data exploration and PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createdAt_Month</th>\n",
       "      <th>Reason</th>\n",
       "      <th>application_id</th>\n",
       "      <th>SystemCustomerID</th>\n",
       "      <th>SystemLoanID</th>\n",
       "      <th>income_source</th>\n",
       "      <th>income_verified</th>\n",
       "      <th>next_loan_number</th>\n",
       "      <th>country</th>\n",
       "      <th>DecisionFlag</th>\n",
       "      <th>...</th>\n",
       "      <th>callFeatures_no_distinct_call_numbers_out</th>\n",
       "      <th>callFeatures_no_calls_over_zero_secs_out</th>\n",
       "      <th>callFeatures_no_calls_out</th>\n",
       "      <th>predictedIncome</th>\n",
       "      <th>installationId</th>\n",
       "      <th>obiResult_scorecard</th>\n",
       "      <th>obiResult_score</th>\n",
       "      <th>obiResult_riskLevel</th>\n",
       "      <th>obiResult_ratingClass</th>\n",
       "      <th>obiResult_ncngav2Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201812</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>WRYRieZlqBsT6w53b2HDpA_1544015103274</td>\n",
       "      <td>334043596</td>\n",
       "      <td>302881418623</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>NG</td>\n",
       "      <td>Declined</td>\n",
       "      <td>...</td>\n",
       "      <td>139.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>105872.674499</td>\n",
       "      <td>374ac87639a74b3e01465993be618360</td>\n",
       "      <td>NC_NGA_V2</td>\n",
       "      <td>337.57000</td>\n",
       "      <td>Decline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a6e99ddb-2c95-4b1e-90cb-d05ffa9d1c9a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201809</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>oVpQz7tgkPa3bmvVDzYqw_1530858694219</td>\n",
       "      <td>524284563</td>\n",
       "      <td>302328949395</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>NG</td>\n",
       "      <td>Declined</td>\n",
       "      <td>...</td>\n",
       "      <td>327.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>34421.220891</td>\n",
       "      <td>e24749e856614a1ae3734c773670f74c</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>2</th>\n",
       "      <td>201810</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>WCONqdL9BlaRetaDRERg_1538822524979</td>\n",
       "      <td>481582700</td>\n",
       "      <td>302536536368</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>NG</td>\n",
       "      <td>Declined</td>\n",
       "      <td>...</td>\n",
       "      <td>139.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>14447.944277</td>\n",
       "      <td>abe46a8fe9345c0b230c3fcaa5ddbbbc</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>3</th>\n",
       "      <td>201812</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>Wi5dgyaoGQ9yciK89PEg_1544071630964</td>\n",
       "      <td>234330495</td>\n",
       "      <td>302887567434</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>NG</td>\n",
       "      <td>Declined</td>\n",
       "      <td>...</td>\n",
       "      <td>46.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>52952.581832</td>\n",
       "      <td>cfe3f3006f8e9799e9d17859df394b18</td>\n",
       "      <td>NC_NGA_V2</td>\n",
       "      <td>394.94000</td>\n",
       "      <td>Decline</td>\n",
       "      <td>18a</td>\n",
       "      <td>da360fe9-8e86-4031-afb9-2143a3f734ee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201901</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>16JgKGioSsWVaWdY7noC8A_1547701922961</td>\n",
       "      <td>563455515</td>\n",
       "      <td>303057983993</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>NG</td>\n",
       "      <td>Declined</td>\n",
       "      <td>...</td>\n",
       "      <td>114.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>31577.275467</td>\n",
       "      <td>7af8400ec773914c8d1fed044aa420b7</td>\n",
       "      <td>RC_NGA_V1L</td>\n",
       "      <td>423.70578</td>\n",
       "      <td>Decline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 289 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   createdAt_Month                               Reason  \\\n",
       "0           201812  Client did not meet approval score.   \n",
       "1           201809  Client did not meet approval score.   \n",
       "2           201810  Client did not meet approval score.   \n",
       "3           201812  Client did not meet approval score.   \n",
       "4           201901  Client did not meet approval score.   \n",
       "\n",
       "                         application_id  SystemCustomerID  SystemLoanID  \\\n",
       "0  WRYRieZlqBsT6w53b2HDpA_1544015103274         334043596  302881418623   \n",
       "1   oVpQz7tgkPa3bmvVDzYqw_1530858694219         524284563  302328949395   \n",
       "2    WCONqdL9BlaRetaDRERg_1538822524979         481582700  302536536368   \n",
       "3    Wi5dgyaoGQ9yciK89PEg_1544071630964         234330495  302887567434   \n",
       "4  16JgKGioSsWVaWdY7noC8A_1547701922961         563455515  303057983993   \n",
       "\n",
       "  income_source  income_verified  next_loan_number country DecisionFlag  \\\n",
       "0        credit             True                 1      NG     Declined   \n",
       "1        credit             True                 4      NG     Declined   \n",
       "2        credit             True                 2      NG     Declined   \n",
       "3        credit             True                 1      NG     Declined   \n",
       "4        credit             True                 2      NG     Declined   \n",
       "\n",
       "                   ...                   \\\n",
       "0                  ...                    \n",
       "1                  ...                    \n",
       "2                  ...                    \n",
       "3                  ...                    \n",
       "4                  ...                    \n",
       "\n",
       "  callFeatures_no_distinct_call_numbers_out  \\\n",
       "0                                     139.0   \n",
       "1                                     327.0   \n",
       "2                                     139.0   \n",
       "3                                      46.0   \n",
       "4                                     114.0   \n",
       "\n",
       "   callFeatures_no_calls_over_zero_secs_out callFeatures_no_calls_out  \\\n",
       "0                                     287.0                     655.0   \n",
       "1                                    1114.0                    2923.0   \n",
       "2                                     583.0                    1115.0   \n",
       "3                                      38.0                     108.0   \n",
       "4                                     549.0                    1474.0   \n",
       "\n",
       "  predictedIncome                    installationId obiResult_scorecard  \\\n",
       "0   105872.674499  374ac87639a74b3e01465993be618360           NC_NGA_V2   \n",
       "1    34421.220891  e24749e856614a1ae3734c773670f74c                 NaN   \n",
       "2    14447.944277  abe46a8fe9345c0b230c3fcaa5ddbbbc                 NaN   \n",
       "3    52952.581832  cfe3f3006f8e9799e9d17859df394b18           NC_NGA_V2   \n",
       "4    31577.275467  7af8400ec773914c8d1fed044aa420b7          RC_NGA_V1L   \n",
       "\n",
       "   obiResult_score  obiResult_riskLevel  obiResult_ratingClass  \\\n",
       "0        337.57000              Decline                    NaN   \n",
       "1              NaN                  NaN                    NaN   \n",
       "2              NaN                  NaN                    NaN   \n",
       "3        394.94000              Decline                    18a   \n",
       "4        423.70578              Decline                    NaN   \n",
       "\n",
       "                    obiResult_ncngav2Id  \n",
       "0  a6e99ddb-2c95-4b1e-90cb-d05ffa9d1c9a  \n",
       "1                                   NaN  \n",
       "2                                   NaN  \n",
       "3  da360fe9-8e86-4031-afb9-2143a3f734ee  \n",
       "4                                   NaN  \n",
       "\n",
       "[5 rows x 289 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('week3_challenge.csv', index_col=0)\n",
    "data. head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11993, 271)\n"
     ]
    }
   ],
   "source": [
    "data1 = data.copy()\n",
    "#Dropiing columns without at least 8395 data points i.e 70% are non-nan values\n",
    "data2 = data1.dropna(axis=1, thresh=8395)\n",
    "print(data2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11993, 248)\n"
     ]
    }
   ],
   "source": [
    "#select only numeric columns\n",
    "\n",
    "data3 = data2.select_dtypes(include=[np.number])\n",
    "print(data3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\maria\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3787: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  downcast=downcast, **kwargs)\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>createdAt_Month</th>\n",
       "      <th>SystemCustomerID</th>\n",
       "      <th>SystemLoanID</th>\n",
       "      <th>next_loan_number</th>\n",
       "      <th>Income</th>\n",
       "      <th>__v</th>\n",
       "      <th>demofeatures_net_income_band</th>\n",
       "      <th>demofeatures_appweek</th>\n",
       "      <th>demofeatures_age_at_application_yrs</th>\n",
       "      <th>demofeatures_num_children</th>\n",
       "      <th>...</th>\n",
       "      <th>callFeatures_vol_calls_1_mnth_out</th>\n",
       "      <th>callFeatures_vol_calls_2_mnth_out</th>\n",
       "      <th>callFeatures_vol_calls_3_mnth_out</th>\n",
       "      <th>callFeatures_dur_calls_out</th>\n",
       "      <th>callFeatures_dur_calls_contacts_out</th>\n",
       "      <th>callFeatures_ratio_contacts_out</th>\n",
       "      <th>callFeatures_no_distinct_call_numbers_out</th>\n",
       "      <th>callFeatures_no_calls_over_zero_secs_out</th>\n",
       "      <th>callFeatures_no_calls_out</th>\n",
       "      <th>predictedIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201812</td>\n",
       "      <td>334043596</td>\n",
       "      <td>302881418623</td>\n",
       "      <td>1</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48872.0</td>\n",
       "      <td>0.985554</td>\n",
       "      <td>0.929771</td>\n",
       "      <td>139.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>105872.674499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201809</td>\n",
