house_credit_default_risk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9ededffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy and pandas for data manipulation\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "\n",
    "# sklearn preprocessing for dealing with categorical variables\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# File system manangement\n",
    "import os\n",
    "\n",
    "# Suppress warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# matplotlib and seaborn for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b7c378c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (307511, 122)\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>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                 0                0                0                0   \n",
       "1  ...                 0                0                0                0   \n",
       "2  ...                 0                0                0                0   \n",
       "3  ...                 0                0                0                0   \n",
       "4  ...                 0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         NaN                        NaN   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         0.0  \n",
       "3                        NaN                         NaN  \n",
       "4                        0.0                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train = pd.read_csv('application_train.csv')\n",
    "print('Training data shape: ', app_train.shape)\n",
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "08a2b7b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows: 0\n"
     ]
    }
   ],
   "source": [
    "# Check for duplicate rows\n",
    "print('Number of duplicate rows:', app_train.duplicated().sum())\n",
    "\n",
    "# Remove duplicate rows\n",
    "app_train = app_train.drop_duplicates()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e2478b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in each column (%):\n",
      "COMMONAREA_MEDI             69.872297\n",
      "COMMONAREA_AVG              69.872297\n",
      "COMMONAREA_MODE             69.872297\n",
      "NONLIVINGAPARTMENTS_MEDI    69.432963\n",
      "NONLIVINGAPARTMENTS_MODE    69.432963\n",
      "                              ...    \n",
      "EXT_SOURCE_2                 0.214626\n",
      "AMT_GOODS_PRICE              0.090403\n",
      "AMT_ANNUITY                  0.003902\n",
      "CNT_FAM_MEMBERS              0.000650\n",
      "DAYS_LAST_PHONE_CHANGE       0.000325\n",
      "Length: 67, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the percentage of missing values in each column\n",
    "missing_values = app_train.isnull().mean() * 100\n",
    "print(\"Missing values in each column (%):\")\n",
    "print(missing_values[missing_values > 0].sort_values(ascending=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "04e10113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME_CONTRACT_TYPE             2\n",
       "CODE_GENDER                    3\n",
       "FLAG_OWN_CAR                   2\n",
       "FLAG_OWN_REALTY                2\n",
       "NAME_TYPE_SUITE                7\n",
       "NAME_INCOME_TYPE               8\n",
       "NAME_EDUCATION_TYPE            5\n",
       "NAME_FAMILY_STATUS             6\n",
       "NAME_HOUSING_TYPE              6\n",
       "OCCUPATION_TYPE               18\n",
       "WEEKDAY_APPR_PROCESS_START     7\n",
       "ORGANIZATION_TYPE             58\n",
       "FONDKAPREMONT_MODE             4\n",
       "HOUSETYPE_MODE                 3\n",
       "WALLSMATERIAL_MODE             7\n",
       "EMERGENCYSTATE_MODE            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.select_dtypes('object').apply(pd.Series.nunique, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2a09b18a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New shape of training data: Index(['OWN_CAR_AGE', 'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'FLOORSMIN_AVG',\n",
      "       'LIVINGAPARTMENTS_AVG', 'NONLIVINGAPARTMENTS_AVG', 'YEARS_BUILD_MODE',\n",
      "       'COMMONAREA_MODE', 'FLOORSMIN_MODE', 'LIVINGAPARTMENTS_MODE',\n",
      "       'NONLIVINGAPARTMENTS_MODE', 'YEARS_BUILD_MEDI', 'COMMONAREA_MEDI',\n",
      "       'FLOORSMIN_MEDI', 'LIVINGAPARTMENTS_MEDI', 'NONLIVINGAPARTMENTS_MEDI',\n",
      "       'FONDKAPREMONT_MODE'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Option to drop columns with a high percentage of missing values\n",
    "threshold = 60  # Threshold for dropping columns\n",
    "columns_to_drop = missing_values[missing_values > threshold].index\n",
    "app_train.drop(columns=columns_to_drop, inplace=True)\n",
    "print('New shape of training data:', columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8d3340d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoding for binary categories\n",
    "for column in app_train.columns:\n",
    "    if app_train[column].dtype == 'object' and len(app_train[column].unique()) == 2:\n",
    "        le = LabelEncoder()\n",
    "        app_train[column] = le.fit_transform(app_train[column])\n",
    "\n",
    "# One-hot encoding for other categorical variables\n",
    "app_train = pd.get_dummies(app_train, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9eab1cef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "92972dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4/cjbI488okWLFulf//qXWrZsqeTkZM2ePVtvvvmmz9/KCAkJUbVq1Xz+Jogk3XjjjYqPj3d7FNYvyrdr107ffvutgoOD1b17d11++eV6/PHHNWDAAK1YscKv34F59913ValSJd1www155nXs2FFRUVGaOXOma9qLL76oZs2auT0WL15cKNsD4OLgGF83+AIAAACAH7hiAQAAAMAawQIAUOgGDRqU5zcesh+DBg0q6fKKVOPGjb1u+6xZs0q6PAAoMtwKBQAodIcOHdKJEyc8zouOjlb16tWLuaLi89///tfr1xbXqFFDUVFRxVwRABQPggUAAAAAa9wKBQAAAMBawL9jkZWVpQMHDigqKqpAvwALAAAAoGwwxigjI0M1a9bM9zeUAg4WBw4cUEJCQqCLAwAAACgjfv75Z9WuXdtnm4CDRfaHz37++WdFR0cH2g0AAACAUurEiRNKSEjw64snAg4W2bc/RUdHEywAAACAcsyfjz7w4W0AAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1kJKugBbWVlZOn78uCQpLCxMERERchynhKsCAAAALi5lPlgcP35ct956q+v50qVLFRERUYIVAQAAABcfboUCAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMBamQ8WxhifzwEAAAAUvTIfLM6ePevzOQAAAICiV+aDBQAAAICSR7AAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArIWUdAGF7dZbby3wMv3799dHH32k06dP69y5c2rcuLG+//57SVJYWJgiIiI0cuRIzZ49W99//72Sk5P12muv+d3/mjVrNHnyZA0dOlRt27YtsmUCVV7XBQAAUBaV1fMlxxhjAlnwxIkTiomJ0fHjxxUdHV3YdfktPT1dvXv3LvL1xMbG6tixY67n7777rurUqZPvcmfOnFG/fv10+PBhVatWTe+9957Cw8MLfZlAldd1AQAAlEWl7XypIOf83Arlp5yhQpIGDRrk13KzZs3SkSNHJElHjhzR7Nmzi2SZQJXXdQEAAJRFZfl8qcwHiy1btpTIek+dOqU5c+b4bLNv3z7Nnj1b2ReFjDGaPXu29u3bV6jLBKq8rgsAAKAsKuvnS34Hi7Nnz+rEiRNuj5KWlZWl5557rsTW/9Zbb+nChQse5xljNHnyZK/TPd2BFsgygSqv6wIAACiLysP5kt/BYvz48YqJiXE9EhISirIuv6xdu7akS9C7777rcfrevXu1bt06ZWZmuk3PzMzUunXrtHfv3kJZJlDldV0AAABlUXk4X/I7WDz++OM6fvy46/Hzzz8XZV1+ufrqq0u6BN19990ep9epU0etWrVScHCw2/Tg4GBdddVVHj/4HcgygSqv6wIAACiLysP5kt/BIiwsTNHR0W6PkhYUFKQnnniixNb/4IMPKiTE8zf2Oo6joUOHep3uOE6hLBOo8rouAACAsqg8nC+V+Q9vN2nSpETWW7FiRfXq1ctnm9q1a6tPnz6ugeA4jvr06aNatWoV6jKBKq/rAgAAKIvK+vlSmQ8WxSU2Ntbt+dSpU/1arm/fvqpataokqVq1aurTp0+RLBOo8rouAACAsqgsny8RLPT7L2/HxsYqLCxMjuMoOTnZNS8sLEyxsbEaOXKka3pycrLf97mFh4dr+PDhqlGjhh555BG/fuAkkGUCVV7XBQAAUBaV5fOlcvfL24sWLVLlypVLrB4AAACgvOCXtwEAAAAUK4IFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwVuaDRVhYmM/nAAAAAIpemQ8WjuP4fA4AAACg6JX5YAEAAACg5BEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgLKekCbMXExGjRokWSpLCwMIWHh5dwRQAAAMDFp8wHi6CgIFWuXLmkywAAAAAuatwKBQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACAtZBAFzTGSJJOnDhRaMUAAAAAKD2yz/Wzz/19CThYZGRkSJISEhIC7QIAAABAGZCRkaGYmBifbRzjT/zwICsrSwcOHFBUVJQcxwmowMJw4sQJJSQk6Oeff1Z0dHSJ1YHCx7Etvzi25RfHtvzi2JZfHNvyqzCOrTFGGRkZqlmzpoKCfH+KIuArFkFBQapdu3agixe66Oho/hjKKY5t+cWxLb84tuUXx7b84tiWX7bHNr8rFdn48DYAAAAAawQLAAAAANbKfLAICwvT6NGjFRYWVtKloJBxbMsvjm35xbEtvzi25RfHtvwq7mMb8Ie3AQAAACBbmb9iAQAAAKDkESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwFqZCBZvvPGG6tWrp/DwcLVo0UL/+te/fLZftWqVWrRoofDwcF166aWaOnVqMVWKgirIsV24cKE6deqkSy65RNHR0WrTpo0+/fTTYqwWBVHQv9tsX3/9tUJCQnTllVcWbYEIWEGP7dmzZ/XXv/5ViYmJCgsL02WXXaZp06YVU7UoiIIe21mzZiklJUUVK1ZUfHy87rnnHh05cqSYqoW/Vq9erZtvvlk1a9aU4zj68MMP812Gc6myoaDHtqjPpUp9sJg3b56GDRumv/71r9q0aZOuueYadevWTXv37vXYfvfu3erevbuuueYabdq0SU888YQefvhhLViwoJgrR34KemxXr16tTp06acmSJdqwYYM6duyom2++WZs2bSrmypGfgh7bbMePH9fdd9+tG264oZgqRUEFcmzvvPNOff7550pNTdX27ds1Z84cNWzYsBirhj8Kemy/+uor3X333brvvvv0ww8/aP78+Vq3bp3uv//+Yq4c+Tl58qRSUlL02muv+dWec6myo6DHtsjPpUwpd9VVV5lBgwa5TWvYsKEZNWqUx/YjR440DRs2dJv2pz/9ybRu3brIakRgCnpsPWnUqJF5+umnC7s0WAr02Pbq1cv87W9/M6NHjzYpKSlFWCECVdBju3TpUhMTE2OOHDlSHOXBQkGP7QsvvGAuvfRSt2lTpkwxtWvXLrIaYU+SWbRokc82nEuVTf4cW08K81yqVF+xOHfunDZs2KDOnTu7Te/cubPWrFnjcZlvvvkmT/suXbpo/fr1On/+fJHVioIJ5NjmlpWVpYyMDFWpUqUoSkSAAj2206dP165duzR69OiiLhEBCuTYLl68WC1bttTEiRNVq1Yt1a9fX48++qhOnz5dHCXDT4Ec27Zt22rfvn1asmSJjDH65Zdf9MEHH+imm24qjpJRhDiXungU9rlUSKH0UkQOHz6szMxM1ahRw216jRo1dPDgQY/LHDx40GP7Cxcu6PDhw4qPjy+yeuG/QI5tbpMmTdLJkyd15513FkWJCFAgx3bnzp0aNWqU/vWvfykkpFS/LF3UAjm2P/30k7766iuFh4dr0aJFOnz4sB566CH9+uuvfM6iFAnk2LZt21azZs1Sr169dObMGV24cEF/+MMf9OqrrxZHyShCnEtdPAr7XKpUX7HI5jiO23NjTJ5p+bX3NB0lr6DHNtucOXM0ZswYzZs3T9WrVy+q8mDB32ObmZmpPn366Omnn1b9+vWLqzxYKMjfbVZWlhzH0axZs3TVVVepe/fueumllzRjxgyuWpRCBTm2W7du1cMPP6ynnnpKGzZs0LJly7R7924NGjSoOEpFEeNcqvwrinOpUv3WYLVq1RQcHJzn3ZJDhw7lSdLZ4uLiPLYPCQlR1apVi6xWFEwgxzbbvHnzdN9992n+/Pm68cYbi7JMBKCgxzYjI0Pr16/Xpk2bNHjwYEm/n4waYxQSEqLly5fr+uuvL5ba4Vsgf7fx8fGqVauWYmJiXNOuuOIKGWO0b98+JSUlFWnN8E8gx3b8+PFq166d/vKXv0iSmjZtqkqVKumaa67RM888w7vaZRjnUuVfUZ1LleorFqGhoWrRooVWrFjhNn3FihVq27atx2XatGmTp/3y5cvVsmVLVahQochqRcEEcmyl39P1wIEDNXv2bO7jLaUKemyjo6O1ZcsWpaWluR6DBg1SgwYNlJaWpquvvrq4Skc+Avm7bdeunQ4cOKDffvvNNW3Hjh0KCgpS7dq1i7Re+C+QY3vq1CkFBbmfRgQHB0v6f+9uo2ziXKp8K9JzqUL5CHgRmjt3rqlQoYJJTU01W7duNcOGDTOVKlUye/bsMcYYM2rUKNO/f39X+59++slUrFjRPPLII2br1q0mNTXVVKhQwXzwwQcltQnwoqDHdvbs2SYkJMS8/vrrJj093fU4duxYSW0CvCjosc2Nb4UqvQp6bDMyMkzt2rXN7bffbn744QezatUqk5SUZO6///6S2gR4UdBjO336dBMSEmLeeOMNs2vXLvPVV1+Zli1bmquuuqqkNgFeZGRkmE2bNplNmzYZSeall14ymzZtMv/973+NMZxLlWUFPbZFfS5V6oOFMca8/vrrJjEx0YSGhprmzZubVatWueYNGDDAXHfddW7tV65caZo1a2ZCQ0NN3bp1zZtvvlnMFcNfBTm21113nZGU5zFgwIDiLxz5KujfbU4Ei9KtoMd227Zt5sYbbzQRERGmdu3aZvjw4ebUqVPFXDX8UdBjO2XKFNOoUSMTERFh4uPjTd++fc2+ffuKuWrk58svv/T5/yfnUmVXQY9tUZ9LOcZwvRIAAACAnVL9GQsAAAAAZQPBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAADKqNWrV+vmm29WzZo15TiOPvzwwwL38emnn6p169aKiorSJZdcottuu027d+8ucD8ECwAAAKCMOnnypFJSUvTaa68FtPxPP/2kW265Rddff73S0tL06aef6vDhw/rjH/9Y4L745W0AAACgHHAcR4sWLVLPnj1d086dO6e//e1vmjVrlo4dO6bk5GQ9//zz6tChgyTpgw8+UO/evXX27FkFBf1+zeGjjz7SLbfcorNnz6pChQp+r58rFgAAAEA5dc899+jrr7/W3Llz9d133+mOO+5Q165dtXPnTklSy5YtFRwcrOnTpyszM1PHjx/XzJkz1blz5wKFCokrFgAAAEC5kPuKxa5du5SUlKR9+/apZs2arnY33nijrrrqKj333HOSfv+cxh133KEjR44oMzNTbdq00ZIlSxQbG1ug9XPFAgAAACiHNm7cKGOM6tevr8jISNdj1apV2rVrlyTp4MGDuv/++zVgwACtW7dOq1atUmhoqG6//XYV9PpDSFFsBAAAAICSlZWVpeDgYG3YsEHBwcFu8yIjIyVJr7/+uqKjozVx4kTXvPfee08JCQlau3atWrdu7ff6CBYAAABAOdSsWTNlZmbq0KFDuuaaazy2OXXqVJ7Qkf08KyurQOvjVigAAACgjPrtt9+UlpamtLQ0SdLu3buVlpamvXv3qn79+urbt6/uvvtuLVy4ULt379a6dev0/PPPa8mSJZKkm266SevWrdPYsWO1c+dObdy4Uffcc48SExPVrFmzAtXCh7cBAACAMmrlypXq2LFjnukDBgzQjBkzdP78eT3zzDN69913tX//flWtWlVt2rTR008/rSZNmkiS5s6dq4kTJ2rHjh2qWLGi2rRpo+eff14NGzYsUC0ECwAAAADWuBUKAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBYByzXEcffjhh4Xeb4cOHTRs2DDX87p16+qVV14p9PV4WldJMcbo//7v/1SlShU5jqO0tLSSLgkAUIoQLACUOQMHDpTjOHIcRxUqVFCNGjXUqVMnTZs2TVlZWW5t09PT1a1bN7/6LUgIWbhwocaNG1fQ0n1auXKlHMfRsWPHinxdgVi2bJlmzJihjz/+WOnp6UpOTvbZvkGDBgoNDdX+/fvzzOvQoYMcx9GECRPyzOvevbscx9GYMWO0Z88e17H29hgzZoxf9S9YsEAdOnRQTEyMIiMj1bRpU40dO1a//vqrJGnGjBlu/daoUUM333yzfvjhB7d+co6/nI+uXbu62tStW9c1PSIiQnXr1tWdd96pL774wq2v7O1LS0vTmDFj8t3WPXv2+LWtAFASCBYAyqSuXbsqPT1de/bs0dKlS9WxY0cNHTpUPXr00IULF1zt4uLiFBYWVmjrPX/+vCSpSpUqioqKKrR+fSnOdfmya9cuxcfHq23btoqLi1NISIjXtl999ZXOnDmjO+64QzNmzPDYJiEhQdOnT3ebduDAAX3xxReKj493tUlPT3c9RowYocaNG7tNe/TRR/Ot/a9//at69eqlVq1aaenSpfr+++81adIkbd68WTNnznS1i46OVnp6ug4cOKBPPvlEJ0+e1E033aRz58659Zc9/nI+5syZ49Zm7NixSk9P1/bt2/Xuu+8qNjZWN954o5599lmPNT766KNu/dWuXdvVR/YjISEh320FgJJCsABQJoWFhSkuLk61atVS8+bN9cQTT+if//ynli5d6nYim/MqxLlz5zR48GDFx8crPDxcdevW1fjx4yX9/g6zJN16661yHMf1fMyYMbryyis1bdo0XXrppQoLC5MxxuPtSRkZGerTp48iIyNVs2ZNvfrqq655Od+Zznbs2DE5jqOVK1dqz5496tixoySpcuXKchxHAwcOlJT3VqijR4/q7rvvVuXKlVWxYkV169ZNO3fudM2fMWOGYmNj9emnn+qKK65QZGSk60TYl1WrVumqq65SWFiY4uPjNWrUKFdIGzhwoIYMGaK9e/e67R9vUlNT1adPH/Xv31/Tpk2TMSZPmx49eujIkSP6+uuv3Wrv3LmzqlevLkkKDg5WXFyc6xEZGamQkJA803z597//reeee06TJk3SCy+8oLZt26pu3brq1KmTFixYoAEDBrjaOo6juLg4xcfHq2XLlnrkkUf03//+V9u3b3frM3v85XxUrlzZrU1UVJTi4uJUp04dXXvttfr73/+uJ598Uk899VSe/iQpMjLSrb/g4GBXHzmnAUBpRbAAUG5cf/31SklJ0cKFCz3OnzJlihYvXqz3339f27dv13vvvec6QV63bp0kafr06UpPT3c9l6Qff/xR77//vhYsWODzcwUvvPCCmjZtqo0bN+rxxx/XI488ohUrVvhVe0JCghYsWCBJ2r59u9LT0zV58mSPbQcOHKj169dr8eLF+uabb2SMUffu3V1XUyTp1KlTevHFFzVz5kytXr1ae/fu9fnO/v79+9W9e3e1atVKmzdv1ptvvqnU1FQ988wzkqTJkydr7Nixql27dp79k1tGRobmz5+vfv36qVOnTjp58qRWrlyZp11oaKj69u3rdtVixowZuvfee33uq4KaNWuWIiMj9dBDD3mcHxsb63H6sWPHNHv2bElShQoVCqWWoUOHyhijf/7zn4XSHwCUJt6vYwNAGdSwYUN99913Huft3btXSUlJat++vRzHUWJiomveJZdcIun3k8y4uDi35c6dO6eZM2e62njTrl07jRo1SpJUv359ff3113r55ZfVqVOnfOsODg5WlSpVJEnVq1f3erK7c+dOLV68WF9//bXatm0r6fcT54SEBH344Ye64447JP1+y9bUqVN12WWXSZIGDx6ssWPHel3/G2+8oYSEBL322mtyHEcNGzbUgQMH9Nhjj+mpp55STEyMoqKiXFcQfJk7d66SkpLUuHFjSdJdd92l1NRU1xWZnO677z61b99ekydP1oYNG3T8+HHddNNNfn9uwh87d+7UpZde6lc4OH78uCIjI2WM0alTpyRJf/jDH9SwYUO3dh9//HGeKyWPPfaYnnzySZ/9V6lSRdWrV+ezEgDKJYIFgHLFGCPHcTzOGzhwoDp16qQGDRqoa9eu6tGjhzp37pxvn4mJifmGCklq06ZNnueF/U1R27ZtU0hIiK6++mrXtKpVq6pBgwbatm2ba1rFihVdoUKS4uPjdejQIZ/9tmnTxm3ftWvXTr/99pv27dunOnXq+F1jamqq+vXr53rer18/XXvttTp27FiewNS0aVMlJSXpgw8+0Jdffqn+/fsX2tWBbL7GRG5RUVHauHGjLly4oFWrVumFF17Q1KlT87Tr2LGj3nzzTbdp2cGwMOsBgLKEYAGgXNm2bZvq1avncV7z5s21e/duLV26VJ999pnuvPNO3Xjjjfrggw989lmpUqWA68k+gQwK+v3O05yfNch565K/PH1WIXt6zpPV3CfnjuN4XdbT8jnXVZCT4K1bt2rt2rVat26dHnvsMdf0zMxMzZkzRw8++GCeZe699169/vrr2rp1q/7973/7vS5/1a9fX1999ZXOnz+fb2gJCgrS5ZdfLun3q18HDx5Ur169tHr1ard2lSpVcrUriCNHjuh///uf1zEKAGUZn7EAUG588cUX2rJli2677TavbaKjo9WrVy+9/fbbmjdvnhYsWOD6utEKFSooMzMz4PV/++23eZ5n30KTfcUj5weoc39eIzQ0VJJ81tCoUSNduHBBa9eudU07cuSIduzYoSuuuCLg2hs1aqQ1a9a4hY81a9YoKipKtWrV8ruf1NRUXXvttdq8ebPS0tJcj5EjRyo1NdXjMn369NGWLVuUnJysRo0aBbwN3vTp00e//fab3njjDY/zc3+9b06PPPKINm/erEWLFhVKLZMnT1ZQUJB69uxZKP0BQGnCFQsAZdLZs2d18OBBZWZm6pdfftGyZcs0fvx49ejRQ3fffbfHZV5++WXFx8fryiuvVFBQkObPn6+4uDjX7Tl169bV559/rnbt2iksLCzPt/zk5+uvv9bEiRPVs2dPrVixQvPnz9cnn3wiSYqIiFDr1q01YcIE1a1bV4cPH9bf/vY3t+UTExPlOI4+/vhjde/eXREREXnu409KStItt9yiBx54QG+99ZaioqI0atQo1apVS7fcckuB6s3poYce0iuvvKIhQ4Zo8ODB2r59u0aPHq3hw4e7rrbk5/z585o5c6bGjh2b5zcu7r//fk2cOFGbN29WSkqK27zKlSsrPT290G+Bynb11Vdr5MiRGjFihPbv369bb71VNWvW1I8//qipU6eqffv2Gjp0qMdlo6Ojdf/992v06NHq2bOn6+pN9vjLKSQkRNWqVXM9z8jI0MGDB3X+/Hnt3r1b7733nv7xj39o/PjxAV3tAIDSjisWAMqkZcuWKT4+XnXr1lXXrl315ZdfasqUKfrnP//p9Ss5IyMj9fzzz6tly5Zq1aqV9uzZoyVLlrhOnCdNmqQVK1YoISFBzZo1K3BNI0aM0IYNG9SsWTONGzdOkyZNUpcuXVzzp02bpvPnz6tly5YaOnSo6xuXstWqVUtPP/20Ro0apRo1amjw4MEe1zN9+nS1aNFCPXr0UJs2bWSM0ZIlS6xOzGvVqqUlS5bo3//+t1JSUjRo0CDdd999ecKPL4sXL9aRI0d066235pmXlJSkJk2aeL1qERsba3XLWX6ef/55zZ49W2vXrlWXLl3UuHFjDR8+XE2bNnX7ullPhg4dqm3btmn+/PmuadnjL+ejffv2bss99dRTio+P1+WXX67+/fvr+PHj+vzzz91uEQOA8sQxvm66BQAAAAA/cMUCAAAAgDWCBQCgTBs0aJAiIyM9PgYNGlTS5QHARYNboQAAZdqhQ4d04sQJj/Oio6NVvXr1Yq4IAC5OBAsAAAAA1rgVCgAAAIA1ggUAAAAAawH/QF5WVpYOHDigqKgo1w8GAQAAACg/jDHKyMhQzZo18/3B1ICDxYEDB5SQkBDo4gAAAADKiJ9//lm1a9f22SbgYBEVFeVaSXR0dKDdAAAAACilTpw4oYSEBNe5vy8BB4vs25+io6MJFgAAAEA55s9HH/jwNgAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrISVdQHlmjNGZM2dKuox8GWN09uxZSVJYWJgcxynhigpHeHh4udkWAACA0o5gUYTOnDmjbt26lXQZF62lS5cqIiKipMsAAAC4KHArFAAAAABrXLEoJr9d2VsmqJTu7szzito8V5KUkXKXFFyhhAsKnJN1QZFpc0q6DAAAgItOKT3TLX9MUEjZOGEPrlA26vTClHQBAAAAFyluhQIAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWAsp6QJsGWN05swZSVJ4eLgcxynhigCUJbyGAABQOMr8FYszZ86oW7du6tatm+vkAAD8xWsIAACFo8wHCwAAAAAlj2ABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWAsp6QIAoLTo1q1bSZeAfAQFBSkrK6tY1xkWFqazZ8+6nleqVEknT56UJEVFRSkjI8Prso7jKCYmRiNHjlTbtm0lSR06dHDNX7lypSRpzZo1mjx5soYOHepql5qaqlmzZqlv376SpFmzZkmSsrKyFBISoqpVq7q1z8/gwYP1/fffu56HhoZq+fLlrueeaihuY8aMce2TatWq6YMPPshTmySvdfraBn/78KZz5846d+5cnv1W2LLr7Ny5s5YvX+5XjdnHNjk5Wa+99lqR1RaInOP4vvvuK+ly/FIYfwsF7WPNmjWaOHGiJGnkyJEaN26cTp8+rYiICC1dujSgGkoCVywAXNSyT9pQNhR3qJDkFiokuUKFJJ+hQpKMMTp27JgmTZqkM2fOuIUK6feQcebMGb300kv65Zdf9NJLL+nMmTM6duyYZs2apaysLM2cOVPvvfeesrKyXNt/4cIFt/b52bt3r1uokKRz585p9erVkuSxhuL2yy+/uEKFJB0+fFjff/+9W22TJk3SpEmTPNbpaxv87cOb1atX69y5c5Lc91thy1nnrFmz/Kox57H9/vvvtXfv3iKpLRA5x/GsWbN07Nixki4pX4Xxt1DQPs6cOaNJkybp2LFjOnbsmJ577jmdPn1aknT69Gl9++23AW1LSSBYALio/frrryVdAi4CR44c0ezZsz3OmzVrlo4cOeLW7sknn3QLUcaYAveb06BBgzxOf+qpp7zWUNwGDx6cZ9qQIUPy1OatTl/b4G8f3mTvJ2/PC0vOOrOPf3415j623o51Scg5jrOysopsvxWmwvhbKGgfOdtL0m+//eY2f9SoUQWuoaSU+Vuhcr7YlsQ7LL641ePlPwUUslI8HlD6cOsTitO7777rcfrMmTNd/zbGuK5O+MMYo9mzZ6tz586qXbu2xzZz5szRqVOnvPbxpz/9ST/++KPr/1N/+ixsy5Yt0//+9788040xbvsn97zsOiVp9uzZHrch9zxvfXjb1ocfftjr9ClTpuS/cX7at2+fxzp91ejp2J46dUpz5sxR7969C622QKxfv15btmxxm/bdd99p/fr1atmyZQlV5VvuYxDI30JB+8hun5+RI0e6bpUqzRzj7W2QXM6ePet2OfjEiRNKSEjQ8ePHFR0dXWQF5ufo0aO69dZbS2z9/spIuUsKrVjSZXiWeV5RG39/4c5o3l8KrlDCBVk4d0pRm+eWdBUAUKxatWqliRMnynEct+kXLlzQjTfeWOD+goOD1bx5c499FrbMzEzdcMMNAS2bXacxRps2bVJmZqbbvGbNmslxHG3cuNFtnqc+PG3r6dOnfb4BsXTpUkVERARUe07GGI0cOVIbNmzwGCo91Zjfsf3ss88UElIy7x9nZWWpZ8+eOnHiRJ550dHR+vDDDxUUVLpumsk+BrnHSkH+FgraR3b7devW+VXjsmXLFB4eXsAts3fixAnFxMT4dc7v91EdP368YmJiXI+EhATrQgEAgL1169Z5vLfe21WS/GRmZnrts7B9/PHHAS+bXef69evzBIfMzEytX79e69at8xoqcvbhaVu9Xa3wd76/9u7dq3Xr1nm9UuWpxvyObaDHvjCsXbvWY6iQfj9JXbt2bTFXlL/sY+BpHPn7t1DQPrLb++svf/mL321Lit9R9vHHH9fw4cNdz7OvWJS0sLAw178XLVpUIknOmzNnzvy/qylBZf6us7Ihx34ubeMBpcvmzZvL1H2rgC9XXXWV6tSpk2f63XffHdAJZnBwsFq0aOGxz8LWo0cPvfzyywEtm11nVlaWxysWzZs3l6R8r1h429YpU6b4vGJRWLdC1alTR61atfJ5xSJ3jfkd27vvvrtQagvE1VdfrejoaI/hIiYmRldffXUJVOVb9jHwdLXB37+FgvaR3d7fcPHCCy/4uTUlx++z3bCwMLeT+NIi5yWl8PDwQrkkWSSK+FIy/n9lZTygxLVu3bqkSwA8KuhX6oaEhGjo0KEeb9MICQnRn/70J7311ltel7/iiiu0Y8cOtxMhx3G89lnYgoODNWrUKE2YMKHAy2bXaYzRgAED8swbNmyYx3me+vC0rREREWratKm+++67PPOuvPLKQvs/JrsGb3V6qtHXsX3wwQdL7DYo6fcx/NRTT+nRRx/NM2/06NGl7jYoyfsxKMjfQkH7yNne11U16ff/s8rCm6Wl78gCQDHJ+dWWQFHz9g5y//79XSccjuOoX79+atKkiV99Oo6jPn36qFatWl7b9O7dWxUrev+M35tvvqk+ffq41ZBfn4Wta9euuuSSS/JMdxzHbf/knpddZ+3atb1uQ+553vrwxttViVdeecXPrfOPtzp91ejp2FasWFG9evUq1NoC0bJlyzzjuGnTpq6rSKWRr3FUVH1kt89PIMG7JBAsAFzUqlSpUtIl4CJQrVo1rycPffv2VdWqVd3ajRs3zu1dXW/vlvrqN6epU6d6nD527FivNRQ3Tz/s9uqrr+apzVudvrbB3z68yd5P3p4Xlpx1Zh///GrMfWy9HeuSkHMcBwUFFdl+K0yF8bdQ0D5ytpekyMhIt/llJVRIBAsAF7nsXzNG2VASt1Dkvg24UqVKrn9HRUX5XNZxHMXGxmr48OEKDw/Pc5Vs5cqVCg8P1/Dhw1WjRg098sgjCg8PV2xsrPr27augoCD1799f/fr1U1BQkGv7Q0JC3Nrnp06dOkpOTnabFhoaqmuvvVaSPNZQ3GrUqOH2A4LVqlVTcnKyW23Dhw/XiBEjPNbpaxv87cOba6+9VqGhoZLc91thy1ln3759/aox57FNTk4uls/F+CvnOO7bt69iY2NLuqR8FcbfQkH7CA8P14gRIxQbG6vY2Fg98cQTrtvsIiIiytStu35/3WxuBfnqqaKU86vgCutr3wpLztpK9de4lqevm82xLaVtPKB0Ks2vIQAAlLQi+bpZAAAAAPCGYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKyFlHQBtsLDw7V06VLXvwGgIHgNAQCgcJT5YOE4jiIiIkq6DABlFK8hAAAUDm6FAgAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYCynpAi4WTtYFmZIuwpvM857/XQY5WRdKugQAAICLEsGimESmzSnpEvwStXluSZcAAACAMohboQAAAABY44pFEQoPD9fSpUtLuox8GWN09uxZSVJYWJgcxynhigpHeHh4SZcAAABw0SBYFCHHcRQREVHSZfilYsWKJV0CAAAAyjBuhQIAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwFpIoAsaYyRJJ06cKLRiAAAAAJQe2ef62ef+vgQcLDIyMiRJCQkJgXYBAAAAoAzIyMhQTEyMzzaO8Sd+eJCVlaUDBw4oKipKjuMEVKC/Tpw4oYSEBP3888+Kjo4u0nVdbNi3RYd9WzTYr0WHfVt02LdFh31bdNi3Racs7VtjjDIyMlSzZk0FBfn+FEXAVyyCgoJUu3btQBcPSHR0dKnf+WUV+7bosG+LBvu16LBviw77tuiwb4sO+7bolJV9m9+Vimx8eBsAAACANYIFAAAAAGtlIliEhYVp9OjRCgsLK+lSyh32bdFh3xYN9mvRYd8WHfZt0WHfFh32bdEpr/s24A9vAwAAAEC2MnHFAgAAAEDpRrAAAAAAYI1gAQAAAMAawQIAAACAtVITLN544w3Vq1dP4eHhatGihf71r3/5bL9q1Sq1aNFC4eHhuvTSSzV16tRiqrRsKch+XblypRzHyfP4z3/+U4wVlw2rV6/WzTffrJo1a8pxHH344Yf5LsOY9U9B9y3j1j/jx49Xq1atFBUVperVq6tnz57avn17vssxbvMXyL5l3PrnzTffVNOmTV0/ItamTRstXbrU5zKMWf8UdN8yZgMzfvx4OY6jYcOG+WxXXsZtqQgW8+bN07Bhw/TXv/5VmzZt0jXXXKNu3bpp7969Htvv3r1b3bt31zXXXKNNmzbpiSee0MMPP6wFCxYUc+WlW0H3a7bt27crPT3d9UhKSiqmisuOkydPKiUlRa+99ppf7Rmz/ivovs3GuPVt1apV+vOf/6xvv/1WK1as0IULF9S5c2edPHnS6zKMW/8Esm+zMW59q127tiZMmKD169dr/fr1uv7663XLLbfohx9+8NieMeu/gu7bbIxZ/61bt05///vf1bRpU5/tytW4NaXAVVddZQYNGuQ2rWHDhmbUqFEe248cOdI0bNjQbdqf/vQn07p16yKrsSwq6H798ssvjSRz9OjRYqiu/JBkFi1a5LMNYzYw/uxbxm1gDh06ZCSZVatWeW3DuA2MP/uWcRu4ypUrm3/84x8e5zFm7fjat4zZgsnIyDBJSUlmxYoV5rrrrjNDhw712rY8jdsSv2Jx7tw5bdiwQZ07d3ab3rlzZ61Zs8bjMt98802e9l26dNH69et1/vz5Iqu1LAlkv2Zr1qyZ4uPjdcMNN+jLL78syjIvGozZose4LZjjx49LkqpUqeK1DeM2MP7s22yMW/9lZmZq7ty5OnnypNq0aeOxDWM2MP7s22yMWf/8+c9/1k033aQbb7wx37bladyWeLA4fPiwMjMzVaNGDbfpNWrU0MGDBz0uc/DgQY/tL1y4oMOHDxdZrWVJIPs1Pj5ef//737VgwQItXLhQDRo00A033KDVq1cXR8nlGmO26DBuC84Yo+HDh6t9+/ZKTk722o5xW3D+7lvGrf+2bNmiyMhIhYWFadCgQVq0aJEaNWrksS1jtmAKsm8Zs/6bO3euNm7cqPHjx/vVvjyN25CSLiCb4zhuz40xeabl197T9ItdQfZrgwYN1KBBA9fzNm3a6Oeff9aLL76oa6+9tkjrvBgwZosG47bgBg8erO+++05fffVVvm0ZtwXj775l3PqvQYMGSktL07Fjx7RgwQINGDBAq1at8noCzJj1X0H2LWPWPz///LOGDh2q5cuXKzw83O/lysu4LfErFtWqVVNwcHCed9EPHTqUJ71li4uL89g+JCREVatWLbJay5JA9qsnrVu31s6dOwu7vIsOY7Z4MW69GzJkiBYvXqwvv/xStWvX9tmWcVswBdm3njBuPQsNDdXll1+uli1bavz48UpJSdHkyZM9tmXMFkxB9q0njNm8NmzYoEOHDqlFixYKCQlRSEiIVq1apSlTpigkJESZmZl5lilP47bEg0VoaKhatGihFStWuE1fsWKF2rZt63GZNm3a5Gm/fPlytWzZUhUqVCiyWsuSQParJ5s2bVJ8fHxhl3fRYcwWL8ZtXsYYDR48WAsXLtQXX3yhevXq5bsM49Y/gexbTxi3/jHG6OzZsx7nMWbt+Nq3njBm87rhhhu0ZcsWpaWluR4tW7ZU3759lZaWpuDg4DzLlKtxWyIfGc9l7ty5pkKFCiY1NdVs3brVDBs2zFSqVMns2bPHGGPMqFGjTP/+/V3tf/rpJ1OxYkXzyCOPmK1bt5rU1FRToUIF88EHH5TUJpRKBd2vL7/8slm0aJHZsWOH+f77782oUaOMJLNgwYKS2oRSKyMjw2zatMls2rTJSDIvvfSS2bRpk/nvf/9rjGHM2ijovmXc+ufBBx80MTExZuXKlSY9Pd31OHXqlKsN4zYwgexbxq1/Hn/8cbN69Wqze/du891335knnnjCBAUFmeXLlxtjGLM2CrpvGbOBy/2tUOV53JaKYGGMMa+//rpJTEw0oaGhpnnz5m5f0zdgwABz3XXXubVfuXKladasmQkNDTV169Y1b775ZjFXXDYUZL8+//zz5rLLLjPh4eGmcuXKpn379uaTTz4pgapLv+yv3cv9GDBggDGGMWujoPuWcesfT/tUkpk+fbqrDeM2MIHsW8atf+69917X/2GXXHKJueGGG1wnvsYwZm0UdN8yZgOXO1iU53HrGPP/fzoEAAAAAAJU4p+xAAAAAFD2ESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAoIxavXq1br75ZtWsWVOO4+jDDz8scB/GGL344ouqX7++wsLClJCQoOeee67A/YQUeAkAAAAApcLJkyeVkpKie+65R7fddltAfQwdOlTLly/Xiy++qCZNmuj48eM6fPhwgfvhl7cBAACAcsBxHC1atEg9e/Z0TTt37pz+9re/adasWTp27JiSk5P1/PPPq0OHDpKkbdu2qWnTpvr+++/VoEEDq/VzKxQAAABQTt1zzz36+uuvNXfuXH333Xe644471LVrV+3cuVOS9NFHH+nSSy/Vxx9/rHr16qlu3bq6//779euvvxZ4XQQLAAAAoBzatWuX5syZo/nz5+uaa67RZZddpkcffVTt27fX9OnTJUk//fST/vvf/2r+/Pl69913NWPGDG3YsEG33357gdfHZywAAACAcmjjxo0yxqh+/fpu08+ePauqVatKkrKysnT27Fm9++67rnapqalq0aKFtm/fXqDbowgWAAAAQDmUlZWl4OBgbdiwQcHBwW7zIiMjJUnx8fEKCQlxCx9XXHGFJGnv3r0ECwAAAOBi16xZM2VmZurQoUO65pprPLZp166dLly4oF27dumyyy6TJO3YsUOSlJiYWKD18a1QAAAAQBn122+/6ccff5T0e5B46aWX1LFjR1WpUkV16tRRv3799PXXX2vSpElq1qyZDh8+rC+++EJNmjRR9+7dlZWVpVatWikyMlKvvPKKsrKy9Oc//1nR0dFavnx5gWohWAAAAABl1MqVK9WxY8c80wcMGKAZM2bo/PnzeuaZZ/Tuu+9q//79qlq1qtq0aaOnn35aTZo0kSQdOHBAQ4YM0fLly1WpUiV169ZNkyZNUpUqVQpUC8ECAAAAgDW+bhYAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwDlhuM4+vDDDwu93w4dOmjYsGGu53Xr1tUrr7xS6OvxtK6SYozR//3f/6lKlSpyHEdpaWklXRIAoJQjWAAo1QYOHCjHceQ4jipUqKAaNWqoU6dOmjZtmrKystzapqenq1u3bn71W5AQsnDhQo0bN66gpfu0cuVKOY6jY8eOFfm6ArFs2TLNmDFDH3/8sdLT05WcnOyzfYMGDRQaGqr9+/fnmdehQwc5jqMJEybkmde9e3c5jqMxY8Zoz549rmPt7TFmzBi/t6Fz584KDg7Wt99+m2de9rjKXdOHH34ox3Fcz7OPU3JysjIzM93axsbGasaMGa7n3sbUsGHD1KFDB7d19+zZ07WMr0efPn1UsWJFzZ49263PrKwstW3bVrfeequfewMAih7BAkCp17VrV6Wnp2vPnj1aunSpOnbsqKFDh6pHjx66cOGCq11cXJzCwsIKbb3nz5+XJFWpUkVRUVGF1q8vxbkuX3bt2qX4+Hi1bdtWcXFxCgkJ8dr2q6++0pkzZ3THHXe4nWjnlJCQoOnTp7tNO3DggL744gvFx8e72qSnp7seI0aMUOPGjd2mPfroo37Vv3fvXn3zzTcaPHiwUlNTPbYJDw/X888/r6NHj+bb365du/Tuu+/6te6CyLltr7zyiqKjo92mvfnmm5owYYKGDBmi9PR013KTJk3Sjz/+qLfeeqvQawKAQBEsAJR6YWFhiouLU61atdS8eXM98cQT+uc//6mlS5d6fcf43LlzGjx4sOLj4xUeHq66detq/Pjxkn6/lUmSbr31VjmO43o+ZswYXXnllZo2bZouvfRShYWFyRjj8fakjIwM9enTR5GRkapZs6ZeffVV17zsd95z3j507NgxOY6jlStXas+ePerYsaMkqXLlynIcRwMHDpSU91aoo0eP6u6771blypVVsWJFdevWTTt37nTNnzFjhmJjY/Xpp5/qiiuuUGRkpCuI+bJq1SpdddVVCgsLU3x8vEaNGuUKaQMHDtSQIUO0d+9et/3jTWpqqvr06aP+/ftr2rRpMsbkadOjRw8dOXJEX3/9tVvtnTt3VvXq1SVJwcHBiouLcz0iIyMVEhKSZ5o/pk+frh49eujBBx/UvHnzdPLkyTxtbrzxRsXFxbnGhS9DhgzR6NGjdebMGb/W76+c2xYTEyPHcfJMGzJkiK688ko98MADkqT//Oc/euqpp/T3v//dte8AoDQgWAAok66//nqlpKRo4cKFHudPmTJFixcv1vvvv6/t27frvffec50gr1u3TtLvJ5/p6emu55L0448/6v3339eCBQt8fq7ghRdeUNOmTbVx40Y9/vjjeuSRR7RixQq/ak9ISNCCBQskSdu3b1d6eromT57sse3AgQO1fv16LV68WN98842MMerevbvraooknTp1Si+++KJmzpyp1atXa+/evT7f2d+/f7+6d++uVq1aafPmzXrzzTeVmpqqZ555RpI0efJkjR07VrVr186zf3LLyMjQ/Pnz1a9fP3Xq1EknT57UypUr87QLDQ1V37593a5azJgxQ/fee6/PfRUIY4ymT5+ufv36qWHDhqpfv77ef//9PO2Cg4P13HPP6dVXX9W+fft89jls2DBduHBBr732WqHXmx/HcTR9+nT961//0ttvv62BAweqV69ertupAKC0IFgAKLMaNmyoPXv2eJy3d+9eJSUlqX379kpMTFT79u3Vu3dvSdIll1wi6fd75OPi4lzPpd+vdMycOVPNmjVT06ZN3e63z6ldu3YaNWqU6tevryFDhuj222/Xyy+/7FfdwcHBqlKliiSpevXqrnemc9u5c6cWL16sf/zjH7rmmmuUkpKiWbNmaf/+/W738p8/f15Tp05Vy5Yt1bx5cw0ePFiff/651/W/8cYbSkhI0GuvvaaGDRuqZ8+eevrppzVp0iRlZWUpJiZGUVFRrisIOfdPbnPnzlVSUpIaN26s4OBg3XXXXV5vPbrvvvv0/vvv6+TJk1q9erWOHz+um266ya99VhCfffaZTp06pS5dukiS+vXr57WmW2+9VVdeeaVGjx7ts8+KFStq9OjRGj9+vI4fP17oNeenTp06euWVVzRo0CAdOHDAaxAFgJJEsABQZhljvJ74Dxw4UGlpaWrQoIEefvhhLV++3K8+ExMTfZ5IZ2vTpk2e59u2bfNrHf7atm2bQkJCdPXVV7umVa1aVQ0aNHBbV8WKFXXZZZe5nsfHx+vQoUM++23Tpo3bvmvXrp1+++23fN+5zy01NVX9+vVzPe/Xr58WLlyY50PpktS0aVMlJSXpgw8+0LRp09S/f39VqFChQOvzt6ZevXq5PhfSu3dvrV27Vtu3b/fY/vnnn9c777yjrVu3+uz3vvvuU7Vq1fT8888Xes3+uOeeexQfH6+HH37YYxAFgJJGsABQZm3btk316tXzOK958+bavXu3xo0bp9OnT+vOO+/U7bffnm+flSpVCrie7BP1oKDfX1pzftYg561L/vL0WYXs6TlDQe6Tc8dxvC7rafmc6/IW1DzZunWr1q5dq5EjRyokJEQhISFq3bq1Tp8+rTlz5nhc5t5779Xrr7+uDz74oEhug/r111/14Ycf6o033nDVVKtWLV24cEHTpk3zuMy1116rLl266IknnvDZd0hIiJ555hlNnjxZBw4cyDM/KirK49WMY8eOFVoQyN4mACiNCBYAyqQvvvhCW7Zs0W233ea1TXR0tHr16qW3335b8+bN04IFC/Trr79K+v1kPPfXhxZE7q8w/fbbb9WwYUNJ/+9Wq5wfoM79eY3Q0FBJ8llDo0aNdOHCBa1du9Y17ciRI9qxY4euuOKKgGtv1KiR1qxZ4xY+1qxZo6ioKNWqVcvvflJTU3Xttddq8+bNSktLcz1Gjhzp9dajPn36aMuWLUpOTlajRo0C3gZvZs2apdq1a+ep6ZVXXtE777zj9i1iOU2YMEEfffSR1qxZ47P/O+64Q40bN9bTTz+dZ17Dhg3zfB7FGKMNGzaoQYMGgW8UAJQRvO0BoNQ7e/asDh48qMzMTP3yyy9atmyZxo8frx49eujuu+/2uMzLL7+s+Ph4XXnllQoKCtL8+fMVFxen2NhYSb9/M9Tnn3+udu3aKSwsTJUrVy5QTV9//bUmTpyonj17asWKFZo/f74++eQTSVJERIRat26tCRMmqG7dujp8+LD+9re/uS2fmJgox3H08ccfq3v37oqIiMjzjUdJSUm65ZZb9MADD+itt95SVFSURo0apVq1aumWW24pUL05PfTQQ3rllVc0ZMgQDR48WNu3b9fo0aM1fPhw19WW/Jw/f14zZ87U2LFj8/zGxf3336+JEydq8+bNSklJcZtXuXJlpaenF8ktUNLvYef222/PU1NiYqIee+wxffLJJx73XZMmTdS3b1+3b/fyZsKECa7Pb+T06KOPasCAAWrYsKE6d+6s06dP6+9//7t27dqlP//5z4FvFACUEVyxAFDqLVu2TPHx8apbt666du2qL7/8UlOmTNE///lPBQcHe1wmMjJSzz//vFq2bKlWrVppz549WrJkievEedKkSVqxYoUSEhLUrFmzAtc0YsQIbdiwQc2aNdO4ceM0adIkt5PNadOm6fz582rZsqWGDh3q+salbLVq1dLTTz+tUaNGqUaNGho8eLDH9UyfPl0tWrRQjx491KZNGxljtGTJEqsT81q1amnJkiX697//rZSUFA0aNEj33XdfnvDjy+LFi3XkyBGPP9CWlJSkJk2aeL1qERsba3XLmTcbNmzQ5s2bPV7FioqKUufOnb3WJEnjxo3zeQtZtuuvv17XX399nqsfd955p2bMmKF33nlHrVq1UufOnbVr1y7961//UmJiYsE3CADKGMf48yoKAAAAAD5wxQIAAACANYIFAKDMGDRokCIjIz0+Bg0aVNLlAcBFjVuhAABlxqFDh3TixAmP86Kjo1W9evVirggAkI1gAQAAAMAat0IBAAAAsBbw71hkZWXpwIEDioqKKtAvtQIAAAAoG4wxysjIUM2aNfP9raOAg8WBAweUkJAQ6OIAAAAAyoiff/5ZtWvX9tkm4GARFRXlWkl0dHSg3QAAAAAopU6cOKGEhATXub8vAQeL7NufoqOjCRYAAABAOebPRx/48DYAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAawQLAAAAANYIFgAAAACsESwAAAAAWCNYAAAAALBGsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAayElXUB5Z4zRmTNniqzvs2fPSpLCwsLkOE6RrCen8PDwYlkPAAAAyhaCRRE7c+aMunXrVtJlFJqlS5cqIiKipMsAAABAKcOtUAAAAACsccWiGP12ZW+ZoELc5ZnnFbV5riQpI+UuKbhC4fWdg5N1QZFpc4qkbwAAAJQPBItiZIJCiuzkX8EViqxvUyS9AgAAoDzhVigAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgLWQki7AljFGZ86ckSSFh4fLcZwSrggXK8YiAAC4mJX5KxZnzpxRt27d1K1bN9dJHVASGIsAAOBiVuaDBQAAAICSR7AAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArBEsAAAAAFgjWAAAAACwRrAAAAAAYI1gAQAAAMAawQIAAACANYIFAAAAAGsECwAAAADWCBYAAAAArIWUdAFAedStW7eSLgEeBAUFKSsrq9iWy7m8pID7CAkJUVhYmE6dOqV+/frpvvvu05o1azRx4kQdO3bM1S4sLEznzp3Tddddp3Xr1unUqVNq3Lixtm7dmmf9derU0b59+5SVlSXHcWSMcVufMUaZmZmuaR06dNDKlSvz1FatWjWdPn3ata4ffvhBwcHBunDhgipVqqSqVatq7969CgoKkuM4yszMVEhIiNq3b69Vq1apYsWK+uMf/6jly5dr6NChatu2bUD7KLe7775be/fuleM4iomJ0ciRI7V8+XKtXLlSHTp00JgxYyRJqampeu+991x1zJkzRxcuXFBycrJee+01r/2npqZq1qxZ6tu3r+677z63eWvWrNHkyZM1dOhQSXL9O/e25WwXyHZ7Wz57bEjSyJEjXfO81bxmzRo98cQTkqSIiAgtXbo03/U+++yzbuMxv3UXdBsCbReoou5f8rz/A11v9viuU6eO3n33Xdd0f8deToHU0KFDB9e/Pb0uFOa6LlZldV85Juf/JgVw4sQJxcTE6Pjx44qOji7suvx2+vRp10nc0qVLFRERUWK1eJKzvozm/aXgCoXXeeZ5RW2cWTR9e1lPadzHpUXOF1qgqDiOo7lz5+qhhx7SkSNHSrqcQpEdbKpVq6b33ntP4eHhVv3t3LlTDzzwgNu0ypUr6+jRo67n8+bNU1hYmG699VZ5+2/w3XffVZ06dfJMP3bsmP74xz8qKytLQUFBWrhwoWJjYyVJZ86cUb9+/XT48GFVrVpVknTkyJE825azXSDb7W35M2fOqG/fvq6xUbVqVc2aNUtnzpzxWPOZM2d0xx13KCMjw9X3hAkT1Lp1a6/r7dOnj3799VdJvx+7RYsWufrytG5v2+XvPrDdV/kp6v4lz2MmPDw8oPXmHt9vv/22kpKS/B57OQWy7c8++6xWrFjhet6pUyf99a9/zbfu4tjP5UVp21cFOefnVigAKEOMMRoyZEi5CRWSXCf2R44c0ezZs637e/DBB/NMyxkqJGnIkCF68sknvYYKSRo0aJDH6U8++aTr6k9WVpaeeuop17xZs2a5js2RI0fc/p1z23K3K+h2e1s+5/Sc87zVPGvWLLdQIUmjRo3yud7sUCH9fuxy9uVp3QXdhkDbBaqo+5c8j5lA15t7fGc/93fs5RRIDTlDhafn3hTHfi4vyvK+KvNXLE6dOqXu3btLkhYtWlTq0u+ZM2d06623SpIymvWTQkILr/PiumJx4ZyiNr0nqXTu49KAW5+AwhESEqIZM2aodu3aAS0/depUzZ07t9Dq+dOf/qTevXu7nq9fv16PPvponnYvvvii4uLiNGDAALdbyHLK3jZJedoVZLv37dvncfkJEybosccey7P+3Le6ZRs1apQmTJjgcR1XXXWV65amnOu9++67Pd7SN2rUKE2cODHPvODgYL3zzjt5tsvbNuTeB/62C1RR9y95HzO5b7H0Z73exvdNN92kZcuW5Tv2bPftDTfc4HEdwcHB+vzzz73WXRz7ubwojfuqIOf8fgeLs2fP6uzZs24rSUhIKPFgcfToUdeJe2mXkXKXFFqx8DosrmBx7pSiNhfef9QA4E1QUJBatGihiRMnynGcAi17/vx5derUqdBr+uyzzxQSEqKsrCz17NlTJ06cyNMmOjpaSUlJSktL83pyFxwcrGbNmslxHG3cuNGtXXBwsJo3b57vdhtjNHLkSI/LV6pUyWNtgVq2bJnrjSRjjP7yl79o/fr1HtsGBwd73e5WrVq5bZevbci5D/xtF6ii7l+SzzGTW37rzW98+/osWGHs219++UW9evXyuv558+apRo0aeaYXx34uL0rrviqSW6HGjx+vmJgY1yMhIcG6UAAAcsrKytK6deu0d+/eAi/76quvFkFFcn04du3atV5PEE+cOKENGzZ4PbmWpMzMTK1fv17r1q3L0y4zM9Ov7d67d6/X5QszVEjSX/7yF7f1egsV2ev3Jvd2+dqGnG39bReoou5f8j1mcstvvfmNb19fDlEY+9ZXqPA1vzj2c3lRHvaV398K9fjjj2v48OGu59lXLEpaWFiY69+l8TadnLdCKaiMfglXjrpL4z4uadwGBRSeoKAgtWzZ0uOHpvMzZMgQLV68uNBruvvuuyVJV199taKjoz2eKMbExOjyyy/P94pF8+bNJcnjO5ItWrTId7vr1KmjVq1aFcsVixdeeMFtvS1btgzoisVVV13ltl2+tiHnPvC3XaCKun/J95jJLb/15je+vd3y5qnvQLZ93rx5+V6x8KQ49nN5UR72ld9XLMLCwhQdHe32KA1yXhIKDw9XREREqXq4nYSX1Ut9pXwfl/SjIF+1B8C3oKAgDR06NKDL/RUqVNBdd91VqPU8+OCDCgkJcdWW84PaOY0ePVqPPPKIz74cx9GwYcNcXwWae54/253dztP00aNHKzg42OM8Tx5//HGv62ndurXb/1/ZtWd/dXJuI0eO9DgvODg4z3b52oacbf1tF6ii7l/yPWZy76/81utrfN98881ej42nvgPZ9ho1angcX9Lvx9nTbVCBrutiVR72Fd8KBRQCwgWKU/Xq1Uu6hCLhOI769OmjWrVqBdzHoEGDXEHAl+rVq6tJkyY+21SsWDHPO7QtW7bMs1zTpk3VvHlz1a5dW3369PH4n3/ObcvdrqDb7W35Fi1aqE+fPnna9+/f32PNXbp0Uf/+/T2uw9OHumvXrq2+ffvmmZ7dl6d5ffv29bhd/u4D232Vn6LuX/I+Zvr27Vvg9Xoa3yEhIRoxYoRfYy+nQLbd2we0fX1wO9B1XazK+r4iWABAGRIUFKRXX33V9T315UH2f6DVqlXzeGJcUG+++WaeaVWqVHF7/uqrr2rcuHE+3wGcOnWqx+njxo1zvTscFBSksWPHuub17dvXdWyqVavm9u+c25a7XUG329vyOafnnOet5r59+yoqKsqtb2/fFJXdPue+zN2Xp3UXdBsCbReoou5f8jxmAl1v7vGd/dzfsZdTIDXk/gC5v1+YUBz7ubwoy/uKYAEUkvx+rRYlz9etAkWxXM7lbfoICQlRpUqV5DiO+vbtqxo1amjEiBGuH2XLFhYWJsdx1KFDB1f75ORkj+uvU6eOa1ruk+uQkJA8tzx4+wHIatWqua3LcRzXO6qVKlVy3RMcFBTk6jMkJEQdOnSQ4ziqVKmS+vXrpxo1auiRRx4plM9wJSUludbrOI5iY2P16KOPurahQ4cOqlGjhmJjY9WvXz9XHf3793fVnpyc7PV+5tjYWPXt21dBQUHq27ev23EIDw/X8OHDVaNGDQ0fPlwjRozwuG052wWy3d6WDw8Pd42N2NhYDR8+XOHh4V5rDg8Pd7slKiIiwuuP42W3f/TRR93GY86+PK27oNsQaLtAFXX/kucxE+h6c47vOnXqKCkpKc92+Bp7OQVSQ+4fw/Pnx/ECXdfFqizvqzL/Oxb88ja/vF1alPaxCAAAUFD88jYAAACAYkWwAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1kJKugBb4eHhWrp0qevfQElhLAIAgItZmQ8WjuMoIiKipMsAGIsAAOCixq1QAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrISVdwMXEybogU5gdZp73/O9C5mRdKLK+AQAAUD4QLIpRZNqcIus7avPcIusbAAAAyA+3QgEAAACwxhWLIhYeHq6lS5cWSd/GGJ09e1aSFBYWJsdximQ9OYWHhxf5OgAAAFD2ECyKmOM4ioiIKLL+K1asWGR9AwAAAP7iVigAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKyFBLqgMUaSdOLEiUIrBgAAAEDpkX2un33u70vAwSIjI0OSlJCQEGgXAAAAAMqAjIwMxcTE+GzjGH/ihwdZWVk6cOCAoqKi5DhOQAUG4sSJE0pISNDPP/+s6OjoYlsvygbGB3xhfMAbxgZ8YXzAm4thbBhjlJGRoZo1ayooyPenKAK+YhEUFKTatWsHuri16OjocnsAYY/xAV8YH/CGsQFfGB/wpryPjfyuVGTjw9sAAAAArBEsAAAAAFgrc8EiLCxMo0ePVlhYWEmXglKI8QFfGB/whrEBXxgf8Iax4S7gD28DAAAAQLYyd8UCAAAAQOlDsAAAAABgjWABAAAAwBrBAgAAAIA1ggUAAAAAa2UuWLzxxhuqV6+ewsPD1aJFC/3rX/8q6ZJgYcyYMXIcx+0RFxfnmm+M0ZgxY1SzZk1FRESoQ4cO+uGHH9z6OHv2rIYMGaJq1aqpUqVK+sMf/qB9+/a5tTl69Kj69++vmJgYxcTEqH///jp27Jhbm7179+rmm29WpUqVVK1aNT388MM6d+5ckW073K1evVo333yzatasKcdx9OGHH7rNL21jYcuWLbruuusUERGhWrVqaezYseJL9opOfuNj4MCBeV5LWrdu7daG8VE+jR8/Xq1atVJUVJSqV6+unj17avv27W5teP24ePkzPnj9KESmDJk7d66pUKGCefvtt83WrVvN0KFDTaVKlcx///vfki4NARo9erRp3LixSU9Pdz0OHTrkmj9hwgQTFRVlFixYYLZs2WJ69epl4uPjzYkTJ1xtBg0aZGrVqmVWrFhhNm7caDp27GhSUlLMhQsXXG26du1qkpOTzZo1a8yaNWtMcnKy6dGjh2v+hQsXTHJysunYsaPZuHGjWbFihalZs6YZPHhw8ewImCVLlpi//vWvZsGCBUaSWbRokdv80jQWjh8/bmrUqGHuuusus2XLFrNgwQITFRVlXnzxxaLbQRe5/MbHgAEDTNeuXd1eS44cOeLWhvFRPnXp0sVMnz7dfP/99yYtLc3cdNNNpk6dOua3335zteH14+Llz/jg9aPwlKlgcdVVV5lBgwa5TWvYsKEZNWpUCVUEW6NHjzYpKSke52VlZZm4uDgzYcIE17QzZ86YmJgYM3XqVGOMMceOHTMVKlQwc+fOdbXZv3+/CQoKMsuWLTPGGLN161YjyXz77beuNt98842RZP7zn/8YY34/aQkKCjL79+93tZkzZ44JCwszx48fL7TthX9ynziWtrHwxhtvmJiYGHPmzBlXm/Hjx5uaNWuarKysQtwT8MRbsLjlllu8LsP4uHgcOnTISDKrVq0yxvD6AXe5x4cxvH4UpjJzK9S5c+e0YcMGde7c2W16586dtWbNmhKqCoVh586dqlmzpurVq6e77rpLP/30kyRp9+7dOnjwoNsxDwsL03XXXec65hs2bND58+fd2tSsWVPJycmuNt98841iYmJ09dVXu9q0bt1aMTExbm2Sk5NVs2ZNV5suXbro7Nmz2rBhQ9FtPPxS2sbCN998o+uuu87tl1a7dOmiAwcOaM+ePYW/A+CXlStXqnr16qpfv74eeOABHTp0yDWP8XHxOH78uCSpSpUqknj9gLvc4yMbrx+Fo8wEi8OHDyszM1M1atRwm16jRg0dPHiwhKqCrauvvlrvvvuuPv30U7399ts6ePCg2rZtqyNHjriOq69jfvDgQYWGhqpy5co+21SvXj3PuqtXr+7WJvd6KleurNDQUMZXKVDaxoKnNtnPGS8lo1u3bpo1a5a++OILTZo0SevWrdP111+vs2fPSmJ8XCyMMRo+fLjat2+v5ORkSbx+4P/xND4kXj8KU0hJF1BQjuO4PTfG5JmGsqNbt26ufzdp0kRt2rTRZZddpnfeecf1walAjnnuNp7aB9IGJas0jQVPtXhbFkWvV69ern8nJyerZcuWSkxM1CeffKI//vGPXpdjfJQvgwcP1nfffaevvvoqzzxeP+BtfPD6UXjKzBWLatWqKTg4OE9aO3ToUJ5kh7KrUqVKatKkiXbu3On6dihfxzwuLk7nzp3T0aNHfbb55Zdf8qzrf//7n1ub3Os5evSozp8/z/gqBUrbWPDUJvuyOeOldIiPj1diYqJ27twpifFxMRgyZIgWL16sL7/8UrVr13ZN5/UDkvfx4QmvH4ErM8EiNDRULVq00IoVK9ymr1ixQm3bti2hqlDYzp49q23btik+Pl716tVTXFyc2zE/d+6cVq1a5TrmLVq0UIUKFdzapKen6/vvv3e1adOmjY4fP65///vfrjZr167V8ePH3dp8//33Sk9Pd7VZvny5wsLC1KJFiyLdZuSvtI2FNm3aaPXq1W5fEbh8+XLVrFlTdevWLfwdgAI7cuSIfv75Z8XHx0tifJRnxhgNHjxYCxcu1BdffKF69eq5zef14+KW3/jwhNcPC8X0IfFCkf11s6mpqWbr1q1m2LBhplKlSmbPnj0lXRoCNGLECLNy5Urz008/mW+//db06NHDREVFuY7phAkTTExMjFm4cKHZsmWL6d27t8evCKxdu7b57LPPzMaNG83111/v8SvgmjZtar755hvzzTffmCZNmnj8CrgbbrjBbNy40Xz22Wemdu3afN1sMcrIyDCbNm0ymzZtMpLMSy+9ZDZt2uT6OunSNBaOHTtmatSoYXr37m22bNliFi5caKKjo8vM1wGWRb7GR0ZGhhkxYoRZs2aN2b17t/nyyy9NmzZtTK1atRgfF4EHH3zQxMTEmJUrV7p9XeipU6dcbXj9uHjlNz54/ShcZSpYGGPM66+/bhITE01oaKhp3ry529eFoezJ/i7xChUqmJo1a5o//vGP5ocffnDNz8rKMqNHjzZxcXEmLCzMXHvttWbLli1ufZw+fdoMHjzYVKlSxURERJgePXqYvXv3urU5cuSI6du3r4mKijJRUVGmb9++5ujRo25t/vvf/5qbbrrJREREmCpVqpjBgwe7fd0bitaXX35pJOV5DBgwwBhT+sbCd999Z6655hoTFhZm4uLizJgxY8rEVwGWVb7Gx6lTp0znzp3NJZdcYipUqGDq1KljBgwYkOfYMz7KJ0/jQpKZPn26qw2vHxev/MYHrx+FyzGmrPyUHwAAAIDSqsx8xgIAAABA6UWwAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECAAAAgDWCBQAAAABrBAsAAAAA1ggWAAAAAKwRLAAAAABYI1gAAAAAsEawAAAAAGCNYAEAAADAGsECQIlyHEcffvhhoffboUMHDRs2zPW8bt26euWVVwp9PZ7WVVKMMfq///s/ValSRY7jKC0traRLAgBcRAgWAArdwIED5TiOHMdRhQoVVKNGDXXq1EnTpk1TVlaWW9v09HR169bNr34LEkIWLlyocePGFbR0n1auXCnHcXTs2LEiX1cgli1bphkzZujjjz9Wenq6kpOTfbZv0KCBQkNDtX///jzzOnToIMdxNGHChDzzunfvLsdxNGbMGO3Zs8d1rL09xowZ41f9CxYs0PXXX6/KlSurYsWKatCgge69915t2rTJrd3p06c1evRoNWjQQGFhYapWrZpuv/12/fDDD3n6/PXXXzVs2DDVrVtXoaGhio+P1z333KO9e/e6tSvImN20aZN69Oih6tWrKzw8XHXr1lWvXr10+PDhfLcx9/6qXLmyrr32Wq1atcpjLSEhIapTp44efPBBHT161K0vT2F506ZNuuOOO1SjRg2Fh4erfv36euCBB7Rjxw6P68/5+Pbbb/OtHwB8IVgAKBJdu3ZVenq69uzZo6VLl6pjx44aOnSoevTooQsXLrjaxcXFKSwsrNDWe/78eUlSlSpVFBUVVWj9+lKc6/Jl165dio+PV9u2bRUXF6eQkBCvbb/66iudOXNGd9xxh2bMmOGxTUJCgqZPn+427cCBA/riiy8UHx/vapOenu56jBgxQo0bN3ab9uijj+Zb+2OPPaZevXrpyiuv1OLFi/XDDz/o73//uy677DI98cQTrnZnz57VjTfeqGnTpmncuHHasWOHlixZoszMTF199dVuJ8e//vqrWrdurc8++0xvvPGGfvzxR82bN0+7du1Sq1at9NNPP7nV4M+YPXTokG688UZVq1ZNn376qbZt26Zp06YpPj5ep06dync7s3322WdKT0/XqlWrFB0dre7du2v37t0ea/nHP/6hjz76SA899JDPPj/++GO1bt1aZ8+e1axZs7Rt2zbNnDlTMTExevLJJz2uP+ejRYsWftcPAB4ZAChkAwYMMLfcckue6Z9//rmRZN5++23XNElm0aJFxhhjzp49a/785z+buLg4ExYWZhITE81zzz1njDEmMTHRSHI9EhMTjTHGjB492qSkpJjU1FRTr1494ziOycrKMtddd50ZOnSoaz2JiYlm7Nixpnfv3qZSpUomPj7eTJkyxTV/9+7dRpLZtGmTa9rRo0eNJPPll1+65ud8DBgwwBhj8qzr119/Nf379zexsbEmIiLCdO3a1ezYscM1f/r06SYmJsYsW7bMNGzY0FSqVMl06dLFHDhwwOd+XblypWnVqpUJDQ01cXFx5rHHHjPnz5937XNP+8ebgQMHmlGjRpmlS5eaSy+91GRlZbnNv+6668yDDz5oqlatar766ivX9GeffdbcfPPNJiUlxYwePTpPv9nHoyC++eYbI8lMnjzZ4/yctU2YMME4jmPS0tLc2mRmZpqWLVuaRo0audoPGjTIVKpUyaSnp7u1PXXqlKlVq5bp2rWra5q/Y3bRokUmJCTEtd8LytM427dvn5Fkpk6d6rWW4cOHmypVqrhNS0xMNC+//LIxxpiTJ0+aatWqmZ49e3pc79GjR72uHwAKC1csABSb66+/XikpKVq4cKHH+VOmTNHixYv1/vvva/v27XrvvfdUt25dSdK6deskSdOnT1d6errruST9+OOPev/997VgwQKfnyt44YUX1LRpU23cuFGPP/64HnnkEa1YscKv2hMSErRgwQJJ0vbt25Wenq7Jkyd7bDtw4ECtX79eixcv1jfffCNjjLp37+66miJJp06d0osvvqiZM2dq9erV2rt3r8939vfv36/u3burVatW2rx5s958802lpqbqmWeekSRNnjxZY8eOVe3atfPsn9wyMjI0f/589evXT506ddLJkye1cuXKPO1CQ0PVt29ft6sWM2bM0L333utzXxXUnDlzFBkZ6fUdecdxXP+ePXu2OnXqpJSUFLc2QUFBeuSRR7R161Zt3rxZWVlZmjt3rvr27au4uDi3thEREXrooYf06aef6tdff/VZW+4xGxcXpwsXLmjRokUyxgSyuXlUrFhRktzGR04//fSTli1bpgoVKnjt49NPP9Xhw4c1cuRIj/NjY2Ot6wSA/BAsABSrhg0bas+ePR7n7d27V0lJSWrfvr0SExPVvn179e7dW5J0ySWXSPr9BCkuLs71XJLOnTunmTNnqlmzZmratKnbiWhO7dq106hRo1S/fn0NGTJEt99+u15++WW/6g4ODlaVKlUkSdWrV1dcXJxiYmLytNu5c6cWL16sf/zjH7rmmmuUkpKiWbNmaf/+/W6fDzl//rymTp2qli1bqnnz5ho8eLA+//xzr+t/4403lJCQoNdee00NGzZUz5499fTTT2vSpEnKyspSTEyMoqKiFBwcnGf/5DZ37lwlJSWpcePGCg4O1l133aXU1FSPbe+77z69//77OnnypFavXq3jx4/rpptu8muf+WvHjh269NJL3W7deumllxQZGel6HD9+3NX2iiuu8NhP9vQdO3bof//7n44dO+azrTFGP/74Y7715RyzrVu31hNPPKE+ffqoWrVq6tatm1544QX98ssvBdlkl5MnT+rxxx9XcHCwrrvuOtf0jz/+WJGRkYqIiNBll12mrVu36rHHHvPaz86dO121+qNt27Zu+zcyMlKZmZkBbQMAZCNYAChWxhivJ/4DBw5UWlqaGjRooIcffljLly/3q8/ExESfJ9LZ2rRpk+f5tm3b/FqHv7Zt26aQkBBdffXVrmlVq1ZVgwYN3NZVsWJFXXbZZa7n8fHxOnTokM9+27Rp47bv2rVrp99++0379u0rUI2pqanq16+f63m/fv20cOHCPB9Kl6SmTZsqKSlJH3zwgaZNm6b+/fv7fOc8ULnHxL333qu0tDS99dZbOnnypF9XB7LbeBtfNm1ztnv22Wd18OBBTZ06VY0aNdLUqVPVsGFDbdmyJd++smWf2EdFRemjjz7SjBkz1KRJE9f8jh07Ki0tTWvXrtWQIUPUpUsXDRkyJN/t8de8efOUlpbm9ggODi5QHwCQG8ECQLHatm2b6tWr53Fe8+bNtXv3bo0bN06nT5/WnXfeqdtvvz3fPitVqhRwPdknjEFBv78c5jxB83Zrii/eTvByn5zmPjl3HMfnyaGnQFaQk+NsW7du1dq1azVy5EiFhIQoJCRErVu31unTpzVnzhyPy9x77716/fXX9cEHHxT6bVCSlJSUpF27drnt79jYWF1++eWqVauWW9v69etr69atHvv5z3/+4+rvkksuUWxsrM+2juO4hTtvPI3ZqlWr6o477tCkSZO0bds21axZUy+++GK+fWWbN2+eNm/erP/973/av3+/W9CTfh/Tl19+uZo2baopU6bo7Nmzevrpp732V79+fdd2+SMhIUGXX3652wMAbBEsABSbL774Qlu2bNFtt93mtU10dLR69eqlt99+W/PmzdOCBQtc98FXqFDB6naN3F+n+e2337puHcm+4pGenu6an/vzGqGhoZLks4b/r727j26yvv8//kpb2nBXKqCVmyI3Iq4rIBRc6wRRXKGKosfpVkA4Tj3gBCtFO2Ru6NwBwYI3825qh2MrMBmirlJOcVA6RJR76FRw3lFFQNDSAm2hzfX7w2/zI03SJvkkTVuej3NyTpPruj7XO+9+Ctcr15UkMTFRNTU1ev/9952PHTt2rMFLeHyRmJiozZs3u4SPzZs3q2PHjm4H3w3Jzc3VyJEjtXv3bpdXq7Ozs71eDjVhwgTt3btXSUlJSkxMDPg5eJORkaETJ07o+eefb3TdX/7yl3rnnXe0e/dul8cdDoeefPJJJSYmavDgwYqIiNBtt92mZcuW6dChQy7rVlZW6vnnn9eYMWOcl7d548ucjY6OVr9+/XTy5MlG66+TkJCgfv36qUuXLj6tP3fuXOXk5OjgwYMel6elpalr165auHChx+WezkYBQLARLACERHV1tQ4dOqSvv/5aO3bs0Lx58zR+/HiNGzdOkydP9rjNk08+qRUrVujjjz/W/v37tXLlSl144YXON5727t1b//73v3Xo0CG3z/T3xbvvvquFCxdq//79eu6557Ry5UplZmZK+uENvSkpKXr88cf14Ycfqri4WA8//LDL9hdddJFsNpvy8/P17bff6sSJE2776N+/v8aPH6+7775bmzZt0u7duzVp0iT16NFD48eP97vmOr/+9a9VWlqqGTNm6OOPP9abb76puXPnKisry3m2pTFnzpzR3/72N2VkZCgpKcnldtddd2n79u1uB+ySdN555+mbb75p8D0gJlJTUzVr1izNmjVLWVlZ2rRpk7788ktt2bJFubm5stlszuc4c+ZMXX755brhhhu0cuVKHThwQFu3btUtt9yijz76yLm+9MMlSxdeeKF+9rOfqaCgQKWlpSouLtaYMWN05swZPffccy51+DJn8/PzNWnSJOXn52v//v3at2+fcnJytGbNGqPfb2NGjRqlH//4x5o3b57H5e3bt9crr7yit99+WzfeeKPeeecdffHFF9q2bZuys7M1bdo0l/WPHTumQ4cOudyqqqpCVj+AcwPBAkBIrF27Vt26dVPv3r01duxYbdiwQc8884zefPNNr9dyd+jQQQsWLNCwYcM0fPhwffHFF1qzZo3zoHLRokVat26dEhISNGTIEL9rmjVrlrZv364hQ4boscce06JFizRmzBjn8r/85S86c+aMhg0bpszMTOcnLtXp0aOHHn30Uc2ePVvx8fGaPn26x/0sWbJEycnJGjdunFJTU2VZltasWWP03oQePXpozZo1+uCDDzR48GBNmzZNd955p1v4achbb72lY8eO6eabb3Zb1r9/fw0cONDrWYu4uDijS84ak5OTo2XLljm/fK5///669dZb5XA49N577yk2NlaSZLfbtX79ek2ZMkVz5szRxRdfrLFjxyoyMlJbtmxRSkqKc8yuXbtqy5YtuvrqqzV16lT17dtXt912m/r27autW7eqb9++LjX4MmcTExPVrl07zZo1S5dddplSUlL02muv6ZVXXtHtt98esv5IUlZWll5++WWVlpZ6XD5+/Hht3rxZbdq00YQJE3TppZcqIyNDx48fd5vL1157rbp16+Zy8/XLJwHAG5sVrM/LAwAAAHDO4owFAAAAAGMECwBASE2bNs3tOxPqbvWv/W/pzqXnCgD1cSkUACCkjhw5ovLyco/LYmNjdcEFFzRxRaFzLj1XAKiPYAEAAADAGJdCAQAAADAWFeiGDodDBw8eVMeOHf361lcAAAAALYNlWaqoqFD37t0b/d6kgIPFwYMHlZCQEOjmAAAAAFqI0tJS9ezZs8F1Ag4WHTt2dO6k7ouLAAAAALQe5eXlSkhIcB77NyTgYFF3+VNsbCzBAgAAAGjFfHnrA2/eBgAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGAsKtwFtBaWZamqqircZbiwLEvV1dWSpJiYGNlstjBX5Jndbm+2tQEAAMA3BIsgqaqqUnp6erjLaJEKCgrUtm3bcJcBAAAAA1wKBQAAAMAYZyxC4MRlGbIimkFra8+o4+4VkqSKwb+UItuEuaD/z+aoUYddy8NdBgAAAIKkGRz9tj5WRFSzOoiX9EM9zagmK9wFAAAAIKi4FAoAAACAMYIFAAAAAGMECwAAAADGCBYAAAAAjBEsAAAAABgjWAAAAAAwRrAAAAAAYIxgAQAAAMAYwQIAAACAMYIFAAAAAGMECwAAAADGCBYAAAAAjBEsAAAAABgjWAAAAAAwRrAAAAAAYIxgAQAAAMAYwQIAAACAMYIFAAAAAGMECwAAAADGCBYAAAAAjBEsAAAAABgjWAAAAAAwRrAAAAAAYIxgAQAAAMAYwQIAAACAMYIFAAAAAGMECwAAAADGCBYAAAAAjBEsAAAAABgjWAAAAAAwRrAAAAAAYIxgAQAAAMAYwQIAAACAMYIFAAAAAGMECwAAAADGCBYAAAAAjBEsAAAAABgjWAAAAAAwRrAAAAAAYCwq3AWYsixLVVVVkiS73S6bzRbmioBzC3+DAABAagVnLKqqqpSenq709HTnwQ2ApsPfIAAAkFpBsAAAAAAQfgQLAAAAAMYIFgAAAACMESwAAAAAGCNYAAAAADBGsAAAAABgjGABAAAAwBjBAgAAAIAxggUAAAAAYwQLAAAAAMYIFgAAAACMESwAAAAAGCNYAAAAADBGsAAAAABgjGABAAAAwBjBAgAAAIAxggUAAAAAYwQLAAAAAMYIFgAAAACMESwAAAAAGCNYAAAAADBGsAAAAABgjGABAAAAwBjBAgAAAIAxggUAAAAAYwQLAAAAAMYIFgAAAACMESwAAAAAGCNYAAAAADBGsAAAAABgjGABAAAAwBjBAgAAAIAxggUAAAAAYwQLAAAAAMYIFgAAAACMESwAAAAAGCNYAAAAADBGsAAAAABgjGABAAAAwFhUuAsA0Hqkp6eHu4RWLyIiQomJiSopKdGoUaNUVFTksszhcDjv22w2WZbVZLXNmzdPixcv1tGjR2Wz2RQdHa3q6mrn8q5duzqX1a8rLi5OZWVlje4jKSlJX331lU6cOKGamhq3/S9cuFCSlJ2drY8++kh5eXmaOHGi7rzzTrexNm/erKefflqZmZmS5Pz5iiuuUG5uboPbehvniiuukCSlpaXp9OnTio6OVmFhYaPbeBojVEaNGuX8+ez5U1dDWlqaCgsLm6SW+qZPn66SkhIlJSXp2WefdavNpCbTMa655ho5HA5FRERo/fr1AdUQKt761hQa6+sjjzyioqIijRo1So888kjI9xeusYItPT1dlZWVatu2rQoKCsJdjs84YwEALYjD4VBJSYkk14PCumVna8pQIUkPP/ywjh496tz32aFCksuy+nwJFZJUUlKisrIyt1AhSXPmzFFZWZnKysqUk5Ojv//973I4HMrLy3Mbv6qqSosXL9bhw4e1aNEiLVq0SIcPH3Y+lpeX53Vbb+MsXrxYVVVVKi4u1unTpyVJp0+fVnFxcYPblJWVuY0RKkuWLPF4/+ya8vLymqSW+g4cOOCc2yUlJTpw4IBbbYHWZDpGQUGB8+/L4XA0qwM9b31rCo319fDhw85/p4qKinT48OGQ7i9cYwXbli1bVFlZKUmqrKzUli1bwlyR7wgWAICgqB9swum7775zBhiHw6Hf//73Lsvz8vJ07NgxSdKxY8dcfp4+fbrLQWT9bRsaZ9myZW7rN7bv3/3ud25jhMpf//pXj/fPrqnuuYe6lvqmTZvm8b6nHvvLdIwFCxY0eD+cvPWtKTTW1+nTp7vcnzFjRkj3F66xgm327NkN3m/OWvylUGe/8hXOtOmy7yZ+lbBFaia/N5jj8ie0BHv27NG2bds0bNgwffXVV1q2bJnHMyeWZenbb7/1uu3Z6o9jWZaWLl3qcf/33XefnnnmGY/b7N2712X/y5YtU1pamnr27Gn0nOu78cYbPT4+btw4VVZWuvUjlLXUt3z5cp06dcrlsVOnTunFF1/UypUrXfrlb02eeu7PGHfccYfXx+ufAWpq3vq2fPlyZWRkhHTfjfV17dq1bn9LR44c0dq1azV27Nig7y9cYwVbdna218frLvVszmyWj+fKq6urXU5rl