importance(weightage) to all\n",
    "KNNmodel.fit(X, y)\n",
    "# MODEL has been trained\n",
    "preds = KNNmodel.predict(test[f])\n",
    "acc_knn = KNNmodel.score(test[f], test[\"Loan_Status\"])\n",
    "print(acc_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Predicted Loan_Status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Loan_Status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Loan_Status  0   1\n",
       "Actual Loan_Status          \n",
       "0                      1   8\n",
       "1                      2  13"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create confusion matrix\n",
    "pd.crosstab(test[\"Loan_Status\"], preds, rownames=[\"Actual Loan_Status\"], \n",
    "            colnames=[\"Predicted Loan_Status\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC graph for K-NN\n",
    "y_score = KNNmodel.predict_proba(test[f])[:,1]\n",
    "false_positive_rate, true_positive_rate, threshold = roc_curve(test[\"Loan_Status\"], y_score)\n",
    "plt.title('Receiver Operating Characteristic (ROC)')\n",
    "roc_auc=auc(false_positive_rate,true_positive_rate)\n",
    "plt.plot(false_positive_rate, true_positive_rate,'b',label=\"AUC=%f\"%roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0, 1], ls=\"--\")\n",
    "plt.plot([0, 0], [1, 0] , c=\"0.7\")\n",
    "plt.plot([1, 1] , c=\"0.7\")\n",
    "plt.ylabel('True Positive Rate (TPR=TP/P=TP/(TP+FN))')\n",
    "plt.xlabel('False Positive Rate (FPR=FP/N=FP/(FP+TN))')\n",
    "#plt.show()\n",
    "plt.savefig('roc_knn.png')\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the Reciever Operating Characterisitc(ROC) graph, we find the Area Under Curve(AUC) for our k-NN classifier model is 0.540741\n",
    "\n",
    "AUC value of k-NN = 0.540741"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76923077 0.84615385 0.76923077 0.53846154 0.83333333]\n",
      "[0.625      0.64705882 0.66666667 0.58333333 0.58823529]\n"
     ]
    }
   ],
   "source": [
    "# Cross-Validate Model Using Recall\n",
    "print(cross_val_score(KNNmodel, final_Dataset[f], final_Dataset[\"Loan_Status\"], cv=5, scoring=\"recall\"))\n",
    "# Cross-validate model using precision\n",
    "print(cross_val_score(KNNmodel, final_Dataset[f], final_Dataset[\"Loan_Status\"], cv=5, scoring=\"precision\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of k-NN classifier:  0.5833333333333334\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.33      0.11      0.17         9\n",
      "           1       0.62      0.87      0.72        15\n",
      "\n",
      "   micro avg       0.58      0.58      0.58        24\n",
      "   macro avg       0.48      0.49      0.44        24\n",
      "weighted avg       0.51      0.58      0.51        24\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of k-NN classifier: \", acc_knn)\n",
    "print(classification_report(test[\"Loan_Status\"],preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Naive Bayes"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We will now train a Gaussian Naive Bayes classifier model using our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required modules\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9166666666666666\n",
      "GaussianNB(priors=None, var_smoothing=1e-09)\n"
     ]
    }
   ],
   "source": [
    "Naive_Bayes = GaussianNB()\n",
    "Naive_Bayes.fit(train[f],train[\"Loan_Status\"])\n",
    "predict = Naive_Bayes.predict(test[f])\n",
    "acc_gnb = accuracy_score(test[\"Loan_Status\"], predict)\n",
    "print(acc_gnb)\n",
    "print(Naive_Bayes)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can see that the accuracy of our Gaussian NB classifier is 91.667%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>Predicted Loan_Status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Loan_Status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Loan_Status  0   1\n",
       "Actual Loan_Status          \n",
       "0                      7   2\n",
       "1                      0  15"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create confusion matrix\n",
    "pd.crosstab(test[\"Loan_Status\"], predict, rownames=[\"Actual Loan_Status\"], colnames=[\"Predicted Loan_Status\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC graph for Naive Bayes\n",
    "y_score = Naive_Bayes.predict_proba(test[f])[:,1]\n",
    "false_positive_rate, true_positive_rate, threshold = roc_curve(test[\"Loan_Status\"], y_score)\n",
    "plt.title('Receiver Operating Characteristic (ROC)')\n",
    "roc_auc=auc(false_positive_rate,true_positive_rate)\n",
    "plt.plot(false_positive_rate, true_positive_rate,'b',label=\"AUC=%f\"%roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0, 1], ls=\"--\")\n",
    "plt.plot([0, 0], [1, 0] , c=\".7\")\n",
    "plt.plot([1, 1] , c=\".7\")\n",
    "plt.ylabel('True Positive Rate (TPR=TP/P=TP/(TP+FN))')\n",
    "plt.xlabel('False Positive Rate (FPR=FP/N=FP/(FP+TN))')\n",
    "#plt.show()\n",
    "plt.savefig('roc_gnb.png')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the Reciever Operating Characterisitc(ROC) graph, we find the Area Under Curve(AUC) for our Gaussian NB classifier model is 0.896296\n",
    "\n",
    "AUC value of Naive Bayes = 0.896296"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.92307692 0.92307692 1.         0.84615385 1.        ]\n",
      "[0.92307692 0.85714286 0.8125     0.91666667 0.75      ]\n"
     ]
    }
   ],
   "source": [
    "# Cross-Validate Model Using Recall\n",
    "print(cross_val_score(Naive_Bayes, final_Dataset[f], final_Dataset[\"Loan_Status\"], cv=5, scoring=\"recall\"))\n",
    "# Cross-validate model using precision\n",
    "print(cross_val_score(Naive_Bayes, final_Dataset[f], final_Dataset[\"Loan_Status\"], cv=5, scoring=\"precision\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9166666666666666\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.78      0.88         9\n",
      "           1       0.88      1.00      0.94        15\n",
      "\n",
      "   micro avg       0.92      0.92      0.92        24\n",
      "   macro avg       0.94      0.89      0.91        24\n",
      "weighted avg       0.93      0.92      0.91        24\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classificaton Report for our trained Naive Bayes classifier model\n",
    "print(\"Accuracy: \", Naive_Bayes.score(test[f], test[\"Loan_Status\"]))\n",
    "print(classification_report(test[\"Loan_Status\"],predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required modules\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7916666666666666\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Decision_Tree = DecisionTreeClassifier()\n",
    "Decision_Tree.fit(train[f],train[\"Loan_Status\"])\n",
    "prediction = Decision_Tree.predict(test[f])\n",
    "acc_dtree = accuracy_score(test[\"Loan_Status\"], prediction)\n",
    "print(acc_dtree)\n",
    "Decision_Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to draw the Decision Tree\n",
    "\n",
    "from sklearn import tree\n",
    "with open(\"FinalDTree.txt\", \"w\") as a:\n",
    "    a = tree.export_graphviz(Decision_Tree, out_file=a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Predicted Loan_Status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Loan_Status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Loan_Status  0   1\n",
       "Actual Loan_Status          \n",
       "0                      6   3\n",
       "1                      2  13"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create confusion matrix\n",
    "pd.crosstab(test[\"Loan_Status\"], prediction, rownames=[\"Actual Loan_Status\"], colnames=[\"Predicted Loan_Status\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC graph for Decision Tree\n",
    "y_score = Decision_Tree.predict_proba(test[f])[:,1]\n",
    "false_positive_rate, true_positive_rate, threshold = roc_curve(test[\"Loan_Status\"], y_score)\n",