       "      <td>524284563</td>\n",
       "      <td>302328949395</td>\n",
       "      <td>4</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65335.0</td>\n",
       "      <td>0.956868</td>\n",
       "      <td>0.917550</td>\n",
       "      <td>327.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>34421.220891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201810</td>\n",
       "      <td>481582700</td>\n",
       "      <td>302536536368</td>\n",
       "      <td>2</td>\n",
       "      <td>39500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98762.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>139.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>14447.944277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201812</td>\n",
       "      <td>234330495</td>\n",
       "      <td>302887567434</td>\n",
       "      <td>1</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>0.762092</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>46.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>52952.581832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201901</td>\n",
       "      <td>563455515</td>\n",
       "      <td>303057983993</td>\n",
       "      <td>2</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29949.0</td>\n",
       "      <td>0.937828</td>\n",
       "      <td>0.938942</td>\n",
       "      <td>114.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>31577.275467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 248 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   createdAt_Month  SystemCustomerID  SystemLoanID  next_loan_number  \\\n",
       "0           201812         334043596  302881418623                 1   \n",
       "1           201809         524284563  302328949395                 4   \n",
       "2           201810         481582700  302536536368                 2   \n",
       "3           201812         234330495  302887567434                 1   \n",
       "4           201901         563455515  303057983993                 2   \n",
       "\n",
       "     Income  __v  demofeatures_net_income_band  demofeatures_appweek  \\\n",
       "0  200000.0  0.0                      200000.0                   1.0   \n",
       "1   80000.0  0.0                      100000.0                   1.0   \n",
       "2   39500.0  0.0                       50000.0                   2.0   \n",
       "3   50000.0  0.0                       50000.0                   1.0   \n",
       "4   20000.0  0.0                       75000.0                   3.0   \n",
       "\n",
       "   demofeatures_age_at_application_yrs  demofeatures_num_children  \\\n",
       "0                                 28.0                        0.0   \n",
       "1                                 33.0                        0.0   \n",
       "2                                 26.0                        0.0   \n",
       "3                                 36.0                        1.0   \n",
       "4                                 28.0                        3.0   \n",
       "\n",
       "        ...         callFeatures_vol_calls_1_mnth_out  \\\n",
       "0       ...                                     655.0   \n",
       "1       ...                                    2923.0   \n",
       "2       ...                                    1115.0   \n",
       "3       ...                                      58.0   \n",
       "4       ...                                    1474.0   \n",
       "\n",
       "   callFeatures_vol_calls_2_mnth_out  callFeatures_vol_calls_3_mnth_out  \\\n",
       "0                                0.0                                0.0   \n",
       "1                                0.0                                0.0   \n",
       "2                                0.0                                0.0   \n",
       "3                               50.0                                0.0   \n",
       "4                                0.0                                0.0   \n",
       "\n",
       "   callFeatures_dur_calls_out  callFeatures_dur_calls_contacts_out  \\\n",
       "0                     48872.0                             0.985554   \n",
       "1                     65335.0                             0.956868   \n",
       "2                     98762.0                             0.000000   \n",
       "3                      1530.0                             0.762092   \n",
       "4                     29949.0                             0.937828   \n",
       "\n",
       "   callFeatures_ratio_contacts_out  callFeatures_no_distinct_call_numbers_out  \\\n",
       "0                         0.929771                                      139.0   \n",
       "1                         0.917550                                      327.0   \n",
       "2                         0.000000                                      139.0   \n",
       "3                         0.777778                                       46.0   \n",
       "4                         0.938942                                      114.0   \n",
       "\n",
       "   callFeatures_no_calls_over_zero_secs_out  callFeatures_no_calls_out  \\\n",
       "0                                     287.0                      655.0   \n",
       "1                                    1114.0                     2923.0   \n",
       "2                                     583.0                     1115.0   \n",
       "3                                      38.0                      108.0   \n",
       "4                                     549.0                     1474.0   \n",
       "\n",
       "   predictedIncome  \n",
       "0    105872.674499  \n",
       "1     34421.220891  \n",
       "2     14447.944277  \n",
       "3     52952.581832  \n",
       "4     31577.275467  \n",
       "\n",
       "[5 rows x 248 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to fill all NAN with 0\n",
    "\n",
    "data3.fillna(0,inplace=True)\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11993, 245)\n"
     ]
    }
   ],
   "source": [
    "#lets remove the first three columns they seem less relevant to our clustering\n",
    "data4 = data3.iloc[:, 3:]\n",
    "print(data4.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to scale the numeric values\n",
    "\n",
    "x = data4.loc[:,:].values\n",
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>235</th>\n",
       "      <th>236</th>\n",
       "      <th>237</th>\n",
       "      <th>238</th>\n",
       "      <th>239</th>\n",
       "      <th>240</th>\n",
       "      <th>241</th>\n",
       "      <th>242</th>\n",
       "      <th>243</th>\n",
       "      <th>244</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.389585</td>\n",
       "      <td>2.488673</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.964747</td>\n",
       "      <td>-1.243848</td>\n",
       "      <td>-0.222474</td>\n",
       "      <td>-0.648491</td>\n",
       "      <td>0.488025</td>\n",
       "      <td>2.247432</td>\n",
       "      <td>0.009235</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016726</td>\n",
       "      <td>-0.218086</td>\n",
       "      <td>-0.113863</td>\n",
       "      <td>0.075120</td>\n",
       "      <td>0.792418</td>\n",
       "      <td>0.703710</td>\n",
       "      <td>0.543044</td>\n",
       "      <td>0.004773</td>\n",
       "      <td>-0.006009</td>\n",
       "      <td>2.802797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.823083</td>\n",
       "      <td>0.541326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082888</td>\n",
       "      <td>-1.243848</td>\n",
       "      <td>0.443094</td>\n",
       "      <td>-0.648491</td>\n",
       "      <td>0.235318</td>\n",
       "      <td>0.050359</td>\n",
       "      <td>0.009230</td>\n",
       "      <td>...</td>\n",
       "      <td>1.841329</td>\n",
       "      <td>-0.218086</td>\n",
       "      <td>-0.113863</td>\n",
       "      <td>0.127209</td>\n",
       "      <td>0.709694</td>\n",
       "      <td>0.667245</td>\n",
       "      <td>2.471878</td>\n",
       "      <td>1.408808</td>\n",
       "      <td>1.776958</td>\n",
       "      <td>-0.149962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.681305</td>\n",
       "      <td>-0.115904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.358041</td>\n",
       "      <td>-0.386424</td>\n",
       "      <td>-0.488701</td>\n",
       "      <td>-0.648491</td>\n",
       "      <td>-0.129878</td>\n",
       "      <td>-0.136774</td>\n",
       "      <td>0.009212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.386796</td>\n",
       "      <td>-0.218086</td>\n",
       "      <td>-0.113863</td>\n",
       "      <td>0.232974</td>\n",
       "      <td>-2.049741</td>\n",
       "      <td>-2.070640</td>\n",
       "      <td>0.543044</td>\n",
       "      <td>0.507306</td>\n",
       "      <td>0.355615</td>\n",
       "      <td>-0.975365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.389585</td>\n",
       "      <td>0.054489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.358041</td>\n",
       "      <td>-1.243848</td>\n",
       "      <td>0.842434</td>\n",
       "      <td>0.170522</td>\n",
       "      <td>-0.129878</td>\n",
       "      <td>0.142770</td>\n",
       "      <td>0.008628</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.463560</td>\n",
       "      <td>0.348488</td>\n",
       "      <td>-0.113863</td>\n",
       "      <td>-0.074673</td>\n",
       "      <td>0.147992</td>\n",
       "      <td>0.250177</td>\n",
       "      <td>-0.411114</td>\n",
       "      <td>-0.417965</td>\n",
       "      <td>-0.436028</td>\n",
       "      <td>0.615854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.681305</td>\n",
       "      <td>-0.432348</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.137577</td>\n",
       "      <td>0.471001</td>\n",
       "      <td>-0.222474</td>\n",
       "      <td>1.808549</td>\n",
       "      <td>-0.129878</td>\n",
       "      <td>-0.226874</td>\n",
       "      <td>0.008966</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675611</td>\n",
       "      <td>-0.218086</td>\n",
       "      <td>-0.113863</td>\n",
       "      <td>0.015246</td>\n",
       "      <td>0.654783</td>\n",
       "      <td>0.731075</td>\n",
       "      <td>0.286550</td>\n",
       "      <td>0.449582</td>\n",
       "      <td>0.637840</td>\n",
       "      <td>-0.267489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1    2         3         4         5         6         7    \\\n",
