FeatureImportance+RF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Succesfull\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import io\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "print ('Succesfull')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import warnings filter\n",
    "from warnings import simplefilter\n",
    "# ignore all future warnings\n",
    "simplefilter(action='ignore', category=FutureWarning)\n",
    "#ignore all Depricated Warning\n",
    "simplefilter(action='ignore', category=DeprecationWarning)\n",
    "simplefilter(action='ignore', category=UserWarning)\n",
    "simplefilter(action='ignore', category=RuntimeWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (307511, 122)\n",
      "Testing data shape:  (48744, 121)\n"
     ]
    }
   ],
   "source": [
    "app_train = pd.read_csv('F:/Project/application_train.csv')\n",
    "app_test = pd.read_csv('F:/Project/application_test.csv')\n",
    "\n",
    "print('Training data shape: ', app_train.shape)\n",
    "print('Testing data shape: ', app_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <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",
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       "      <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",
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       "      <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  \\\n",
       "0             ...                             0                0   \n",
       "1             ...                             0                0   \n",
       "2             ...                             0                0   \n",
       "3             ...                             0                0   \n",
       "4             ...                             0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "3                0                0                        NaN   \n",
       "4                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\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_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\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_YEAR  \n",
       "0                         1.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "3                         NaN  \n",
       "4                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "le_count = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 columns were label encoded.\n"
     ]
    }
   ],
   "source": [
    "# Iterate through the columns\n",
    "for col in app_train:\n",
    "    if app_train[col].dtype == 'object':\n",
    "        # If 2 or fewer unique categories\n",
    "        if len(list(app_train[col].unique())) <= 2:\n",
    "            # Train on the training data\n",
    "            le.fit(app_train[col])\n",
    "            # Transform both training and testing data\n",
    "            app_train[col] = le.transform(app_train[col])\n",
    "            app_test[col] = le.transform(app_test[col])\n",
    "            \n",
    "            # Keep track of how many columns were label encoded\n",
    "            le_count += 1\n",
    "\n",
    "print('%d columns were label encoded.' % le_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features shape:  (307511, 243)\n",
      "Testing Features shape:  (48744, 239)\n"
     ]
    }
   ],
   "source": [
    "app_train = pd.get_dummies(app_train)\n",
    "app_test = pd.get_dummies(app_test)\n",
    "\n",
    "print('Training Features shape: ', app_train.shape)\n",
    "print('Testing Features shape: ', app_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features shape:  (307511, 240)\n",
      "Testing Features shape:  (48744, 239)\n"
     ]
    }
   ],
   "source": [
    "train_labels = app_train['TARGET']\n",
    "\n",
    "# Align the training and testing data, keep only columns present in both dataframes\n",
    "app_train, app_test = app_train.align(app_test, join = 'inner', axis = 1)\n",
    "\n",
    "# Add the target back in\n",
    "app_train['TARGET'] = train_labels\n",
    "\n",
    "print('Training Features shape: ', app_train.shape)\n",
    "print('Testing Features shape: ', app_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    307511.000000\n",
       "mean         43.936973\n",
       "std          11.956133\n",
       "min          20.517808\n",
       "25%          34.008219\n",
       "50%          43.150685\n",
       "75%          53.923288\n",
       "max          69.120548\n",
       "Name: DAYS_BIRTH, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(app_train['DAYS_BIRTH'] / -365).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    307511.000000\n",
       "mean      63815.045904\n",
       "std      141275.766519\n",
       "min      -17912.000000\n",
       "25%       -2760.000000\n",
       "50%       -1213.000000\n",
       "75%        -289.000000\n",
       "max      365243.000000\n",
       "Name: DAYS_EMPLOYED, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train['DAYS_EMPLOYED'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "app_train['DAYS_EMPLOYED'].plot.hist(title = 'Days Employment Histogram');\n",
    "plt.xlabel('Days Employment');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Positive Correlations:\n",
      " DAYS_REGISTRATION                                    0.041975\n",
      "OCCUPATION_TYPE_Laborers                             0.043019\n",