5eXKyEhQcePH1dsbGzICmzM999/r5tvvjls+/ekYvAvpeh24S5Dqj2jjjv+JkmqGHq7FNkmzAWd5fQpddy9ItxVADiHxMbGavXq1Zo9e7Z27Nih2tpav7Z94403FBHxw4l+y7KUnZ3t1zhr1qzR3LlzG90mMjJSQ4cO1cKFC2Wz2XyusSFlZWW66aab/N4uFLXUV1NTo2uvvTYkNXn7Pfk6xsmTJ3X99dd7Xf7222+rffv2PtceTI317Z133lFUVGheP26sr/Pnz9eYMWM8zvPIyEgVFhYqMjIyaPvzZ34Gc6xgq6qqajB0rV27Vna7vQkr+kF5ebk6derk0zG/z5dCzZ8/X506dXLeEhISjAsFAKCplJeXKz8/X1u3bvUrVNRt+/777zvvHzhwwO9x7rnnHp+2qa2t1datW4N6rXygr16Hopb6vJ3l8cafmrz9nnwd4+677zZaHkqN9c3fvvqjsb4uXbrU6zyvra1Vfn5+UPfnz/wM5ljB9uCDDxotbw58jrIPPfSQsrKynPfrzliEW0xMjPPn1atXhyXJST+kTOeZk4gWf4VZ6J3Vo3D+3mCGy6DQknTq1Enjxo3Tpk2b/D5j0alTJ/3kJz9x3u/Vq5eGDx/u1zgvvPCCz2cskpOT1atXL5/ra8zy5csDPmMR7Frqmzx5sl8Hwf7U5O335OsYL7/8coNnLF5++WWf6w62xvo2efLkkO27sb5OnjxZeXl5Hud5VFSUxo0bF9T9+TM/gzlWsD3xxBMNnrF44oknmrCawPh8BBwTE+NyEN9cnH26ym63q23btmGs5v+E6RRai9Icf2/wW91HCAItwdy5cxUZGanMzExNmTLF723rLoOSfvi/x59xLrvsMrVr186nberGDublGHFxcYqNjVV5ebnbsg4dOqiystLjQWAoaqkvKipKU6dO1Z///Ge3ZRkZGXrttddcavOnJm+/J1/HaN++vfr06aPPP//cbVm/fv3CdhmU1HDf7rnnnpBdBiU13teoqCg9+OCDevzxx922zc7O9usyKF/258/8DOZYwWa323X55Zfrgw8+cFuWkpLSIl6E5VOhABhpTh+7CHgzaNAgDR06VJLUs2dPTZgwweMBhM1m0/nnn+9127PVH8dms3l9lfipp57yus3AgQNd7k+YMEE9evQI7Ik24K233vL4eH5+vsd+hLKW+jIyMtSunet7E9u1a6epU6e69cvfmjz13J8xvL1BOzc31+caQsVb337xi1+EfN+N9XXs2LFuf0sXXHCB0tLSQrK/cI0VbN7eoO0ppDVHBAsAQFD4+ypkKHXu3Nl50BAREaE//OEPLssnTpyoLl26SPrhi/vO/vnZZ591np3wtG1D40yYMMFt/cb2/dhjj7mNESr1X6Wtu392TXXPPdS11Pfiiy96vO+px/4yHeM3v/lNg/fDyVvfmkJjfa3/ZX1/+tOfQrq/cI0VbPVDREsJFRLBAgBalIiICCUlJUly/QblumVna+pT+o899pi6du3q3Hf9y2fPXlZfXFycT/tISkpSXFycx8s85s2bp7i4OMXFxemBBx7QpEmTFBERoYkTJ7qNb7fblZWVpfj4eGVlZWnWrFmKj4/XzJkzFR8fr4kTJ3rd1ts4M2fOlN1u18iRIxUdHS1Jio6O1siRIxvcJi4uzm2MUKn/0al198+uaeLEiU1SS329evVyzu2kpCTnte6eeuwv0zHS09NdwmZzen+Zt741hcb6Gh8f7/x3atSoUYqPjw/p/sI1VrClpKQ4LxFv27atUlJSwlyR73z+uNn6/PnoqVCqrKx0/oEXFBSE7Vr9s+toNh/t2pw/bvas2sL5e4O55vI3CAAAgi8kHzcLAAAAAN4QLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgLGocBdgym63q6CgwPkzgKbF3yAAAJBaQbCw2Wxq27ZtuMsAzln8DQIAAIlLoQAAAAAEAcECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMEawAAAAAGCMYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAAMBYV7gJaI5ujRla4i5Ck2jOef24GbI6acJcAAACAICJYhECHXcvDXYKbjrtXhLsEAAAAtGJcCgUAAADAGGcsgsRut6ugoCDcZbiwLEvV1dWSpJiYGNlstjBX5Jndbg93CQAAADBEsAgSm82mtm3bhrsMN+3atQt3CQAAADgHcCkUAAAAAGMECwAAAADGCBYAAAAAjBEsAAAAABgjWAAAAAAwRrAAAAAAYIxgAQAAAMAYwQIAAACAMYIFAAAAAGMECwAAAADGCBYAAAAAjBEsAAAAABgjWAAAAAAwRrAAAAAAYIxgAQAAAMAYwQIAAACAMYIFAAAAAGMECwAAAADGogLd0LIsSVJ5eXnQigEAAADQfNQd69cd+zck4GBRUVEhSUpISAh0CAAAAAAtQEVFhTp16tTgOjbLl/jhgcPh0MGDB9WxY0fZbLaACvRHeXm5EhISVFpaqtjY2JDv71xBX0OH3oYOvQ0dehsa9DV06G3o0NvQaUm9tSxLFRUV6t69uyIiGn4XRcBnLCIiItSzZ89ANw9YbGxss/8FtET0NXTobejQ29Cht6FBX0OH3oYOvQ2dltLbxs5U1OHN2wAAAACMESwAAAAAGGsxwSImJkZz585VTExMuEtpVehr6NDb0KG3oUNvQ4O+hg69DR16GzqttbcBv3kbAAAAAOq0mDMWAAAAAJovggUAAAAAYwQLAAAAAMYIFgAAAACMESwAAAAAGGs2weL5559Xnz59ZLfblZycrP/85z8Nrr9x40YlJyfLbrerb9++evHFF5uo0pbHn94WFRXJZrO53T7++OMmrLhlKC4u1g033KDu3bvLZrPpjTfeaHQb5m3j/O0rc9Z38+fP1/Dhw9WxY0ddcMEFuummm7Rv375Gt2PeNiyQvjJvffPCCy9o0KBBzm8nTk1NVUFBQYPbMF99429vmbOBmT9/vmw2m+6///4G12st87ZZBIt//OMfuv/++/Xb3/5WO3fu1IgRI5Senq4DBw54XP/zzz/XddddpxEjRmjnzp2aM2eO7rvvPq1ataqJK2/+/O1tnX379umbb75x3vr3799EFbccJ0+e1ODBg/Xss8/6tD7z1jf+9rUOc7ZxGzdu1L333qstW7Zo3bp1qqmpUVpamk6ePOl1G+Zt4wLpax3mbcN69uypxx9/XNu2bdO2bdt0zTXXaPz48frvf//rcX3mq+/87W0d5qzvtm7dqpdeekmDBg1qcL1WNW+tZuDyyy+3pk2b5vLYpZdeas2ePdvj+tnZ2dall17q8tjUqVOtlJSUkNXYUvnb2w0bNliSrO+//74Jqms9JFmrV69ucB3mrf986StzNnBHjhyxJFkbN270ug7z1n++9JV5G7jzzjvPeuWVVzwuY76aaai3zFn/VFRUWP3797fWrVtnXXXVVVZmZqbXdVvTvA37GYvTp09r+/btSktLc3k8LS1Nmzdv9rjNe++957b+mDFjtG3bNp05cyZktbY0gfS2zpAhQ9StWzeNHj1aGzZsCGWZ5wzmbWgxZ/13/PhxSVLnzp29rsO89Z8vfa3DvPVdbW2tVqxYoZMnTyo1NdXjOszXwPjS2zrMWd/ce++9uv7663Xttdc2um5rmrdhDxZHjx5VbW2t4uPjXR6Pj4/XoUOHPG5z6NAhj+vX1NTo6NGjIau1pQmkt926ddNLL72kVatW6fXXX9eAAQM0evRoFRcXN0XJrRrzNjSYs4GxLEtZWVm68sorlZSU5HU95q1/fO0r89Z3e/fuVYcOHRQTE6Np06Zp9erVSkxM9Lgu89U//vSWOeu7FStWaMeOHZo/f75P67emeRsV7gLq2Gw2l/uWZbk91tj6nh6Hf70dMGCABgwY4Lyfmpqq0tJS5eTkaOTIkSGt81zAvA0+5mxgpk+frj179mjTpk2Nrsu89Z2vfWXe+m7AgAHatWuXysrKtGrVKk2ZMkUbN270egDMfPWdP71lzvqmtLRUmZmZKiwslN1u93m71jJvw37GomvXroqMjHR7Bf3IkSNu6a3OhRde6HH9qKgodenSJWS1tjSB9NaTlJQUffLJJ8Eu75zDvG06zNmGzZgxQ2+99ZY2bNignj17Nrgu89Z3/vTVE+atZ9HR0br44os1bNgwzZ8/X4MHD9bTTz/tcV3mq3/86a0nzFl327dv15EjR5ScnKyoqChFRUVp48aNeuaZZxQVFaXa2lq3bVrTvA17sIiOjlZycrLWrVvn8vi6det0xRVXeNwmNTXVbf3CwkINGzZMbdq0CVmtLU0gvfVk586d6tatW7DLO+cwb5sOc9Yzy7I0ffp0vf7661q/fr369OnT6DbM28YF0ldPmLe+sSxL1dXVHpcxX8001FtPmLPuRo8erb1792rXrl3O27BhwzRx4kTt2rVLkZGRbtu0qnkblreM17NixQqrTZs2Vm5urvXhhx9a999/v9W+fXvriy++sCzLsmbPnm3dfvvtzvU/++wzq127dtbMmTOtDz/80MrNzbXatGlj/fOf/wzXU2i2/O3tk08+aa1evdrav3+/VVJSYs2ePduSZK1atSpcT6HZqqiosHbu3Gnt3LnTkmQtXrzY2rlzp/Xll19alsW8DZS/fWXO+u6ee+6xOnXqZBUVFVnffPON83bq1CnnOsxb/wXSV+atbx566CGruLjY+vzzz609e/ZYc+bMsSIiIqzCwkLLspivJvztLXM2cPU/Fao1z9tmESwsy7Kee+4566KLLrKio6OtoUOHunxM35QpU6yrrrrKZf2ioiJryJAhVnR0tNW7d2/rhRdeaOKKWw5/ertgwQKrX79+lt1ut8477zzryiuvtN5+++0wVN381X30Xv3blClTLMti3gbK374yZ33nqa+SrCVLljjXYd76L5C+Mm9986tf/cr5/9f5559vjR492nnga1nMVxP+9pY5G7j6waI1z1ubZf3fu0MAAAAAIEBhf48FAAAAgJaPYAEAAADAGMECAAAAgDGCBQAAAABjBAsAAAAAxggWAAAAAIwRLAAAAAAYI1gAAAAALVRxcbFuuOEGde/eXTabTW+88YbfY1iWpZycHF1yySWKiYlRQkKC5s2b5/c4UX5vAQAAAKBZOHnypAYPHqw77rhDt9xyS0BjZGZmqrCwUDk5ORo4cKCOHz+uo0eP+j0O37wNAAAAtAI2m02rV6/WTTfd5Hzs9OnTevjhh5WXl6eysjIlJSVpwYIFGjVqlCTpo48+0qBBg1RSUqIBAwYY7Z9LoQAAAIBW6o477tC7776rFStWaM+ePbr11ls1duxYffLJJ5Kkf/3rX+rbt6/y8/PVp08f9e7dW3fddZe+++47v/dFsAAAAABaoU8//VTLly/XypUrNWLECPXr108PPPCArrzySi1ZskSS9Nlnn+nLL7/UypUrtXTpUr366qvavn27fv7zn/u9P95jAQAAALRCO3bskGVZuuSSS1wer66uVpcuXSRJDodD1dXVWrp0qXO93NxcJScna9++fX5dHkWwAAAAAFohh8OhyMhIbd++XZGRkS7LOnToIEnq1q2boqKiXMLHj370I0nSgQMHCBYAAADAuW7IkCGqra3VkSNHNGLECI/r/PSnP1VNTY0+/fRT9evXT5K0f/9+SdJFF13k1/74VCgAAACghTpx4oT+97//SfohSCxevFhXX321OnfurF69emnSpEl69913tWjRIg0ZMkRHjx7V+vXrNXDgQF133XVyOBwaPny4OnTooKeeekoOh0P33nuvYmNjVVhY6FctBAsAAACghSoqKtLVV1/t9viUKVP06quv6syZM/rjH/+opUuX6uuvv1aXLl2UmpqqRx99VAMHDpQkHTx4UDNmzFBhYaHat2+v9PR0LVq0SJ07d/arFoIFAAAAAGN83CwAAAAAYwQLAAAAAMYIFgAAAACMESwAAAAAGCNYAAAAADBGsAAAAABgjGABAAAAwBjBAgAAAIAxggUAAAAAYwQLAAAAAMYIFgAAAACM/T+AfInD5bwWpgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# List of numerical features you might want to check for outliers\n",
    "numerical_features = ['AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE']\n",
    "\n",
    "# Creating box plots\n",
    "fig, axes = plt.subplots(len(numerical_features), 1, figsize=(8, 12))\n",
    "for i, feature in enumerate(numerical_features):\n",
    "    sns.boxplot(x=app_train[feature], ax=axes[i])\n",
    "    axes[i].set_title(f'Distribution of {feature}', fontsize=10)\n",
    "    axes[i].set_xlabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "322a31c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMT_INCOME_TOTAL - Filtered 14035 outliers\n",
      "AMT_CREDIT - Filtered 5752 outliers\n",
      "AMT_ANNUITY - Filtered 4998 outliers\n",
      "AMT_GOODS_PRICE - Filtered 1868 outliers\n"
     ]
    }
   ],
   "source": [
    "def filter_outliers(dataframe, feature):\n",
    "    Q1 = dataframe[feature].quantile(0.25)\n",
    "    Q3 = dataframe[feature].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    \n",
    "    # Filtering out the outliers\n",
    "    filtered_data = dataframe[(dataframe[feature] >= lower_bound) & (dataframe[feature] <= upper_bound)]\n",
    "    return filtered_data\n",
    "\n",
    "# Apply the filter to each numerical feature\n",
    "for feature in numerical_features:\n",
    "    original_size = app_train.shape[0]\n",
    "    app_train = filter_outliers(app_train, feature)\n",
    "    new_size = app_train.shape[0]\n",
    "    print(f'{feature} - Filtered {original_size - new_size} outliers')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "79157650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replotting a box plot for 'AMT_INCOME_TOTAL'\n",
    "numerical_features = ['AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE']\n",
    "\n",
    "# Creating box plots\n",
    "fig, axes = plt.subplots(len(numerical_features), 1, figsize=(8, 12))\n",
    "for i, feature in enumerate(numerical_features):\n",
    "    sns.boxplot(x=app_train[feature], ax=axes[i])\n",
    "    axes[i].set_title(f'Distribution of {feature}', fontsize=10)\n",
    "    axes[i].set_xlabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "6e50c686",
   "metadata": {},
   "outputs": [],
   "source": [
    "app_train_domain = app_train.copy()\n",
    "\n",