    "plt.title('Receiver Operating Characteristic (ROC)')\n",
    "roc_auc=auc(false_positive_rate,true_positive_rate)\n",
    "plt.plot(false_positive_rate, true_positive_rate,'b',label=\"AUC=%f\"%roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0, 1], ls=\"--\")\n",
    "plt.plot([0, 0], [1, 0] , c=\".7\")\n",
    "plt.plot([1, 1] , c=\".7\")\n",
    "plt.ylabel('True Positive Rate (TPR=TP/P=TP/(TP+FN))')\n",
    "plt.xlabel('False Positive Rate (FPR=FP/N=FP/(FP+TN))')\n",
    "#plt.show()\n",
    "plt.savefig('roc_dtree.png')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the Reciever Operating Characterisitc(ROC) graph, we find the Area Under Curve(AUC) for our Decision Tree classifier model is 0.766667\n",
    "\n",
    "AUC value of Decision Tree = 0.76667"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.53846154 0.46153846 0.92307692 0.69230769 1.        ]\n",
      "[0.77777778 0.75       0.75       0.81818182 0.71428571]\n"
     ]
    }
   ],
   "source": [
    "# Cross-Validate Model Using Recall\n",
    "print(cross_val_score(Decision_Tree, final_Dataset[f], final_Dataset[\"Loan_Status\"], cv=5, scoring=\"recall\"))\n",
    "# Cross-validate model using precision\n",
    "print(cross_val_score(Decision_Tree, final_Dataset[f], final_Dataset[\"Loan_Status\"], cv=5, scoring=\"precision\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7916666666666666\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.67      0.71         9\n",
      "           1       0.81      0.87      0.84        15\n",
      "\n",
      "   micro avg       0.79      0.79      0.79        24\n",
      "   macro avg       0.78      0.77      0.77        24\n",
      "weighted avg       0.79      0.79      0.79        24\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classificaton Report for our trained Decision Tree classifier model\n",
    "print(\"Accuracy: \", Decision_Tree.score(test[f], test[\"Loan_Status\"]))\n",
    "print(classification_report(test[\"Loan_Status\"],prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required modules\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept for the model is: [-0.62505495]\n",
      "The coefficients for the model is: [[-1.90032183e-04 -6.94385392e-05 -1.41847856e-03  1.50926139e+00\n",
      "   7.27142583e-01]]\n",
      "0.8333333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogisticRegressionModel = LogisticRegression(solver='lbfgs')\n",
    "LogisticRegressionModel.fit(train[f],train[\"Loan_Status\"])\n",
    "print(\"The intercept for the model is:\",LogisticRegressionModel.intercept_)\n",
    "print(\"The coefficients for the model is:\",LogisticRegressionModel.coef_)\n",
    "predictions = LogisticRegressionModel.predict(test[f])\n",
    "acc_lr = accuracy_score(test[\"Loan_Status\"], predictions)\n",
    "print(acc_lr)\n",
    "LogisticRegressionModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>Predicted Loan_Status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Loan_Status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted Loan_Status  0   1\n",
       "Actual Loan_Status          \n",
       "0                      6   3\n",
       "1                      1  14"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create confusion matrix\n",
    "pd.crosstab(test[\"Loan_Status\"], predictions, rownames=[\"Actual Loan_Status\"], colnames=[\"Predicted Loan_Status\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xd4FWX2wPHvoRMINRQJhNB7ESMoKk1FRERFBEQsq7uubXXBAmJZ266uXX9WLIsFsSDNioogqCCgIk1QOgGkd5KQcn5/vJN4CUnmJuTm5ibn8zx5cqfcmTNzy7nzzsx5RVUxxhhjAMqEOwBjjDHFhyUFY4wxWSwpGGOMyWJJwRhjTBZLCsYYY7JYUjDGGJPFkkIJJyKXicgX4Y6jOBGRgyLSNAzrjRcRFZFyRb3uUBCR5SLSqwDPK/B7UkT6isjUgjy3oESkooisFJG6RbnecLGkUIREZL2IJHlfSn+IyHgRqRrKdarqBFXtG8p1BBKR7iLytYgcEJF9IvKRiLQtqvXnEM9sEflr4DhVraqqa0O0vpYi8oGI7PS2f4mIjBKRsqFYX0F5yan58SxDVdup6myf9RyTCI/zPfkf4JGA5auIHPI+U5tF5Mns+1pEBojIAm++XSIyQUQaZpvnBBF5TUS2eu/dlSJyv4hUUdUU4HVgdAFjjiiWFIre+apaFegMnAjcGeZ4CiSnX7sicirwBTANaAA0AX4BvgvFL/Pi9otbRJoBPwCbgA6qWh24BEgAogt5XWHb9nCtW0ROBqqr6vxskzp5n6mewFDg6oDnDAbeAZ4BYoB2QArwrYjU9OapBcwDKgOnqmo0cDZQA2jmLeod4EoRqRiizSs+VNX+iugPWA+cFTD8KPBJwHBF4HFgI7ANeAmoHDD9AmAxsB9YA/TzxlcHXgO2ApuBh4Cy3rSrgG+9xy8Bj2eLaRowynvcAPgQ2AGsA24OmO8+YBLwtrf+v+awfXOBF3IY/xnwpve4F5AIjAV2evvksmD2QcBzRwN/AG8BNYGPvZj3eI8bevP/G0gHkoGDwHPeeAWae4/HA88DnwAHcF/qzQLi6QusAvYBLwDf5LTt3rxvB76eOUyP99Z9pbd9O4G7AqZ3xX057fVey+eACgHTFbgR+B1Y5417BpeE9gM/AmcEzF/W289rvG37EWgEzPGWdcjbL0O9+Qfg3l97ge+Bjtneu6OBJbgv1XIEvJ+92Bd5cWwDnvTGb/TWddD7O5WA96Q3TzvgS2C399yxuey/e4FXs43Lei294feB573HAmwA7sj2nDLAMuABb/ghYClQxufz+zvQM9zfI6H+C3sApekv24eoofdGfCZg+tPAdKAW7pflR8DD3rSu3hfT2d6bOhZo7U2bCrwMVAHqAguAv3vTsj6AQA/vC0S84ZpAEi4ZlPG+NO4FKgBNgbXAOd689wGpwIXevJWzbVsU7gu4dw7b/Rdgq/e4F5AGPIlLAD1xX06tgtgHmc/9r/fcykBt4GJv/dHAB8DUgHXPJtuXOMcmhd3e/i0HTADe9abF4L7kBnnTbvH2QW5J4Q/gL3m8/vHeul/xYu+E+4Jt400/CTjFW1c88Cvwz2xxf+ntm8xEOcLbB+WAW70YKnnTbse9x1rhviA7AbWz7wNvuAuwHeiGSyZX4t6vFQPeu4txSaVywLjM9/M84HLvcVXglGzbXC5gXVfx53syGpcAbwUqecPdctl/HwC35/FatvaWNTJgWIEmOSzrfmCe93g+cH8Qn9/pBPxQKql/YQ+gNP15H6KDuF9tCswEanjTBPflGPgr9VT+/EX4MvBUDsus532xBB5RXArM8h4HfgAF98uthzf8N+Br73E3YGO2Zd8J/M97fB8wJ49ta+htU+scpvUDUr3HvXBf7FUCpr8P3BPEPugFHMH70ssljs7AnoDh2fgnhVcDpvUHVnqPr8j84gjYf5uyLy9geire0Vsu0+O9dTcMGLcAGJbL/P8EpmSLu4/Pe2wPrjkF3BHOBbnMlz0pvAg8mG2eVXi/jL337tU5vJ8zk8Ic3BdtTC7bnFtSuBT4OcjPz5fAdTlsx37vfaPARP5MZKd74455vwDXAb97j3/Pvtxc1j8BuDeYWCP5z84pFL0L1bVZ9sL9konxxtfB/dr9UUT2ishe4HNvPLhfaGtyWF5joDywNeB5L+OOGI6i7p39Lu6DCDAc90bPXE6DzGV4yxmLSzqZNuWxXXuADOCEHKadgGsqyZpXVQ8FDG/AHa347QOAHaqanDkgIlEi8rKIbBCR/bgvpxr5PLH7R8Djw7hfungxZW2zt/8S81jOLnLe/qDW552k/ti7CGE/7qRqTLbnHvUaiMitIvKrd1J7L64pMfM5ub1nctIYuDXb698Itw9yXHc21wAtgZUislBEBgS53vzEuIecz810we3DobgfN1W88ZnvOb/3ZLCvWzSuaa1Es6QQJqr6De5X6uPeqJ24ppx2qlrD+6uu7gQauA9ks2OXxCbckUJMwPOqqWq7XFY9ERgsIo1xH6APA5azLmAZNVQ1WlX7B4adx/YcwjUhXJLD5CG4o6JMNUWkSsBwHLAliH2QUwy34ppHuqlqNVwTGbhf9XnGHIStuCMgt0ARCRzOwVe4pqyCehFYCbTwtmUsf25HpqztEZEzcO38Q4CaqloD18SY+Zzc3jM52QT8O9vrH6WqE3Nad3aq+ruqXor7MfJfYJL3Gvvt//zEuASXeHJav6rq+7j34L3e6FW4JH7Ue1JEyuBep8z35FfARd74vLTBXThRollSCK+ngbNFpLOqZuDamp/KvB5aRGJF5Bxv3teAv4jImSJSxpvWWlW34q74eUJEqnnTmolIz5xWqKo/407KvgrMUNXMXz4LgP0iMlpEKotIWRFp713xEawxuCs0bhaRaBGpKSIP4ZqA7s827/0iUsH7YhsAfBDEPshJNC6R7PWuIvlXtunbcOdHCuIToIOIXOhdcXMjUD+P+f8FdBeRx0Skvhd/cxF5W0RqBLG+aFxTyEERaQ1cH8T8abjXs5yI3AtUC5j+KvCgiLQQp6OI1PamZd8vrwDXiUg3b94qInKeiAR11ZSIjBCROt5rmPmeSvdiyyD31+BjoL6I/FPc/QDRItItl3k/xZ2DyssjwLUiUt87srsNuFtEhnvv6/q4/VINeMp7zpPe8Bvej6XM992TItIxcxh3Lif7lU8ljiWFMFLVHcCbuPZ0cL/6VgPzveaDr3C/glHVBbgTtk/hfg1+gzvkB9f2XQFYgTvEnkTeh8MTgbNwl9llxpIOnI9rk1+H+9X+Kq45Itjt+RY4B3didiuuWehE4HRV/T1g1j+8OLfgmq+uU9WVfvsgF0/jTtruxH1gP882/RnckdEeEXk22G3xtmcn7lfmo7gmhra4K2xScpl/DS4BxgPLRWQf7khsEe48kp/bcE16B3Bf0u/5zD8Dd2XXb7h9nczRTTxP4s7XfIFLNq/h9hW4c0RveE1FQ1R1Ee4c03O412Y1ru0/WP1w23wQt8+HqWqyqh7GXQX2nbeuUwKfpKoHcBdPnI97X/wO9M5pBar6E7Avj6SBqi7FfTZu94bfAy4HRuLeIyu8fXCaqu7y5tkNdMedE/pBRA7gjiL2efsB3Ovyhrp7Fkq0zKtQjCkS4u6AfVtV82qGKZa85oVE3CW0s8IdT2kkIn2BG1T1wiJcZ0Vcs1EPVd1eVOsNl2J1848xxY3XdPUDronqdlx7fYlvQiiuVPUL3JFPUa4zBXdRSKlgzUfG5O1U3NUxO3FNHBeqalJ4QzImdKz5yBhjTBY7UjDGGJMl4s4pxMTEaHx8fLjDMMaYiPLjjz/uVNU6fvNFXFKIj49n0aJF4Q7DGGMiiohsCGY+az4yxhiTxZKCMcaYLEE1H4lIZ+AMXHGsJFwt8pmqui+EsRljjClieR4pePVMfsTVramJu5V+P65Ewmxx3ddF3J2pxhhjcuZ3pFAbd2v3oZwmikgCrnJgXuWEjTHGRIg8jxRU9ZncEoI3fZGqfpnTNBF5XUS2i8iyXKaLiDwrIqvFdW7eJX+hG2OMKWy+5xREpAKuN6rs5xQ+DahsmZPxuIqLb+Yy/VyghffXDVdLPtfqh8YYY0Ivz6QgInfjOqOYg+u/90tcP6otcTXvBbhNVY85GlDVOSISn8fiL8B15q64Msk1ROQEr3+AQjdnzhyOHDlCVFRUKBZvCmjrVthe4utOGnN8FEVFkYya3HBDbv1nFQ6/I4WlqvpQLtMeFZETcN3pFUQsR9d+T/TGHZMURORa4FqAuLi4Aq0sJSWF9PT0Aj3XhM727XDwIFSt6j+vMaVRetlU0iofAhXKHwymr6bjk2dSUNVpPtO3ksOXeJCydzMIuXTdp6rjgHEACQkJBargV6WK6/2xe/fuBXm6CZGxY93/2bPDGoYxxc6+pFQe/vRX3l24ifjaUTxycUdOaVrb/4nHya/56BVy72NVVfXvx7HuRI4+ymiI64nLGGNKtfQM5eIXv2ftjoP8vWdTRp7VkkrlyxbJuv2aj77KYVwscAtQHjiepDAduElE3sWdYN4XqvMJxhgTCfYcOkKNqPKULSPc1rcVDWpUomPD0DcZBfJrPsrqI9br0PpO4EzgcVwfsrkSkYlALyBGRBJxnZqX95b7Eq4T7v64PlAP4/ofNsaYUkdVmbp4M/d/tILR/Vpzadc4+rWvH5ZYgrkktQVwF+7X/BPAP1Q11e95qnqpz3QFbgwyTmOMKZG27E3irilLmbVqByfG1SChcc2wxuN3TmEifyaDUUAaUFlEKgOo6v6QR2iMMSXUtMWbuWvKMtIzlHsHtOXK7vGULZPTNThFx+9I4QzcieYxwGhvXGbEChTs+lBjjDFUr1yezo1q8PCgDjSqVTzuofI7p2DF7owxppCkpWfw2rfrSE3P4KY+LejVqi49W9bB3QdcPPhVSb0+4HHr0IdjjDEl04ot+7nohe95+LOV/PrHAdxpVYpVQgD/5qO/4WoSAbwDWNE6Y4zJh5S0dJ77ejUvzl5DjajyvHBZF85tX7/YJYNM+emjuXhugTHGFGPrdx7mpW/WMLBzA+45ry01q1QId0h58ksKNUTkfFwzUzURGRg4UVWnhywyE1LjxsE774Q7Cli8GDp3DncUxhSuQylpfLliGxeeGEur+tHMHNWLuNrF40SyH7+k8B0wxHv8PXBJwDTF3ZVsItA77xSPL+TOnWH48PDGYExhmvv7Du6cvJTNe5NoH1uN5nWjIyYhgP/VR5cXVSCm6HXubIXojCks+w6n8u9PV/D+okSaxlThvWtPpXnd6HCHlW/5OaeAiPRR1a9DFYwxxkSi9Azl4pe+Z93OQ9zQqxk3n9miyArYFbZ8JQVczSO7AskYY4Ddh45Qo7IrYHf7Oa2IrVGZ9rHVwx3WccnzPgVjjDHHUlU+/DGR3o/P5t2Frq+wc9rVj/iEAMEVxMvsU0GARiIyLnOaql4bwtiMMabYSdxzmLFTljHntx2c1LgmXZvUCndIhSqY5qN3Ax73At7LZT5jjCnRpvycyN1TlqHA/QPbcfkpjSkT5gJ2hc03KajqzMzHInIgcNgYY0qTWlUqclJ8Lf5zUXsa1oycy0zzI78nmtNDEoUxxhRDqekZvDJ3LWnpys1ntqBnyzr0aBFTbEtUFIZ8JQVVPTlUgRhjTHGybPM+Rn+4hOVb9nN+pwaoKiJSohMC+HeyUxFXFK85sBQYr6p2tGCMKbGSU9N5dubvvDxnLTWjKvDSiC70a39CuMMqMn5HCuNxVx3NBS4E2gMjQxyTMcaEzYZdh3ll7loGnRjL3ee1pXpU+XCHVKT8kkJ7Ve0A4F2K+kPoQzLGmKJ1KCWNGcv/YFCXhrSqH83Xt/YqNj2hFTW/pJCa+UBVU0t6W5oxpvT55rcdjJ28lC37kujYsDrN60aX2oQA/kmhk4js9h4LEO0NC6CqWrLu2jDGlBp7Dh3hwU9WMPmnzTSrU4UP/h6ZBewKm19SqIxdhmqMKWEyC9ht2HWYm3o356Y+zSO2gF1h80sK81XVCuAZY0qEXQdTqBlVgbJlhDH9WhNbszLtGkR+vaLC5FcQz04iGGMinqry/qJN9H58NhMXbgSgb7v6lhBy4HekUEdEbs5toqo+W8jxGGNModq0+zBjpyxl7u876Rpfi1Ob1g53SMWaX1IoC8RgRwzGmAg0+adE7p66DAEevLA9l3WNK3EF7AqbX1LYqqr3FkkkxhhTyGKqVqRrk1r8+6IOxNaoHO5wIoJfUrCUaoyJGKnpGbz8zRrSM+CWs1rQo2UderSsE+6wIopfUjjfbwEiEqWqhwspHmOMKZBlm/dx+6Ql/Lp1Pxd0/rOAnckfv6TwpogsBKYBP6lqMoCIxAG9gaG4+kjvhzJIY4zJTXJqOk9/9TuvzF1LrSoVePnykzinXf1whxWx/C5JPRP4DrgFWC0ie707micB8cDfVDXXhCAi/URklYisFpExOUyPE5FZIvKziCwRkf4F3xRjTGm0cfdhXvt2LYO7NOSrkT0tIRwnvyOFMqo6HZie3wWLSFngeeBsIBFYKCLTVXVFwGx3A++r6osi0hb4FJdsjDEmVweSU/l82R9cktCIlvWimXVbrxLbE1pR80sKC0RkPfAZ8LmqJuZj2V2B1aq6FkBE3gUuAAKTggLVvMfVgS35WH5EGjcO3nkn3FHA4sXQuXO4ozAm/2at3M5dU5byx/5kToyrQfO60ZYQClGezUeqehIwGqgEvCQiP4jIYyLSR0T8iozHApsChhO9cYHuA0aISCLuKOEfOS1IRK4VkUUismjHjh0+qy3e3nnHfSGHW+fOMHx4uKMwJni7Dx1h5HuL+cv4hVSpWI5J13e3AnYh4Nsdp6quBp4DnvN6YusJDAAeFZHNqnpBLk/N6bS/Zhu+FNeb2xMicirwloi0V9WMbDGMA8YBJCQkZF9GxOncGWbPDncUxkSO9Axl8Ivfs3H3YW4+swU39m5GxXJWwC4UfJOCiHQAmgHLVfV34AvvL/MqpNwkAo0ChhtybPPQNUA/AFWdJyKVcHdQbw92A4wxJdeOAynUruIK2I3t34bYmpVpc0I1/yeaAsuz+UhExgJTgcuAL0Xk6sDpqroxj6cvBFqISBMRqQAM49gT1htxVzghIm1wzVSR3T5kjDluqsp7CzfS54nZvLPAfc2c1baeJYQi4HekcBnQUVUPiUgdXLv/68EsWFXTROQmYAauhtLrqrpcRB4AFnlXNd0KvCIiI3FNS1epasQ3DxljCm7jrsOMmbyE79fsoluTWpzePCbcIZUqfkkhRVUPAajqDhHxu6/hKKr6KS6RBI67N+DxCuC0/CzTGFNyTfoxkXumLqNsGeHfF7Xn0pOtgF1R80sKTUVksvdYgGYBw6jqoJBFZowpdepVq0j3ZrV56KL2nFDdCtiFg19SuDjb8HOhCsQYU/ocScvgxdlryFBl5NktOaNFHc5oYQXswskvKQxX1WuKJBJjTKnyy6a93DFpCau2HWDQibFWwK6Y8EsKJxZJFMaYUiPpSDpPfrmK175dR93oSrx6RQJnta0X7rCMxy8pRHn3KeSYvlV1SeGHZIwpyTbtOcwb329gWNc4xpzbmmqV/IojmKLklxRicUXtcrs7uUehR2SMKXH2ewXshngF7Gbf3osG1hNaseSXFFarqn3xG2MK7OuV2xg7eRnbDyTTJa4mzetWtYRQjPmWuTDGmILYdTCFBz5ewbTFW2hVL5qXLj+J5nWrhjss48MvKYwtkiiMMSVKeoZyyUvz2LTnMCPPasn1vZpRoVy+7n01YZJnUlDVzzIfi8gTqnpr5v/Qh2aMiTTbDyQTU6UiZcsId53XhoY1o2hV38pbR5L8pO7e3v8+oQjEGBO5MjKUCT9soM/j3zDBK2B3Zpt6lhAikJ1TMMYcl/U7DzFm8hLmr91N92a16Wl3JEc0SwrGmAJ7f9Em7pm6jAply/DIoA4MPbmR3ZUc4SwpGGMKLLZGZXq0rMODF7SnfvVK4Q7HFAJLCsaYoKWkpfPCrDWoKqP6tuK05jGcZv0dlCj5SQrvef/fDUUgxpji7eeNexj94RJ+23aQi7s0tAJ2JVTQSUFV/xv43xhTOhw+ksYTX/zG69+to361Srx+VQJ9WlsBu5LKr4/mk0XkRxHZKyJzRaR1UQVmjCkeNu9J4q35G7isWxxfjOxhCaGE8ztSeAG4G5gDDASeBvqFOihjTHjtS0rls6VbGdY1jhb1ovnm9l7WE1op4ZcUygbc1TxRRG4PdUDGmPD6Yvkf3D11GbsOHSEhvhbN61a1hFCK+CWFGiIyMLdhVZ0emrCMMUVt58EU7pu+nI+XbKV1/WhevTLBCtiVQn5J4TvgklyGFbCkYEwJkJ6hDH7xe7bsTea2vi35e89mlC9rBexKI7+k8KyqLiySSIwxRW7b/mTqVHUF7P51fjsa1qxMi3pWr6g08/sp8HcR+VlE3haRESJiRU2MKQEyMpS35m/gzCe+YcIPGwDo3bquJQTjWzr7rwAi0h44F3eyuRLwNfA5MF9VM0IepTGm0KzdcZAxk5eyYN1uTm8eQ69WdcMdkilGgrp5TVWXAcuAx0SkCnAmcDnwHNAldOEZYwrTews3cu+05VQsV4ZHB3fkkpMa2l3J5ih5JgURqQj8DWgOLAXGq+oh3AlmO8lsTIRpWDOKXq1cAbu61ayAnTmW35HCeECAucCFQHtgZIhjMsYUkpS0dP5v5moAbjvHCtgZf35Job2qdgAQkXHAD6EPyRhTGH7csJs7Ji1hzY5DDEmwAnYmOH5JITXzgaqm2hvKmOLvUEoaj81YxRvz1tOgemXeuLorPVvahYMmOH6XpHYSkd3e3x6gY+ZjEdntt3AR6Sciq0RktYiMyWWeISKyQkSWi8g7BdkIY8yftuxN4p0FG7nilMbMGNnDEoLJF78jhcpAekEWLCJlgeeBs4FEYKGITFfVFQHztADuBE5T1T0iYtfGGVMA+w6n8snSrQzv5grYzb2jN/XsRLIpAL+kMF9VC3rJaVdgtaquBRCRd4ELgBUB8/wNeF5V9wCo6vYCrsuYUuvzZX9wz7Rl7D50hG5Na9GsTlVLCKbA/JqPjuckQiywKWA40RsXqCXQUkS+E5H5IpJjWW4RuVZEFonIoh07dhxHSMaUHNsPJHPDhB+57u0fqVO1ItNuPI1mdayAnTk+fkcKdUTk5twmquqzeTw3p4SiOay/BdALaAjMFZH2qro323rGAeMAEhISsi/DmFInPUMZ8tI8tuxL5vZzWnFtj6ZWwM4UCt/+FIAYCnbEkAg0ChhuCGzJYZ75qpoKrBORVbgkYUX4jMnB1n1J1Iuu5ArYDWxHo5pRVt7aFCq/pLBVVe8t4LIXAi1EpAmwGRgGDM82z1TgUmC8iMTgmpPWFnB9xpRYGRnKm/PW8+iMVYw5tzVXnBpPb6tZZELALykU+JyCqqaJyE3ADNwRx+uqulxEHgAWeR30zAD6isgK3FVOt6vqroKu05iSaPX2g4z5cAmLNuyhR8s69GltycCEjl9S6Hs8C1fVT4FPs427N+CxAqO8P2NMNu8u2Mi905dTuXxZnrikE4O6xNpdySak8jwzpapZl/qIyDeB/40xoRdXO4qz2tTlq1E9udgqmpoiEFTpbE9m7xvVQhGIMQaSU9N5dubvANzRrzXdm8XQvZkVsDNFx65hM6aYWLR+N/2fncsLs9ew+9ARXOuqMUUrP0cKxpgQOJiSxmOfr+TN+RuIrVGZN6/uSg+rV2TCxJKCMWH2x74k3l24iStPjef2c1pRpaJ9LE345OfdZ2e4jCkkew4d4eOlW7n8lMY0r+sK2FlPaKY4yE9SuC3bf2NMPqkqny37g3unLWPv4VS6N6tNszpVLSGYYiPopKCqMwP/i0hDVU0MVWDGlDTb9ydzz7RlzFi+jQ6x1Xnz6m5WwM4UO75JQUROxlU3/VZVd4pIO2A00AdXz8gY4yM9Q7nk5Xn8sS+ZO89tzTWnN6GcFbAzxVCeSUFEHgYuBn4B7haRKcAtwH+B60IfnjGRbcveJOpXcwXsHrigPY1qVqapHR2YYszvSOECoJOqJolILVyV006quir0oRkTudIzC9h9voo7+7sCdtYtpokEfkkhWVWTAFR1t4istIRgTN5Wbz/AHZOW8NPGvfRqVYcz29QLd0jGBM0vKTQVkcneYwHiA4ZR1UEhi8yYCPTODxu5b/pyqlQsy1NDO3FhZytgZyKLX1K4ONvwc6EKxJiSID4mir7t6nHfwHbEVK0Y7nCMybc8k4KqzhSRDkAzYLmq/l40YRkTGZJT03nqq98QhDHnWgE7E/nyvCZORMbieke7DPhSRK4ukqiMiQA/rN3Fuc/M5eVv1nIgOdUK2JkSwa/56DKgo6oeEpE6uA5zXg99WMYUXweSU/nv5yt5e/5G4mpF8c5fu9G9uR0dmJLBLymkqOohcB3uiIjdbWNKvW37U5j0YyJ/Pb0Jo/q2JKqCFbAzJUd+rz5qZlcfmdJo96EjfLJkC5efGk/zulWZe0cf6kTbiWRT8tjVR8bkQVX5eMlW7pu+nP3JqZzWPIamdapaQjAlll9SGK6q1xRJJMYUM9v2J3PXlGV89es2OjaszoTB3axEhSnx/JLCiUUShTHFTHqGMsQrYHdX/zb85bR4K2BnSgW/pBDl3aeQ4y2Zqrqk8EMyJnwS9xzmhOqVKVtGePCC9sTViiI+pkq4wzKmyPglhVjgeXJOCgr0KPSIjAmD9Azlf9+t4/EvVnHnuW24snu89ZNsSiW/pLBaVe2L35Roq/44wB0fLuGXTXs5s3Vd+razAnam9LILrE2p9vb8Ddz/0XKiK5XnmWGdGdipgRWwM6WaX1IYWyRRGFPEVBURoXndqvTvcAL3DmhLbStgZ4xvUvi796vpS1VNC5wgIo2BK4FEVbXSFyYiJB1J58kvV1GmjHDnuW04pWltTmlaO9xhGVNs+CWFG4FbgedFZBuwA6gENAE2Ac+r6oehDdGYwjFvzS7GTF7Chl2HufyUxllHC8aYP/mVzt4MjAJGiUhz4AQgCVilqgeKID5jjtv+5FQe/nQlExdspHHtKN75Wzcrb21MLoI+0aw1v9VSAAAgAElEQVSqq4HVIYzFmJDYvj+FqT9v5toeTRl5VksqVygb7pCMKbZCeoumiPQTkVUislpExuQx32ARURFJCGU8pvTYdTCF8d+tA6B53ap8O7o3Y/u3sYRgjI+QXZIqImVxN76dDSQCC0VkuqquyDZfNHAz8EOoYjGlh6oy/Zct3Dd9OQdT0ujRsg5N61S1K4uMCVLQRwoiUsE7rxCsrrib39aq6hHgXeCCHOZ7EHgUSM7Hso05xpa9SVzzxiJueXcxjWtX4ZObz7ACdsbkU1BJQUTOA5YCX3rDnUVkis/TYnFXKGVK9MYFLvdEoJGqfuyz/mtFZJGILNqxY0cwIZtSJi09g2Hj5jNvzS7uGdCWD6/vTst60eEOy5iIE2zz0QNAN2AWgKouDuKoIbd6SW6i68XtKeAqv5Wr6jhgHEBCQoJ1hGuybNp9mAY1KlOubBn+c1EH4mpFEVc7KtxhGROxgm0+SlXVvdnG+X05JwKNAoYbAlsChqOB9sBsEVkPnAJMt5PNJhhp6RmMm7OGs578hrfmrQfg9BYxlhCMOU7BHin8KiJDgDIi0gS4BZjv85yFQAtv/s3AMGB45kRV3QdkXSwuIrOB21R1UfDhm9Lo1637Gf3hEpYk7uPstvU4t8MJ4Q7JmBIj2COFm4CTgAxgMu6k8C15PcEri3ETMAP4FXhfVZeLyAMiMrDgIZvS7K156zn//75l854knht+IuMuP4l61SqFOyxjSoxgjxTOUdXRwOjMESIyCJcgcqWqnwKfZht3by7z9goyFlMKZZakaFkvmvM7NeCeAW2pVaVCuMMypsQJNinczbEJ4K4cxhlTqA4fSePxGb9Rrqwwtn8bujWtTTcrYGdMyOSZFETkHKAfECsiTwZMqoZrSjImZL5bvZMxk5ewaXcSV3WPtwJ2xhQBvyOF7cAy3DmE5QHjDwC5lq0w5njsS0rlP5/8ynuLNtEkpgrv//1UujapFe6wjCkV/Kqk/gz8LCITVNXuODZFYufBFD5asoXrejbjn2e1oFJ5q1dkTFEJ9pxCrIj8G2iL608BAFVtGZKoTKmz40AKH/2yhatPb0KzOlX5dnQfO5FsTBgEmxTGAw8BjwPnAn/BzimYQqCqTF28mfs/WsHhlHR6t65Lk5gqlhCMCZNg71OIUtUZAKq6RlXvBnqHLixTGmzem8Rfxi9k5Hu/0DSmCp/ecjpNYqqEOyxjSrVgjxRSxF32sUZErsPdoVw3dGGZks4VsJvHroNHuO/8tlx+ajxly9iVRcaEW7BJYSRQFdfvwb+B6sDVoQrKlFwbdx0mtqYrYPfIoI7E1YqiUS2rV2RMcRFU85Gq/qCqB1R1o6perqoDgQ0hjs2UIGnpGbw4ew1nPfUNb85bD8BpzWMsIRhTzPgeKYjIybh+EL5V1Z0i0g5X7qIPrvKpMXlavmUfoz9cwrLN+zmnXT3OswJ2xhRbfnc0PwxcDPwC3O11rHML8F/gutCHZyLdG9+v58GPV1AjqgIvXtbFKpoaU8z5HSlcAHRS1SQRqYXrD6GTqq4KfWgmkmWWpGhdP5oLOsdyz4A21Iiyy0yNKe78kkKyqiYBqOpuEVlpCcHk5VBKGo/NWEX5ssJd57W1AnbGRBi/pNBURDIroQoQHzCMqg4KWWQm4sz5bQd3Tl7Kln1JXHmqFbAzJhL5JYWLsw0/F6pATOTadziVBz9ZwaQfE2laxxWwOzneCtgZE4n8CuLNLKpATOTaeSiFz5Zu5YZezbj5TCtgZ0wkC/bmNWOOsv1AMtMXb+GvZzTNKmBX0+oVGRPxLCmYfFFVPvxpMw9+vIKk1HTObFOPJjFVLCEYU0LkKymISEVVTQlVMKZ427T7MGOnLGXu7ztJaFyTRy7uaAXsjClhgkoKItIVeA1X8yhORDoBf1XVf4QyOFN8pKVncOkr89lz6AgPXtCOy7o1powVsDOmxAn2SOFZYAAwFUBVfxERK51dCqzfeYhGtaIoV7YMjw52Bewa1rR6RcaUVMH2p1BGVbMXwEsv7GBM8ZGansHzs1bT96k5WQXsujeLsYRgTAkX7JHCJq8JSUWkLPAP4LfQhWXCadnmfdwxaQkrtu7nvA4nMKBjg3CHZIwpIsEmhetxTUhxwDbgK2+cKWH+9906HvrkV2pVqcBLI06iX/v64Q7JGFOEgk0Kaao6LKSRmLDKLEnRrkF1Bp0Yy93ntaV6VPlwh2WMKWLBJoWFIrIKeA+YrKoHQhiTKUIHU9J49POVVChbhrsHtKVrk1p0bWIlKowprYLtea0Z8BBwErBURKaKiB05RLjZq7ZzzlNzeGv+BhR3tGCMKd2CvfoIVf1eVW8GugD7gQkhi8qE1J5DRxj1/mKu+t9CKlcoy6TrunPPgLZW0dQYE/TNa1VxHe4MA9oA04DuIYzLhNCew0f4Yvk2bu7TnBv7NKdiOStgZ4xxgj1SWAacAjyqqs1V9VZV/cHvSSLST0RWichqERmTw/RRIrJCRJaIyEwRaZzP+E2Qtu9PZtycNagqTetU5bvRfRjVt5UlBGPMUYI90dxUVTPys2DvfobngbOBRNzJ6umquiJgtp+BBFU9LCLXA48CQ/OzHpM3VeWDRYk8+MkKjqRlcHbb+jSJqWJXFhljcpRnUhCRJ1T1VuBDETnmLKRPz2tdgdWqutZb1ru4JqispKCqswLmnw+MyEfs+bJ1K2zfDmPHhmoNwVm8GDp3Lpp1bdp9mDsnL+Xb1Tvp2qQWjwzqYAXsjDF58jtSeM/7X5Ae12KBTQHDiUC3POa/Bvgspwkici1wLUBcXFwBQnEJ4eDBAj21UHXuDMOHh349mQXs9h5O5aEL2zO8a5wVsDPG+PLreW2B97CNqh6VGETkJiCvntly+gbK8ZpHERkBJAA9c4ljHDAOICEhocDXTVatCrNnF/TZkWHdzkPEeQXsHhvcica1o2hQo3K4wzLGRIhgTzRfncO4a3yekwg0ChhuCGzJPpOInAXcBQy0vhoKLjU9g/+b+TvnPDWHN75fD8CpzWpbQjDG5IvfOYWhuMtQm4jI5IBJ0cBen2UvBFqISBNgs7ecoxpORORE4GWgn6puz2fsxrMkcS93TFrCyj8OcH6nBgzsbAXsjDEF43dOYQGwC/cr//mA8QdwVw7lSlXTvCamGUBZ4HVVXS4iDwCLVHU68BhQFfjAu3Fqo6oOLNCWlFKvf7uOhz5ZQZ3oirxyRQJnt60X7pCMMRHM75zCOmAdripqvqnqp8Cn2cbdG/D4rIIs1/xZwK5jw+oMPbkRY85tQ/XKdpmpMeb4+DUffaOqPUVkD0efJBZAVdUqpxWxA8mpPPLZSiqWK8u957clIb4WCfH2MhhjCodf81Fml5sxoQ7E+Ju1cjtjpyxl2/5k/npG06yjBWOMKSx+zUeZdzE3Arao6hEROR3oCLyNK4xnQmz3oSM88NFypi7eQst6VXnhsu6cGFcz3GEZY0qgYC9JnYrrirMZ8CauKN47IYvKHGVfUiozf93OLWe24ON/nGEJwRgTMsHWPspQ1VQRGQQ8rarPikieVx+Z4/PHvmSmLt7M33s0pUlMFb4d08dOJBtjQi7o7jhF5BLgcuBCb5x9Q4WAqvLuwk3855NfSc3IoF+7+sTHVLGEYIwpEsEmhauBG3Cls9d6N6RNDF1YpdOGXYcY8+FS5q3dxSlNa/HIoI7EWwE7Y0wRCiopqOoyEbkZaC4irXHVT/8d2tBKl7T0DIa/8gP7klL5z0UdGHZyIytgZ4wpcsH2vHYG8BauXIUA9UXkclX9LpTBlQZrdhyksVfA7okhroDdCdWtXpExJjyCvfroKaC/qp6mqt2B84BnQhdWyXckLYOnv/qNfk/P4c15GwA4pWltSwjGmLAK9pxChcAe01T1VxGpEKKYSrzFm/YyetISVm07wAWdG3DhibHhDskYY4Dgk8JPIvIyrgkJ4DJ8CuKZnL327Tr+/ckK6kZX4rUrEzizjRWwM8YUH8EmheuAm4E7cOcU5gD/F6qgSqLMkhSdG1VnWNc4xpzbmmqV7DJTY0zx4psURKQD0AyYoqqPhj6kkmV/cioPf7qSSuXL8K/z23FS41qc1NgK2Bljiqc8TzSLyFhciYvLgC9FJKce2EwuvlqxjbOf/Ib3Fm6kQrkyqBa4J1FjjCkSfkcKlwEdVfWQiNTB9Y3weujDimy7DqZw/0crmP7LFlrXj2bc5Ql0alQj3GEZY4wvv6SQoqqHAFR1h4gEewlrqXYgOY1Zq7Yz8qyWXN+rGRXK2W4zxkQGv6TQNKBvZgGaBfbVrKqDQhZZhNmyN4kpP2/mhl7NiI+pwndj+tiJZGNMxPFLChdnG34uVIFEqowM5Z0FG3nks5WkZyjndTiB+JgqlhCMMRHJr5OdmUUVSCRat/MQYz5cwg/rdnNa89o8fFFH4mpHhTssY4wpsGDvUzDZpKVnMOLVH9ifnMqjF3fkkoSG1jWmMSbiWVLIp9XbDxBfuwrlypbhqaGdaVw7inrVKoU7LGMKTWpqKomJiSQnJ4c7FFMAlSpVomHDhpQvX7Am7HwlBRGpqKopBVpThEtJS+f5WWt4YdZq7uzfhmtOb0LXJnYTmil5EhMTiY6OJj4+3o5+I4yqsmvXLhITE2nSpEmBlhHUtZIi0lVElgK/e8OdRKTUlLn4aeMeBjz7Lc/O/J2BnRowyArYmRIsOTmZ2rVrW0KIQCJC7dq1j+soL9gjhWeBAbi7m1HVX0Skd4HXGkFembOW/3z2KydUq8T//nIyvVvVDXdIxoScJYTIdbyvXbBJoYyqbsi2svTjWnMxl5GhlCkjdGlcg8u6xTG6X2ui7TJTY0wJF+yttptEpCugIlJWRP4J/BbCuMJmX1Iqd0z6hfs/Wg7ASY1r8dCFHSwhGFPEpkyZgoiwcuVKAGbPns2AAQOOmueqq65i0qRJgDtBPmbMGFq0aEH79u3p2rUrn332WVDrSklJYejQoTRv3pxu3bqxfv36HOd76qmnaNeuHe3bt+fSSy/NaqaZOXMmXbp0oXPnzpx++umsXr0agDlz5tClSxfKlSuXFWeg/fv3Exsby0033ZQ1buLEiXTo0IGOHTvSr18/du7cCcAHH3xAu3btKFOmDIsWLQpquwoi2KRwPTAKiAO2Aad440qUGcv/4Ownv+HDnzZTpWI5K2BnTBhNnDiR008/nXfffTeo+e+55x62bt3KsmXLWLZsGR999BEHDhwI6rmvvfYaNWvWZPXq1YwcOZLRo0cfM8/mzZt59tlnWbRoEcuWLSM9PT0rtuuvv54JEyawePFihg8fzkMPPQRAXFwc48ePZ/jw4bnG3LNnz6zhtLQ0brnlFmbNmsWSJUvo2LEjzz3n7hlu3749kydPpkePHkFtU0EF1XykqtuBYSGNJIx2HkzhX9OW88nSrbQ9oRqvX3Uy7WOrhzssY8Lun/+ExYsLd5mdO8PTT+c9z8GDB/nuu++YNWsWAwcO5L777stz/sOHD/PKK6+wbt06KlasCEC9evUYMmRIUDFNmzYtax2DBw/mpptuyuoDJVBaWhpJSUmUL1+ew4cP06BBA8C14+/fvx+Affv2ZY2Pj48HoEyZY39///jjj2zbto1+/fpl/fJXVVSVQ4cOUbt2bfbv30/z5s0BaNOmTVDbcryCSgoi8gpwzM9mVb220CMKg4PJacz9fQe3n9OKa3s0pXxZK2BnTDhNnTqVfv360bJlS2rVqsVPP/2U5/yrV68mLi6OatWq5Th96NChrFq16pjxo0aN4oorrmDz5s00atQIgHLlylG9enV27dpFTExM1ryxsbHcdtttxMXFUblyZfr27Uvfvn0BePXVV+nfvz+VK1emWrVqzJ8/P894MzIyuPXWW3nrrbeYOfPPwhHly5fnxRdfpEOHDlSpUoUWLVrw/PPP57mswhbsieavAh5XAi4CNhV+OEVn894kpvyUyI29mxMfU4Xv7zyTqhXtXj5jAvn9og+ViRMn8s9//hOAYcOGMXHixGPOJ2QK5mqb9957L8/pOTUVZ1/unj17mDZtGuvWraNGjRpccsklvP3224wYMYKnnnqKTz/9lG7duvHYY48xatQoXn311VzX98ILL9C/f/+sRJQpNTWVF198kZ9//pmmTZvyj3/8g4cffpi7777bdxsLS7DNR0ftURF5C/jS73ki0g94BigLvKqqj2SbXhF4EzgJ2AUMVdX1QUVeQBkZyoQfNvDIZyvJUBjQsQHxMVUsIRhTTOzatYuvv/6aZcuWISKkp6cjIlxxxRXs2bPnqHl3795NTEwMzZs3Z+PGjRw4cIDo6Ohjlul3pNCwYUM2bdpEw4YNSUtLY9++fdSqdfTNqV999RVNmjShTp06AAwaNIjvv/+ec845h19++YVu3bplratfv355buO8efOYO3cuL7zwAgcPHuTIkSNUrVqViy92NUibNWsGwJAhQ3jkkUfyWlShK+g3YROgcV4ziEhZ4HngbCARWCgi01V1RcBs1wB7VLW5iAwD/gsMLWBMvjLKpDNs3HwWrN/NGS1i+M9FHWhUywrYGVOcTJo0iSuuuIKXX345a1zPnj3ZvXs3W7Zs4ddff6VNmzZs2LCBX375hc6dOxMVFcU111zDzTffzMsvv0yFChXYunUrM2fOZMSIEb5HCgMHDuSNN97g1FNPZdKkSfTp0+eYI4W4uDjmz5/P4cOHqVy5MjNnziQhIYGaNWuyb98+fvvtN1q2bMmXX37p2/4/YcKErMfjx49n0aJFPPLII2zZsoUVK1awY8cO6tSpE9SyCl3miY28/oA9wG7vby+wFhji85xTgRkBw3cCd2abZwZwqve4HLATkLyWe9JJJ2lBPP/Cd/rkm59oh399ru8v3KgZGRkFWo4xJd2KFSvCuv6ePXvqZ599dtS4Z555Rq+77jr99ttvtVu3btqpUydNSEjQL774ImuelJQUvf3227VZs2barl077dq1q37++edBrTMpKUkHDx6szZo105NPPlnXrFmjqqqbN2/Wc889N2u+e++9V1u1aqXt2rXTESNGaHJysqqqTp48Wdu3b68dO3bUnj17Zj1/wYIFGhsbq1FRUVqrVi1t27btMev+3//+pzfeeGPW8IsvvqitW7fWDh066IABA3Tnzp1Z64iNjdUKFSpo3bp1tW/fvrluT06vIbBIg/i+F/W57FJcumwEbPZGZajfk9zzBgP9VPWv3vDlQDdVvSlgnmXePIne8Bpvnp3ZlnUtcC1AXFzcSRs2bPBb/TFeeGE5ySQzYkQ76loBO2NylflL3ESunF5DEflRVRP8nuvbfKSqKiJTVPWkfMaV09mf7MkkmHlQ1XHAOICEhIQC3Txwww3tCvI0Y4wpVYK99nKBiHTJ57ITcUcYmRoCW3KbR0TKAdVxTVTGGGPCIM+k4H1RA5yOSwyrROQnEflZRPK+cBgWAi1EpImIVMDd/DY92zzTgSu9x4OBr4NpmjLGhJZ9DCPX8b52fs1HC4AuwIX5XbCqponITbiTyWWB11V1uYg8gDvhMR14DXhLRFbjjhBK7F3TxkSKSpUqsWvXLiufHYHU60+hUqWCnzf1SwrirWhNQRauqp8Cn2Ybd2/A42TgkoIs2xgTGg0bNiQxMZEdO3aEOxRTAJk9rxWUX1KoIyKjcpuoqk8WeM3GmGKpfPnyBe61y0Q+v6RQFqhKzlcJGWOMKWH8ksJWVX2gSCIxxhgTdn6XpNoRgjHGlCJ53tEsIrVUtVjdNyAiO4D839LsxOBKaZQmts2lg21z6XA829xYVev4zeRb5qIkEZFFwdzmXZLYNpcOts2lQ1Fss/UmY4wxJoslBWOMMVlKW1IYF+4AwsC2uXSwbS4dQr7NpeqcgjHGmLyVtiMFY4wxebCkYIwxJkuJTAoi0s8r871aRMbkML2iiLznTf9BROKLPsrCFcQ2jxKRFSKyRERmikiefWxHAr9tDphvsIioiET85YvBbLOIDPFe6+Ui8k5Rx1jYgnhvx4nILK+k/xIR6R+OOAuLiLwuItu9nilzmi4i8qy3P5YUoK+bvAXTZ2ck/eHqNa0BmgIVgF+AttnmuQF4yXs8DHgv3HEXwTb3BqK8x9eXhm325osG5gDzgYRwx10Er3ML4GegpjdcN9xxF8E2jwOu9x63BdaHO+7j3OYeuC4LluUyvT/wGa7ixCnAD4W5/pJ4pNAVWK2qa1X1CPAucEG2eS4A3vAeTwLOlMguHO+7zao6S1UPe4PzcT3hRbJgXmeAB4FHgeSiDC5EgtnmvwHPq+oeAFXdXsQxFrZgtlmBat7j6hzbw2NEUdU55N0D5QXAm+rMB2qIyAmFtf6SmBRigU0Bw4neuBznUdU0YB9Qu0iiC41gtjnQNbhfGpHMd5tF5ESgkap+XJSBhVAwr3NLoKWIfCci80WkX5FFFxrBbPN9wAgRScT13/KPogktbPL7ec8XvyqpkSinX/zZr7sNZp5IEvT2iMgIIAHoGdKIQi/PbRaRMsBTwFVFFVARCOZ1LodrQuqFOxqcKyLtVXVviGMLlWC2+VJgvKo+ISKn4npzbK+qGaEPLyxC+v1VEo8UEoFGAcMNOfZwMmserx/q6uR9uFbcBbPNiMhZwF3AQFVNKaLYQsVvm6OB9sBsEVmPa3udHuEnm4N9b09T1VRVXQeswiWJSBXMNl8DvA+gqvOASrjCcSVVUJ/3giqJSWEh0EJEmohIBdyJ5OnZ5pkOXOk9Hgx8rd4ZnAjlu81eU8rLuIQQ6e3M4LPNqrpPVWNUNV5V43HnUQaq6qLwhFsognlvT8VdVICIxOCak9YWaZSFK5ht3gicCSAibXBJoST3JToduMK7CukUYJ+qbi2shZe45iNVTRORm4AZuCsXXlfV5SLyALBIVacDr+EOMVfjjhCGhS/i4xfkNj+G60XvA++c+kZVHRi2oI9TkNtcogS5zTOAviKyAkgHblfVXeGL+vgEuc23Aq+IyEhcM8pVkfwjT0Qm4pr/YrzzJP8CygOo6ku48yb9gdXAYeAvhbr+CN53xhhjCllJbD4yxhhTQJYUjDHGZLGkYIwxJoslBWOMMVksKRhjjMliSaEYE5F0EVkc8Befx7zxuVVVzOc6Z3sVKX/xSiW0KsAyrhORK7zHV4lIg4Bpr4pI20KOc6GIdA7iOf8UkagCrOtpEemRbb2Zr8lgb3zma7VMRD7IXE+28R+JSI0CbGNQ6/KmvSwip4nIeBHZLCIVvfEx3k18IV/38e4zn7ie956zQkSSApeZ1zaLSB0R+TxgOR1EZHyw+6NUCXdFQPvLs1riwXzMG08uVRXzuc7ZeNVEgWuB6YW1vELeN4Fx/gX4MojnrAdi8rmeWsB8v+0JfK2ACcCoHMa/Adx1vPsut3V5w4tx1/OPx93UlVk9NIZ8VA89znUf1z4LGNcLV74iqPe73zYD/wNOCxj+Cogr7PdmpP/ZkUKE8Y4I5orIT95f9xzmaSciC7xfUEtEpIU3fkTA+JdFpKzP6uYAzb3nnimuXv1ScfXeM3+NPSJ/9tPwuDfuPhG5zftFmABM8NZZ2fvVmCAi