      "FLAG_DOCUMENT_3                                      0.044346\n",
      "REG_CITY_NOT_LIVE_CITY                               0.044395\n",
      "FLAG_EMP_PHONE                                       0.045982\n",
      "NAME_EDUCATION_TYPE_Secondary / secondary special    0.049824\n",
      "REG_CITY_NOT_WORK_CITY                               0.050994\n",
      "DAYS_ID_PUBLISH                                      0.051457\n",
      "CODE_GENDER_M                                        0.054713\n",
      "DAYS_LAST_PHONE_CHANGE                               0.055218\n",
      "NAME_INCOME_TYPE_Working                             0.057481\n",
      "REGION_RATING_CLIENT                                 0.058899\n",
      "REGION_RATING_CLIENT_W_CITY                          0.060893\n",
      "DAYS_BIRTH                                           0.078239\n",
      "TARGET                                               1.000000\n",
      "Name: TARGET, dtype: float64\n",
      "\n",
      "Most Negative Correlations:\n",
      " EXT_SOURCE_3                           -0.178919\n",
      "EXT_SOURCE_2                           -0.160472\n",
      "EXT_SOURCE_1                           -0.155317\n",
      "NAME_EDUCATION_TYPE_Higher education   -0.056593\n",
      "CODE_GENDER_F                          -0.054704\n",
      "NAME_INCOME_TYPE_Pensioner             -0.046209\n",
      "ORGANIZATION_TYPE_XNA                  -0.045987\n",
      "DAYS_EMPLOYED                          -0.044932\n",
      "FLOORSMAX_AVG                          -0.044003\n",
      "FLOORSMAX_MEDI                         -0.043768\n",
      "FLOORSMAX_MODE                         -0.043226\n",
      "EMERGENCYSTATE_MODE_No                 -0.042201\n",
      "HOUSETYPE_MODE_block of flats          -0.040594\n",
      "AMT_GOODS_PRICE                        -0.039645\n",
      "REGION_POPULATION_RELATIVE             -0.037227\n",
      "Name: TARGET, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Find correlations with the target and sort\n",
    "correlations = app_train.corr()['TARGET'].sort_values()\n",
    "\n",
    "# Display correlations\n",
    "print('Most Positive Correlations:\\n', correlations.tail(15))\n",
    "print('\\nMost Negative Correlations:\\n', correlations.head(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.07823930830982712"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Effect of Age on Repayment\n",
    "# Find the correlation of the positive days since birth and target\n",
    "app_train['DAYS_BIRTH'] = abs(app_train['DAYS_BIRTH'])\n",
    "app_train['DAYS_BIRTH'].corr(app_train['TARGET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5M3Dv7ecDBw5kypQpODg4mG8d8fb2NhffNm3a4OXlxbBhw5gxYwZXrlxhypQpvPTSS7oSVh7Z6Omz+S3hRo77OZS+yz8/nJEPGYlIThVqsTxw4ABdunQxfw8ODiY4OJh+/foRGhoKwMaNG7lx4wb9+/fP0N/GxoZjx46xdu1arl69ipOTE82bN2fFihVUrlzZHPf+++9jY2PDoEGDSElJoUWLFnzyySfY2NiY1xMWFkZQUBAdOnSgfPny9OrVixkz9EMlj+63hBvs9n0tx/2e2fNRPmQjIrlRqMWyefPmJCYmZhkzYMAABgwYkGmbra0tGzdutLqd8uXLM3v27CwfMODm5kZYWJjVdYmISMlTbK6GFSlsuT2cGnPmPOjUt0ixpmIpkk25PZxa5dTb+ZCNiBSkYnE1rIiISGFSsRQREbFCxVJERMQKnbMUKaLO/v4bncZMzVGfOvYV+WjK+PxJSKQEU7EUKaJSSlfI+QVFB5fkTzIiJZwOw4qIiFihYikiImKFiqWIiIgVOmcp8hg5FX0ixxcFgS4MErFGxVLkMZJsUz5XTxnShUEiWdNhWBEREStULEVERKxQsRQREbFC5yylSMjt6690YYqIFAQVSykScvv6K12YIiIFQYdhRURErFCxFBERsULFUkRExAoVSxERESsKtVju3r2bvn374uXlhdFoZPXq1Rbtw4cPx2g0Wnzatm1rEXPr1i3Gjx9P7dq1cXFxoW/fvly8eNEi5vz58/Tp0wcXFxdq167Nm2++ye3bty1ifvzxR1q2bImTkxN/+ctfWL58ef7stIiIFDuFWixv3LhBgwYNmDlzJra2tpnGtGrViujoaPMnPDzcon3SpEl89dVXLFu2jM2bN3P9+nX69OlDamoqAKmpqfTp04ekpCQ2b97MsmXLiIiI4O233zav48yZM/Tu3Rt/f39++OEH3njjDd58802+/PLL/Nt5EREpNgr11pF27drRrl07AEaMGJFpTLly5XBycsq07erVq6xatYqFCxfSunVrAD799FN8fHzYsWMHgYGBbN++nePHj3P48GFcXV0BmDZtGqNHj+add96hSpUqrFixAmdnZ2bPng2Ap6cnP//8MwsWLKBbt255vdsiIlLMFPn7LPfu3UvdunWxs7OjWbNmvPPOOzg4OABw8OBB7ty5Q5s2bczxrq6ueHp6sn//fgIDA4mKisLT09NcKAECAwO5desWBw8epEWLFkRFRVmsIz3mX//6F3fu3KFMmTIFs7OSY3rLhogUhCJdLNu2bUuXLl2oWbMm586dY8aMGXTt2pUdO3ZQrlw54uLisLGxwd7e3qKfg4MDcXFxAMTFxZmLazp7e3tsbGwsYlq1apVhHXfv3iUhIQFnZ+dM84uJicnR/uQ0vrh675OVnLt2J0d9zl76E3xzvq0kyubqYQYnVo/hyGsTctQntzmmpqblvFMu5XZbyTeTi9XfZ3HKtaBpbDKXnXHx8PB4aFuRLpY9e/Y0/9vb2xtfX198fHzYtm0bXbt2fWg/k8mEwWAwf7//3/fLKsZkMmXZF7Ie2AfFxMTkKL44++/d0vyn6fAc9amy/m3rQZmwscndafc75Srxn6ajc9SnoHMsyG3ZVrAtNn+fJem/pZzS2GQuL8alWN06Ur16dVxcXDh9+jQAjo6OpKamkpCQYBEXHx9vnk06OjqaZ5DpEhISSE1NzTImPj6e0qVL88QTT+TX7oiISDFRrIplQkICly5dMl/w4+vrS5kyZYiMjDTHXLx4kejoaAICAgDw9/cnOjra4naSyMhIypUrh6+vrzlmx44dFtuKjIzk6aef1vlKEREp3MOwSUlJ5lliWloaFy5c4NChQ1StWpWqVasyc+ZMunbtipOTE+fOnWP69Ok4ODjQuXNnAOzs7Bg4cCBTpkzBwcGBqlWr8vbbb+Pt7W0+B9mmTRu8vLwYNmwYM2bM4MqVK0yZMoWXXnqJKlWqADBo0CCWLFnCxIkTGTRoEPv372fNmjUsXbq0UMZFpKDpQimRrBVqsTxw4ABdunQxfw8ODiY4OJh+/foxZ84cjh07xtq1a7l69SpOTk40b96cFStWULlyZXOf999/HxsbGwYNGkRKSgotWrTgk08+wcbGBgAbGxvCwsIICgqiQ4cOlC9fnl69ejFjxgzzOmrVqsW6det46623WL58Oc7OzoSEhOi2ESkxkm3K660vIlko1GLZvHlzEhMTH9q+ceNGq+soX748s2fPNt8jmRk3NzfCwsKyXM9zzz3HDz/8YHV7IiJS8hSrc5YiIiKFQcVSRETEChVLERERK1QsRURErCjST/ARESkMo6fP5reEGznup1tpHl8qliIiD/gt4YZupRELKpYikmu5eZiBZl9SHKlYykPl9lBUzJnzuXo7hxQ/uXqYgWZfUgypWMpD5fZQVJVTuXs7h4hIUaWrYUVERKxQsRQREbFCxVJERMQKnbMUkcdabi5U00Vq8iAVSxF5rOXmQjVdpCYP0mFYERERK1QsRURErFCxFBERsULnLEWkQOXmEXlQPB6T9zjvW0mnYikiBSpXj8iDYvGYvMd530o6HYYVERGxQsVSRETEikItlrt376Zv3754eXlhNBpZvXq1ue3OnTu8++67NG3aFBcXFzw9PRkyZAjnz5+3WEenTp0wGo0Wn8GDB1vEJCYmMnToUNzd3XF3d2fo0KEkJiZaxBw9epQXXngBZ2dnvLy8CAkJwWQy5d/Oi4hIsVGo5yxv3LhBgwYN6NevH8OGDbNou3nzJr/++itBQUH4+Phw7do1Jk+eTK9evdi9ezelS/8v9f79+zNlyhTz9/Lly1usa8iQIVy4cIHw8HAMBgOjR4/m9ddfJywsDIBr167x4osv0rRpU7Zv305MTAwjR46kQoUK/P3vf8/HERCR7Eq/eCb5ZjK2FWyz3U9P45G8UKjFsl27drRr1w6AESNGWLTZ2dnxxRdfWCybO3cuTZo0ITo6Gm9vb/PyChUq4OTklOk2oqOj+f7779m6dSsBAQHm9XTs2JGYmBg8PDwIDw8nOTmZ0NBQbG1tadCgASdPnmTRokWMGjUKg8GQl7stIrmQ24tn9DQeyQvF6pzl9evXATAajRbLN2zYQO3atWnSpAmTJ082xwFERUVRqVIlc6EEaNKkCRUrVmT//v3mmGeffRZb2//9v9XAwEAuXbrE2bNn83OXRESkGCg2t47cvn2byZMn06FDB2rUqGFe/te//hU3NzecnZ05ceIE06ZN48iRI+ZZaVxcHPb29hazQ4PBQLVq1YiLizPHuLi4WGzPwcHB3FarVq183jsRKclyc3+m7s0sWMWiWN69e5ehQ4dy9epV/vWvf1m0vfLKK+Z/e3t7U6tWLQIDAzl48CC+vvdOVGR2GNVkMmUooA+2P6xvupiYmBztR07jC1vyzeRc9UtNTSuQPgXdr6BzLMhtPc7jWBxyTKJsjg8xJ+/5KNPflOL2O1NQsjMuHh4eD20r8sXy7t27vPrqqxw7doyvv/6aJ554Isv4p59+GhsbG06fPo2vry+Ojo7Ex8dbFEeTyURCQoJ59ujo6GieZaaLj48H/jfDzExWA/ug9POjxUlOLqK4n41Nzo/u56ZPQfcr6BwLcluP8zg+rjnaVrDN8JtSHH9nCkJejEuRPmd5584dBg0axNGjR/nqq68eehHP/Y4ePUpqaqo51t/fn6SkJKKioswxUVFR3Lhxw3we09/fn71795KSkmKOiYyMpHr16tSsWTOP90pERIqbQi2WSUlJHDp0iEOHDpGWlsaFCxc4dOgQ58+f5+7du7z88sv8/PPPLF26FIPBQGxsLLGxsSQn3zs8+PvvvxMSEsKBAwc4e/Ys3377La+++ioNGzakSZMmAHh6etK2bVvGjRvHTz/9RFRUFOPGjaN9+/bm/6fRq1cvbG1tGTFiBMeOHSMiIoJ58+YxYsQIXQkrIiKFexj2wIEDdOnSxfw9ODiY4OBg+vXrx8SJE9m8eTMArVq1sui3cOFC+vfvT5kyZdi5cyeffPIJN27coEaNGrRr146JEydiY2Njjl+yZAkTJkygR48eAHTs2JFZs2aZ2+3s7Ni0aRNBQUG0bt0ao9HIyJEjGTVqVD7uvYhI7mV2UVB27kHVhUG5U6jFsnnz5hmepHO/rNoAXF1dzQU1K1WrVmXx4sVZxnh7e7Nlyxar6xIRKQr00PaCVaTPWYqIiBQFKpYiIiJWqFiKiIhYoWIpIiJihYqliIiIFSqWIiIiVqhYioiIWKFiKSIiYoWKpYiIiBUqliIiIlaoWIqIiFihYikiImKFiqWIiIgVKpYiIiJWqFiKiIhYoWIpIiJiRY6KZUhICMeOHXto+/HjxwkJCXnkpERERIqSHBXLmTNncvTo0Ye2q1iKiMjjKE8PwyYlJVGmTJm8XKWIiEihK20t4MiRIxw+fNj8fe/evdy9ezdDXGJiIsuXL6du3bp5m6GIiOSZU9En6DRmao771bGvyEdTxud9QsWE1WL59ddfmw+tGgwGVqxYwYoVKzKNrVy5MkuWLMnbDEVEJM8k25Rnt+9rOe94sGT/tlstli+99BJt27bFZDLRrl07Jk6cSGBgoEWMwWCgQoUK1KlTh7Jly+ZbsiIiIoXB6jlLFxcX/Pz8aNy4MV999RWDBw/Gz8/P4tOoUSO8vLxyXCh3795N37598fLywmg0snr1aot2k8lEcHAw9evXx9nZmU6dOnH8+HGLmMTERIYOHYq7uzvu7u4MHTqUxMREi5ijR4/ywgsv4OzsjJeXFyEhIZhMJouYL7/8koCAABwdHQkICOCrr77K0b6IiMjjK0cX+Dz33HM4ODjk2cZv3LhBgwYNmDlzJra2thna58+fz8KFCwkJCWH79u04ODjw4osvcv36dXPMkCFDOHToEOHh4axfv55Dhw7x+uuvm9uvXbvGiy++iKOjI9u3b2fmzJl8/PHHLFiwwBwTFRXF4MGD+etf/8quXbv461//yiuvvMLPP/+cZ/sqIiLFl9XDsA/asWMHK1eu5MyZM1y5ciXDDM1gMHDw4MFsratdu3a0a9cOgBEjRli0mUwmQkNDGTt2LN26dQMgNDQUDw8P1q9fz6BBg4iOjub7779n69atBAQEADB37lw6duxITEwMHh4ehIeHk5ycTGhoKLa2tjRo0ICTJ0+yaNEiRo0ahcFgIDQ0lObNmxMUFASAp6cnu3btIjQ0lGXLluV0iERE5DGTo5llaGgoPXr0YPfu3VSvXp2mTZvSrFkzi0/Tpk3zJLGzZ88SGxtLmzZtzMtsbW1p2rQp+/fvB+7NCCtVqmQulABNmjShYsWKFjHPPvusxcw1MDCQS5cucfbsWQB++ukni+2kx6SvQ0RESrYczSwXLlxIs2bN2LBhQ75fyBMbGwuQ4bCvg4MDly5dAiAuLg57e3sMBoO53WAwUK1aNeLi4swxLi4uGdaR3larVi1iY2Mz3U76Oh4mJiYmR/uU0/jClnwzOVf9UlPTCqRPQfcr6BwLcluP8zgqx7zpl3wzudj9ht0vO7l7eHg8tC1HxTIhIYH/+7//K9ArXu8vhHDv8OyDxfFB1mLSDx1bi8ls3ffLamAflH5YuDixrZDxPHJ22Njk/FkXuelT0P0KOseC3NbjPI7KMW/62VawLXa/Yeny4vc3R6Pm6+vLuXPnHmmD2eXk5ASQYXYXHx9vngU6OjoSHx9vcd7UZDKRkJBgEZPZOuB/M0wnJ6cstyMiIiVbjmaW7733Hv369aN169a0aNEiv3ICoGbNmjg5OREZGckzzzwDQEpKCnv37mX69OkA+Pv7k5SURFRUlPm8ZVRUFDdu3DB/9/f3Z+rUqaSkpFC+fHkAIiMjqV69OjVr1gSgcePGREZGMnr0aPP2IyMjLc6FioiUZLl58s/j9NSfHBXL4OBgqlSpQvfu3alTpw5ubm7Y2NhYxBgMBtatW5et9SUlJXH69GkA0tLSuHDhAocOHaJq1aq4ubkxfPhwPvzwQzw8PKhbty4ffPABFStWpFevXsC9q1bbtm3LuHHjmD9/PiaTiXHjxtG+fXvzlLtXr16EhIQwYsQIgoKCOHXqFPPmzePNN980H2YdNmwYL7zwAnPmzKFz5858/fVixQWnAAAgAElEQVTX7Nq1i61bt+ZkeEREHlu5evLPY/TUnxwVyxMnTmAwGHB1deXWrVucOnUqQ4y183z3O3DgAF26dDF/Dw4OJjg4mH79+hEaGsqYMWNITk5m/PjxJCYm0qhRIzZu3EjlypXNfZYsWcKECRPo0aMHAB07dmTWrFnmdjs7OzZt2kRQUBCtW7fGaDQycuRIRo0aZY4JCAhg+fLlzJgxg+DgYJ588kmWL1+On59fToZHREQeUzkqlvc/UD0vNG/ePMPTdu5nMBiYNGkSkyZNemhM1apVWbx4cZbb8fb2ZsuWLVnGdOvWzXw/p4iIyP0K7jI9ERGRYipHM8vz589nK87NzS1XyYiIiBRFOSqWDRs2zNY5ycuXL+c6IRERkaImR8VywYIFGYplamoqZ8+eZe3atTg6OjJkyJA8TVBERKSw5ahY9u/f/6FtY8eOpU2bNiQlJT1yUiIiIkVJnl3gU6lSJfr378+iRYvyapUiIiJFQp5eDVumTBnzQ85FREQeF3lWLA8fPswnn3yCp6dnXq1SRESkSMiTq2GvXr3KtWvXqFSpEgsXLsyz5ERERIqCHBXLZs2aZSiWBoMBo9FI7dq16dmzJ0ajMU8TFBERKWw5KpahoaH5lYeIiEiR9UjnLK9fv87169fzKhcREZEiKcfF8ty5c7z++uvUrl2bmjVrUrNmTWrXrs2wYcMK7MXQIiIiBSlHh2FjYmJo3749V69epVWrVnh6emIymYiJiSE8PJzvvvuObdu2Ubdu3fzKV0REpMDlqFhOmzYNk8lEZGQkDRs2tGg7fPgw3bp1Y9q0aaxatSpPkxQRESlMOToM++OPP/L6669nKJQAPj4+vPbaa+zatSvPkhMRESkKclQsb9++TZUqVR7abmdnx+3btx85KRERkaIkR8WyQYMGhIWFkZycnKHt1q1bhIWF0aBBgzxLTkREpCjI0TnLN954gwEDBtC6dWteffVVPDw8ADh58iTLly8nJiaGzz//PF8SldwbPX02vyXcyHG/mDPnwTcfEhIRKWZyVCxfeOEFFi9ezOTJk3nzzTfNT/MxmUw4OTmxePFiOnbsmC+JSu79lnCD3b6v5bhflVNv50M2IiLFT46KJUCvXr3o3r07Bw8eNN9X6e7ujq+vL6VL53h1IiIiRV6unuBTunRp/Pz86NGjBz169MDPzy9fCqWPjw9GozHDp3fv3gAEBwdnaKtXr57FOkwmE8HBwdSvXx9nZ2c6derE8ePHLWISExMZOnQo7u7uuLu7M3ToUBITE/N8f0REpHiyWixjY2Np3Lgx//jHP7KM+8c//oG/vz/x8fF5llxkZCTR0dHmz86dOzEYDHTv3t0c4+HhYRGzZ88ei3XMnz+fhQsXEhISwvbt23FwcODFF1+0eEzfkCFDOHToEOHh4axfv55Dhw7x+uuv59l+iIhI8Wa1WH7yySdcvnyZsWPHZhk3ZswYEhIS+PTTT/MsuWrVquHk5GT+fPfdd1SuXNmiWJYuXdoiplq1auY2k8lEaGgoY8eOpVu3bjRo0IDQ0FCSkpJYv349ANHR0Xz//ffMmzePgIAA/P39mTt3Ltu2bSMmJibP9kVERIovq8Xy22+/pUePHlSuXDnLuCpVqtCzZ0+2bNmSZ8ndz2QysWrVKvr06UOFChXMy8+cOYOXlxcNGzZk8ODBnDlzxtx29uxZYmNjadOmjXmZra0tTZs2Zf/+/QBERUVRqVIlAgICzDFNmjShYsWK5hgRESnZrJ5o/P333xk6dGi2Vubt7Z1vt45ERkZy9uxZBg4caF7m5+fHokWL8PDwID4+ntmzZ9OuXTv27dvHE088QWxsLAAODg4W63JwcODSpUsAxMXFYW9vb/GeToPBQLVq1YiLi8syp5zOPAtrppp8M+N9sdmRmppWYP0Kclu57VfQORbkth7ncVSOhdcv+WZykTlCl5080m+HzIzVYmkwGEhLy94gpaWlZXg5dF5ZuXIlzzzzjMWj9p5//nmLGD8/P3x9fVmzZg2jRo0yL38wJ5PJlKE4PujBmMxkNbAPiomJyVF8XrKtYJurfjY2uXuDW276FeS2ctuvoHMsyG09zuOoHAuvn20F20L73btfXvz+Wt17d3d3fvnll2yt7D//+Q/u7u6PlFBm/vvf/7J582ZefvnlLOMqVapE/fr1OX36NABOTk4AGWaI8fHx5tmmo6Mj8fHxmEwmc7vJZCIhISHDjFREREomq8Wyffv2bNiwgZMnT2YZd/LkSdavX0+HDh3yLLl0q1evply5cvTo0SPLuJSUFGJiYsxFsmbNmjg5OREZGWkRs3fvXvM5Sn9/f5KSkoiKijLHREVFcePGDYvzmCIiUnJZPQw7atQoPv/8c7p06cJ7771H9+7dLe6pvHv3Ll988QWTJ0+mcuXKFoc/84LJZOKzzz7L9CKjyZMn06FDB1xdXc3nLG/evEm/fv2Ae4dXhw8fzocffoiHhwd169blgw8+oGLFivTq1QsAT09P2rZty7hx45g/fz4mk4lx48bRvn37InH44EG5eXSdHlsnIvJorBbLatWqER4eTv/+/Rk6dCijR4+mbt26VKpUiaSkJE6dOkVKSgrVq1dn7dq12Nvb52mCu3bt4vTp0yxZsiRD2x9//MGQIUNISEigWrVq+Pn58d1331kcCh4zZgzJycmMHz+exMREGjVqxMaNGy0K75IlS5gwYYJ55tqxY0dmzZqVp/uRV3Lz6Do9tk5E5NFk67E7Tz/9NHv37mXFihVs3bqV6Ohorl+/TuXKlWnYsCEdO3bklVdewc7OLs8TbNGixUOfprN8+XKr/Q0GA5MmTWLSpEkPjalatSqLFy/OdY4iIvJ4y/Yz6uzs7Bg7dqzVhxOIiIg8bgrumnYREZFiSsVSRETEChVLERERK1QsRURErFCxFBERsULFUkRExAoVSxEREStULEVERKxQsRQREbFCxVJERMQKFUsRERErVCxFRESsULEUERGxQsVSRETEChVLERERK1QsRURErFCxFBERsULFUkRExAoVSxEREStULEVERKwo0sUyODgYo9Fo8alXr5653WQyERwcTP369XF2dqZTp04cP37cYh2JiYkMHToUd3d33N3dGTp0KImJiRYxR48e5YUXXsDZ2RkvLy9CQkIwmUwFso8iIlL0FeliCeDh4UF0dLT5s2fPHnPb/PnzWbhwISEhIWzfvh0HBwdefPFFrl+/bo4ZMmQIhw4dIjw8nPXr13Po0CFef/11c/u1a9d48cUXcXR0ZPv27cycOZOPP/6YBQsWFOh+iohI0VW6sBOwpnTp0jg5OWVYbjKZCA0NZezYsXTr1g2A0NBQPDw8WL9+PYMGDSI6Oprvv/+erVu3EhAQAMDcuXPp2LEjMTExeHh4EB4eTnJyMqGhodja2tKgQQNOnjzJokWLGDVqFAaDoUD3V0REip4iP7M8c+YMXl5eNGzYkMGDB3PmzBkAzp49S2xsLG3atDHH2tra0rRpU/bv3w9AVFQUlSpVMhdKgCZNmlCxYkWLmGeffRZbW1tzTGBgIJcuXeLs2bMFsIciIlLUFeli6efnx6JFiwgPD+ejjz4iNjaWdu3acfnyZWJjYwFwcHCw6OPg4EBcXBwAcXFx2NvbW8wODQYD1apVs4jJbB3pbSIiIkX6MOzzzz9v8d3Pzw9fX1/WrFlD48aNATIcJjWZTBmK44OsxaRf3GPtEGxMTEw29iL38ZlJvpmc4z6pqWm52lZB9lOOeUPjmDf9lGPe9Eu+mZwnv3t5ITt5eHh4PLStSBfLB1WqVIn69etz+vRpOnfuDNyb/bm6uppj4uPjzTNDR0dH4uPjLYqjyWQiISHBIubBGWR8fDyQcdb6oKwG9kHp50gflW0FW+tBD7Cxyd0BhILspxzzhsYxb/opx7zpZ1vBNk9+9x5VXvz+FunDsA9KSUkhJiYGJycnatasiZOTE5GRkRbte/fuNZ+j9Pf3JykpiaioKHNMVFQUN27csIjZu3cvKSkp5pjIyEiqV69OzZo1C2jPRESkKCvSxXLy5Mn8+OOPnDlzhp9//pmXX36Zmzdv0q9fPwwGA8OHD2fevHlERERw7NgxRowYQcWKFenVqxcAnp6etG3blnHjxvHTTz8RFRXFuHHjaN++vfn/ZfTq1QtbW1tGjBjBsWPHiIiIYN68eYwYMUJXwoqICFDED8P+8ccfDBkyhISEBKpVq4afnx/fffcd7u7uAIwZM4bk5GTGjx9PYmIijRo1YuPGjVSuXNm8jiVLljBhwgR69OgBQMeOHZk1a5a53c7Ojk2bNhEUFETr1q0xGo2MHDmSUaNGFezOiohIkVWki+Xy5cuzbDcYDEyaNIlJkyY9NKZq1aosXrw4y/V4e3uzZcuWXOUoIiKZOxV9gk5jpua4Xx37inw0ZXzeJ/QIinSxFBGR4ivZpjy7fV/LeceDS/I+mUdUpM9ZioiIFAUqliIiIlaoWIqIiFihYikiImKFiqWIiIgVKpYiIiJWqFiKiIhYoWIpIiJihYqliIiIFSqWIiIiVqhYioiIWKFiKSIiYoWKpYiIiBUqliIiIlaoWIqIiFihYikiImKFiqWIiIgVKpYiIiJWqFiKiIhYoWIpIiJihYqliIiIFUW6WM6ZM4fWrVvj5uZGnTp16NOnD8eOHbOIGT58OEaj0eLTtm1bi5hbt24xfvx4ateujYuLC3379uXixYsWMefPn6dPnz64uLhQu3Zt3nzzTW7fvp3v+ygiIkVfkS6WP/74I6+++irbtm0jIiKC0qVL0717d65cuWIR16pVK6Kjo82f8PBwi/ZJkybx1VdfsWzZMjZv3sz169fp06cPqampAKSmptKnTx+SkpLYvHkzy5YtIyIigrfffrvA9lVERIqu0oWdQFY2btxo8f3TTz/F3d2dffv20bFjR/PycuXK4eTklOk6rl69yqpVq1i4cCGtW7c2r8fHx4cdO3YQGBjI9u3bOX78OIcPH8bV1RWAadOmMXr0aN555x2qVKmST3soIiLFQZEulg9KSkoiLS0No9FosXzv3r3UrVsXOzs7mjVrxjvvvIODgwMABw8e5M6dO7Rp08Yc7+rqiqenJ/v37ycwMJCoqCg8PT3NhRIgMDCQW7ducfDgQVq0aJHn+zJ6+mx+S7iR434xZ86Db56nIyJSZJyKPkGnMVNz3K+OfUU+mjI+7xOimBXLiRMn4uPjg7+/v3lZ27Zt6dKlCzVr1uTcuXPMmDGDrl27smPHDsqVK0dcXBw2NjbY29tbrMvBwYG4uDgA4uLizMU1nb29PTY2NuaYzMTExOQo//vjj5yP5z9NR+eoP0DF6Ek57pOampbjPgXdTznmDY1j3vRTjnnTL7fbSqIsu31fy3G/5D0fPfR3OTu/1x4eHg9tKzbF8q233mLfvn1s3boVGxsb8/KePXua/+3t7Y2vry8+Pj5s27aNrl27PnR9JpMJg8Fg/n7/v+/3sOWQ9cA+KCYmxiLetoJttvvez8Ym56eZc9OnoPspx7yhccybfsoxb/oVdI62FWwz/V1+8Pc3N4r0BT7pJk2axIYNG4iIiKBWrVpZxlavXh0XFxdOnz4NgKOjI6mpqSQkJFjExcfHm2eTjo6OGWaQCQkJpKamZphxiohIyVPki+WECRNYv349ERER1KtXz2p8QkICly5dMl/w4+vrS5kyZYiMjDTHXLx4kejoaAICAgDw9/cnOjra4naSyMhIypUrh6+vThCKiJR0RfowbFBQEGFhYXz++ecYjUZiY2MBqFixIpUqVSIpKYmZM2fStWtXnJycOHfuHNOnT8fBwYHOnTsDYGdnx8CBA5kyZQoODg5UrVqVt99+G29vb1q1agVAmzZt8PLyYtiwYcyYMYMrV64wZcoUXnrpJV0JKyIiRbtYLl26FIBu3bpZLJ8wYQKTJk3CxsaGY8eOsXbtWq5evYqTkxPNmzdnxYoVVK5c2Rz//vvvY2Njw6BBg0hJSaFFixZ88skn5nOfNjY2hIWFERQURIcOHShfvjy9evVixowZBbezIiJSZBXpYpmYmJhlu62tbYZ7MTNTvnx5Zs+ezezZsx8a4+bmRlhYWI5zFBGRx1+RP2cpIiJS2FQsRURErFCxFBERsULFUkRExAoVSxEREStULEVERKxQsRQREbFCxVJERMQKFUsRERErVCxFRESsULEUERGxQsVSRETEChVLERERK1QsRURErFCxFBERsULFUkRExAoVSxEREStULEVERKxQsRQREbFCxVJERMQKFUsRERErVCwfsHTpUho2bIiTkxMtW7Zkz549hZ2SiIgUMhXL+2zcuJGJEyfyf//3f/zwww/4+/vz17/+lfPnzxd2aiIiUohULO+zcOFC/va3v/Hyyy/j6enJ7NmzcXJyYvny5YWdmoiIFCJDYmKiqbCTKApu375N9erVWbZsGd27dzcvDwoK4tixY2zevLkQsxMRkcKkmeX/l5CQQGpqKg4ODhbLHRwciIuLK6SsRESkKFCxfIDBYLD4bjKZMiwTEZGSRcXy/7O3t8fGxibDLDI+Pj7DbFNEREoWFcv/r2zZsvj6+hIZGWmxPDIykoCAgELKSkREioLShZ1AUTJy5Ehef/11GjVqREBAAMuXL+fPP/9k0KBBhZ2aiIgUIs0s79OjRw+Cg4OZPXs2zZs3Z9++faxbtw53d/ds9Z8zZw6tW7fGzc2NOnXq0KdPH44dO2YRYzKZCA4Opn79+jg7O9OpUyeOHz+eH7tTZCxZsoSmTZvi5uaGm5sbzz//PNu2bTO3l8QxycyHH36I0Whk/Pjx5mUldWyCg4MxGo0Wn3r16pnbS+q4APz5558MGzaMOnXq4OTkREBAAD/++KO5vSSOjY+PT4a/F6PRSO/evc0xj/rAGRXLBwwZMoTDhw8TFxfHzp07adasWbb7/vjjj7z66qts27aNiIgISpcuTffu3bly5Yo5Zv78+SxcuJCQkBC2b9+Og4MDL774ItevX8+P3SkSXFxcmDZtGjt37iQyMpIWLVrQv39/jhw5ApTMMXnQTz/9xMqVK/H29rZYXpLHxsPDg+joaPPn/h+3kjouiYmJtG/fHpPJxLp169i/fz+zZs2yuK6iJI5NZGSkxd/Kzp07MRgM5tsA8+KBM7rPMh8lJSXh7u7O6tWr6dixIyaTifr16/Paa68RFBQEQHJyMh4eHvzjH/8oUYd7a9Wqxbvvvssrr7xS4sfk6tWrtGzZkvnz5zNr1iwaNGjA7NmzS/TfS3BwMBEREezduzdDW0kel+nTp7N7926LIzP3K8ljc78PPviAjz76iBMnTlChQgUCAwPx9vbmo48+Msc888wzdOvWjXfffTdb69TMMh8lJSWRlpaG0WgE4OzZs8TGxtKmTRtzjK2tLU2bNmX//v2FlWaBSk1NZcOGDdy4cQN/f3+NCTB27Fi6detGy5YtLZaX9LE5c+YMXl5eNGzYkMGDB3PmzBmgZI/LN998Q6NGjRg0aBB169blueeeY/HixZhM9+Y8JXls0plMJlatWkWfPn2oUKECt2/f5uDBgxZjAtCmTZscjYku8MlHEydOxMfHB39/fwBiY2MBMn3wwaVLlwo8v4J09OhR2rVrR0pKChUrVuTzzz/H29vb/MdaEscEYOXKlZw+fZpPP/00Q1tJ/nvx8/Nj0aJFeHh4EB8fz+zZs2nXrh379u0r0eNy5swZli1bxogRIxg7diyHDx9mwoQJAAwdOrREj026yMhIzp49y8CBA4G8e+CMimU+eeutt9i3bx9bt27FxsbGoq0kPvjAw8ODXbt2cfXqVSIiIhg+fDhff/21ub0kjklMTAzTp09ny5YtlC1b9qFxJXFsnn/+eYvvfn5++Pr6smbNGho3bgyUzHFJS0vj6aefNh86/Mtf/sLp06dZunQpQ4cONceVxLFJt3LlSp555hkaNmxosfxRx0SHYfPBpEmT2LBhAxEREdSqVcu83MnJCaBEPvigbNmy1K5d2/wfuo+PD4sWLSrRYxIVFUVCQgLPPvss9vb22Nvbs3v3bpYuXYq9vT1PPPEEUDLH5kGVKlWifv36nD59ukT/zTg5OeHp6WmxrF69ely4cMHcDiVzbAD++9//snnzZl5++WXzsrx64IyKZR6bMGEC69evJyIiwuJSd4CaNWvi5ORk8eCDlJQU9u7dW+IefJCWlsbt27dL9Jh06tSJPXv2sGvXLvPn6aefpmfPnuzatYu6deuW2LF5UEpKCjExMTg5OZXov5kmTZpw6tQpi2WnTp3Czc0N0G/M6tWrKVeuHD169DAvy6sHzthMnDhxal4lWtIFBQWxdu1a/vnPf+Lq6sqNGze4ceMGcO9/MIPBQGpqKnPnzqVu3bqkpqby9ttvExsby7x58yhXrlwh70H+mDp1KmXLliUtLY2LFy8SGhrKunXrmDp1KnXq1CmRYwJQvnx5HBwcLD7h4eG4u7vTv3//Evv3AjB58mTz38ypU6cYP348p0+fZu7cuRiNxhI7Lq6uroSEhFCqVCmcnZ3ZuXMnM2bMYNy4cTRq1KhE/82YTCZGjhxJ+/btLd4cBVC5cmWCg4NxdnamfPnyzJ49mz179rBgwQLs7OyytX6ds8xDS5cuBaBbt24WyydMmMCkSZMAGDNmDMnJyYwfP57ExEQaNWrExo0bqVy5coHnW1BiY2MZOnQocXFxVKlSBW9vb9avX09gYCBQMscku0rq2Pzxxx8MGTKEhIQEqlWrhp+fH9999535ASEldVyeeeYZVq9ezfTp05k9ezaurq689dZbDBkyxBxTUsdm165dnD59miVLlmRo69GjB5cvX2b27NnExsbi5eWVowfOgO6zFBERsUrnLEVERKxQsRQREbFCxVJERMQKFUsRERErVCxFRESsULEUERGxQsVSpIRbtGgRPj4+3Llzp7BTeWSTJ082378rkpdULEUK0Jo1azAajTz99NOFnQoAN27cYO7cuYwZM4YyZcoUdjqPbOTIkRw+fJjNmzcXdirymFGxFClA6U8N+f3334mKiirsdFizZg03btygX79+hZ1KnqhevTodOnTg448/LuxU5DGjYilSQP78809++OEHJk+ejJubG+vWrSvslPj8889p3749FStWLOxUHurmzZs5iu/Rowd79+7lt99+y6eMpCRSsRQpIOHh4ZQvX54XXniBnj17snHjxkzPE6akpPDWW29Rt25dXFxcePHFF4mJicHHx4fhw4dbxF67do3Jkyfj4+ODo6MjTz31FFOnTuXWrVtW8zl//jy//vorrVq1sljevn17mjVrlmmf1q1bZ4jfsGEDgYGBVK9eHXd3d/r06cOJEycsYo4cOcLw4cPx9fXFycmJOnXq8Oqrr5pfLZVu9erVGI1GfvjhByZOnEi9evVwcXEB4O7du8yePZtGjRrh7OxM7dq1adeuHV9++aXFOtLz++abb6yOgUh2qViKFJCwsDDat29PpUqV6NWrF5cvX+b777/PEDdq1CgWLVpEixYtmD59OjVq1KB79+4ZZljJycl07tyZVatW0aNHD2bNmkX79u1ZsGABgwYNsprPvn37APD19bVY/re//Y2jR49y5MgRi+WnTp3iwIED9OnTx7xs3rx5vPrqq7i4uDB9+nTeeOMNjh49Svv27Tlz5ow5LjIykpiYGHr37s2sWbMYMGAA3333HV26dCE5OTlDbhMmTODgwYO88cYbvPXWWwDMnDmT999/n2bNmhESEkJQUBC1a9fm559/tuhrNBp58skn2bt3r9UxEMkuvXVEpAAcP36cI0eOMGHCBACeeuop85sPOnbsaI779ddfWb9+PYMHD2bOnDnm5TNmzOCDDz6wWOeiRYuIiYlhx44dFi8E9vLyIigoiD179tC0adOH5nTy5Eng3jsQ79e9e3cmTJjAunXreOqpp8zL165dS+nSpenVqxdwb2Y6Y8YMi7fqAPTt2xd/f38++OADFixYAMCrr77K3//+d4vtdOjQgY4dO/LVV1/Ru3dvi7YKFSrw9ddfU7r0/36itm3bRrt27fjoo48euk/patWqZd4/kbygmaVIAQgLC6NKlSq0a9fOvKxnz55s2bKFa9eumZelzzSHDh1q0f/Bw68AmzZtIiAggGrVqpGQkGD+pB+G/OGHH7LM6fLly5QqVYoqVapYLLezs6NTp06sX7+etLQ04N67AsPDw2nTpo357fJfffUVd+/epWfPnhbbL1OmDH5+fhbbr1ChgvnfSUlJXL58mXr16mFnZ8fBgwcz5Pbyyy9bFEq4907C48ePZ3j5cWaqVq1KQkKC1TiR7NLMUiSfmUwm1q9fT7Nmzfjzzz/Nyxs3bkxKSgoREREMGDAAuDdbMxgM1K5d22Id9vb2GI1Gi2W//fYbR44coU6dOpluNz4+Plu5ZaZfv35s2LCBXbt20bJlS/bt28fZs2eZMmWKxfYB/P39M13H/QUyMTGRqVOn8uWXX3LlyhWLuKtXr2boW6tWrQzLJk2axIABA/Dz86N+/fq0adOGXr168cwzz2S6XwaDIdO8RHJDxVIkn+3atYsLFy5w4cIFtmzZkqF93bp15mKZlQcLW1paGi1atOCNN97IND79wpiHeeKJJzCZTFy7di1DIW7dujXOzs6EhYXRsmVL1q1bR+XKlXnhhRcstg+wfv36DLNAgFKl/nfgavDgwezZs4dRo0bRsGFDKleujMFgYPDgweb13M/W1jbDsubNm/Prr7+yZcsWIiMjWbt2LaGhobzzzjsZxiAxMRF7e/ss918kJ1QsRfLZunXrqFq1aqb3/u3cuZNly5bxxx9/4OLigpubGyaTidOnT1O/fn1zXEJCQoYZ2JNPPklSUlKGq1OzK/0855kzZzJc5GNjY0Pv3r1ZsWIF77//Pps2baJr164WRezJJ58EwNXV1SLXByUmJrJ9+3YmTpzIxIkTzRRUZFIAAAPSSURBVMtTUlJITEzMUc5Go5F+/frRr18/kpOT6dWrFyEhIYwZMwYbGxtz3O+//06DBg1ytG6RrOicpUg+Sj/M+vzzz9O5c+cMn5EjR5KWlsb69esBaNu2LQCLFy+2WE9oaGiGdffo0YP//Oc/mT6tJjk5maSkpCxzCwgIAMj0nCHcOxR7/fp1xo0bR2JiIn379rVo79q1K6VLlyY4ODjT2WH6YeD0GeaDM+NFixZl2u9hLl++bPHd1tYWT09Pbt26ZXGl8JUrVzhz5ox5/0TygmaWIvko/QKe+w9f3q9WrVp4eXkRFhbG6NGj+ctf/kKPHj1Yvnw5iYmJNGvWjAMHDrBjxw7s7e0tzsP9/e9/59tvv2XgwIH07t2bRo0acevWLU6dOsWmTZsIDw+ncePGD83Nzc0NHx8fIiMjeeWVVzK0e3l54evry6ZNm3B1deW5557LkPu0adN4++23adu2LV26dKFq1aqcP3+eb7/9Fj8/P+bOnUuVKlV47rnn+Oijj7hz5w5ubm7s3buXPXv28MQTT2R7LP39/WnatCnPPPMMTzzxBEeOHOGzzz6jffv2VK5c2RwXGRkJ8NAxF8kNFUuRfBQWFkbZsmWzfLh3hw4dmDt3LkeOHOGpp55i0aJFODo6Eh4eztatW/H392fTpk20b9+e8uXLm/vZ2toSERHB/Pnz2bhxIxs2bKBixYrUqlWL4cOH4+HhYTW/gQMH8u6775KUlESlSpUytPfr14+DBw/Su3fvTC+YGTlyJHXr1uXjjz9mzpw53L17l+rVq9OkSRMGDhxojlu6dCkTJ05kxYoV3L17l6ZNmxIREUG3bt2s5phu+PDhbNmyhR9++IGUlBRq1KjB2LFjGTt2rEXcF198QUBAQLb2XyS7DImJiZlfDiciRUZiYiK1atXi/7VzxygOQlEYhf9WCKR1EYKSBZjKziJkAe4hIQiCnY3Y2wUDadOkTGcVJGQDaVO5Ae1TzUBmBl6jjDOcbwHe1x30cU3TVLvdbrDndl0nz/MUx/G3dRVJOhwO2mw2ut1ub7ucU9W2rVzXVVVVCsPwt4+Df4Q7S2Bifvqjzcdyv+/7g86azWbabrefn0i/Oh6PWiwWfyKUklSWpRzHIZQYHG+WwMTkea77/a7lcinLsnS9XnU+nxUEgU6n0+jz+77X5XJR0zTa7/eqqkrr9Xr0ucCUEUtgYuq6VlEUejwe6rpOtm1rtVopSZK3Rf+xPJ9Pua6r+XyuKIqUZdnoM4GpI5YAABhwZwkAgAGxBADAgFgCAGBALAEAMCCWAAAYEEsAAAxeQ9lD0RzWmFIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the style of plots\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# Plot the distribution of ages in years\n",
    "plt.hist(app_train['DAYS_BIRTH'] / 365, edgecolor = 'k', bins = 25)\n",
    "plt.title('Age of Client'); plt.xlabel('Age (years)'); plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 8))\n",
    "\n",
    "# KDE plot of loans that were repaid on time\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'target == 0')\n",
    "\n",
    "# KDE plot of loans which were not repaid on time\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 1, 'DAYS_BIRTH'] / 365, label = 'target == 1')\n",