       "0 -0.389585  2.488673  0.0  0.964747 -1.243848 -0.222474 -0.648491  0.488025   \n",
       "1  2.823083  0.541326  0.0  0.082888 -1.243848  0.443094 -0.648491  0.235318   \n",
       "2  0.681305 -0.115904  0.0 -0.358041 -0.386424 -0.488701 -0.648491 -0.129878   \n",
       "3 -0.389585  0.054489  0.0 -0.358041 -1.243848  0.842434  0.170522 -0.129878   \n",
       "4  0.681305 -0.432348  0.0 -0.137577  0.471001 -0.222474  1.808549 -0.129878   \n",
       "\n",
       "        8         9      ...          235       236       237       238  \\\n",
       "0  2.247432  0.009235    ...     0.016726 -0.218086 -0.113863  0.075120   \n",
       "1  0.050359  0.009230    ...     1.841329 -0.218086 -0.113863  0.127209   \n",
       "2 -0.136774  0.009212    ...     0.386796 -0.218086 -0.113863  0.232974   \n",
       "3  0.142770  0.008628    ...    -0.463560  0.348488 -0.113863 -0.074673   \n",
       "4 -0.226874  0.008966    ...     0.675611 -0.218086 -0.113863  0.015246   \n",
       "\n",
       "        239       240       241       242       243       244  \n",
       "0  0.792418  0.703710  0.543044  0.004773 -0.006009  2.802797  \n",
       "1  0.709694  0.667245  2.471878  1.408808  1.776958 -0.149962  \n",
       "2 -2.049741 -2.070640  0.543044  0.507306  0.355615 -0.975365  \n",
       "3  0.147992  0.250177 -0.411114 -0.417965 -0.436028  0.615854  \n",
       "4  0.654783  0.731075  0.286550  0.449582  0.637840 -0.267489  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pd.DataFrame(data = x, columns = [columns]).head()\n",
    "pd.DataFrame(data = x).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create pca\n",
    "pca = PCA(n_components=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit the data to the model\n",
    "principalComponents = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe containing principal components\n",
    "principalDf = pd.DataFrame(data = principalComponents, columns = ['principal component 1', 'principal component 2','principal component 3','principal component 4', 'principal component 5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>principal component 3</th>\n",
       "      <th>principal component 4</th>\n",
       "      <th>principal component 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.221909</td>\n",
       "      <td>-2.273156</td>\n",
       "      <td>0.760809</td>\n",
       "      <td>-1.595706</td>\n",
       "      <td>4.226088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.461862</td>\n",
       "      <td>-3.655075</td>\n",
       "      <td>-2.135398</td>\n",
       "      <td>-0.890569</td>\n",
       "      <td>-0.204470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.898813</td>\n",
       "      <td>-2.348909</td>\n",
       "      <td>0.101638</td>\n",
       "      <td>0.483877</td>\n",
       "      <td>0.256985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.883405</td>\n",
       "      <td>1.373125</td>\n",
       "      <td>-0.209396</td>\n",
       "      <td>3.304525</td>\n",
       "      <td>0.288002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.635306</td>\n",
       "      <td>1.729701</td>\n",
       "      <td>0.546533</td>\n",
       "      <td>-2.891157</td>\n",
       "      <td>-0.892884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2  principal component 3  \\\n",
       "0               4.221909              -2.273156               0.760809   \n",
       "1              13.461862              -3.655075              -2.135398   \n",
       "2               5.898813              -2.348909               0.101638   \n",
       "3              -1.883405               1.373125              -0.209396   \n",
       "4               2.635306               1.729701               0.546533   \n",
       "\n",
       "   principal component 4  principal component 5  \n",
       "0              -1.595706               4.226088  \n",
       "1              -0.890569              -0.204470  \n",
       "2               0.483877               0.256985  \n",
       "3               3.304525               0.288002  \n",
       "4              -2.891157              -0.892884  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display principal components\n",
    "principalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3553984 , 0.0941872 , 0.03832466, 0.03382516, 0.02766936])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5494047776089314"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\maria\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "principalDf = principalDf.as_matrix(columns= ['principal component 1', 'principal component 2','principal component 3','principal component 4', 'principal component 5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-MEANS CLUSTERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# declare a Kmeans instance with 3 classes\n",
    "model = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit data \n",
    "model.fit(principalDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get clusters\n",
    "clusters = model.predict(principalDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "#print(model.labels_) \n",
    "print(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.88362589e+00,  3.08427065e-02, -1.81761408e-02,\n",
       "         3.51751183e-02,  5.09611533e-03],\n",
       "       [ 1.10778545e+02, -5.64192907e+00, -4.90307183e+00,\n",
       "         6.64483477e-01,  5.92196804e-01],\n",
       "       [ 1.95435268e+01, -1.60691160e-01,  4.37133341e-01,\n",
       "        -4.78709615e-01, -8.99944465e-02]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What are the cluster center vectors? first row contains values for the coordinates of the first centroid , the second row contains values for the coordinates of the other centroid i\n",
    "model.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_means = model.fit_predict(principalDf)\n",
    "\n",
    "#Visualizing the clusters for k=4\n",
    "plt.scatter(principalDf[y_means==0,0],principalDf[y_means==0,1],s=50, c='purple',label='Cluster1')\n",
    "plt.scatter(principalDf[y_means==1,0],principalDf[y_means==1,1],s=50, c='blue',label='Cluster2')\n",
    "plt.scatter(principalDf[y_means==2,0],principalDf[y_means==2,1],s=50, c='green',label='Cluster3')\n",
    "\n",
    "\n",
    "plt.scatter(model.cluster_centers_[:,0], model.cluster_centers_[:,1],s=50,marker='s', c='red', alpha=0.7, label='Centroids')\n",
    "plt.title('Customer segments')\n",
    "plt.xlabel('Dimension 1')\n",
    "plt.ylabel('Dimension 2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster\n",
       "0        0\n",
       "1        2\n",
       "2        0\n",
       "3        0\n",
       "4        0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#name the column cluster\n",
    "df = DataFrame(clusters)\n",
    "df.columns = ['cluster']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11092\n",
       "2      865\n",
       "1       36\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>cluster</th>\n",
       "      <th>createdAt_Month</th>\n",
       "      <th>Reason</th>\n",
       "      <th>application_id</th>\n",
       "      <th>SystemCustomerID</th>\n",
       "      <th>SystemLoanID</th>\n",
       "      <th>income_source</th>\n",
       "      <th>income_verified</th>\n",
       "      <th>next_loan_number</th>\n",
       "      <th>country</th>\n",
       "      <th>...</th>\n",
       "      <th>callFeatures_no_distinct_call_numbers_out</th>\n",
       "      <th>callFeatures_no_calls_over_zero_secs_out</th>\n",
       "      <th>callFeatures_no_calls_out</th>\n",
       "      <th>predictedIncome</th>\n",
       "      <th>installationId</th>\n",
       "      <th>obiResult_scorecard</th>\n",
       "      <th>obiResult_score</th>\n",
       "      <th>obiResult_riskLevel</th>\n",
       "      <th>obiResult_ratingClass</th>\n",
       "      <th>obiResult_ncngav2Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>201812</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>WRYRieZlqBsT6w53b2HDpA_1544015103274</td>\n",
       "      <td>334043596</td>\n",
       "      <td>302881418623</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>NG</td>\n",
       "      <td>...</td>\n",
       "      <td>139.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>105872.674499</td>\n",
       "      <td>374ac87639a74b3e01465993be618360</td>\n",
       "      <td>NC_NGA_V2</td>\n",
       "      <td>337.57000</td>\n",
       "      <td>Decline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a6e99ddb-2c95-4b1e-90cb-d05ffa9d1c9a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>201809</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>oVpQz7tgkPa3bmvVDzYqw_1530858694219</td>\n",
       "      <td>524284563</td>\n",
       "      <td>302328949395</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>NG</td>\n",
       "      <td>...</td>\n",
       "      <td>327.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>34421.220891</td>\n",
       "      <td>e24749e856614a1ae3734c773670f74c</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>2</th>\n",
       "      <td>0</td>\n",
       "      <td>201810</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>WCONqdL9BlaRetaDRERg_1538822524979</td>\n",
       "      <td>481582700</td>\n",
       "      <td>302536536368</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>NG</td>\n",
       "      <td>...</td>\n",
       "      <td>139.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>14447.944277</td>\n",
       "      <td>abe46a8fe9345c0b230c3fcaa5ddbbbc</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>3</th>\n",
       "      <td>0</td>\n",
       "      <td>201812</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>Wi5dgyaoGQ9yciK89PEg_1544071630964</td>\n",
       "      <td>234330495</td>\n",
       "      <td>302887567434</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>NG</td>\n",
       "      <td>...</td>\n",
       "      <td>46.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>52952.581832</td>\n",
       "      <td>cfe3f3006f8e9799e9d17859df394b18</td>\n",
       "      <td>NC_NGA_V2</td>\n",
       "      <td>394.94000</td>\n",
       "      <td>Decline</td>\n",
       "      <td>18a</td>\n",
       "      <td>da360fe9-8e86-4031-afb9-2143a3f734ee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>201901</td>\n",
       "      <td>Client did not meet approval score.</td>\n",
       "      <td>16JgKGioSsWVaWdY7noC8A_1547701922961</td>\n",
       "      <td>563455515</td>\n",
       "      <td>303057983993</td>\n",
       "      <td>credit</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>NG</td>\n",