14vIowExXyUi/1fAOOcRUBBMRF4UkUXi+hO43xt3M9AAmCUis7xxfUVknrcfPxCRqjksezDweQ7j8zI3c7/lFWchyVqXuLt5f1PVdG/a08BIcaVcQiG3dRfmPsuvvLZ5KnBZwPBHRPiNq6FgSaF4qxxweDzFG7cdOFtVuwBDgWdzeN51wDOq2hn3pZzofWiH4n4pdcbd7XpZDs8NdD6wVEQq4X6FDVXVDrg74a8XkVrARUA7Ve0IPBT4ZFWdBCwCLlPVzqqaFDB5EjAoYHgo8F4B4+yH+8BnuktVE4COQE8R6aiqz+Lqw/RW1d7iSkDcDZzl7ctFwKgcln0a8GO2cRMCXpejqut6X0bnAkuzjS+LK8Uw3RuOlqObBgP/ApvX8rOuczn6y3gj8C1webbnhXrdhbLPCijHbfYsAs7IY9hQAstclDBJ3hdjoPLAc+La0NNxtW2ymwfcJSINgcmq+ruInAmcBCwUV+aiMi7B5GSCiCThmlv+AbQC1qnqb970N4Abgedw/RS8KiKfAEGXqFbVHSKyVlztlt+9dXznLTc/cVbBNVkE9j41RESuxb2/T8B1vLIk23NP8cZ/562nAm6/ZXcCx9bRuUyPraFUWUQWe4/n4kqpBI6Px31Rfult/wHA9zxIPtd1DseWPPgPLhF9kjmiCNZ9XPtMRH4AKuLKstQKmGe0qs4IIu5jttmzHXe0mNuwwZJCJBoJbAM64Y70juk8RlXf8T5Y5wEzROSvuHK7b6jqnUGs46gPcPZfdgHrSRORrrhfwMOAm4A++diW94AhwEpgiqqquG/ooOPE9cT1CPA8MEhEmgC3ASer6h7vZGKlHJ4ruPMQl/qsIymX5x8zXw4JPGu8iFTHJc0bgWdFJBr3RZiT4aq6Ij/rEneStoaqHlUtU1VXe1+qQwLmDfW6j2ufqWo3b7m9cHWMrgpiWYHPP2abPZW82HIbNlhSiETVgURVzRCRK3G/ko8iIk2Btar6rPe4I/AFME1EnlLV7V7TT7SqbghinSuBeBFprqqrcYfm33ht8FGq+qmIzMcV6MruAK6MdU4m40p5bwBGe+Nm5idOVU0VkbuBNV7TUwXgELBPROrhmiVmZ4tlJ65q6vOZ2+R9sTUMOBrK9CuurXs2x0FV93nnNaaJyIv5+LUerN7ArFym/ZuCHSkUdN2Fss+O01Hb7GkJLMtj2GDnFCLRC8CV3pdwS9wXYHZDgWXer6XWuK77VuDa0L8QkSW4ZoyguvBT1WRc08AHIrIUyABewn3Bfuwt7xvcUUx244GXvLbkytmWuwdYATRW1QXeuHzH6Z2reAK4TVV/wfVRvBx4HdcklWkc8JmIzFLVHbgOeCZ665mP21fZfYK7Cua4qerPuCObUJzczH4+IXC9ywUSttUAAACVSURBVIGfQrDO3NZdaPusoHLZ5t4cnSiyDxusSqoxvkTkW2CAFuPey0TkJ6CbqqYWh3UXx30mInOAC7xmxYq4HzKnq+uS13gsKRjjQ0S64dq/s5+sNrkobvtMROrgrmib6g23AGJVdXZYAyuGLCkYY4zJYucUjDHGZLGkYIwxJoslBWOMMVksKRhjjMliScEYY0yW/wc5hQJk62mAtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC graph for Logistic Regression\n",
    "y_score = LogisticRegressionModel.predict_proba(test[f])[:,1]\n",
    "false_positive_rate, true_positive_rate, threshold = roc_curve(test[\"Loan_Status\"], y_score)\n",
    "plt.title('Receiver Operating Characteristic (ROC)')\n",
    "roc_auc=auc(false_positive_rate,true_positive_rate)\n",
    "plt.plot(false_positive_rate, true_positive_rate,'b',label=\"AUC=%f\"%roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0, 1], ls=\"--\")\n",
    "plt.plot([0, 0], [1, 0] , c=\".7\")\n",
    "plt.plot([1, 1] , c=\".7\")\n",
    "plt.ylabel('True Positive Rate (TPR=TP/P=TP/(TP+FN))')\n",
    "plt.xlabel('False Positive Rate (FPR=FP/N=FP/(FP+TN))')\n",
    "#plt.show()\n",
    "plt.savefig('roc_lr.png')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the Reciever Operating Characterisitc(ROC) graph, we find the Area Under Curve(AUC) for our Logistic Regression classifier model is 0.881481\n",
    "\n",
    "AUC value of Logistic Regression classifier = 0.881481"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.92307692 0.92307692 1.         0.84615385 1.        ]\n",
      "[0.75       0.75       0.8125     0.91666667 0.75      ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:758: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n",
      "  \"of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# Cross-Validate Model Using Recall\n",
    "print(cross_val_score(LogisticRegressionModel, final_Dataset[f], final_Dataset[\"Loan_Status\"], cv=5, scoring=\"recall\"))\n",
    "# Cross-validate model using precision\n",
    "print(cross_val_score(LogisticRegressionModel, final_Dataset[f], final_Dataset[\"Loan_Status\"], cv=5, scoring=\"precision\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression RMSE: 0.4082\n"
     ]
    }
   ],
   "source": [
    "# RMSE for Logistic Regression\n",
    "lin_mse = mean_squared_error(predictions, test[\"Loan_Status\"])\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "print('Logistic Regression RMSE: %.4f' % lin_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction :  [1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1 0 1 0 1 1]\n",
      "Logistic Regression R squared: 0.8333\n"
     ]
    }
   ],
   "source": [
    "# R-squared for Logistic Regression\n",
    "print (\"Prediction : \",predictions)\n",
    "print('Logistic Regression R squared: %.4f' % LogisticRegressionModel.score(test[f], test[\"Loan_Status\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8333333333333334\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.67      0.75         9\n",
      "           1       0.82      0.93      0.87        15\n",
      "\n",
      "   micro avg       0.83      0.83      0.83        24\n",
      "   macro avg       0.84      0.80      0.81        24\n",
      "weighted avg       0.84      0.83      0.83        24\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classificaton Report for our trained Logistic Regression classifier model\n",
    "print(\"Accuracy: \", LogisticRegressionModel.score(test[f], test[\"Loan_Status\"]))\n",
    "print(classification_report(test[\"Loan_Status\"],predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN: 0.5833333333333334\n",
      "GNB: 0.9166666666666666\n",
      "LR: 0.8333333333333334\n",
      "DTree: 0.7916666666666666\n"
     ]
    }
   ],
   "source": [
    "# printing the accuracy scores of all the 4 models:\n",
    "print(\"KNN:\", acc_knn)\n",
    "print(\"GNB:\", acc_gnb)\n",
    "print(\"LR:\", acc_lr)\n",
    "print(\"DTree:\", acc_dtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comapring accuracies of 4 models using barplot\n",
    "modelss = [\"KNN\", \"Gaussian NB\", \"Decision Tree\", \"Logistic Regression\"]\n",
    "accuracies = [acc_knn, acc_gnb, acc_dtree, acc_lr]\n",
    "barlist = plt.bar(modelss, accuracies, width=0.5, alpha=0.6)\n",
    "plt.ylim(0, 1.0)\n",
    "barlist[1].set_color('r')\n",
    "barlist[2].set_color('g')\n",
    "barlist[3].set_color('y')\n",
    "plt.xlabel('Classification Model')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Comaprison of Accuracy')\n",
    "plt.grid()\n",
    "#plt.show()\n",
    "plt.savefig('accu_models.png')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the graph we can see that Naive Bayes classifier has the highest accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Dataset:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now we will re-train the 4 models using 100% of our train dataset and use the models to predict the target attribute for our given test dataset. We have to pre-process the test dataset in the same manner as we pre-processed our train dataset to remove any miscalculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Application_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>514.000000</td>\n",
       "      <td>502</td>\n",
       "      <td>511</td>\n",
       "      <td>499</td>\n",
       "      <td>514</td>\n",
       "      <td>488</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>497.000000</td>\n",
       "      <td>505.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>405</td>\n",
       "      <td>329</td>\n",
       "      <td>285</td>\n",
       "      <td>403</td>\n",
       "      <td>419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2163.075875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5652.608949</td>\n",
       "      <td>1605.816965</td>\n",
       "      <td>148.742455</td>\n",
       "      <td>342.059406</td>\n",
       "      <td>0.843220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>467.056621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6574.855143</td>\n",
       "      <td>3081.978215</td>\n",
       "      <td>89.057125</td>\n",
       "      <td>65.870517</td>\n",
       "      <td>0.363979</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1345.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1760.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2894.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2150.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3862.000000</td>\n",