    "\n",
    "# Labeling of plot\n",
    "plt.xlabel('Age (years)'); plt.ylabel('Density'); plt.title('Distribution of Ages');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nidhi\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\nidhi\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<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>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "      <th>YEARS_BINNED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9461</td>\n",
       "      <td>25.920548</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>16765</td>\n",
       "      <td>45.931507</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19046</td>\n",
       "      <td>52.180822</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>19005</td>\n",
       "      <td>52.068493</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>19932</td>\n",
       "      <td>54.608219</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>16941</td>\n",
       "      <td>46.413699</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>13778</td>\n",
       "      <td>37.747945</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>18850</td>\n",
       "      <td>51.643836</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>20099</td>\n",
       "      <td>55.065753</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>14469</td>\n",
       "      <td>39.641096</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  DAYS_BIRTH  YEARS_BIRTH  YEARS_BINNED\n",
       "0       1        9461    25.920548  (25.0, 30.0]\n",
       "1       0       16765    45.931507  (45.0, 50.0]\n",
       "2       0       19046    52.180822  (50.0, 55.0]\n",
       "3       0       19005    52.068493  (50.0, 55.0]\n",
       "4       0       19932    54.608219  (50.0, 55.0]\n",
       "5       0       16941    46.413699  (45.0, 50.0]\n",
       "6       0       13778    37.747945  (35.0, 40.0]\n",
       "7       0       18850    51.643836  (50.0, 55.0]\n",
       "8       0       20099    55.065753  (55.0, 60.0]\n",
       "9       0       14469    39.641096  (35.0, 40.0]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average failure to repay loans by age bracket\n",
    "\n",
    "# Age information into a separate dataframe\n",
    "age_data = app_train[['TARGET', 'DAYS_BIRTH']]\n",
    "age_data['YEARS_BIRTH'] = age_data['DAYS_BIRTH'] / 365\n",
    "\n",
    "# Bin the age data\n",
    "age_data['YEARS_BINNED'] = pd.cut(age_data['YEARS_BIRTH'], bins = np.linspace(20, 70, num = 11))\n",
    "age_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BINNED</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(20.0, 25.0]</th>\n",
       "      <td>0.123036</td>\n",
       "      <td>8532.795625</td>\n",
       "      <td>23.377522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25.0, 30.0]</th>\n",
       "      <td>0.111436</td>\n",
       "      <td>10155.219250</td>\n",
       "      <td>27.822518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30.0, 35.0]</th>\n",
       "      <td>0.102814</td>\n",
       "      <td>11854.848377</td>\n",
       "      <td>32.479037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35.0, 40.0]</th>\n",
       "      <td>0.089414</td>\n",
       "      <td>13707.908253</td>\n",
       "      <td>37.555913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40.0, 45.0]</th>\n",
       "      <td>0.078491</td>\n",
       "      <td>15497.661233</td>\n",
       "      <td>42.459346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(45.0, 50.0]</th>\n",
       "      <td>0.074171</td>\n",
       "      <td>17323.900441</td>\n",
       "      <td>47.462741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50.0, 55.0]</th>\n",
       "      <td>0.066968</td>\n",
       "      <td>19196.494791</td>\n",
       "      <td>52.593136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55.0, 60.0]</th>\n",
       "      <td>0.055314</td>\n",
       "      <td>20984.262742</td>\n",
       "      <td>57.491131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60.0, 65.0]</th>\n",
       "      <td>0.052737</td>\n",
       "      <td>22780.547460</td>\n",
       "      <td>62.412459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(65.0, 70.0]</th>\n",
       "      <td>0.037270</td>\n",
       "      <td>24292.614340</td>\n",
       "      <td>66.555108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TARGET    DAYS_BIRTH  YEARS_BIRTH\n",
       "YEARS_BINNED                                     \n",
       "(20.0, 25.0]  0.123036   8532.795625    23.377522\n",
       "(25.0, 30.0]  0.111436  10155.219250    27.822518\n",
       "(30.0, 35.0]  0.102814  11854.848377    32.479037\n",
       "(35.0, 40.0]  0.089414  13707.908253    37.555913\n",
       "(40.0, 45.0]  0.078491  15497.661233    42.459346\n",
       "(45.0, 50.0]  0.074171  17323.900441    47.462741\n",
       "(50.0, 55.0]  0.066968  19196.494791    52.593136\n",
       "(55.0, 60.0]  0.055314  20984.262742    57.491131\n",
       "(60.0, 65.0]  0.052737  22780.547460    62.412459\n",
       "(65.0, 70.0]  0.037270  24292.614340    66.555108"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by the bin and calculate averages\n",
    "age_groups  = age_data.groupby('YEARS_BINNED').mean()\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 8))\n",
    "\n",