    "app_train_domain['CREDIT_INCOME_PERCENT'] = app_train_domain['AMT_CREDIT'] / app_train_domain['AMT_INCOME_TOTAL']\n",
    "app_train_domain['ANNUITY_INCOME_PERCENT'] = app_train_domain['AMT_ANNUITY'] / app_train_domain['AMT_INCOME_TOTAL']\n",
    "app_train_domain['CREDIT_TERM'] = app_train_domain['AMT_ANNUITY'] / app_train_domain['AMT_CREDIT']\n",
    "app_train_domain['DAYS_EMPLOYED_PERCENT'] = app_train_domain['DAYS_EMPLOYED'] / app_train_domain['DAYS_BIRTH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "c79ad8f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy='median')  \n",
    "\n",
    "numerical_cols = app_train.select_dtypes(include=['float64', 'int64']).columns\n",
    "app_train[numerical_cols] = imputer.fit_transform(app_train[numerical_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "82afc4ad",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input X contains infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[97], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m imputer \u001b[38;5;241m=\u001b[39m SimpleImputer(strategy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m'\u001b[39m)  \n\u001b[1;32m      4\u001b[0m numerical_cols \u001b[38;5;241m=\u001b[39m app_train_domain\u001b[38;5;241m.\u001b[39mselect_dtypes(include\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloat64\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mint64\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m----> 5\u001b[0m app_train_domain[numerical_cols] \u001b[38;5;241m=\u001b[39m imputer\u001b[38;5;241m.\u001b[39mfit_transform(app_train_domain[numerical_cols])\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_set_output.py:140\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m    139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 140\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    142\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m    143\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m    144\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m    145\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m    146\u001b[0m         )\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/base.py:878\u001b[0m, in \u001b[0;36mTransformerMixin.fit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    874\u001b[0m \u001b[38;5;66;03m# non-optimized default implementation; override when a better\u001b[39;00m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;66;03m# method is possible for a given clustering algorithm\u001b[39;00m\n\u001b[1;32m    876\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    877\u001b[0m     \u001b[38;5;66;03m# fit method of arity 1 (unsupervised transformation)\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n\u001b[1;32m    879\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001b[39;00m\n\u001b[1;32m    881\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/impute/_base.py:390\u001b[0m, in \u001b[0;36mSimpleImputer.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    381\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeprecated\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    382\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    383\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mverbose\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m parameter was deprecated in version \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    384\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1.1 and will be removed in 1.3. A warning will \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    388\u001b[0m     )\n\u001b[0;32m--> 390\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_input(X, in_fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    392\u001b[0m \u001b[38;5;66;03m# default fill_value is 0 for numerical input and \"missing_value\"\u001b[39;00m\n\u001b[1;32m    393\u001b[0m \u001b[38;5;66;03m# otherwise\u001b[39;00m\n\u001b[1;32m    394\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/impute/_base.py:344\u001b[0m, in \u001b[0;36mSimpleImputer._validate_input\u001b[0;34m(self, X, in_fit)\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m new_ve \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    343\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 344\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ve\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m in_fit:\n\u001b[1;32m    347\u001b[0m     \u001b[38;5;66;03m# Use the dtype seen in `fit` for non-`fit` conversion\u001b[39;00m\n\u001b[1;32m    348\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_dtype \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mdtype\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/impute/_base.py:327\u001b[0m, in \u001b[0;36mSimpleImputer._validate_input\u001b[0;34m(self, X, in_fit)\u001b[0m\n\u001b[1;32m    324\u001b[0m     force_all_finite \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 327\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(\n\u001b[1;32m    328\u001b[0m         X,\n\u001b[1;32m    329\u001b[0m         reset\u001b[38;5;241m=\u001b[39min_fit,\n\u001b[1;32m    330\u001b[0m         accept_sparse\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    331\u001b[0m         dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[1;32m    332\u001b[0m         force_all_finite\u001b[38;5;241m=\u001b[39mforce_all_finite,\n\u001b[1;32m    333\u001b[0m         copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy,\n\u001b[1;32m    334\u001b[0m     )\n\u001b[1;32m    335\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m ve:\n\u001b[1;32m    336\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcould not convert\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(ve):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/base.py:565\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    563\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation should be done on X, y or both.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    564\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 565\u001b[0m     X \u001b[38;5;241m=\u001b[39m check_array(X, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[1;32m    566\u001b[0m     out \u001b[38;5;241m=\u001b[39m X\n\u001b[1;32m    567\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py:921\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    916\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    917\u001b[0m             \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[1;32m    918\u001b[0m         )\n\u001b[1;32m    920\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[0;32m--> 921\u001b[0m         _assert_all_finite(\n\u001b[1;32m    922\u001b[0m             array,\n\u001b[1;32m    923\u001b[0m             input_name\u001b[38;5;241m=\u001b[39minput_name,\n\u001b[1;32m    924\u001b[0m             estimator_name\u001b[38;5;241m=\u001b[39mestimator_name,\n\u001b[1;32m    925\u001b[0m             allow_nan\u001b[38;5;241m=\u001b[39mforce_all_finite \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow-nan\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    926\u001b[0m         )\n\u001b[1;32m    928\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_min_samples \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    929\u001b[0m     n_samples \u001b[38;5;241m=\u001b[39m _num_samples(array)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py:161\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m    146\u001b[0m     \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[1;32m    147\u001b[0m     msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    148\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    149\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    160\u001b[0m     )\n\u001b[0;32m--> 161\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n",
      "\u001b[0;31mValueError\u001b[0m: Input X contains infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy='median')  \n",
    "\n",
    "numerical_cols = app_train_domain.select_dtypes(include=['float64', 'int64']).columns\n",
    "app_train_domain[numerical_cols] = imputer.fit_transform(app_train_domain[numerical_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "60063b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assume the target variable is named 'TARGET'\n",
    "X = app_train.drop(columns=['TARGET'])\n",
    "y = app_train['TARGET']\n",
    "\n",