       "      <td>1031.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2543.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>2229.750000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2990.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Application_ID Gender Married Dependents Education Self_Employed  \\\n",
       "count       514.000000    502     511        499       514           488   \n",
       "unique             NaN      2       2          4         2             2   \n",
       "top                NaN      M     Yes          0  Graduate            No   \n",
       "freq               NaN    405     329        285       403           419   \n",
       "mean       2163.075875    NaN     NaN        NaN       NaN           NaN   \n",
       "std         467.056621    NaN     NaN        NaN       NaN           NaN   \n",
       "min        1345.000000    NaN     NaN        NaN       NaN           NaN   \n",
       "25%        1760.250000    NaN     NaN        NaN       NaN           NaN   \n",
       "50%        2150.000000    NaN     NaN        NaN       NaN           NaN   \n",
       "75%        2543.750000    NaN     NaN        NaN       NaN           NaN   \n",
       "max        2990.000000    NaN     NaN        NaN       NaN           NaN   \n",
       "\n",
       "        ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count        514.000000         514.000000  497.000000        505.000000   \n",
       "unique              NaN                NaN         NaN               NaN   \n",
       "top                 NaN                NaN         NaN               NaN   \n",
       "freq                NaN                NaN         NaN               NaN   \n",
       "mean        5652.608949        1605.816965  148.742455        342.059406   \n",
       "std         6574.855143        3081.978215   89.057125         65.870517   \n",
       "min          150.000000           0.000000    9.000000         12.000000   \n",
       "25%         2894.250000           0.000000  100.000000        360.000000   \n",
       "50%         3862.000000        1031.000000  128.000000        360.000000   \n",
       "75%         6000.000000        2229.750000  170.000000        360.000000   \n",
       "max        81000.000000       41667.000000  700.000000        480.000000   \n",
       "\n",
       "        Credit_History Property_Area  \n",
       "count       472.000000           514  \n",
       "unique             NaN             3  \n",
       "top                NaN     Semiurban  \n",
       "freq               NaN           192  \n",
       "mean          0.843220           NaN  \n",
       "std           0.363979           NaN  \n",
       "min           0.000000           NaN  \n",
       "25%           1.000000           NaN  \n",
       "50%           1.000000           NaN  \n",
       "75%           1.000000           NaN  \n",
       "max           1.000000           NaN  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# laoding the test dataset\n",
    "Data = pd.read_csv(\"test.csv\")\n",
    "# making a copy of the main Data to manipulate\n",
    "X = Data.copy()\n",
    "# viewing the numeric dataset\n",
    "Data.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4288</td>\n",
       "      <td>3263.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4843</td>\n",
       "      <td>3806.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4652</td>\n",
       "      <td>3583.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3816</td>\n",
       "      <td>754.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3052</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11417</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3800</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2071</td>\n",
       "      <td>754.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2929</td>\n",
       "      <td>2333.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3572</td>\n",
       "      <td>4114.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3167</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2214</td>\n",
       "      <td>1398.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5568</td>\n",
       "      <td>2142.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10408</td>\n",
       "      <td>0.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5667</td>\n",
       "      <td>2667.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2137</td>\n",
       "      <td>8980.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4300</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3865</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10513</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6080</td>\n",
       "      <td>2569.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>2787</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>4283</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>2297</td>\n",
       "      <td>1522.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>2165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>4750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>2726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>3000</td>\n",
       "      <td>3416.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>9357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>3859</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>16120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>3833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>6383</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>9963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>5780</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>416</td>\n",
       "      <td>41667.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2894</td>\n",
       "      <td>2792.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>5703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>3676</td>\n",
       "      <td>4301.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>12000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>2400</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>3400</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>3987</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>3232</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Credit_History  \\\n",
       "0               4288             3263.0       133.0             1.0   \n",
       "1               4843             3806.0       151.0             1.0   \n",
       "2              13650                0.0         NaN             1.0   \n",
       "3               4652             3583.0         NaN             1.0   \n",
       "4               3816              754.0       160.0             1.0   \n",
       "5               3052             1030.0       100.0             1.0   \n",
       "6              11417             1126.0       225.0             1.0   \n",
       "7               7333                0.0       120.0             1.0   \n",
       "8               3800             3600.0       216.0             0.0   \n",
       "9               2071              754.0        94.0             1.0   \n",
       "10              5316                0.0       136.0             1.0   \n",
       "11              2929             2333.0       139.0             1.0   \n",
       "12              3572             4114.0       152.0             0.0   \n",
       "13              7451                0.0         NaN             1.0   \n",
       "14              5050                0.0       118.0             1.0   \n",
       "15             14583                0.0       185.0             1.0   \n",
       "16              3167             2283.0       154.0             1.0   \n",
       "17              2214             1398.0        85.0             NaN   \n",
       "18              5568             2142.0       175.0             1.0   \n",
       "19             10408                0.0       259.0             1.0   \n",
       "20              5667             2667.0       180.0             1.0   \n",