    "# Graph the age bins and the average of the target as a bar plot\n",
    "plt.bar(age_groups.index.astype(str), 100 * age_groups['TARGET'])\n",
    "\n",
    "# Plot labeling\n",
    "plt.xticks(rotation = 75); plt.xlabel('Age Group (years)'); plt.ylabel('Failure to Repay (%)')\n",
    "plt.title('Failure to Repay by Age Group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155317</td>\n",
       "      <td>-0.160472</td>\n",
       "      <td>-0.178919</td>\n",
       "      <td>-0.078239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>-0.155317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213982</td>\n",
       "      <td>0.186846</td>\n",
       "      <td>0.600610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>-0.160472</td>\n",
       "      <td>0.213982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109167</td>\n",
       "      <td>0.091996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>-0.178919</td>\n",
       "      <td>0.186846</td>\n",
       "      <td>0.109167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.205478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <td>-0.078239</td>\n",
       "      <td>0.600610</td>\n",
       "      <td>0.091996</td>\n",
       "      <td>0.205478</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TARGET  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  DAYS_BIRTH\n",
       "TARGET        1.000000     -0.155317     -0.160472     -0.178919   -0.078239\n",
       "EXT_SOURCE_1 -0.155317      1.000000      0.213982      0.186846    0.600610\n",
       "EXT_SOURCE_2 -0.160472      0.213982      1.000000      0.109167    0.091996\n",
       "EXT_SOURCE_3 -0.178919      0.186846      0.109167      1.000000    0.205478\n",
       "DAYS_BIRTH   -0.078239      0.600610      0.091996      0.205478    1.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the EXT_SOURCE variables and show correlations\n",
    "ext_data = app_train[['TARGET', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH']]\n",
    "ext_data_corrs = ext_data.corr()\n",
    "ext_data_corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exterior sources with strongest correlations with target \n",
    "\n",
    "plt.figure(figsize = (10, 12))\n",
    "\n",
    "# iterate through the sources\n",
    "for i, source in enumerate(['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']):\n",
    "    \n",
    "    # create a new subplot for each source\n",
    "    plt.subplot(3, 1, i + 1)\n",
    "    # plot repaid loans\n",
    "    sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, source], label = 'target == 0')\n",
    "    # plot loans that were not repaid\n",
    "    sns.kdeplot(app_train.loc[app_train['TARGET'] == 1, source], label = 'target == 1')\n",
    "    \n",
    "    # Label the plots\n",
    "    plt.title('Distribution of %s by Target Value' % source)\n",
    "    plt.xlabel('%s' % source); plt.ylabel('Density');\n",
    "    \n",
    "plt.tight_layout(h_pad = 2.5)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>NAME_CONTRACT_TYPE</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>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>...</th>\n",
       "      <th>HOUSETYPE_MODE_terraced house</th>\n",
       "      <th>WALLSMATERIAL_MODE_Block</th>\n",
       "      <th>WALLSMATERIAL_MODE_Mixed</th>\n",
       "      <th>WALLSMATERIAL_MODE_Monolithic</th>\n",
       "      <th>WALLSMATERIAL_MODE_Others</th>\n",
       "      <th>WALLSMATERIAL_MODE_Panel</th>\n",
       "      <th>WALLSMATERIAL_MODE_Stone, brick</th>\n",
       "      <th>WALLSMATERIAL_MODE_Wooden</th>\n",
       "      <th>EMERGENCYSTATE_MODE_No</th>\n",
       "      <th>EMERGENCYSTATE_MODE_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>351000.0</td>\n",
       "      <td>0.018801</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>297000.0</td>\n",
       "      <td>0.008019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 239 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  NAME_CONTRACT_TYPE  FLAG_OWN_CAR  FLAG_OWN_REALTY  \\\n",
       "0      100002                   0             0                1   \n",
       "1      100003                   0             0                0   \n",
       "2      100004                   1             1                1   \n",
       "3      100006                   0             0                1   \n",
       "4      100007                   0             0                1   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0             0          202500.0    406597.5      24700.5         351000.0   \n",
       "1             0          270000.0   1293502.5      35698.5        1129500.0   \n",
       "2             0           67500.0    135000.0       6750.0         135000.0   \n",
       "3             0          135000.0    312682.5      29686.5         297000.0   \n",
       "4             0          121500.0    513000.0      21865.5         513000.0   \n",
       "\n",
       "   REGION_POPULATION_RELATIVE           ...             \\\n",
       "0                    0.018801           ...              \n",
       "1                    0.003541           ...              \n",
       "2                    0.010032           ...              \n",
       "3                    0.008019           ...              \n",
       "4                    0.028663           ...              \n",
       "\n",