       "      <td>...</td>\n",
       "      <td>114.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>31577.275467</td>\n",
       "      <td>7af8400ec773914c8d1fed044aa420b7</td>\n",
       "      <td>RC_NGA_V1L</td>\n",
       "      <td>423.70578</td>\n",
       "      <td>Decline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 290 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster  createdAt_Month                               Reason  \\\n",
       "0        0           201812  Client did not meet approval score.   \n",
       "1        2           201809  Client did not meet approval score.   \n",
       "2        0           201810  Client did not meet approval score.   \n",
       "3        0           201812  Client did not meet approval score.   \n",
       "4        0           201901  Client did not meet approval score.   \n",
       "\n",
       "                         application_id  SystemCustomerID  SystemLoanID  \\\n",
       "0  WRYRieZlqBsT6w53b2HDpA_1544015103274         334043596  302881418623   \n",
       "1   oVpQz7tgkPa3bmvVDzYqw_1530858694219         524284563  302328949395   \n",
       "2    WCONqdL9BlaRetaDRERg_1538822524979         481582700  302536536368   \n",
       "3    Wi5dgyaoGQ9yciK89PEg_1544071630964         234330495  302887567434   \n",
       "4  16JgKGioSsWVaWdY7noC8A_1547701922961         563455515  303057983993   \n",
       "\n",
       "  income_source  income_verified  next_loan_number country  \\\n",
       "0        credit             True                 1      NG   \n",
       "1        credit             True                 4      NG   \n",
       "2        credit             True                 2      NG   \n",
       "3        credit             True                 1      NG   \n",
       "4        credit             True                 2      NG   \n",
       "\n",
       "                   ...                   \\\n",
       "0                  ...                    \n",
       "1                  ...                    \n",
       "2                  ...                    \n",
       "3                  ...                    \n",
       "4                  ...                    \n",
       "\n",
       "  callFeatures_no_distinct_call_numbers_out  \\\n",
       "0                                     139.0   \n",
       "1                                     327.0   \n",
       "2                                     139.0   \n",
       "3                                      46.0   \n",
       "4                                     114.0   \n",
       "\n",
       "  callFeatures_no_calls_over_zero_secs_out  callFeatures_no_calls_out  \\\n",
       "0                                    287.0                      655.0   \n",
       "1                                   1114.0                     2923.0   \n",
       "2                                    583.0                     1115.0   \n",
       "3                                     38.0                      108.0   \n",
       "4                                    549.0                     1474.0   \n",
       "\n",
       "  predictedIncome                    installationId  obiResult_scorecard  \\\n",
       "0   105872.674499  374ac87639a74b3e01465993be618360            NC_NGA_V2   \n",
       "1    34421.220891  e24749e856614a1ae3734c773670f74c                  NaN   \n",
       "2    14447.944277  abe46a8fe9345c0b230c3fcaa5ddbbbc                  NaN   \n",
       "3    52952.581832  cfe3f3006f8e9799e9d17859df394b18            NC_NGA_V2   \n",
       "4    31577.275467  7af8400ec773914c8d1fed044aa420b7           RC_NGA_V1L   \n",
       "\n",
       "  obiResult_score  obiResult_riskLevel  obiResult_ratingClass  \\\n",
       "0       337.57000              Decline                    NaN   \n",
       "1             NaN                  NaN                    NaN   \n",
       "2             NaN                  NaN                    NaN   \n",
       "3       394.94000              Decline                    18a   \n",
       "4       423.70578              Decline                    NaN   \n",
       "\n",
       "                    obiResult_ncngav2Id  \n",
       "0  a6e99ddb-2c95-4b1e-90cb-d05ffa9d1c9a  \n",
       "1                                   NaN  \n",
       "2                                   NaN  \n",
       "3  da360fe9-8e86-4031-afb9-2143a3f734ee  \n",
       "4                                   NaN  \n",
       "\n",
       "[5 rows x 290 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MERGE THE CLUSTER COLUMN WITH THE ORIGINAL DATAFRAME\n",
    "\n",
    "data5 = pd.concat([df,data1], axis=1)\n",
    "data5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXPLORATORY DATA ANALYSIS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Income</th>\n",
       "      <th>predictedIncome</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.50</td>\n",
       "      <td>801776.7</td>\n",
       "      <td>45873.975033</td>\n",
       "      <td>37594.733000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25000.00</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>84844.563889</td>\n",
       "      <td>57441.201649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.17</td>\n",
       "      <td>696986.0</td>\n",
       "      <td>54904.418173</td>\n",
       "      <td>43168.254559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Income                         predictedIncome\n",
       "              min       max          mean            mean\n",
       "cluster                                                  \n",
       "0           18.50  801776.7  45873.975033    37594.733000\n",
       "1        25000.00  300000.0  84844.563889    57441.201649\n",
       "2           74.17  696986.0  54904.418173    43168.254559"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets get basic statistics variables for customer's income\n",
    "#f = {'Income':['sum','mean'], 'B':['prod']}\n",
    "f = {'Income':['min', 'max','mean'],'predictedIncome':['mean']}\n",
    "data5.groupby('cluster').agg(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">demofeatures_age_at_application_yrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>29.499729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>31.863584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        demofeatures_age_at_application_yrs                  \n",
       "                                        min    max       mean\n",
       "cluster                                                      \n",
       "0                                      17.0  147.0  29.499729\n",
       "1                                      21.0   45.0  31.500000\n",
       "2                                      19.0   57.0  31.863584"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets get basic statistics variables for customer's ages\n",
    "a = {'demofeatures_age_at_application_yrs':['min', 'max','mean']}\n",
    "data5.groupby('cluster').agg(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\maria\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\maria\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Density')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the data - displot\n",
    "plot_income = sns.distplot(data3[\"Income\"])\n",
    "plot_spend = sns.distplot(data3[\"predictedIncome\"])\n",
    "\n",
    "plt.xlabel('Income / Predicted Income')\n",
    "plt.title('Distribution plot of Customers income')\n",
    "plt.ylabel('Density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets visualize further with some histograms\n",
    "def plot_histogram(incomes, cols, bins = 10):\n",
    "    for col in cols:\n",
    "        fig = plt.figure(figsize=(6,6)) # define plot area\n",
    "        ax = fig.gca() # define axis    \n",
    "        data3[col].plot.hist(ax = ax, bins = bins) # Use the plot.hist method on subset of the data frame\n",
    "        ax.set_title('Histogram of ' + col) # Give the plot a main title\n",
    "        ax.set_xlabel(col) # Set text for the x axis\n",
    "        ax.set_ylabel('Number of customers')# Set text for y axis\n",
    "        plt.show()\n",
    "        \n",
    "num_cols = ['Income', 'predictedIncome']    \n",
    "plot_histogram('incomes', num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24892b0d320>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.factorplot('Pclass',data=titanic_df,hue='Sex')\n",
    "#df = sns.load_dataset('titanic')\n",
    "#sns.countplot(x='survived',hue='class',data=df)\n",
    "sns.countplot(x= 'cluster', hue= 'demofeatures_marital_status', data = data5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24892ae7e48>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x= 'cluster', hue= 'demofeatures_gender', data = data5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV4AAAFrCAYAAACZnOEEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xm8XHV9//HXGxICgYSACRSSNGGJCwhSuLIUfoALYXEJWrHwQ1mM4laliwq0FGSpYq1arEWhUAlqCRQRUdEQgUBVCNwEWcJSIlsiESIBEkDBhE//+H4vnIwzc+fOvXPm3pn38/GYxz3ne7bPWe5nvvM9myICMzMrzwbtDsDMrNs48ZqZlcyJ18ysZE68ZmYlc+I1MyuZE6+ZWcmceIeYpIslnd2mZUvSNyU9JenWdsQw0rRyf0laIOmDrZh3P8vdV9IDkp6VdHjZy+8kkh6W9Nahnm/HJ9684R6XtGmh7IOSFrQxrFbZDzgImBIRe1YbQdI2ki6StELSGkn3STqjuH2aISkk7TiYediQORP4WkRsFhFXDWZGrUo83a7jE282Cjix3UEMlKQNBzjJNODhiHiuxvy2BG4GNgH2iYhxpEQ9AdhhMLEOF5JGtTuGYWAasKTdQUD37o9+1zsiOvoDPAycDKwCJuSyDwILcvd0IIBRhWkWAB/M3ccBPwe+AjwNPAj8eS5fBjwBHFuY9mLgG8B8YA1wIzCtMPy1edgq4H7gvRXTfh24BngOeGuV9dkWuDpPvxT4UC6fDfweWAc8C5xRZdqzgbuADWpsq/62xY55fZ4BfgtclstvytM9l5f9l7n8QznGVTnmbQvzDeBjwAN5O51FSv43A6uBy4GNCuO/Hfhl3ge/AHat2McnAXcCL5C+aE8Cfp3nfT/wlhrrXHN/Af8OfKli/B8Af11jXgcB9+Xt87U8rw8Whn8AuBd4CphXcVwMdHtU3bbAr4CXgN/lfTEG2By4CFiRt8nZwIZ5/B2A64En8z79Dq/8n3yrYl6fAQ4Ellf5H3tr7v4scAXw7Rz3B0kVvJNzbE/mddkyj79xHvfJvG9vA7bu5396O9Ixtwb4ad5P3y4M3zsfI08DdwAHVhzPZ5H+p9cA1wITC8PfDzyS4/mHinWrtx7T8z6cDTwK3FR3HdqdGFv96dtwwJXA2blsoIl3LXA8sGE+aB/NO3sMMDPvwM0K/8hrgP3z8HOBn+Vhm5KS9fGk5LB7Pth3Lkz7DLBv3skbV1mfG4Hz8gG7G7CSnFRyrD+rsy1uoUpCLgzvb1tcmg/GDfLy96tIHDsW+t+c1233vB3+rXgw5vGvBsYDO5MS5nXA9qREcQ/5Cy3P4wlgr7wPjs37dUxhH/8SmEqqzb8mb+dtC+u1Q411rre/9gQeI39RAROB56mSGPKw1cB7gNHA3+Tjpm/bHU5KlK/L+/5U4BdNbo/+tu3DFL60gauA80nH31bArcCH87AdSV8YY4BJpIT2r3XmdSD9J94/5PXdIO+PvyYde1Pycs4HLs3jf5j0ZTY279s9gPH9/E/fDPwLsBGpeW01OfECk0lJ8bC8/INy/6TC8fwr4NU5tgXAOXnYTqQvmL5j4ct5H/atW731mJ734SV5O29Sdx3anRhb/eGVxPt6UlKbxMAT7wOFYbvk8bculD0J7Fb4R55bGLYZqRY6FfhL4H8q4jsfOL0w7SV11mVqnte4QtnngYsLsdZLvA8AH6kzvL9tcQlwAakNuXLaysR7EfDPFdvhD8D0wvj7FoYvAk4q9H+JnABIvwLOqlje/cABhX38gcKwHUmJ+q3A6H6Oj5r7K/ffCxyUu/8KuKbGfI4Bbin0C1he2HY/BmYXhm9ASuLTmtge/W3bh3klWWxNSuKbFMY/CrihxnocDtxe+f9T6D+Q/hPvTRXD76XwiwPYJsc7ivQrYL1fMP3srz8lJcOxhbJv80riPQn4VsU083jlS2sBcGph2MeAn+Tu0yqOhU2BFwvrVm89pud9uH0j69EtbbxExN3AD0k/FQbq8UL37/L8Kss2K/QvKyz3WdLPwW1JbW97SXq67wMcDfxJtWmr2BZYFRFrCmWPkL7lG/Ek6WBp1mdICeVWSUskfaDOuNvm2ICXt8OTrB9r5TastU2nAX9Xsd2m5mX0KW7zpaTayWeBJyTNlVQct1Kt/QUwB3hf7n4f6ed3NdtWzCdYf19OA84txL+KtC2b2R6NbNvickcDKwrLPp9U80XSVnn7/FrSalISm1hjHRtVeQxPA75XWP69pC+3rUnbcx4wV9Jjkv5Z0ug68+77H3i+xvKmAUdUHCv7sf5x/5tC9/Osv12L+/A50nZtZD1qrXtVXZN4s9NJbWPFA7TvRNTYQlkxETZjal+HpM2ALUk/WZcBN0bEhMJns4j4aGHaqDPfx4AtJY0rlP0pqd2uET8F3iWp1n6vuy0i4jcR8aGI2Jb0E/G8OlcyPEY6UAHIV028agCxFi0D/qliu42NiEsL46y33SLivyJivxxDAF+oM/9a+wtSIpol6Q2kZoJaVwmsqJiPiv15HT5csQ6bRMQv6sRVy0C27TJSjXdiYbnjI2LnPPzzpO2za0SMJ325qDB95fH4HIXjI58AnlQxTuU0y4BDK9Z944j4dUT8ISLOiIidSOdO3k769VDLCtL/QPEYrdzO36pY1qYRcU6deRbnXdyHY0nbtd/1qLPuVXVV4s01ocuATxbKVpIO2PdJ2jDX4gZ7hv8wSftJ2ojUkL8wIpaRatyvlvR+SaPz542SXtdg/MtIP8s+L2ljSbuSGvO/02BcXya1Ic6RNA1A0mRJX5a0a3/bQtIRkqbk3qdIB9m63P84qT2yz38Bx0vaTdIY4HN5OzzcYKxF/wF8RNJe+VrlTSW9reIL6GWSXiPpzXm5vyfVFtdVGzertb+IiOWkEz7fAr4bEb+rMY8fATtLenc+o/1J1v8C/wZwiqSdc4ybSzqi0Q1QoeFtGxErSCeQviRpvKQNJO0g6YA8yjhSu+bTkiYDn66YReV+/V9g47z9R5Paqsf0E+83gH8qHHOTJM3K3W+StEtO4KtJP91r7quIeAToBT4raSNJ+wDvKIzybeAdkg7Ox/DGkg4sHLf1XAG8vXAsnMn6ObLmegxUVyXe7ExS203Rh0gH3JOkExvN1EKK/otUu15FOllwNEBuIpgJHEmqtfyGVBPr78AtOorUnvQY8D1S+/D8RiaMiFWkWsUfgIWS1pBO4DxDOvED9bfFG/N0z5JOBJ0YEQ/lYZ8lJfSnJb03Iq4D/hH4LqkmsUNe7wGLiN4c19dICX8pqT27ljHAOaQTUL8h/az++zrjV91fBXNIbfu1mhmIiN8CR+TlPgnMIJ057xv+PdK+npt/0t8NHFonppqa2LbHkE5E3UPaflfwyk/vM0gn6Z4hfXlcWTHt54FT8379VEQ8Q2oXvZD0Jf0cqS27nnNJx8u1+Zi7hXSiFNKX0xWkpHsv6eTxt/uZ39HAPqTtfDapMvUCvFw5mUXa3ytJtdRP00Cui4glwMdJx8MK0rYqrlu99RgQ5UZiM6tB0v6kZDA9Il5qdzy2PkmXAfdFxOntjqVR3VjjNWtY/jl9InChk+7wkJvndsjNJoeQariDukOvbF15V4lZI3Lbey/pIvzj2xxOV8nNWdUcSrrT8krSia/lwEcj4vayYhsKbmowMyuZmxrMzErmxGtmVrKua+OdOHFiTJ8+vd1hmFmHWbRo0W8jovJmkqpalngl/SfpLpQnIuL1uWxL0jV300n3d783Ip7Kd/mcS3qwxfPAcRGxOE9zLOkibUgPuZmTy/cg3We/CelpXidGAw3W06dPp7e3d4jW0swskfRI/2MlrWxquBg4pKLsZOC6iJhBunC/77kJh5IuOJ8BnEB6KEpfoj6ddJHynsDpkrbI03w9j9s3XeWyzMyGpZYl3oi4iXQnUNEs0l1A5L+HF8ovieQWYIKkbYCDgfkRsSoiniI9M/WQPGx8RNyca7mXFOZlZjaslX1ybet873jfPeRb5fLJrP9Un+W5rF758irlVUk6QVKvpN6VK1cOeiXMzAZjuFzVoCpl0UR5VRFxQUT0RETPpEkNtX2bmbVM2Yn38dxMQP77RC5fzvqPdptCeghMvfIpVcrNzIa9shPv1aTXtpD/fr9Qfkx+5N/ewDO5KWIeMFPSFvmk2kxgXh62RtLe+YqIYwrzMjMb1lp5OdmlpNeETJS0nHR1wjnA5ZL6XgjX9zzSa0iXki0lXU52PKTHGEo6i/Q8VIAz86MNAT7KK5eT/Th/zMyGva57VkNPT0/4Ol4zG2qSFkVETyPjDpeTa2ZmXcOJ18ysZE68ZmYl67qH5AwXqnYlstFlpxysS7nGa2ZWMideM7OSOfGamZXMidfMrGROvGZmJXPiNTMrmROvmVnJnHjNzErmxGtmVjInXjOzkjnxmpmVzInXzKxkTrxmZiVz4jUzK5kTr5lZyZx4zcxK5sRrZlYyJ14zs5I58ZqZlcyJ18ysZE68ZmYlc+I1MyuZE6+ZWcmceM3MSubEa2ZWMideM7OSOfGamZXMidfMrGROvGZmJXPiNTMrmROvmVnJnHjNzErmxGtmVjInXjOzkjnxmpmVzInXzKxkTrxmZiVz4jUzK5kTr5lZyZx4zcxK1pbEK+lvJC2RdLekSyVtLGk7SQslPSDpMkkb5XHH5P6lefj0wnxOyeX3Szq4HetiZjZQpSdeSZOBTwI9EfF6YEPgSOALwFciYgbwFDA7TzIbeCoidgS+ksdD0k55up2BQ4DzJG1Y5rqYmTWjXU0No4BNJI0CxgIrgDcDV+Thc4DDc/es3E8e/hZJyuVzI+KFiHgIWArsWVL8ZmZNKz3xRsSvgX8BHiUl3GeARcDTEbE2j7YcmJy7JwPL8rRr8/ivKpZXmcbMbNhqR1PDFqTa6nbAtsCmwKFVRo2+SWoMq1VebZknSOqV1Lty5cqBB21mNoTa0dTwVuChiFgZEX8ArgT+HJiQmx4ApgCP5e7lwFSAPHxzYFWxvMo064mICyKiJyJ6Jk2aNNTrY2Y2IO1IvI8Ce0sam9tq3wLcA9wAvCePcyzw/dx9de4nD78+IiKXH5mvetgOmAHcWtI6mJk1bVT/owytiFgo6QpgMbAWuB24APgRMFfS2bnsojzJRcC3JC0l1XSPzPNZIulyUtJeC3w8ItaVujJmZk1Qqjx2j56enujt7W13GKhaC7XRZYejdRBJiyKip5FxfeeamVnJnHjNzErmxGtmVjInXjOzkjnxmpmVzInXzKxkTrxmZiVz4jUzK5kTr5lZyZx4zcxK5sRrZlYyJ14zs5I58ZqZlcyJ18ysZE68ZmYlc+I1MyuZE6+ZWcmceM3MSubEa2ZWMideM7OS9Zt4JZ0oabySiyQtljSzjODMzDpRIzXeD0TEamAmMAk4HjinpVGZmXWwRhJv34vIDwO+GRF3FMrMzGyAGkm8iyRdS0q88ySNA15qbVhmZp1rVL2BkgScRmpieDAinpf0KlJzg5mZNaFu4o2IkHRVROxRKHsSeLLlkZmZdahGmhpukfTGlkdiZtYl6tZ4szcBH5H0MPAc6cRaRMSurQzMzKxTNZJ4D215FGZmXaTfpoaIeASYCrw5dz/fyHRmZlZdI3eunQ6cBJySi0YD325lUGZmnayRmuu7gHeS2neJiMeAca0MysyskzWSeF+MiAACQNKmrQ3JzKyzNZJ4L5d0PjBB0oeAnwL/0dqwzMw6V79XNUTEv0g6CFgNvAY4LSLmtzwyM7MO1cjlZETEfEkL+8aXtGVErGppZGZmHarfxCvpw8CZwO9ID8cRqb13+9aGZmbWmRqp8X4K2DkiftvqYMzMukEjJ9d+RbppwszMhkAjNd5TgF/kNt4X+goj4pMti8rMrIM1knjPB64H7sIPQDczG7RGEu/aiPjblkdiZtYlGmnjvUHSCZK2kbRl36flkZmZdahGarz/P/89pVDmy8nMzJrUyJ1r25URiJlZt2jksZCjJX1S0hX581eSRg9moZIm5HndJ+leSfvkJoz5kh7If7fI40rSVyUtlXSnpN0L8zk2j/+ApGMHE5OZWVkaaeP9OrAHcF7+7JHLBuNc4CcR8VrgDcC9wMnAdRExA7gu90N6A8aM/Dmhb9m5nfl0YC9gT+D0vmRtZjacNdLG+8aIeEOh/3pJdzS7QEnjgf2B4wAi4kXgRUmzgAPzaHOABaQHsM8CLsmPprwl15a3yePO73tmhKT5wCHApc3GZmZWhkZqvOsk7dDXI2l7YN0glrk9sBL4pqTbJV2Yn/G7dUSsAMh/t8rjTwaWFaZfnstqlf+RfFVGr6TelStXDiJ0M7PBayTxfpp0SdkCSTeSbqb41CCWOQrYHfh6RPwZ6c0WJ9cZX1XKok75HxdGXBARPRHRM2nSpIHGa2Y2pBppavgZqX31NaRkd98gl7kcWB4RC3P/FaTE+7ikbSJiRW5KeKIw/tTC9FOAx3L5gRXlCwYZm5lZyzVS4705Il6IiDsj4o6IeAG4udkFRsRvgGWSXpOL3gLcA1wN9F2ZcCzw/dx9NXBMvrphb+CZ3BQxD5gpaYt8Um1mLjMzG9Zq1ngl/QmpzXQTSX/GKz/txwNjB7ncTwDfkbQR8CBwPOlL4HJJs4FHgSPyuNcAhwFLSU9JOx4gIlZJOgu4LY93ph/ObmYjgdLFAlUGpOtijwN6SMmtL/GuAS6OiCvLCHCo9fT0RG9vb7vDQNVaqI0ah6PZsCdpUUT0NDJuzRpvRMwB5kj6i4j47pBFZ2bW5Rpp450iaXxuY71Q0mJJM1semZlZh2ok8X4gIlaTTl5tRWpjPaelUZmZdbBGEm9fa+RhwDcj4g6qX0NrZmYNaCTxLpJ0LSnxzpM0Dr+JwsysaY3cQDEb2A14MCKel/Qq8iVdZmY2cI0k3v3y313la6DMzAatkcT76UL3xqRHMC4C3tySiMzMOlwjb6B4R7Ff0lTgn1sWkZlZh2vk5Fql5cDrhzoQM7Nu0W+NV9K/8crjFjcgnWhr+kHoZmbdrpE23uKDDdYCl0bEz1sUj5lZx6v3dLJJwKT8zIZi+c6SJkWEX+VgZtaEem28/wZUe13DFNLLKs3MrAn1Eu8uEXFjZWFEzAN2bV1IZmadrV7iHd3kMDMzq6Ne4n1A0mGVhZIOJb01wszMmlDvqoa/AX4o6b2kO9UgvY1iH+DtrQ7MzKxT1azxRsT/ArsANwLT8+dGYNc8zMzMmlD3Ot78RuFvlhSLmVlXaOaWYTMzGwQnXjOzktVMvJKuy3+/UF44Zmadr14b7zaSDgDeKWkuFe9Zi4jFLY3MzKxD1Uu8pwEnk24R/nLFsMAPQjcza0rNxBsRVwBXSPrHiDirxJjMzDpaI2+gOEvSO4H9c9GCiPhha8MyM+tc/V7VIOnzwInAPflzYi4zM7MmNPIg9LcBu0XESwCS5gC3A6e0MjAzs07V6HW8Ewrdm7ciEDOzbtFIjffzwO2SbiBdUrY/ru2amTWtkZNrl0paALyRlHhPiojftDowM7NO1UiNl4hYAVzd4ljMzLqCn9VgZlYyJ14zs5LVTbySNpB0d1nBmJl1g7qJN1+7e4ekPy0pHjOzjtfIybVtgCWSbgWe6yuMiHe2LCozsw7WSOI9o+VRmJl1kUau471R0jRgRkT8VNJYYMPWh2Zm1pkaeUjOh4ArgPNz0WTgqlYGZWbWyRq5nOzjwL7AaoCIeADYqpVBmZl1skYS7wsR8WJfj6RRpDdQmJlZExpJvDdK+ntgE0kHAf8N/KC1YZmZda5GEu/JwErgLuDDwDXAqYNdsKQNJd0u6Ye5fztJCyU9IOkySRvl8jG5f2kePr0wj1Ny+f2SDh5sTGZmZeg38eabKOYAZ5EuLZsTEUPR1HAicG+h/wvAVyJiBvAUMDuXzwaeiogdga/k8ZC0E3AksDNwCHCeJF9tYWbDXiNXNbwN+BXwVeBrwFJJhw5moZKmkN5scWHuF+mtxVfkUeYAh+fuWbmfPPwtefxZwNyIeCEiHgKWAnsOJi4zszI0cgPFl4A3RcRSAEk7AD8CfjyI5f4r8BlgXO5/FfB0RKzN/ctJl62R/y4DiIi1kp7J408GbinMsziNmdmw1Ugb7xN9STd7EHii2QVKenue56JicZVRo59h9aapXOYJknol9a5cuXJA8ZqZDbWaNV5J786dSyRdA1xOSmxHALcNYpn7Au+UdBiwMTCeVAOeIGlUrvVOAR7L4y8HpgLL86VsmwOrCuV9itOsJyIuAC4A6Onp8aVwZtZW9Wq878ifjYHHgQOAA0lXOGzR7AIj4pSImBIR00knx66PiKOBG4D35NGOBb6fu6/O/eTh1+eTe1cDR+arHrYDZgC3NhuXmVlZatZ4I+L4MgMBTgLmSjqb9Pr4i3L5RcC3JC0l1XSPzPEtkXQ5cA+wFvh4RKwrOWYzswFTf1eG5drkJ4DpFBL1SH0sZE9PT/T29rY7DFSthdoYkgsVzdpA0qKI6Glk3EauariKVOv8AfDSYAIzM7PGEu/vI+KrLY/EzKxLNJJ4z5V0OnAt8EJfYUQsbllUZmYdrJHEuwvwftKdZX1NDZH7zcxsgBpJvO8Cti8+GtLMzJrXyJ1rdwATWh2ImVm3aKTGuzVwn6TbWL+Nd0ReTmZm1m6NJN7TWx6FmVkXaegtw2UEYmbWLfpNvJLW8MpTvzYCRgPPRcT4VgZmZtapGqnxjiv2SzocP3DczKxpjVzVsJ6IuApfw2tm1rRGmhreXejdAOjBr3c3M2taI1c1vKPQvRZ4mPS+MzMza0IjbbxlP5fXzKyj1Xv1z2l1pouIOKsF8ZiZdbx6Nd7nqpRtCswmveXXidfMrAn1Xv3zpb5uSeOAE4HjgbmkV76bmVkT6rbxStoS+FvgaGAOsHtEPFVGYGZmnapeG+8XgXeTXou+S0Q8W1pUZmYdrN4NFH8HbAucCjwmaXX+rJG0upzwzMw6T7023gHf1WZmZv1zcjUzK5kTr5lZyZx4zcxK5sRrZlYyJ14zs5I58ZqZlcyJ18ysZE68ZmYlc+I1MyuZE6+ZWcmceM3MSubEa2ZWMideM7OSOfGamZXMidfMrGROvGZmJXPiNTMrmROvmVnJ6r5l2MyGB6ndEQxPEe2OoDmu8ZqZlcyJ18ysZE68ZmYlc+I1MytZ6YlX0lRJN0i6V9ISSSfm8i0lzZf0QP67RS6XpK9KWirpTkm7F+Z1bB7/AUnHlr0uZmbNaEeNdy3wdxHxOmBv4OOSdgJOBq6LiBnAdbkf4FBgRv6cAHwdUqIGTgf2AvYETu9L1mZmw1npiTciVkTE4ty9BrgXmAzMAubk0eYAh+fuWcAlkdwCTJC0DXAwMD8iVkXEU8B84JASV8XMrCltbeOVNB34M2AhsHVErICUnIGt8miTgWWFyZbnslrl1ZZzgqReSb0rV64cylUwMxuwtiVeSZsB3wX+OiJW1xu1SlnUKf/jwogLIqInInomTZo08GDNzIZQWxKvpNGkpPudiLgyFz+emxDIf5/I5cuBqYXJpwCP1Sk3MxvW2nFVg4CLgHsj4suFQVcDfVcmHAt8v1B+TL66YW/gmdwUMQ+YKWmLfFJtZi4zMxvW2vGshn2B9wN3SfplLvt74BzgckmzgUeBI/Kwa4DDgKXA88DxABGxStJZwG15vDMjYlU5q2Bm1jzFSH3KRJN6enqit7e33WH4oSc1dNnh2DAfL9UNp+NF0qKI6GlkXN+5ZmZWMideM7OSOfGamZXMidfMrGROvGZmJXPiNTMrmROvmVnJnHjNzErmxGtmVjInXjOzkjnxmpmVzInXzKxkTrxmZiVz4jUzK5kTr5lZyZx4zcxK5sRrZlYyJ14zs5I58ZqZlcyJ18ysZE68ZmYlc+I1MyuZE6+ZWcmceM3MSubEa2ZWMideM7OSOfGamZXMidfMrGROvGZmJXPiNTMrmROvmVnJnHjNzErmxGtmVjInXjOzkjnxmpmVzInXzKxkTrxmZiVz4jUzK5kTr5lZyZx4zcxK5sRrZlYyJ14zs5I58ZqZlcyJ18ysZCM+8Uo6RNL9kpZKOrnd8ZiZ9WdEJ15JGwL/DhwK7AQcJWmn9kZlZlbfiE68wJ7A0oh4MCJeBOYCs9ock5lZXSM98U4GlhX6l+cyM7Nha1S7AxgkVSmLPxpJOgE4Ifc+K+n+lkY18kwEftvuIABUbY/acOPjpbppjY440hPvcmBqoX8K8FjlSBFxAXBBWUGNNJJ6I6Kn3XHYyODjZfBGelPDbcAMSdtJ2gg4Eri6zTGZmdU1omu8EbFW0l8B84ANgf+MiCVtDsvMrK4RnXgBIuIa4Jp2xzHCuRnGBsLHyyAp4o/ORZmZWQuN9DZeM7MRx4nXzKxkTrxmZiVz4u0ykj5T6D6iYtjnyo/IhjMfL63hxNt9jix0n1Ix7JAyA7ERwcdLCzjxdh/V6K7Wb+bjpQWceLtP1Oiu1m/m46UFfB1vl5G0DniOVFvZBHi+bxCwcUSMbldsNvz4eGkNJ14zs5K5qcFeJunRdsdgI4ePl+Y58VqRT5bYQPh4aZITrxW53ckGwsdLk0b808lsYCT9ba1BwGZlxmLDn4+X1nDi7T7j6gw7t7QobKTw8dICvqrBXibpjRFxW7vjsJHBx0vzXOPtcpJ2It0WehTwDOB3aVlNPl6GhhNvF5I0jfSPcxSwlvR21J6IeLidcdnw5ONl6Pmqhi4j6RekVyWNBt4TEXsAa/xPZNX4eGkNJ97us5J0wmRrYFIuc0O/1eLjpQV8cq0LSdoc+AvST8cdgQnAwRFxa1sDs2HJx8vQc+LtcpK2Bv6SdMJkakRMbXNINoz5eBkaTrz2MknTIuKRdsdhI4OPl+b5qoYuI+nqfkZ5ZymB2Ijg46U1nHi7zz7AMuBSYCF+0InV5+OlBdzU0GUkbQgcRDpRsivwI+DSiFjS1sBsWPLx0hq+nKzLRMS6iPhJRBwL7A0sBRZI+kSbQ7NhyMdLa7ipoQtJGgO8jVSLmQ58FbiynTHZ8OXjZei5qaHLSJoDvB74MTA3Iu5uc0g2jPl4aQ0n3i4j6SXSywth/TuQBEREjC8/KhuufLy0hhOvmVnJfHLNzGrKmIfIAAAFFElEQVSS9OZC93YVw95dfkSdwTVeM6tJ0uKI2L2yu1q/Nc41XjOrRzW6q/Vbg5x4zayeqNFdrd8a5Ot4zaye7fPzGlToJvdvV3syq8dtvGZWk6QD6g2PiBvLiqWTuMZrZvU8FBGPtjuITuM2XjOr56q+DknfbWcgncSJ18zqKV65sH3bougwTrxmVk+9qxqsST65ZmY1SVpHelaDgE2A5/sG4Wc1NM2J18ysZG5qMDMrmROvmVnJnHjNzErmxGtDStJnJX2qhfM/QtK9km5oYtoJkj7WirjaSdLDkia2Ow5rnBOvjTSzgY9FxJuamHYCMODEm9+02zE6bX1GIideGzRJ/yDpfkk/BV6Ty3aQ9BNJiyT9j6TX5vKLJX1d0g2SHpR0gKT/zLXYiwvzPErSXZLulvSFXHYasB/wDUlflLRh/nubpDslfTiPt5mk6yQtzvOYlWd7DrCDpF/m6Q6U9MPCMr8m6bjc/bCk0yT9DDiizvockWO8Q9JNdbbRWEmX5zgvk7RQUk8eNlPSzTne/5a0WSGGMwrr0bfMV0m6VtLtks6ncJODpPdJujWv4/l9SVbSs5LOlLQQ2KfZfW1DJCL88afpD7AHcBcwFhhPev33p4DrgBl5nL2A63P3xcBcUrKYBawGdiFVAhYBuwHbAo8Ck0jPE7keODxPvwDoyd0nAKfm7jFAL+mJWaOA8bl8Yo5JpDfk3l2I/UDgh4X+rwHH5e6Hgc8UhtVan7uAybl7Qp3t9Cng/Nz9emAt0JPjuwnYNA87CTitEMMncvfHgAtz91cL47yNdGPDROB1wA+A0XnYecAxuTuA97b7ePEnffyQHBus/wd8LyKeB8iPDdwY+HPgv6WXK2NjCtP8ICJC0l3A4xFxV552CSk5TgMWRMTKXP4dYH8Kzw3IZgK7SnpP7t8cmAEsBz4naX/gJWAysHUT63ZZXv5mddbn58DFki6n/ivP9wPOBYiIuyXdmcv3BnYCfp7nvRFwc2G6vnkuAvpetbN/X3dE/EjSU7n8LaQvwtvyvDYBnsjD1gF+1sIw4cRrQ6HyLpwNgKcjYrca47+Q/75U6O7rH0WqDTZCpBrhvPUKU3PBJGCPiPiDpIdJXwaV1rJ+c1vlOH1v1625PhHxEUl7kWqev5S0W0Q8WSPWWuswPyKOqjG8b/usY/3/12p3PgmYExGnVBn2+4hYV2MZVjK38dpg3QS8S9ImksYB7yDdVvqQpCMAlLxhAPNcCBwgaWJuozwKqPbc13nARyWNzst5taRNSTXfJ3LSfROpBg2wBhhXmP4RYCdJYyRtTqox/pGIWF1rfSTtEBELI+I04LfA1Brr9DPgvXmanUjNKwC3APtK2jEPGyvp1XW3TtrmR+fxDwW2yOXXAe+RtFUetqWkadVnYe3kxGuDEhGLST/Jf0n6Kfs/edDRwGxJdwBLSO25jc5zBXAKcANwB7A4Ir5fZdQLgXuAxZLuBs4n1Qq/A/RI6s1x3Jfn+yTpJ/3dkr4YEcuAy4E78zS31wmr1vp8se8kICkh3lFj+