       "21              4166                0.0        44.0             1.0   \n",
       "22              2137             8980.0       137.0             0.0   \n",
       "23              2957                0.0        81.0             1.0   \n",
       "24              4300             2014.0       194.0             1.0   \n",
       "25              3692                0.0        93.0             NaN   \n",
       "26             23803                0.0       370.0             1.0   \n",
       "27              3865             1640.0         NaN             1.0   \n",
       "28             10513             3850.0       160.0             0.0   \n",
       "29              6080             2569.0       182.0             NaN   \n",
       "..               ...                ...         ...             ...   \n",
       "484             2787             1917.0       146.0             0.0   \n",
       "485             4283             3000.0       172.0             1.0   \n",
       "486             2297             1522.0       104.0             1.0   \n",
       "487             2165                0.0        70.0             1.0   \n",
       "488             4750                0.0        94.0             1.0   \n",
       "489             2726                0.0       106.0             0.0   \n",
       "490             3000             3416.0        56.0             1.0   \n",
       "491             6000                0.0       205.0             1.0   \n",
       "492             9357                0.0       292.0             1.0   \n",
       "493             3859             3300.0       142.0             1.0   \n",
       "494            16120                0.0       260.0             1.0   \n",
       "495             3833                0.0       110.0             1.0   \n",
       "496             6383             1000.0       187.0             1.0   \n",
       "497             2987                0.0        88.0             0.0   \n",
       "498             9963                0.0       180.0             1.0   \n",
       "499             5780                0.0       192.0             1.0   \n",
       "500              416            41667.0       350.0             NaN   \n",
       "501             2894             2792.0       155.0             1.0   \n",
       "502             5703                0.0       128.0             1.0   \n",
       "503             3676             4301.0       172.0             1.0   \n",
       "504            12000                0.0       496.0             1.0   \n",
       "505             2400             3800.0         NaN             1.0   \n",
       "506             3400             2500.0       173.0             1.0   \n",
       "507             3987             1411.0       157.0             1.0   \n",
       "508             3232             1950.0       108.0             1.0   \n",
       "509             2900                0.0        71.0             1.0   \n",
       "510             4106                0.0        40.0             1.0   \n",
       "511             8072              240.0       253.0             1.0   \n",
       "512             7583                0.0       187.0             1.0   \n",
       "513             4583                0.0       133.0             0.0   \n",
       "\n",
       "    Property_Area  \n",
       "0           Urban  \n",
       "1       Semiurban  \n",
       "2           Urban  \n",
       "3       Semiurban  \n",
       "4           Urban  \n",
       "5           Urban  \n",
       "6           Urban  \n",
       "7           Rural  \n",
       "8           Urban  \n",
       "9       Semiurban  \n",
       "10          Urban  \n",
       "11      Semiurban  \n",
       "12          Rural  \n",
       "13      Semiurban  \n",
       "14      Semiurban  \n",
       "15          Rural  \n",
       "16      Semiurban  \n",
       "17          Urban  \n",
       "18          Rural  \n",
       "19          Urban  \n",
       "20          Rural  \n",
       "21      Semiurban  \n",
       "22      Semiurban  \n",
       "23      Semiurban  \n",
       "24          Rural  \n",
       "25          Rural  \n",
       "26          Rural  \n",
       "27          Rural  \n",
       "28          Urban  \n",
       "29          Rural  \n",
       "..            ...  \n",
       "484         Rural  \n",
       "485         Rural  \n",
       "486         Urban  \n",
       "487     Semiurban  \n",
       "488     Semiurban  \n",
       "489     Semiurban  \n",
       "490     Semiurban  \n",
       "491     Semiurban  \n",
       "492     Semiurban  \n",
       "493         Rural  \n",
       "494         Urban  \n",
       "495         Rural  \n",
       "496         Rural  \n",
       "497     Semiurban  \n",
       "498         Rural  \n",
       "499         Urban  \n",
       "500         Urban  \n",
       "501         Rural  \n",
       "502         Urban  \n",
       "503         Rural  \n",
       "504     Semiurban  \n",
       "505         Urban  \n",
       "506     Semiurban  \n",
       "507         Rural  \n",
       "508         Rural  \n",
       "509         Rural  \n",
       "510         Rural  \n",
       "511         Urban  \n",
       "512         Urban  \n",
       "513     Semiurban  \n",
       "\n",
       "[514 rows x 5 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping the values which are not the principal attributes\n",
    "for i in X.columns:\n",
    "    if i not in f:\n",
    "        X = X.drop([i], axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>514.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>497.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5652.608949</td>\n",
       "      <td>1605.816965</td>\n",
       "      <td>148.742455</td>\n",
       "      <td>0.843220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6574.855143</td>\n",
       "      <td>3081.978215</td>\n",
       "      <td>89.057125</td>\n",
       "      <td>0.363979</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2894.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3862.000000</td>\n",
       "      <td>1031.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6000.000000</td>\n",
       "      <td>2229.750000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ApplicantIncome  CoapplicantIncome  LoanAmount  Credit_History  \\\n",
       "count        514.000000         514.000000  497.000000      472.000000   \n",
       "unique              NaN                NaN         NaN             NaN   \n",
       "top                 NaN                NaN         NaN             NaN   \n",
       "freq                NaN                NaN         NaN             NaN   \n",
       "mean        5652.608949        1605.816965  148.742455        0.843220   \n",
       "std         6574.855143        3081.978215   89.057125        0.363979   \n",
       "min          150.000000           0.000000    9.000000        0.000000   \n",
       "25%         2894.250000           0.000000  100.000000        1.000000   \n",
       "50%         3862.000000        1031.000000  128.000000        1.000000   \n",
       "75%         6000.000000        2229.750000  170.000000        1.000000   \n",
       "max        81000.000000       41667.000000  700.000000        1.000000   \n",
       "\n",
       "       Property_Area  \n",
       "count            514  \n",
       "unique             3  \n",
       "top        Semiurban  \n",
       "freq             192  \n",
       "mean             NaN  \n",
       "std              NaN  \n",
       "min              NaN  \n",
       "25%              NaN  \n",
       "50%              NaN  \n",
       "75%              NaN  \n",
       "max              NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Credit_History       float64\n",
       "Property_Area          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making the non numeric values numeric\n",