    "# Splitting the dataset into the training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "975326e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TARGET\n",
      "0.0    20545\n",
      "1.0     1923\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Use a smaller subset of the training data for hyperparameter tuning\n",
    "subsample_index = np.random.choice(X_train.index, size=int(len(X_train) * 0.1), replace=False)\n",
    "X_train_subsample = X_train.loc[subsample_index]\n",
    "y_train_subsample = y_train.loc[subsample_index]\n",
    "# grid_log_reg.fit(X_train_subsample, y_train_subsample)\n",
    "print((y_train_subsample.value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "33d6c2f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TARGET\n",
      "0.0    20545\n",
      "1.0    20545\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Create an instance of SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "# Use the instance to resample the data\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train_subsample, y_train_subsample)\n",
    "\n",
    "print((y_train_resampled.value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "4b6662d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "to_scale = [col for col in app_train.columns if app_train[col].max() > 1]\n",
    "\n",
    "app_train[to_scale] = scaler.fit_transform(app_train[to_scale])\n",
    "\n",
    "# app_train remains a DataFrame\n",
    "print(type(app_train))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e2b75fff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9266\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Initializing the Logistic Regression model\n",
    "model = LogisticRegression(random_state=42, max_iter=1000)\n",
    "\n",
    "# Training the model\n",
    "model.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "# Making predictions\n",
    "y_pred = model.predict(X_train_resampled)\n",
    "\n",
    "# Evaluating the model\n",
    "accuracy = accuracy_score(y_train_resampled, y_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')\n",
    "\n",
    "cm = confusion_matrix(y_train_resampled, y_pred)\n",
    "\n",
    "# Plotting the confusion matrix\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('Actual Labels')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5116f75a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "to_scale = [col for col in app_train.columns if app_train[col].max() > 1]\n",
    "\n",
    "app_train[to_scale] = scaler.fit_transform(app_train[to_scale])\n",
    "\n",
    "# app_train remains a DataFrame\n",
    "print(type(app_train))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9672eb23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "print(type(app_train))\n",
    "\n",
    "# Split the data into features and target label\n",
    "X = app_train.drop(columns=['TARGET'])\n",
    "y = app_train['TARGET']\n",
    "\n",
    "# Splitting data into training and validation sets\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c360a0a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TARGET\n",
      "0.0    22631\n",
      "1.0    22631\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Create an instance of SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "# Use the instance to resample the data\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train_subsample, y_train_subsample)\n",
    "\n",
    "print((y_train_resampled.value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c8d468d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9926cb6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression setup\n",
    "log_reg = LogisticRegression(random_state=42)\n",
    "param_log_reg = {\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10],\n",
    "}\n",
    "\n",
    "\n",
    "# K-Nearest Neighbors setup\n",
    "knn = KNeighborsClassifier()\n",
    "param_knn = {\n",
    "    'n_neighbors': [5,10,15],\n",
    "}\n",
    "\n",
    "# Decision Tree setup\n",
    "decision_tree = DecisionTreeClassifier(random_state=42)\n",
    "param_decision_tree = {\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 10, 20],\n",
    "    'min_samples_leaf': [1, 5, 10]\n",
    "}\n",
    "\n",
    "# Support Vector Machine setup\n",
    "svm = LinearSVC(random_state = 42)\n",
    "params_svm = {'C': [0.001, 0.01, 0.1, 1, 10]   }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9fd5d030",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# Grid Search for Logistic Regression\n",
    "grid_log_reg = GridSearchCV(log_reg, param_log_reg, cv=5)\n",
    "grid_log_reg.fit(X_train_resampled, y_train_resampled)\n",
    "print('1')\n",
    "\n",
    "# Grid Search for K-Nearest Neighbors\n",
    "grid_knn = GridSearchCV(knn, param_knn, cv=5)\n",
    "grid_knn.fit(X_train_resampled, y_train_resampled)\n",
    "print('2')\n",
    "\n",
    "# Grid Search for Decision Trees\n",
    "grid_decision_tree = GridSearchCV(decision_tree, param_decision_tree, cv=5)\n",
    "grid_decision_tree.fit(X_train_resampled, y_train_resampled)\n",
    "print('3')\n",
    "\n",
    "# Grid Search for Support Vector Machine\n",
    "grid_svm = GridSearchCV(svm, params_svm, cv=5)\n",
    "grid_svm.fit(X_train_resampled, y_train_resampled)\n",
    "print('4')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a57d8b53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for Logistic Regression: {'C': 1}\n",
      "Best score for Logistic Regression: 0.8772207349720127\n",
      "Best parameters for KNN: {'n_neighbors': 5}\n",
      "Best score for KNN: 0.8257483572645412\n",
      "Best parameters for Decision Tree: {'max_depth': 20, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
      "Best score for Decision Tree: 0.8959844244341688\n",
      "Best parameters for SVM: {'C': 0.1}\n",
      "Best score for SVM: 0.5\n"
     ]
    }
   ],
   "source": [
    "# Output the best settings and performance metrics\n",
    "print(\"Best parameters for Logistic Regression:\", grid_log_reg.best_params_)\n",
    "print(\"Best score for Logistic Regression:\", grid_log_reg.best_score_)\n",
    "\n",
    "print(\"Best parameters for KNN:\", grid_knn.best_params_)\n",
    "print(\"Best score for KNN:\", grid_knn.best_score_)\n",
    "\n",
    "print(\"Best parameters for Decision Tree:\", grid_decision_tree.best_params_)\n",
    "print(\"Best score for Decision Tree:\", grid_decision_tree.best_score_)\n",
    "\n",
    "print(\"Best parameters for SVM:\", grid_svm.best_params_)\n",
    "print(\"Best score for SVM:\", grid_svm.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6a5a0566",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = grid_decision_tree.best_estimator_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "bdaed356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Score: 0.5427\n",
      "Accuracy: 0.8437\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.91      0.91     51527\n",
      "         1.0       0.13      0.16      0.14      4645\n",
      "\n",
      "    accuracy                           0.84     56172\n",
      "   macro avg       0.53      0.53      0.53     56172\n",
      "weighted avg       0.86      0.84      0.85     56172\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score, accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "y_pred_proba = best_model.predict_proba(X_test)[:, 1]\n",
    "roc_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "print(f'ROC AUC Score: {roc_auc:.4f}')\n",
    "\n",
    "# Predict class labels for accuracy and other classification metrics\n",
    "y_pred = best_model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')\n",
    "\n",
    "# Detailed classification report\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5109f0f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_feature_importance(model):\n",
    "    importance = pd.DataFrame(model.feature_importances_, index=X_test.columns, columns=['Importance'])\n",
    "    importance.sort_values(by='Importance', ascending=False, inplace=True)\n",
    "    importance.head(10).plot(kind='barh')\n",
    "    plt.title('Top 10 Important Features')\n",
    "    plt.show()\n",
    "    \n",
    "plot_feature_importance(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9833e42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic Example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  }
 ],
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