       "   HOUSETYPE_MODE_terraced house  WALLSMATERIAL_MODE_Block  \\\n",
       "0                              0                         0   \n",
       "1                              0                         1   \n",
       "2                              0                         0   \n",
       "3                              0                         0   \n",
       "4                              0                         0   \n",
       "\n",
       "   WALLSMATERIAL_MODE_Mixed  WALLSMATERIAL_MODE_Monolithic  \\\n",
       "0                         0                              0   \n",
       "1                         0                              0   \n",
       "2                         0                              0   \n",
       "3                         0                              0   \n",
       "4                         0                              0   \n",
       "\n",
       "   WALLSMATERIAL_MODE_Others  WALLSMATERIAL_MODE_Panel  \\\n",
       "0                          0                         0   \n",
       "1                          0                         0   \n",
       "2                          0                         0   \n",
       "3                          0                         0   \n",
       "4                          0                         0   \n",
       "\n",
       "   WALLSMATERIAL_MODE_Stone, brick  WALLSMATERIAL_MODE_Wooden  \\\n",
       "0                                1                          0   \n",
       "1                                0                          0   \n",
       "2                                0                          0   \n",
       "3                                0                          0   \n",
       "4                                0                          0   \n",
       "\n",
       "   EMERGENCYSTATE_MODE_No  EMERGENCYSTATE_MODE_Yes  \n",
       "0                       1                        0  \n",
       "1                       1                        0  \n",
       "2                       0                        0  \n",
       "3                       0                        0  \n",
       "4                       0                        0  \n",
       "\n",
       "[5 rows x 239 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = app_train.drop(columns = ['TARGET'])\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature names\n",
    "features = list(train.columns)\n",
    "\n",
    "# Copy of the testing data\n",
    "test = app_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (307511, 239)\n",
      "Testing data shape:  (48744, 239)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Median imputation of missing values\n",
    "imputer = SimpleImputer(strategy = 'median')\n",
    "\n",
    "# Scale each feature to 0-1\n",
    "scaler = MinMaxScaler(feature_range = (0, 1))\n",
    "\n",
    "# Fit on the training data\n",
    "imputer.fit(train)\n",
    "\n",
    "# Transform both training and testing data\n",
    "train = imputer.transform(train)\n",
    "test = imputer.transform(app_test)\n",
    "\n",
    "# Repeat with the scaler\n",
    "scaler.fit(train)\n",
    "train = scaler.transform(train)\n",
    "test = scaler.transform(test)\n",
    "\n",
    "print('Training data shape: ', train.shape)\n",
    "print('Testing data shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded packages\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "print(\"Loaded packages\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the random forest classifier\n",
    "random_forest = RandomForestClassifier(n_estimators = 100, random_state = 2019, verbose = 1, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   20.9s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   55.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "            oob_score=False, random_state=2019, verbose=1,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train on the training data\n",
    "random_forest.fit(train, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed:    0.4s finished\n"
     ]
    }
   ],
   "source": [
    "# Extract feature importances\n",
    "feature_importance_values = random_forest.feature_importances_\n",
    "feature_importances = pd.DataFrame({'feature': features, 'importance': feature_importance_values})\n",
    "\n",
    "#Make predictions on test data\n",
    "predictions = random_forest.predict(test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nidhi\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Make a submission dataframe\n",
    "submit = app_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = predictions\n",
    "\n",
    "# Save the submission dataframe\n",
    "submit.to_csv('random_forest_baseline.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100013</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100028</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100038</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET\n",
       "0      100001       0\n",
       "1      100005       0\n",
       "2      100013       0\n",
       "3      100028       0\n",
       "4      100038       0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    48737\n",
       "1        7\n",
       "Name: TARGET, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit['TARGET'].value_counts()"
   ]
  }
 ],
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