    "X.Property_Area.replace(['Rural','Semiurban', 'Urban'], [0, 1, 2], inplace=True)\n",
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>514.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>514.00000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>514.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5652.608949</td>\n",
       "      <td>1605.816965</td>\n",
       "      <td>148.66537</td>\n",
       "      <td>0.774319</td>\n",
       "      <td>0.964981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6574.855143</td>\n",
       "      <td>3081.978215</td>\n",
       "      <td>87.94022</td>\n",
       "      <td>0.418437</td>\n",
       "      <td>0.791487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2894.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3862.000000</td>\n",
       "      <td>1031.000000</td>\n",
       "      <td>128.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6000.000000</td>\n",
       "      <td>2229.750000</td>\n",
       "      <td>171.50000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Credit_History  \\\n",
       "count       514.000000         514.000000   514.00000      514.000000   \n",
       "mean       5652.608949        1605.816965   148.66537        0.774319   \n",
       "std        6574.855143        3081.978215    87.94022        0.418437   \n",
       "min         150.000000           0.000000     9.00000        0.000000   \n",
       "25%        2894.250000           0.000000   100.00000        1.000000   \n",
       "50%        3862.000000        1031.000000   128.00000        1.000000   \n",
       "75%        6000.000000        2229.750000   171.50000        1.000000   \n",
       "max       81000.000000       41667.000000   700.00000        1.000000   \n",
       "\n",
       "       Property_Area  \n",
       "count     514.000000  \n",
       "mean        0.964981  \n",
       "std         0.791487  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         1.000000  \n",
       "75%         2.000000  \n",
       "max         2.000000  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filling na values in credit history with 0 (as stated in the ppt)\n",
    "X.Credit_History.fillna(0, inplace=True)\n",
    "# filling NaN values with mean +- std in float type columns\n",
    "avg = X.LoanAmount.mean()\n",
    "std = X.LoanAmount.std()\n",
    "count = X.LoanAmount.isnull().sum()\n",
    "random = np.random.randint(avg-std,avg+std,size=count)\n",
    "X['LoanAmount'][np.isnan(X['LoanAmount'])]=random\n",
    "X.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ApplicantIncome MAD :\n",
      " 3860.5\n",
      "CoapplicantIncome MAD :\n",
      " 1030.5\n",
      "LoanAmount MAD :\n",
      " 128.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>514.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>514.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4067.149805</td>\n",
       "      <td>1209.268327</td>\n",
       "      <td>130.367704</td>\n",
       "      <td>0.774319</td>\n",
       "      <td>0.964981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1743.417951</td>\n",
       "      <td>1379.440909</td>\n",
       "      <td>46.348698</td>\n",
       "      <td>0.418437</td>\n",
       "      <td>0.791487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2894.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3860.500000</td>\n",
       "      <td>1030.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4836.500000</td>\n",
       "      <td>2082.000000</td>\n",
       "      <td>157.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9538.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Credit_History  \\\n",
       "count       514.000000         514.000000  514.000000      514.000000   \n",
       "mean       4067.149805        1209.268327  130.367704        0.774319   \n",
       "std        1743.417951        1379.440909   46.348698        0.418437   \n",
       "min         150.000000           0.000000    9.000000        0.000000   \n",
       "25%        2894.250000           0.000000  100.000000        1.000000   \n",
       "50%        3860.500000        1030.500000  128.000000        1.000000   \n",
       "75%        4836.500000        2082.000000  157.750000        1.000000   \n",
       "max        9538.000000        5500.000000  260.000000        1.000000   \n",
       "\n",
       "       Property_Area  \n",
       "count     514.000000  \n",
       "mean        0.964981  \n",
       "std         0.791487  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         1.000000  \n",
       "75%         2.000000  \n",
       "max         2.000000  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# handling outliers with MAD\n",
    "\n",
    "mad=1.4826*np.median(np.abs(X.ApplicantIncome-X.ApplicantIncome.median()))\n",
    "size_outlr_mad=X.ApplicantIncome\n",
    "size_outlr_mad[((X.ApplicantIncome-X.ApplicantIncome.median()).abs()>3*mad)]=X.ApplicantIncome.median()\n",
    "print(\"ApplicantIncome MAD :\\n\",size_outlr_mad.median())\n",
    "\n",
    "mad=1.4826*np.median(np.abs(X.CoapplicantIncome-X.CoapplicantIncome.median()))\n",
    "size_outlr_mad=X.CoapplicantIncome\n",
    "size_outlr_mad[((X.CoapplicantIncome-X.CoapplicantIncome.median()).abs()>3*mad)]=X.CoapplicantIncome.median()\n",
    "print(\"CoapplicantIncome MAD :\\n\",size_outlr_mad.median())\n",
    "\n",
    "mad=1.4826*np.median(np.abs(X.LoanAmount-X.LoanAmount.median()))\n",
    "size_outlr_mad=X.LoanAmount\n",
    "size_outlr_mad[((X.LoanAmount-X.LoanAmount.median()).abs()>3*mad)]=X.LoanAmount.median()\n",
    "print(\"LoanAmount MAD :\\n\",size_outlr_mad.median())\n",
    "X.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "514"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogisticRegressionModel.fit(final_Dataset[f],final_Dataset[\"Loan_Status\"])\n",
    "predictions = LogisticRegressionModel.predict(X[f])\n",
    "#print(\"1:\", predictions.count('1'), \"\\n0:\", predictions.count('0'))\n",
    "len(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "514"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Naive_Bayes.fit(final_Dataset[f],final_Dataset[\"Loan_Status\"])\n",
    "predict = Naive_Bayes.predict(X[f])\n",
    "len(predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "514"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNNmodel.fit(final_Dataset[f],final_Dataset[\"Loan_Status\"])\n",
    "preds = KNNmodel.predict(X[f])\n",
    "len(preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "514"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Decision_Tree.fit(final_Dataset[f],final_Dataset[\"Loan_Status\"])\n",
    "prediction = Decision_Tree.predict(X[f])\n",
    "len(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213\n"
     ]
    }
   ],
   "source": [
    "print(sum(1 for i,j,k,l in zip(predict,prediction,predictions, preds) if i==j==k==l))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Variable Name                      Model Name\n",
    "--------------                  ----------------\n",
    "predictions                     Logistic Regression\n",
    "predict                         Naive Bayes\n",
    "prediction                      Decision Tree\n",
    "preds                           KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 165, 1: 349}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(predictions, return_counts = True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 247, 1: 267}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(predict, return_counts = True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 259, 1: 255}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(prediction, return_counts = True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 142, 1: 372}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(preds, return_counts = True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}