vOASbmJ4aS8zGdyc8pxwKV52C3Aa+tuIDgD2F/SYlJzy6N5/e4BTgWuzfOaD2zTz7ysDfysBrMS5Jr76Ij4vaQdSLXTV0fEi20OzdrAbbxm5RgL3JCbRQR81Em3e7nGazaEJB0MfKGi+KGIeFc74rHhyYnXzKxkPrlmZlYyJ14zs5I58ZqZlcyJ18ysZE68ZmYl+z87SpV73iBa9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAAGNCAYAAACllDJRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XmcHFW5//HPlyQQlkASGBCSQFgiCoIQh034KYuETRYXFDciRAJevOLFq4BXQQGvKBfBDSSCEBCIAQUCoiEiywVkmYR9u4lsiUGIBMIqkPD8/qgzUBl6emomXdNLvu/Xq19ddepU9VPdPc+cPnWqShGBmZnV1gr1DsDMrBU5uZqZlcDJ1cysBE6uZmYlcHI1MyuBk6uZWQmcXBuIpPMlnVyn15ak8yQ9J+mOesTQbGr5eUl6SdJGtdiWNQYn1yokPS7paUmr5sq+JOmGOoZVlp2A3YGREbFtpQqS1pV0rqSnJL0o6WFJ38u/P30hKSRtsizbaHYRsVpEPFrvOKx2nFx7NhA4qt5B9JakAb1cZQPg8Yh4uZvtDQf+CqwM7BARQ8iS8VBg42WJtVFIGljvGKy6ZvqMnFx7dirwn5KGdl0gaXRqdQ3Mld0g6Utp+ouSbpF0uqTnJT0q6YOpfK6kZySN77LZtSTNSC3DGyVtkNv2e9KyhZIekfSp3LLzJZ0l6RpJLwO7VIh3PUnT0vpzJB2WyicA5wA7pJ+n36vwPhwNvAh8PiIeB4iIuRFxVETcW+C92CTtzyJJ/5T021R+U6p+T3rtT6fyw1KMC1PM6+W2G5L+TdLs9D6dJGljSX+V9IKkqZJWzNX/qKS702dwq6Qtc8sel3SMpHuBlyUNTPN/T9t+RNJuFd6Pqp+XpF9IOq3L+3+VpK9V2ki+9Z4+y19I+kPa7u2SNs7V3Tz3PXha0rdS+UqSzpA0Pz3OkLRSWrazpHmSvpm+d09JOkDS3pL+L23rW7nXWEHSsZL+JunZ9J4Or/I+IGmwpN+k+s9LulPSOmlZxe9ebn9Pzs3vLGleD5/RKEm/l7Qgvd7Pc/UPlfSQsi6u6cr9DfWriPCjmwfwOPAR4PfAyansS8ANaXo0EMDA3Do3AF9K018EFgOHAAOAk4EngV8AKwHjyBLWaqn++Wn+Q2n5T4Cb07JVgblpWwOBscA/gc1z6y4CdiT7pzm4wv7cCJwJDAa2AhYAu+VivbnKe3Eb8L0qy3t6Ly4B/qszNmCnXL0ANsnN75r2bWx6H34G3NSl/jRgdWBz4DXgOmAjYA3gQWB8qjsWeAbYLn0G49PnulLuM74bGEXWKt80vc/r5fZr4272udrntS0wH1ghza8FvAKs08223noP0nYXpm0MBC4CpqRlQ4CngK+n93EIsF1admL6nNYG2oBbgZPSsp3JvovHA4OAw9Lnf3HaxubAv4CNUv2vpW2NTPt2NnBJD38vhwNXAauk9/oDwOoFvnvnk/6+crHO6/J3mP+MBgD3AKeT/V289X0CDgDmAO9N7923gVvrkj/qlbia4cHbyfV9ZImrjd4n19m5ZVuk+uvkyp4Ftsp9yabklq0GLElfqk8D/9slvrOBE3LrXlBlX0albQ3Jlf0AOD8Xa7XkOhs4osrynt6LC4BJZH26XdftmlzPBX7U5X14Axidq79jbvlM4Jjc/GnAGWn6LFKCyS1/BPhw7jM+NLdsE7Jk/BFgUA/fj24/rzT/ELB7mv4KcE2VbXVNrufklu0NPJymPwPc1c02/gbsnZvfg6yrB7KE9SowIM0PSa+5XZf38YBc7Lvllq2bPoOBVfbhULKEvmUvv3vn03NyzX9GO5Al53fEAvwRmJCbX4Hsn9oG1T7LMh7uFiggIu4HrgaO7cPqT+emX03b61q2Wm5+bu51XyJrwaxH1ie6Xfq59byk54HPAe+qtG4F6wELI+LFXNkTwIiC+/Es2R9YX30TEHCHpAckHVql7nopNuCt9+FZlo6163vY3Xu6AfD1Lu/bqPQanfLv+RyyVtt3gWckTcl3SVTQ3ecFMBn4fJr+PHBhle109Y/c9Cu5/RlFlkQrWep9S9P52J+NiCVp+tX0XO19uzz3nj1EliDXqRLzhcB0YErqlviRpEEs+3cPlv5ujwKeiIjFFeptAPwkF/dCsu9db16rJpxcizuB7KdU/kPqPPizSq4sn+z6YlTnhKTVgOFkPy/nAjdGxNDcY7WI+HJu3WqXOJsPDJc0JFe2PvD3gnH9GfiYpO6+M1Xfi4j4R0QcFhHrkf18PFPdjxCYT/ZHAoCy0Qhr9iLWvLnA97u8b6tExCW5Oku9bxFxcUTslGII4IdVtt/d5wXwG2B/Se8n+5l6RR/i72ou3R9AXOp9I/t853dTt8jr7NXlfRscEd1+BhHxRkR8LyI2Az4IfBQ4mJ6/ey/T899Q/jOaC6yvyge35gKHd4l75Yi4tacdrjUn14JSi+a3wFdzZQvIviCflzQgtcaW9cj53pJ2SgdkTgJuj4i5ZC3nd0v6gqRB6bGNpPcWjH8u2U+2H6QDD1sCE8j684r4MVkf5+TcQZsRkn4sacue3gtJB0oamWafI/tj6WxFPU3WX9rpYuAQSVulAzL/nd6HxwvGmvcr4AhJ2ymzqqR9uvyhv0XSppJ2Ta/7L7LW3JJKdZPuPi8iYh5wJ1mL7ncR8WqV7RR1NfAuSV9LB7CGSNouLbsE+LakNklrkfWv/qaPr/NL4Pu5z7pN0v7VVpC0i6QtlI1UeYGsG2FJge/e3WTv43BJ7yL75VDNHWT9zqekz3OwpB1zcR8nafMU0xqSDuztzteCk2vvnEjWgZ53GPANsp+tm5N9iZbFxWSt5IVkBwQ+B5B+Uo0DDiJrCfyDrEW1Ui+2/RmyvtH5wOVk/bUziqwYEQvJWiNvALdLepHsINIisgMIUP292Cat9xLZwaijIuKxtOy7ZEn7eUmfiojrgO8AvyP7I9o47XevRURHiuvnZEl9Dln/cndWAk4hO6D2D7KDQ9+qUr/i55UzmayvvTddAt1K34PdgX1TfLN5e2TIyUAHcC9wHzArlfXFT8g+p2vTZ30b2UHBat4FXEaWWB8iO4jVmdyrffcuJDtA9ThwLVkjplupa2Nfsv7xJ4F5ZMckiIjLyf4upkh6Abgf2KvA/tacUqevmZVA0ofIEszoiHiz3vFY/3HL1awk6WDOUWRH/p1YlzNOrmYlSH3hz5ONsDijzuHUjKTPKTvZo+vjgXrH1mjcLWBmVgK3XM3MSuDkamZWgqa5wkxvrLXWWjF69Oh6h2FmLWbmzJn/jIi2InVbMrmOHj2ajo6OeodhZi1G0hM918q4W8DMrAROrmZmJXByNTMrgZOrmVkJnFzNzErg5GpmVgInVzOzEji5mpmVwMnVzKwETq5mZiVwcjUzK0FLXlugr6T+fT1fStesdbnlamZWAidXM7MSOLmamZXAydXMrAROrmZmJXByNTMrgZOrmVkJSk2ukv5D0gOS7pd0iaTBkjaUdLuk2ZJ+K2nFVHelND8nLR+d285xqfwRSXuUGbOZWS2UllwljQC+CrRHxPuAAcBBwA+B0yNiDPAcMCGtMgF4LiI2AU5P9ZC0WVpvc2BP4ExJA8qK28ysFsruFhgIrCxpILAK8BSwK3BZWj4ZOCBN75/mSct3k6RUPiUiXouIx4A5wLYlx21mtkxKS64R8Xfgf4AnyZLqImAm8HxELE7V5gEj0vQIYG5ad3Gqv2a+vMI6ZmYNqcxugWFkrc4NgfWAVYG9KlTtPMO+0pn9UaW86+tNlNQhqWPBggV9C9rMrEbK7Bb4CPBYRCyIiDeA3wMfBIambgKAkcD8ND0PGAWQlq8BLMyXV1jnLRExKSLaI6K9ra2tjP0xMyuszOT6JLC9pFVS3+luwIPA9cAnU53xwJVpelqaJy3/S0REKj8ojSbYEBgD3FFi3GZmy6y0Sw5GxO2SLgNmAYuBu4BJwB+AKZJOTmXnplXOBS6UNIesxXpQ2s4DkqaSJebFwJERsaSsuM3MakHRghcVbW9vj46Ojl6v5+u5mlk1kmZGRHuRuj5Dy8ysBE6uZmYlcHI1MyuBk6uZWQmcXM3MSuDkamZWAidXM7MSOLmamZXAydXMrAROrmZmJXByNTMrgZOrmVkJnFzNzErg5GpmVgInVzOzEji5mpmVwMnVzKwETq5mZiVwcjUzK4GTq5lZCZxczcxK4ORqZlaC0pKrpE0l3Z17vCDpa5KGS5ohaXZ6HpbqS9JPJc2RdK+ksbltjU/1Z0saX1bMZma1UlpyjYhHImKriNgK+ADwCnA5cCxwXUSMAa5L8wB7AWPSYyJwFoCk4cAJwHbAtsAJnQnZzKxR9Ve3wG7A3yLiCWB/YHIqnwwckKb3By6IzG3AUEnrAnsAMyJiYUQ8B8wA9uynuM3M+qS/kutBwCVpep2IeAogPa+dykcAc3PrzEtl3ZWbmTWs0pOrpBWB/YBLe6paoSyqlHd9nYmSOiR1LFiwoPeBmpnVUH+0XPcCZkXE02n+6fRzn/T8TCqfB4zKrTcSmF+lfCkRMSki2iOiva2trca7YGbWO/2RXD/D210CANOAziP+44Erc+UHp1ED2wOLUrfBdGCcpGHpQNa4VGZm1rAGlrlxSasAuwOH54pPAaZKmgA8CRyYyq8B9gbmkI0sOAQgIhZKOgm4M9U7MSIWlhm3mdmyUsQ7ui+bXnt7e3R0dPR6PVXq3S1RC771Zi1N0syIaC9S12domZmVwMnVzKwETq5mZiVwcjUzK4GTq5lZCZxczcxK4ORqZlYCJ1czsxI4uZqZlcDJ1cysBE6uZmYlcHI1MyuBk6uZWQmcXM3MSuDkamZWAidXM7MSOLmamZXAydXMrAROrmZmJXByNTMrgZOrmVkJnFzNzErQY3KVdJSk1ZU5V9IsSeOKbFzSUEmXSXpY0kOSdpA0XNIMSbPT87BUV5J+KmmOpHsljc1tZ3yqP1vS+L7vrplZ/yjScj00Il4AxgFtwCHAKQW3/xPgTxHxHuD9wEPAscB1ETEGuC7NA+wFjEmPicBZAJKGAycA2wHbAid0JmQzs0ZVJLkqPe8NnBcR9+TKul9JWh34EHAuQES8HhHPA/sDk1O1ycABaXp/4ILI3AYMlbQusAcwIyIWRsRzwAxgz0J7Z2ZWJ0WS60xJ15Il1+mShgBvFlhvI2ABcJ6kuySdI2lVYJ2IeAogPa+d6o8A5ubWn5fKuis3M2tYVZOrJAHHk/103yYiXgFWJOsa6MlAYCxwVkRsDbzM210AFV+uQllUKe8a60RJHZI6FixYUCA8M7PyVE2uERHAFRExK/2kJyKejYh7C2x7HjAvIm5P85eRJdun08990vMzufqjcuuPBOZXKe8a66SIaI+I9ra2tgLhmZmVp0i3wG2StunthiPiH8BcSZumot2AB4FpQOcR//HAlWl6GnBwGjWwPbAodRtMB8ZJGpYOZI1LZWZmDWtggTq7AEdIepzsp73IGrVbFlj334GLJK0IPErWnbACMFXSBOBJ4MBU9xqyft05wCupLhGxUNJJwJ2p3okRsbDAa5uZ1Y2yX/5VKkgbVCqPiCdKiagG2tvbo6Ojo9frqccxELXVw1tvZg1G0syIaC9St8dugZRERwG7pulXiqxnZrY8K3KG1gnAMcBxqWgQ8JsygzIza3ZFWqAfA/Yj628lIuYDQ8oMysys2RVJrq+nIVkBkE4EMDOzKook16mSziY7HfUw4M/Ar8oNy8ysufU4FCsi/kfS7sALwKbA8RExo/TIzMyaWJFxrkTEDEm3d9aXNNxjTc3MutdjcpV0OHAi8CrZBVtE1v+6UbmhmZk1ryIt1/8ENo+If5YdjJlZqyhyQOtvZCcOmJlZQUVarscBt6Y+19c6CyPiq6VFZWbW5Iok17OBvwD3Uewi2WZmy70iyXVxRBxdeiRmZi2kSJ/r9ekq/+umO7cOTzcNNDOzbhRpuX42PR+XK/NQLDOzKoqcobVhfwRiZtZKipxEMAj4MtltsgFuAM6OiDdKjMvMrKkV6RY4i+warmem+S+ksi+VFZSZWbMrkly3iYj35+b/IumesgIyM2sFRUYLLJG0ceeMpI2AJeWFZGbW/Iq0XL9BNhzrUbKLtmwAHFpqVGZmTa5Icr0ZGEN2LVcBD5cakZlZCyjSLfDXiHgtIu6NiHsi4jXgr0U2LulxSfdJultSRyobLmmGpNnpeVgql6SfSpoj6V5JY3PbGZ/qz5Y0vi87ambWn7ptuUp6FzACWFnS1mStVoDVgVV68Rq7dLlc4bHAdRFxiqRj0/wxwF5kLeQxwHZkIxK2S2eDnQC0k528MFPStIh4rhcxmJn1q2rdAnsAXwRGAqfxdnJ9EfjWMrzm/sDOaXoy2bjZY1L5BelmiLdJGipp3VR3RuedDyTNAPYELlmGGMzMStVtco2IycBkSZ+IiN/1cfsBXCspyE48mASsExFPpdd4StLaqe4IYG5u3XmprLtyM7OGVaTPdaSk1VOf6DmSZkkaV3D7O0bEWLKf/EdK+lCVuqpQFlXKl145u7hMh6SOBQsWFAzPzKwcRZLroRHxAjAOWBs4BDilyMYjYn56fga4HNgWeDr93Cc9P5OqzwNG5VYfCcyvUt71tSZFRHtEtLe1tRUJz8ysNEWSa2fLcW/gvIi4h8qtyaVXklaVNKRzmiw53w9MAzqP+I8HrkzT04CDUwt5e2BR6j6YDoyTNCyNLBiXyszMGlaRca4zJV0LbAgclxJmkTsSrANcLqnzdS6OiD9JuhOYKmkC8CRwYKp/DVkCn0N2z65DACJioaSTgDtTvRN9W28za3TKDs5XqSCtAGwFPBoRz0taExgREff2R4B90d7eHh0dHb1eTz22x2urh7fezBqMpJkR0V6kbpGW607peUv1d/YxM2tSRa8t0Gkw2UGpmcCupURkZtYCityJYN/8vKRRwI9Ki8jMrAUUGS3Q1TzgfbUOxMyslRS5zcvPeHvQfufBLV8s28ysiiJ9rvnD7ouBSyLilpLiMTNrCdWuitUGtKVrDOTLN5fUFhE+x9TMrBvV+lx/BlQ6j3Qk8JNywjEzaw3VkusWEXFj18KImA5sWV5IZmbNr1pyHdTHZWZmy71qyXW2pL27FkraC3i0vJDMzJpftdEC/wFcLelTZGdkQXarlR2Aj5YdmJlZM+u25RoR/wdsAdwIjE6PG4Et0zIzM+tG1XGu6U6v5/VTLGZmLaMvp7+amVkPnFzNzErQbXKVdF16/mH/hWNm1hqq9bmuK+nDwH6SptDlvlkRMavUyMzMmli15Ho8cCzZ6a4/7rIs8MWyzcy61W1yjYjLgMskfSciTurHmMzMml6ROxGcJGk/4EOp6IaIuLrcsMzMmluPowUk/QA4CngwPY5KZWZm1o0iQ7H2AXaPiF9HxK+BPVNZIZIGSLpL0tVpfkNJt0uaLem3klZM5Sul+Tlp+ejcNo5L5Y9I2qM3O2hmVg9Fx7kOzU2v0cvXOAp4KDf/Q+D0iBgDPAdMSOUTgOciYhPg9FQPSZsBBwGbkyX2MyUN6GUMZmb9qkhy/QFwl6TzJU0mu4jLfxfZuKSRZK3cc9K8yEYZXJaqTAYOSNP7p3nS8t1S/f2BKRHxWkQ8Bswhu723mVnDKnJA6xJJNwDbkI11PSYi/lFw+2cA3wSGpPk1gecjYnGanweMSNMjgLnpNRdLWpTqjwBuy20zv46ZWUMqcoNCIuIpYFpvNizpo8AzETFT0s6dxZU238OyauvkX28iMBFg/fXX702oZmY1V+a1BXYkO7vrcWAKWXfAGcBQSZ1JfSQwP03PA0YBpOVrAAvz5RXWeUtETIqI9ohob2urdOsvM7P+U1pyjYjjImJkRIwmOyD1l4j4HHA98MlUbTxwZZqeluZJy/8SEZHKD0qjCTYExgB3lBW3mVktVE2uklaQdH+NX/MY4GhJc8j6VM9N5ecCa6byo8lOvSUiHgCmko2x/RNwZEQsqXFMZmY1paxxWKWCdBFwXEQ82T8hLbv29vbo6Ojo9Xqq1Ltboh7eejNrMJJmRkR7kbpFDmitCzwg6Q7g5c7CiNivj/GZmbW8Isn1e6VHYWbWYoqMc71R0gbAmIj4s6RVAJ8hZWZWRZELtxxGdsbU2aloBHBFmUGZmTW7IkOxjiQbs/oCQETMBtYuMygzs2ZXJLm+FhGvd86kAf4+zm1mVkWR5HqjpG8BK0vaHbgUuKrcsMzMmluR5HossAC4DzgcuAb4dplBmZk1uyKjBd5Mlxq8naw74JHo6cwDM7PlXI/JVdI+wC+Bv5FdoWpDSYdHxB/LDs7MrFkVOYngNGCXiJgDIGlj4A+Ak6uZWTeK9Lk+05lYk0eBZ0qKx8ysJXTbcpX08TT5gKRryK5MFcCBwJ39EJuZWdOq1i2wb276aeDDaXoBMKy0iMzMWkC3yTUiDunPQMzMWkmR0QIbAv8OjM7X9yUHzcy6V2S0wBVkdwm4Cniz3HDMzFpDkeT6r4j4aemRmJm1kCLJ9SeSTgCuBV7rLIyIWaVFZWbW5Iok1y2AL5DdGruzWyDSvJmZVVAkuX4M2Ch/2UEzM6uuyBla9wBDyw7EzKyVFEmu6wAPS5ouaVrno6eVJA2WdIekeyQ9IOl7qXxDSbdLmi3pt5JWTOUrpfk5afno3LaOS+WPSNqjb7tqZtZ/inQLnNDHbb8G7BoRL0kaBNws6Y/A0cDpETFF0i+BCcBZ6fm5iNhE0kHAD4FPS9oMOAjYHFgP+LOkd0fEkj7GZWZWukJ3f+3LhtM1X19Ks4PSo/NA2GdT+WTgu2TJdf80DdkNEX8uSal8SkS8BjwmaQ6wLfDXvsRlZtYfitz99UVJL6THvyQtkfRCkY1LGiDpbrKraM0guybs8xGxOFWZR3Y3WdLzXIC0fBGwZr68wjr515ooqUNSx4IFC4qEZ2ZWmh6Ta0QMiYjV02Mw8Ang50U2HhFLImIrYCRZa/O9laqlZ3WzrLvyrq81KSLaI6K9ra2tSHhmZqUpckBrKRFxBb0c4xoRzwM3ANsDQ9MdZCFLuvPT9DxgFLx1h9k1gIX58grrmJk1pCLdAh/PPT4p6RQK3FpbUpukoWl6ZeAjwEPA9cAnU7XxwJVpelqaJy3/S+q3nQYclEYTbAiMAe4ovIdmZnVQZLRA/rqui4HHyQ4y9WRdYLKkAWRJfGpEXC3pQWCKpJOBu8guCkN6vjAdsFpINkKAiHhA0lTgwfT6R3qkgJk1OrXijVzb29ujo6Oj1+upUu9uiVrwrTdraZJmRkR7kbrVbvNyfJX1IiJO6nVkZmbLiWrdAi9XKFuVbLD/moCTq5lZN6rd5uW0zmlJQ4CjgEOAKWS32zYzs25UPaAlaTjZ6aqfIzubamxEPNcfgZmZNbNqfa6nAh8HJgFbRMRL3dU1M7OlVRvn+nWyC6V8G5ifOwX2xaKnv5qZLa+q9bn2+uwtMzPLOIGamZXAydXMrAROrmZmJXByNTMrgZOrmVkJnFzNzErg5GpmVgInVzOzEji5mpmVwMnVzKwETq5mZiVwcjUzK0GRGxRai/A9wsz6j1uuZmYlKC25Shol6XpJD0l6QNJRqXy4pBmSZqfnYalckn4qaY6keyWNzW1rfKo/W9L4smI2M6uVMluui4GvR8R7ge2BIyVtBhwLXBcRY4Dr0jzAXsCY9JgInAVv3WrmBGA7YFvghM6EbGbWqEpLrhHxVETMStMvAg8BI4D9ye7HRXo+IE3vD1wQmduAoZLWBfYAZkTEwnT/rhnAnmXFbWZWC/3S5yppNLA1cDuwTkQ8BVkCBtZO1UYAc3OrzUtl3ZWbmTWs0pOrpNWA3wFfi4hq996qdCw7qpR3fZ2JkjokdSxYsKBvwZqZ1UipyVXSILLEelFE/D4VP51+7pOen0nl84BRudVHAvOrlC8lIiZFRHtEtLe1tdV2R8zMeqnM0QICzgUeiogf5xZNAzqP+I8HrsyVH5xGDWwPLErdBtOBcZKGpQNZ41KZmVnDKvMkgh2BLwD3Sbo7lX0LOAWYKmkC8CRwYFp2DbA3MAd4BTgEICIWSjoJuDPVOzEiFpYYt5nZMlO04Gk07e3t0dHR0ev1Wv0MplbfP7OySZoZEe1F6voMLTOzEji5mpmVwMnVzKwETq5mZiVwcjUzK4GTq5lZCZxczcxK4ORqZlYCJ1czsxI4uZqZlcDJ1cysBE6uZmYlcHI1MyuBk6uZWQmcXM3MSuDkamZWAidXM7MSOLmamZXAydXMrAROrmZmJXByNTMrgZOrmVkJSkuukn4t6RlJ9+fKhkuaIWl2eh6WyiXpp5LmSLpX0tjcOuNT/dmSxpcVr5lZLZXZcj0f2LNL2bHAdRExBrguzQPsBYxJj4nAWZAlY+AEYDtgW+CEzoRsZtbISkuuEXETsLBL8f7A5DQ9GTggV35BZG4DhkpaF9gDmBERCyPiOWAG70zYZmYNp7/7XNeJiKcA0vPaqXwEMDdXb14q667czKyhDax3AIkqlEWV8nduQJpI1qXA+uuvX7vIrGmo0relRFHxm2iW6e+W69Pp5z7p+ZlUPg8Ylas3EphfpfwdImJSRLRHRHtbW1vNAzcz643+Tq7TgM4j/uOBK3PlB6dRA9sDi1K3wXRgnKRh6UDWuFRmZtbQSusWkHQJsDOwlqR5ZEf9TwGmSpoAPAkcmKpfA+wNzAFeAQ4BiIiFkk4C7kz1ToyIrgfJzMwajqIFO47a29ujo6Oj1+u1ep+d96+2WvBPx3ogaWZEtBep6zO0zMxK4ORqZlYCJ1czsxI4uZqZlcDJ1cysBE6uZmYlcHI1MyuBk6uZWQmcXM3MSuDkamZWAidXM7MSOLmamZXAydXMrASNcicCM+uBr/rVXNxyNTMrgZOrmVkJnFzNzErg5GpmVgInVzOzEji5mpmVwMnVzKwETq5mZiVomuQqaU9Jj0iaI+nYesdjZlZNUyRXSQOAXwB7AZsBn5G0WX2jMjPrXlMkV2BbYE5EPBoRrwNTgP3rHJOZWbeaJbmOAObm5uelMjOzhtQsF26pdMmKpS4rIWkiMDHNviTpkdKjettawD97u1J/X4hjGXj/KvD+NYQ+7dsy2KBoxWZJrvOAUbn5kcDeRC1aAAAN9klEQVT8fIWImARM6s+gOknqiIj2erx2f/D+NbdW3r9G3rdm6Ra4ExgjaUNJKwIHAdPqHJOZWbeaouUaEYslfQWYDgwAfh0RD9Q5LDOzbjVFcgWIiGuAa+odRzfq0h3Rj7x/za2V969h903hy42bmdVcs/S5mpk1FSdXM7MSOLlaRemUYzPrIyfXPpL0wyJlTWyOpFNb+RoOkj4o6bOSDu581DumWpH0vnrHUCZJO0k6JE23Sdqw3jF15QNafSRpVkSM7VJ2b0RsWa+YaknSELLxxIeQ/RP+NTAlIl6oa2A1IulCYGPgbmBJKo6I+Gr9oqodSTcDKwLnAxdHxPP1jah2JJ0AtAObRsS7Ja0HXBoRO9Y5tKU4ufaSpC8D/wZsBPwtt2gIcEtEfL4ugZVI0oeAS4ChwGXASRExp75RLRtJDwGbRQv/AUgaAxwKHAjcAZwXETPqG9Wyk3Q3sDUwKyK2TmUN17BpmnGuDeRi4I/AD4D8dWVfjIiF9Qmp9lKf6z5kLdfRwGnARcD/Ixtv/O66BVcb9wPvAp6qdyBliYjZkr4NdAA/BbaWJOBbEfH7+ka3TF6PiJAUAJJWrXdAlTi59l5ExOOSjuy6QNLwFkqws4HrgVMj4tZc+WWpJdvs1gIelHQH8FpnYUTsV7+QakfSlmT/GPcBZgD7RsSs9BP6r0AzJ9epks4Ghko6jKx1/qs6x/QO7hboJUlXR8RHJT1GdmWu/LWDIiI2qlNoNZNarf8VESfWO5aySPpwpfKIuLG/YymDpJvIEs5lEfFql2VfiIgL6xNZbUjaHRhH9vc3vRG7O5xcrSJJ10fELvWOo0yS1gG2SbN3RMQz9YynliR9HPhDRLzWY+UmJWl1cr++G+1Xo5NrL0kaW215RMzqr1jKJOn7wBrAb4GXO8tbaP8+BZwK3EDW+vl/wDci4rJ6xlUrks4DdgVuIrtzx/SIWFzfqGpD0uHAicCrwJtkn1/D/Wp0cu0lSdenycFkw0HuIftwtwRuj4id6hVbLeX2My8iYtd+D6YEku4Bdu9srUpqA/4cEe+vb2S1I2kQ2X3nPg3sBMyIiC/VN6plJ2k2sENE9OdFsnvNB7R6qfOnsqQpwMSIuC/Nvw/4z3rGVkut3iUArNClG+BZWuykmoh4Q9IfyY4NrEx237mmT65kQyBfqXcQPXFy7bv3dCZWgIi4X9JW9Qyo1iTtA2xO1koHoIUOcv1J0nSy8buQte4a9ZKWvSZpT7KTQHYh6/o4B/hUPWOqoeOAWyXdztIjPRrqBBB3C/SRpEvI+iJ/Q9Yy+DywWkR8pq6B1YikXwKrkP1xngN8kuygz4S6BlZDkj4B7EjWrXNTRFxe55BqJv2ymgL8sdUOaqXhczcD95H1uQIQEZPrFlQFTq59JGkw8GWgc8znTcBZEfGv+kVVO51nvOSeVwN+HxHj6h2bLd8k3RoRH6x3HD1xt0AfRcS/UuvumojozzvN9pfOsZGvpIHnzwINd3GM3pJ0c0TsJOlFlr6DcOcR59XrFFpNtPr+Jdenuz1fxdLdAh6K1Qok7Uc2lGfFiNgw9bee2EJn+HwH+BmwG/ALsj/UcyLiO3UNzJZ76QSerjwUq1VImkk2jvCGRr54RC1IWgkYHBGL6h1LrUjaGJgXEa9J2plsKN0FrXT1KKsvdwv03eKIWJRdB6N1pDN7ultGk1/wI+93QLukTYBzyW7VfjGwd12jsh6l8bv54x03AGdHxBt1C6oCJ9e+u1/SZ4EB6dJuXwVu7WGdZrBvlWVBc1/wI+/NdMv2jwFnRMTPJN1V76CskLOAQcCZaf4LqayhxvC6W6CPJK0C/BfZxSMApgMnt8pogVaXxkieQfYZ7hsRj0m6PyJa+gr+rUDSPV3PpKtUVm9uufZBumrU9yLiG2R/nC2pxU8iOAQ4Avh+Sqwbko1Ztsa3RNLGEfE3AEkb8fbdJBqGW659JOkvrXKefSXLw0kE1pwk7QacBzxKNsRsA+CQiKh0PYy6cXLtI0mnAWOAS1n6qlEt0SfZ6icRSNoR+C7ZH+ZAGvTKSlZZGsGyKdnn9nAjnoXmboG+G042sD7fem2lAz6dfcedJxEspAVOIsg5F/gPYCYN+JPSupfuAnJRRNyb5odJmhARZ/awar9ycu27FYCjOsdFShpGdp+pVnGVpKFkJ0rMIvvH0XC30lgGiyLij/UOwvrksIj4RedMRDyXbvfi5NoitswPOE8f8Nb1DKjGHgaWRMTvJG0GjAWuqHNMtXS9pFPJfmnkT6FsiYuBt7gVJKnzzr3pAPOKdY7pHZxc+24FScMi4jnIbk5Ia72f34mISyXtBOxO1io/C9iuvmHVTOd+tOfKgqW7eawxTSe7SeEvyT6zI4A/1Tekd2qlZNDfTiO7puRlZB/wp4Dv1zekmursh9wH+GVEXCnpu3WMp6aWg4uBt7JjgMPJztIScC3ZiJaG4tECyyD9XN6V7AO+LiIerHNINSPpauDvwEeAD5BdJeuORhuo3Vfp5oT/DawXEXulz3KHiDi3zqFZi3BytYrSGWh7AvdFxGxJ6wJbRMS1dQ6tJtLtT84ju4X4+yUNBO6KiC3qHJr1oFmG0Tm52nJJ0p0RsY2ku3JXNbs7IlrqVj2tSNLDVBhGFxHP1i2oCtznasurlyWtSbqgtKTtgZa5pGKLa4phdG652nJJ0liyi4FvDjwAtAGf7ByYbo1L0inAABp8GJ1brra8ehC4nOwWzS+SjeH9v7pGZEU1xTA6t1xtuSRpKvACcFEq+gwwLCIOrF9U1kqcXG251CzXBLW3Sfp8RPxG0tGVlkfEj/s7pmrcLWDLq7skbR8RtwFI2g64pc4xWXWrpuchdY2iILdcbbkk6SGyS9Y9mYrWBx4C3iQbM9lyN5q0/uXkasslSRtUWx4RT/RXLNZ3kmZFxNh6x1GJuwVsueTk2TIa9vbLK9Q7ADOzZfCHegfQHXcLmFlTkfQVsjsRPFfvWKpxy9XMms27gDslTZW0p6SG7Bpwy9XMmk5KqOPIbpHeDkwFzu283XYjcMvVzJpOusXLP9JjMTAMuEzSj+oaWI5brmbWVCR9FRgP/JPsDgRXRMQbklYAZkfExnUNMPFQLDNrNmsBH+86nC4i3pT00TrF9A7uFjCzZnMNsLBzRtKQdPoyEfFQ3aLqwt0CZtZUJN0FjM3dWnsFoKPRztRyy9XMmo0i1yqMiDdpwC5OJ1czazaPSvqqpEHpcRTwaL2D6srJ1cyazRHAB8lu/T6P7M4EE+saUQXuczUzK0HD9VOYmVUjaTAwgezmkoM7yyPi0LoFVYG7Bcys2VxIdn2BPYAbgZFkN5lsKO4WMLOmIumuiNha0r0RsaWkQcD0iGiou7+65WpmzeaN9Py8pPcBawCj6xdOZe5zNbNmM0nSMOA7wDRgNeD4+ob0Tu4WMDMrgVuuZtZUJB1doXgRMDMi7u7veLrjPlczazbtZCcSjEiPicDOwK8kfbOOcS3F3QJm1lQkTQc+EREvpfnVgMuAj5G1XjerZ3yd3HI1s2azPvB6bv4NYIOIeBV4rT4hvZP7XM2s2VwM3CbpyjS/L3CJpFWBB+sX1tLcLWBmTUfSB4CdAAE3R0RHnUN6B3cLmFkzWhl4ISLOAJ6QtGG9A+rKLVczayqSTiAbMbBpRLxb0nrApRGxY51DW4pbrmbWbD4G7Ae8DBAR84EhdY2oAidXM2s2r6fbvHTeQ2vVOsdTkZOrmTWbqZLOBoZKOgz4M/CrOsf0Du5zNbOmI2l3YBzZaIHpETGjziG9g5OrmVkJfBKBmTUFSS+S+lm7LgIiIlbv55CqcsvVzKwEPqBlZlYCJ1czsxI4uZqZlcDJ1fqVpFvrHYNZf/ABLbN+IGlARCypdxzWf9xytX4lqfPq8TtLukHSZZIelnSRJKVl20i6VdI9ku6QNETSYEnnSbpP0l2Sdkl1vyjpCklXSXpM0lckHZ3q3CZpeKq3saQ/SZop6X8lvadKjAdKuj+9/k2prNrr/zy37tWSdu7cV0knSrod2KGb/Rog6VRJd0q6V9Lh5bzz1t88ztXqaWtgc2A+cAuwo6Q7gN8Cn46IOyWtDrwKHAUQEVukxHitpHen7bwvbWswMAc4JiK2lnQ6cDBwBjAJOCIiZkvaDjgT2LWbuI4H9oiIv0samsqOrPL63VkVuD8ijpe0IvBwhf2aACyKiG0krQTcIunaiHis2FtojcrJ1erpjoiYByDpbmA02V08n4qIOwEi4oW0fCfgZ6nsYUlPAJ3J7fqIeBF4UdIi4KpUfh+wZbrH0geBS1PjGGClKnHdApwvaSrw+1RW7fW7swT4XZretJv9Gpdi/GSqtwYwBnBybXJOrlZP+fsdLSH7Poruz8Ipsp03c/Nvpm2uADwfEVsVCSoijkit232AuyVtVeX1F7N099rg3PS/cv2s1fbr3yNiepHYrHm4z9UazcPAepK2AUj9kgOBm4DPpbJ3k92k7pEiG0ytxMckHZjWl6T3d1df0sYRcXtEHA/8ExhV5fUfB7aStIKkUcC2vdyv6cCXJQ3q3HajXkLPesctV2soEfG6pE8DP5O0Mlm/5EfI+kh/Kek+stbiFyPitdzP/J58DjhL0reBQcAU4J5u6p4qaQxZq/K6VO/hbl7/FrKf8PcB9wOzerlf55B1h8xKB/QWAAcU3SlrXB6KZWZWAncLmJmVwN0CttyS9F/AgV2KL42I79cjHmst7hYwMyuBuwXMzErg5GpmVgInVzOzEji5mpmVwMnVzKwE/x8OySkvjL2v7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analysis of personal categorical information\n",
    "\n",
    "def plot_bars(Customers, cols):\n",
    "    for col in cols:\n",
    "        fig = plt.figure(figsize=(5,5)) # define plot area\n",
    "        ax = fig.gca() # define axis    \n",
    "        counts = data5[col].value_counts() # find the counts for each unique category\n",
    "        counts.plot.bar(ax = ax, color = 'blue') # Use the plot.bar method on the counts data frame\n",
    "        ax.set_title('Number of Customers by' + ' ' + col) # Give the plot a main title\n",
    "        ax.set_xlabel(col) # Set text for the x axis\n",
    "        ax.set_ylabel('Number of Customers')# Set text for y axis\n",
    "        plt.show()\n",
    "\n",
    "plot_cols = ['demofeatures_marital_status','demofeatures_gender','demofeatures_num_children', 'income_source']\n",
    "plot_bars('Customers', plot_cols)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAGLCAYAAACSigZYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xe8HGXZ//HPN6FDQogEpIcSQJAiHprwKCJFQCkqiqKGomB5FNvzAMpPqgJ2lEeKtIAIIghERCEiRaWe0DsRkMQgBEIJoNTr98d9L5lsdveck5yzM7v5vl+v8zoz97RrZnbn2pm55x5FBGZmZlUzrOwAzMzMGnGCMjOzSnKCMjOzSnKCMjOzSnKCMjOzSnKCMjOzSqpcgpJ0lqRjSlq2JJ0p6RlJN5cRQ6cZyv0l6RpJnxmKefex3K0kPSTpBUm7t3v53URSSFqr7DisOUmPStoud39T0mllx1TTZ4LKwT8haclC2WckXTOkkZVja2B7YOWI2KzRCJJWkHS6pMclzZJ0v6Qji9tnXviLXClHASdGxFIRccn8zKj45bfqk3SEpF/O5zz2kfTXAYw/Nn//F5qf5Q6GiPhuRMz3j8LBWqf+nkEtBBw0Pwsqg6ThA5xkNeDRiHixyfxGAzcAiwNbRsQIUkIbBaw5P7FWRRW+JBWwGnBP2UGA94ct4CKi5R/wKHAIMBMYlcs+A1yTu8cCASxUmOYa4DO5ex/gb8CPgWeBh4F35fKpwJPA+MK0ZwEnA5OAWcC1wGqF4evmYTOBB4CP1k17EnA58CKwXYP1WRGYmKefAnw2l+8P/Ad4HXgBOLLBtMcAdwHDmmyrvrbFWnl9ngOeAn6dy6/L072Yl/2xXP7ZHOPMHPOKhfkG8AXgobydjiYlyRuA54ELgEUK438AuD3vg+uBDev28cHAncDLpB8kBwP/zPN+AHhfk3Vuur+A/wN+WDf+74CvNJnX9sD9efucmOf1mcLw/YD7gGeAK+o+FwPdHg23LfB34A3g33lfLAosDZwOPJ63yTHA8Dz+msCfgafzPj2X2d+Tc+rm9b/ANsC0Bt+x7XL3EcCFwC9z3J8h/ZA8JMf2dF6X0Xn8xfK4T+d9ewuwfB/f6Vbrsw+D+30NYK3Ccs8GZgD/AA7L67Zo3g8bFKZbLm+3MbVtlrffkznu3YGdgQfztN8sTNtqe43NMY0HHsv77Ft52PuBV4BX8/66o4/tuE/ePrOAR4C9gbcx53Hk2TzuLsBteZ9OBY4ozOexHNML+W/L/Dn4ZbNjS6Nl9+NY/lnS92cWcC+wSZPPX3G5W5COF88CdwDb1B3bjiZ9XmYBVwLLtlinhse/ljH3Y6UeBbYDfgscE/OWoF4D9gWGk74Mj5EOXosCO+SVW6rwgZ8FvDsPPwH4ax62ZN65+5IOopvkFV2/MO1zwFakD+liDdbnWuDnpC/2xqQvy/sKsf61xba4kQaJq9mHqMG2OA/4Vi02YOtGX+Tcv21et03ydvgZcF3d+BOBkcD6pMRyFbAG6UBwL/lAkufxJLB53gfj835dtLCPbwdWIZ0drpO384qF9VqzyTq32l+bAdPJCR1YFniJBgfQPOx54CPAwsBX8+emtu12JyWUt+V9fxhw/Txuj7627aMUftwAlwCnkD5/ywE3AwfmYWuREuuipIPpdcBPWsxrG/pOUK/m9R2W98dXSJ+9lfNyTgHOy+MfSEr6S+R9+05gZB/f6Vbrsw+D9H2t/1yTktOlwAjSZ+pBYP887OfA8YXpDgJ+V9hmrwHfJn02Pkv63v4qz2t9UlJYI4/fanuNzTH9Im/bjUiflbc1OkC32IZLkj6v6+T+FZh9HNqHuuNIXocN8j7dEHgC2L3FcWOOOIrjtFp2i3j3JP0Y2RQQ6XO7WpPP3y9z90qkBL9zjnv73D+mcGz7O7B23pbXAMe1WKemx7+mcfdjRzxKSlBvJx38xzDwBPVQYdgGefzlC2VPAxsXPvDnF4YtRfo1sgrwMeAvdfGdAhxemPbsFuuySp7XiELZscBZzT5YddM/BHyuxfC+tsXZwKmke1z109YnqNOB79Vth1eBsYXxtyoMnwwcXOj/IflASTqrPLpueQ8A7yns4/0Kw9YiJbTtgIX7+Hw03V+5/z5g+9z938DlTebzaeDGQr9Iv5pr2+4P5INZ7h9GSna1L9lAtkdf2/ZRZn9hlycdwBYvjP9x4Oom67E7cFv996fQvw19J6jr6obfR+EMlnRAepV0sNqPujPiPvZXy/VhEL+vxc81Kdm9DKxXGPdAZh9HNif9KKr9mOklXx3J2+zfzD7LG5Hnu3nd/t69H9trbJ525cLwm4G9Ctu/vwnqWeDDxW1Z2IZNjyN5nJ8AP47mx4054mDuBNVw2S2WdwVwUJNh9Z+/WoI6GDinwXzG5+5rgMMKw74A/LHFOjU9/jX763ctvoi4G7iMdOo8UE8Uuv+d51dftlShf2phuS+QTuFXJN0b2FzSs7U/0mn1WxtN28CKwMyImFUo+wfpl0J/PE36sM+r/yUdeG+WdI+k/VqMu2KODXhzOzzNnLHWb8Nm23Q14Ot1222VvIya4jafQvoVegTwpKTzJRXHrddsfwFMAD6Zuz9JuuzVyIp18wnm3JerAScU4p9J2pbzsj36s22Ly10YeLyw7FNIZx5IWi5vn39Kep50uW3ZJuvYX/Wf4dWAiwvLv4+UBJYnbc8rgPMlTZf0PUkLt5h3y/XJBuv7WrQssAiF7U7huxcRN5Eucb9H0rqkpDaxMO7TEfF6MaYGcRY/7822V82/Ct0v1a1PnyLdp/4Y8DnStvx9jrshSZtLulrSDEnP5enm6XMy0GVnq5DOdgZiNWDPuuPG1sx5DBzIdhzI8Q8YeDXzw0mn18Uvcq1CwRKFsmLCmBer1DokLQWMJl0qmgpcGxGjCn9LRcTnC9NGi/lOB0ZLGlEoW5V06tsffwL2kNRsu7XcFhHxr4j4bESsSPr1+PMWNfemkz4gAORagm8ZQKxFU4Hv1G23JSLivMI4c2y3iPhVRGydYwjg+Bbzb7a/IB2wd5O0EenyXLNacY/XzUfF/rwOB9atw+IRcX2LuJoZyLadSvrlv2xhuSMjYv08/FjS9tkwIkaSkrAK09d/Hl+k8PnIFXnG1I1TP81UYKe6dV8sIv4ZEa9GxJERsR7pXtEHSGejzfS1PvOi1f6veYp0FrNaoaz+u1f7MfMp4MKI+M88xtN0e/Vj2lbHjzlHjLgiIrYnHbDvJ102bDaPX5ES7ioRsTTpvp1ajD/H54S6Y2qLZTczlYFX5JpKOoMqbsclI+K4fkw71zoN8PgHDDBB5V/Wvwa+XCibQfqQfVLS8JwV57dG286Stpa0COkm3E0RMZV0Bre2pE9JWjj/bSrpbf2MfyrpcsixkhaTtCGpcsS5/YzrR6R7HBMkrQYgaSVJP5K0YV/bQtKeklbOvc+QdmLtV+ETpPslNb8C9pW0saRFge/m7fBoP2Mt+gXwufwrTpKWlLRLXaJ+k6R1JG2bl/sf0q/T1xuNmzXbX0TENNKN+3OAiyLi303m8XtgfUkfyjXXvsycX8qTgUMlrZ9jXFrSnv3dAHX6vW0j4nHSzd8fShopaZikNSW9J48ygnwzXNJKwP/UzaJ+vz4ILJa3/8Kke2mL9hHvycB3Cp+5MZJ2y93vlbRBTnTPk5JA033Vj/WZF033f2G5r5MqK3xH0oi8Ll8j/YCpOQfYg5Skzp6PeJpur354Ahjb4kcoeZ7LS9o1/7h5mfQZKH6XV87bo2YE6erNfyRtBnyiMGwGqTJN8XNyO/BuSatKWho4tJ/LbuY04BuS3pmPAWvVtk8LvwQ+KGnHfDxbTNI2hWNYK3OtUx/Hv4bm5UHdo0jXQIs+S/piPk26YTkvv2qLfkU6W5tJuum7N0C+NLcDsBfpF9q/SL/s+/qCF32cdH10OnAx6f7VpP5MGBEzSb9SXwVukjSLdCP+OdINfGi9LTbN071A+jV1UEQ8kocdQUp8z0r6aERcBfw/4CLS2cWaeb0HLCJ6c1wnkj4YU0jXyZtZFDiO9Kv3X6TLP99sMX7D/VUwgXQvo9nlPSLiKdKN3ONI224cqXZQbfjFpH19vtKltLuBnVrE1NQ8bNtPky5P3Uvafhcy+zLHkaTKFs+Rkuxv66Y9Fjgs79dvRMRzpGv1p5F+zLxIutfWygmkz8uV+TN3I+meDaQkfiEpOd1HqgTU13M8rdZnXvS1/2u+RFrfh4G/5unOqA3MP2ZuJR24/jIf8bTaXn35Tf7/tKRbW4w3DPg66TgyE3gPab9CqtV5D/AvSU/lsi8AR+V4vk1K1gBExEvAd4C/5c/JFvmY9GtSzdrJpB/n/Vl2QxHxm7yMX5EqtVxCOtNtNc1UYDfSd38G6Yzqf+hH3mi0TrQ+/jWkfPPKbMhIejfpoDk2It4oOx4bPJLOIlX6OGyQ5ncGMH2w5medzQ8B2pDKl7EOAk5zcrJWJI0FPgS8o9xIrCoq1xafdY98b/BZ0uWjn5QczgJFqR3BRn//VXZsjUg6mnTZ9vt9XfZpp07ajpJObhLryWXHNq98ic/MzCrJZ1BmZlZJvgfVD8suu2yMHTu27DDMrMtMnjz5qYiofw7OMieofhg7diy9vb1lh2FmXUbSP/oea8HlS3xmZlZJTlBmZlZJTlBmZlZJTlBmZlZJTlBmZlZJTlBmZlZJTlBmZlZJTlBmZlZJTlBmZlZJTlBmZlZJTlBmZlZJbotvCEntW5bfmmJm3cZnUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVkkdk6AkjZJ0oaT7Jd0naUtJoyVNkvRQ/r9MHleSfippiqQ7JW1SmM/4PP5DksaXt0ZmZtZKxyQo4ATgjxGxLrARcB9wCHBVRIwDrsr9ADsB4/LfAcBJAJJGA4cDmwObAYfXkpqZmVVLRyQoSSOBdwOnA0TEKxHxLLAbMCGPNgHYPXfvBpwdyY3AKEkrADsCkyJiZkQ8A0wC3t/GVTEzs37qiAQFrAHMAM6UdJuk0yQtCSwfEY8D5P/L5fFXAqYWpp+Wy5qVz0XSAZJ6JfXOmDFjcNfGzMz61CkJaiFgE+CkiHgH8CKzL+c10qiRoWhRPndhxKkR0RMRPWPGjBlovGZmNp86JUFNA6ZFxE25/0JSwnoiX7oj/3+yMP4qhelXBqa3KDczs4rpiAQVEf8CpkpaJxe9D7gXmAjUauKNBy7N3ROBT+fafFsAz+VLgFcAO0haJleO2CGXmZlZxXRSa+ZfAs6VtAjwMLAvKcFeIGl/4DFgzzzu5cDOwBTgpTwuETFT0tHALXm8oyJiZvtWwczM+kvh9zT0qaenJ3p7ewc8nV+3YWatSJocET1lx1FVHXGJz8zMFjxOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVklOUGZmVkkdk6AkPSrpLkm3S+rNZaMlTZL0UP6/TC6XpJ9KmiLpTkmbFOYzPo//kKTxZa2PmZm11jEJKntvRGwcET25/xDgqogYB1yV+wF2AsblvwOAkyAlNOBwYHNgM+DwWlIzM7Nq6bQEVW83YELungDsXig/O5IbgVGSVgB2BCZFxMyIeAaYBLy/3UGbmVnfOilBBXClpMmSDshly0fE4wD5/3K5fCVgamHaabmsWflcJB0gqVdS74wZMwZxNczMrD8WKjuAAdgqIqZLWg6YJOn+FuOqQVm0KJ+7MOJU4FSAnp6ehuOYmdnQ6ZgzqIiYnv8/CVxMuof0RL50R/7/ZB59GrBKYfKVgektys3MrGI6IkFJWlLSiFo3sANwNzARqNXEGw9cmrsnAp/Otfm2AJ7LlwCvAHaQtEyuHLFDLjMzs4rplEt8ywMXS4IU868i4o+SbgEukLQ/8BiwZx7/cmBnYArwErAvQETMlHQ0cEse76iImNm+1TAzs/5ShG+v9KWnpyd6e3sHPJ0a3fEaIt6NZp1H0uTCYzNWpyMu8ZmZ2YLHCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCqp7QlK0kGSRuaXCZ4u6VZJO7Q7DjMzq7YyzqD2i4jnSW+zHUN6meBxJcRhZmYVVkaCqr3Gb2fgzIi4o1BmZmYGlJOgJku6kpSgrpA0AnijhDjMzKzCFmrnwiQJ+Dbp0t7DEfGSpLeQLvOZmZm9qa0JKiJC0iUR8c5C2dPA0+2Mw8zMqq+MS3w3Stq0hOWamVkHaesZVPZe4HOSHgVeJFWQiIjYsIRYzMysospIUDuVsEwzM+swbb/EFxH/AFYBts3dL5URh5mZVVsZLUkcDhwMHJqLFgZ+2e44zMys2so4c9kD2JV0/4mImA6MKCEOMzOrsDIS1CsREUAASFqyhBjMzKziykhQF0g6BRgl6bPAn4BflBCHmZlVWNtr8UXEDyRtDzwPrAN8OyImtTsOMzOrtjKqmRMRkyTdVFu+pNERMbOMWMzMrJranqAkHQgcBfyb1EisSPej1mh3LGZmVl1lnEF9A1g/Ip4qYdlmZtYhyqgk8XfSw7lmZmZNlZGgDgWul3SKpJ/W/vozoaThkm6TdFnuX13STZIekvRrSYvk8kVz/5Q8fGxhHofm8gck7TgE62dmZoOgjAR1CvBn4EZgcuGvPw4C7iv0Hw/8OCLGAc8A++fy/YFnImIt4Md5PCStB+wFrA+8H/i5pOHztTZmZjYkykhQr0XE1yLizIiYUPvrayJJKwO7AKflfgHbAhfmUSYAu+fu3XI/efj78vi7AedHxMsR8QgwBdhssFbMzMwGTxkJ6mpJB0haQdLo2l8/pvsJ8L/Mfj38W4BnI+K13D8NWCl3rwRMBcjDn8vjv1neYJo55Bh7JfXOmDFjAKtnZmaDoYxafJ/I/w8tlLWsZi7pA8CTETFZ0ja14gajRh/DWk0zZ2HEqcCpAD09PQ3HWZCp0ZYcQuE9YLbAKaMlidXnYbKtgF0l7QwsBowknVGNkrRQPktaGZiex59GeqXHNEkLAUsDMwvlNcVpzMysQsp43cbCkr4s6cL899+SFm41TUQcGhErR8RYUiWHP0fE3sDVwEfyaOOBS3P3xNxPHv7n3EDtRGCvXMtvdWAccPOgrqCZmQ2KMi7xnUR6B9TPc/+nctln5mFeBwPnSzoGuA04PZefDpwjaQrpzGkvgIi4R9IFwL3Aa8AXI+L1eV0RMzMbOoo2X9yXdEdEbNRXWZX09PREb2/vgKdr532adt+j8T0os/knaXJE9JQdR1WVUYvvdUlr1nokrQH4LMbMzOZQxiW+/yFVNX+YVKtuNWC/EuIwM7MKKyNB/ZVUOWEdUoK6v4QYzMys4sq4xHdDbsnhzoi4IyJeBm4oIQ4zM6uwtp1BSXorqdWGxSW9g9kPzY4ElmhXHGZm1hnaeYlvR2Af0sOxP2R2gpoFfLONcZiZWQdoW4LKDcJOkPThiLioXcs1M7POVMY9qJUljVRymqRbJe1QQhxmZlZhZSSo/SLieWAHYDlgX+C4EuIwM7MKKyNB1e497QycGRF30LiVcTMzW4CVkaAmS7qSlKCukDSC2e94MjMzA8p5UHd/YGPg4Yh4SdJbSJf5zMzM3lRGgto6/99Q7W5x1MzMOkZZbfHVLAZsBkwGti0hFjMzq6gy3qj7wWK/pFWA77U7DjMzq7YyKknUmwa8vewgzMysWtp+BiXpZ0Dt9XPDSBUm7mh3HGZmVm1l3IMqvpr2NeC8iPhbCXGYmVmFtbM18zHAmNwmX7F8fUljImJGu2IxM7Pqa+c9qJ8BYxqUrwyc0MY4zMysA7QzQW0QEdfWF0bEFcCGbYzDzMw6QDsT1MLzOMzMzBZA7UxQD0naub5Q0k7Aw22Mw8zMOkA7a/F9FbhM0kdJLUcA9ABbAh9oYxxmZtYB2nYGFREPAhsA1wJj89+1wIZ5mJmZ2Zva+hxURLwMnNnOZZqZWWeqQlNHZmZmc3GCMjOzSmpbgpJ0Vf5/fLuWaWZmnaud96BWkPQeYFdJ5wNzvK0wIm5tYyxmZlZx7UxQ3wYOITVt9KO6YYFfWGhmZgVtS1ARcSFwoaT/FxFHt2u5ZmbWmdpeSSIijpa0q6Qf5L8+H9KVtJikmyXdIekeSUfm8tUl3STpIUm/lrRILl8090/Jw8cW5nVoLn9A0o5DtZ5mZjZ/2p6gJB0LHATcm/8OymWtvAxsGxEbkV5w+H5JWwDHAz+OiHHAM8D+efz9gWciYi3gx3k8JK0H7AWsD7wf+Lmk4YO5fmZmNjjKqGa+C7B9RJwREWeQEsUurSaI5IXcu3D+q923ujCXTwB2z9275X7y8PdJUi4/PyJejohHgCnAZoOzWmZmNpjKeg5qVKF76f5MIGm4pNuBJ4FJwN+BZyPitTzKNGCl3L0SMBUgD38OeEuxvME09cs7QFKvpN4ZM/wuRTOzdivjle/HArdJuppU1fzdwKF9TRQRrwMbSxoFXAy8rdFo+b+aDGtW3mh5pwKnAvT09DQcx8zMhk7bE1REnCfpGmBTUsI4OCL+NYDpn83TbwGMkrRQPktaGZieR5sGrAJMk7QQ6SxtZqG8pjiNmZlVSCmX+CLi8YiYGBGX9ic5SRqTz5yQtDiwHXAfcDXwkTzaeODS3D0x95OH/zkiIpfvlWv5rQ6MA24erPUyM7PBU8YlvnmxAjAh17gbBlwQEZdJuhc4X9IxwG3A6Xn804FzJE0hnTntBRAR90i6gFR78DXgi/nSoZmZVYzSiYW10tPTE729vQOeTo3ueA2Rdu/Gdq4btH/9zNpB0uSI6Ck7jqpq6yU+ScMk3d3OZZqZWWdqa4KKiDeAOySt2s7lmplZ5ynjHtQKwD2SbgZerBVGxK4lxGJmZhVVRoI6soRlmplZhynjOahrJa0GjIuIP0laAnB7eGZmNocyGov9LKl9vFNy0UrAJe2Ow8zMqq2MB3W/CGwFPA8QEQ8By5UQh5mZVVgZCerliHil1pObIvJTLmZmNocyEtS1kr4JLC5pe+A3wO9KiMPMzCqsjAR1CDADuAs4ELgcOKyEOMzMrMLKqMX3hqQJwE2kS3sPhNtbMjOzOm1PUJJ2AU4mvXBQwOqSDoyIP7Q7FjMzq64yHtT9IfDeiJgCIGlN4PeAE5SZmb2pjHtQT9aSU/Yw6TXuZmZmb2rbGZSkD+XOeyRdDlxAuge1J3BLu+IwM7PO0M5LfB8sdD8BvCd3zwCWaWMcZmbWAdqWoCJi33Yty8zMOl8ZtfhWB74EjC0u36/bMDOzojJq8V0CnE5qPeKNEpZvZmYdoIwE9Z+I+GkJyzUzsw5SRoI6QdLhwJXAy7XCiLi1hFjMzKyiykhQGwCfArZl9iW+yP1mZmZAOQlqD2CN4is3zMzM6pXRksQdwKgSlmtmZh2kjDOo5YH7Jd3CnPegXM3czMzeVEaCOryEZZqZWYcp431Q17Z7mWZm1nnKaEliFqnWHsAiwMLAixExst2xmJlZdZVxBjWi2C9pd2CzdsdhZmbVVkYtvjlExCX4GSgzM6tTxiW+DxV6hwE9zL7kZ2ZmBpRTi6/4XqjXgEeB3UqIw8zMKqyMe1B+L5SZmfWpna98/3aLwRERR7eYdhXgbOCtpPb7To2IEySNBn5NerfUo8BHI+IZSQJOAHYGXgL2qTVGK2k8cFie9TERMWG+VszMzIZEOytJvNjgD2B/4OA+pn0N+HpEvA3YAviipPWAQ4CrImIccFXuB9gJGJf/DgBOAsgJ7XBgc1LNwcMl+XXzZmYV1M5Xvv+w1i1pBHAQsC9wPvDDZtPlaR8HHs/dsyTdB6xEune1TR5tAnANKdntBpwdEQHcKGmUpBXyuJMiYmaOYxLwfuC8QVlJMzMbNG2tZi5ptKRjgDtJyXGTiDg4Ip4cwDzGAu8AbgKWz8mrlsSWy6OtBEwtTDYtlzUrb7ScAyT1SuqdMWNGf8MzM7NB0rYEJen7wC3ALGCDiDgiIp4Z4DyWAi4CvhIRz7catUFZtCifuzDi1IjoiYieMWPGDCRMMzMbBO08g/o6sCKpgsJ0Sc/nv1mSWiUbACQtTEpO50bEb3PxE/nSHfl/7UxsGrBKYfKVgektys3MrGLalqAiYlhELB4RIyJiZOFvRF/t8OVaeacD90XEjwqDJgLjc/d44NJC+aeVbAE8ly8BXgHsIGmZXDlih1xmZmYVU8aDuvNiK9Jr4u+SdHsu+yZwHHCBpP2Bx4A987DLSVXMp5Cqme8LEBEzJR1NutQIcFStwoSZmVWLUkU3a6Wnpyd6e3sHPJ0a3fEaIu3eje1cN2j/+pm1g6TJEdFTdhxVVXpjsWZmZo04QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSU5QZmZWSV1TIKSdIakJyXdXSgbLWmSpIfy/2VyuST9VNIUSXdK2qQwzfg8/kOSxpexLmZm1reOSVDAWcD768oOAa6KiHHAVbkfYCdgXP47ADgJUkIDDgc2BzYDDq8lNTMzq5aOSVARcR0ws654N2BC7p4A7F4oPzuSG4FRklYAdgQmRcTMiHgGmMTcSc/MzCqgYxJUE8tHxOMA+f9yuXwlYGphvGm5rFn5XCQdIKlXUu+MGTMGPXAzM2ut0xNUM2pQFi3K5y6MODUieiKiZ8yYMYManFWf1N4/M5tbpyeoJ/KHNKRaAAAXcElEQVSlO/L/J3P5NGCVwngrA9NblJuZWcV0eoKaCNRq4o0HLi2UfzrX5tsCeC5fArwC2EHSMrlyxA65zMzMKmahsgPoL0nnAdsAy0qaRqqNdxxwgaT9gceAPfPolwM7A1OAl4B9ASJipqSjgVvyeEdFRH3FCzMzqwBFNLwFYwU9PT3R29s74OnaeW+h3bux3fdNvH7WjSRNjoiesuOoqk6/xGdmZl3KCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCrJCcrMzCppobIDMLP2k9q7vIj2Ls+6g8+gzMyskpygzMyskhbIBCXp/ZIekDRF0iFlx2NmZnNb4BKUpOHA/wE7AesBH5e0XrlRmZlZvQWxksRmwJSIeBhA0vnAbsC9pUZlZoPGlUC6w4KYoFYCphb6pwGb148k6QDggNz7gqQH2hBbzbLAUwOZoN1fyPkw4HUDr1+FeP0amI/1W22ep1wALIgJqtFHaa7fPxFxKnDq0IczN0m9EdFTxrKHWjevG3j9Ol23r1+nWeDuQZHOmFYp9K8MTC8pFjMza2JBTFC3AOMkrS5pEWAvYGLJMZmZWZ0F7hJfRLwm6b+BK4DhwBkRcU/JYdUr5dJim3TzuoHXr9N1+/p1FIWrn5iZWQUtiJf4zMysAzhBmZlZJTlBmZlZJTlBmc0nSYv2p8zMBmaBq8VXVZIOiogT+iqzSroB2KQfZR1F0uhWwyNiZrtiGQqSvtZqeET8qF2xWGNOUNUxHqhPRvs0KOsYkmbRoJWOmogY2cZwBp2kt5Kazlpc0juY3UrJSGCJ0gIbPJNJ+0/AqsAzuXsU8BiwenmhDYoR+f86wKbMfh7yg8B1pURkc3CCKpmkjwOfAFaXVHxgeATwdDlRDY6IGAEg6SjgX8A5pAPc3sw+OHSyHUk/IlYGir+2ZwHfLCOgwRQRqwNIOhmYGBGX5/6dgO3KjG0wRMSRAJKuBDaJiFm5/wjgNyWGZpmfgyqZpNVIv0SPBYrvppoF3BkRr5US2CCSdFNEbN5XWaeS9OGIuKjsOIaKpMkR8c66sq5ps07S/cBGEfFy7l8UuCMi1i03MvMZVMki4h/AP4Aty45lCL0uaW/gfNIlo48Dr5cb0qC6TNIngLEUvlMRcVRpEQ2upyQdBvyStP8+SYef3dc5B7hZ0sWk9dsDOLvckAx8BlUZkj4EHA8sR7oMJiA6/T4NgKSxpHtpW5EOAH8DvhIRj5YX1eCR9EfgOdI9mzcTb0T8sLSgBlGuLHE48G7S/rsOOKrTK0kUSdoE+K/ce11E3FZmPJY4QVWEpCnAByPivrJjsYGRdHdEvL3sOIaapKUi4oWy4xgKkrYGxkXEmZLGAEtFxCNlx7Wg83NQ1fFEtyYnSWtLukrS3bl/w3zJqFtcL2mDsoMYKpLeJele8lunJW0k6eclhzVoJB0OHAwcmosWJl3OtJL5DKoiJJ0AvBW4BHi5Vh4Rvy0tqEEi6Vrgf4BTIuIduaxrzjrywXst4BHSvqtdnt2w1MAGiaSbgI+QavJ14/67HXgHcGth/e7slv3XyVxJojpGAi8BOxTKAuj4BAUsERE3a873Ynd87cSCncoOYKhFxNS6/ddNlVxeiYiQFACSliw7IEucoCoiIvYtO4Yh9JSkNckP7Ur6CPB4uSENnoj4R6N7GGXHNYimSnoXEPkln18Guuly9AWSTgFGSfossB/wi5JjMnyJrzIkrQ2cBCwfEW+XtCGwa0QcU3Jo803SGqQXwb2L1BrBI8DeuYp9x8v3MHqAdSJibUkrAr+JiK1KDm1QSFqWVAtzO9LlyyuBgyKia6qaS9qedPVCwBURMankkAwnqMro5vs0koZHxOv50smw2hP73aLb72FIGt1NVcqLJA0nJaSObxmjG7kWX3UsERE315V1y32aKZK+D6zabckpeyXSL71uvYdxk6TfSNpJdTeiOl1EvA68JGnpsmOxufkeVHV0832aDYG9gNMlDQPOAM6PiOfLDWvQdPs9jLVJl/f2A06U9GvgrIh4sNywBs1/gLskTQJerBVGxJfLC8nAl/gqo8l9mk92S2sLNZLeDZxHahH7QuDoiJhSblTzb0G5hyHpvaRnhJYE7gAOiYgbyo1q/kga36g8Iia0OxabkxNUxXTjfZp8nX8XYF9Se3XnAOeSmpb5bkSsXV501hdJbyG1v/cp4AngdNKrKTYmVQbp9NduWEX5El9F5BaUP0xucLR2qb9LGhx9CLga+H5EXF8ovzCfUXWkbn/fVcENpB8Vu0fEtEJ5b34VR0eSdEFEfFTSXTTYj91SyaWT+QyqIrq5wdFubsMNmr/vKiK+V2pgg0SS8oOsI0gtZHTFvpS0QkQ8nl95M5dueQyikzlBVUS3VClvRNLKwM+ArYE3gL+SnqOZ1nLCDrEAvO/q7aTkO5qUgGcA4yPi7lIDGySSjo+Ig/sqs/ZzNfPq6OYGR88k3bNYgfSK9N/lsm7xuqS9JQ2XNCy/+6qbmgI6FfhaRKwWEasCX89l3WL7BmVd33xVJ/AZVEV0c4Ojkm6PiI37KutUC8D7ru6IiI36Kus0kj4PfAFYA/h7YdAI4PqI2LuUwOxNriRRHd38i+0pSZ8kVS+H9EbdrmkmJyei3cqOYwg9LOn/kS7zQarR1w3vSvoV8AfgWOCQQvmsbm05o9P4DKpiJC0HLFbrj4jHSgxnUEhaFTiR9Fr7AK4n3YPqipvQks6kcS2w/UoIZ9BJWgY4knQPUaQ36h4REc+UGtggqmvsd1lSJZduSMIdzQmqIiTtCvwQWBF4ElgNuC8i1i81MOuTpA8XehcD9gCmuyWCztDtjf12Ml/iq46jgS2AP0XEO/IT+x8vOab5IulntH5OqCsO4BFxUbFf0nnAn0oKZ9BI+h2t99+ubQxnKO1BbuwXICKm5yr1VjInqOp4NSKezrXAhkXE1ZKOLzuo+dRbdgAlGQesWnYQg+AHZQfQJn5hYUU5QVXHs5KWIl3fP1fSk3R4a+b1bZlJGpmKu6cZJ2jYosS/gI5/hiYirq115xcVrktazwci4pXSAht8jRr7Pa3kmAzfg6qM/KvtP8xuiWBp4NxueCmcpB7Sc08jSOv3LLBfREwuNTDrF0m7ACeTqmILWB04MCL+UGpgg2hBaey30zhBVUw+y3jzzLYbqrtKuhP4YkT8JfdvDfy8G57xApB0VUS8r6+yTiXpfuADtVbn82thfh8R65Yb2dDIjRvvFRHnlh3Lgs6X+CpC0oHAUcC/Sc0BiXQ5ZY0y4xoks2rJCSAi/povi3U0SYsBSwDL5qrYtZf5jSTVxuwWT9a9EuVhUk3TjpZ/DH6R1LrJRGBS7v8f4HZSi/tWIp9BVYSkh4AtI+KpsmMZbJJ+TDqQn0dKuh8jvfPqIoCIuLW86OadpIOAr5CS0T8Lg2YBv4iIE0sJbJBJOon02MMFpP23J/AAqcUMIuK35UU37yRdSvoc3gC8D1gGWIT0jN7tZcZmiRNUReTWzD8UES+VHctgk3R1i8EREdu2LZhBJGlTYBrwkYj4WX7x3YeBR0kPsnb85Vl480HkZqJTH0iWdFdEbJC7hwNPAat2WyWeTuYEVRGS3kGqSHATqS0+oHueFepGkm4FtouImfm9VucDXyK9yO9tEfGRUgO0liTdGhGbNOu38vkeVHWcAvwZuIt0D6prSBoFfJr8MsZaeRck3+GFs6SPAafmh3YvktQ1l4gkrU5KvGOZc/91+oO6G0l6PncLWDz31xpq7pYXTnYsJ6jqeC0ivlZ2EEPkcuBGui/5Dpe0UES8RrqHcUBhWDd9ty4hveb9d3TR/ouI4WXHYK1105eo010t6QDSQaB4ia8b7mMs1qXJ9zzgWklPkWpf1qrRr0V6O3K3+E9E/LTsIGzB43tQFSGpUcvJEREdX81c0leBF4DL6LLkK2kL0osYr4yIF3PZ2sBSnVo7sZ6kT5Cab7qSOfdfV6yfVZcTVAVIGkaqYv63smMZCpK+CHyH1IJE7QPXFcl3QSDpWOBTpJYkapf4Orb2pXUOJ6iKkHRDRGxZdhxDQdLfgc278RmvBUFuSWLDLmt/zzrAsLIDsDddKenDktT3qB3nHqDrnu9agNwBjCo7CFvwuJJEdXwNWBJ4XdK/6a6qrq8Dt+cHdv2MV+dZHrhf0i3Muf86vZq5VZwTVEVERDe/IO2S/Ged6fCyA7AFk+9BVUS+tLc3sHpEHC1pFWCFiLi55NDmmaSREfF8k2GrRsRj7Y7J5o2k1YBxEfEnSUuQHlJ2k0A2pHwPqjp+DmwJfCL3vwD8X3nhDIprah2Srqob5jOqDpFf4nchqbUTSK1/e//ZkHOCqo7NI+KLpJcWEhHPkFpW7mTFCh+jWwyzavsisBXwPEBEPAQsV2pEtkBwgqqOV3OLygEgaQyd36xMNOlu1G/V9XKxirmkhfD+szZwJYnq+ClwMbCcpO8AHwEOKzek+bacpK+RzpZq3eT+MeWFZQN0raRvkhpT3R74AqlJLrMh5UoSFSJpXVKjowKuioj7Sg5pvkhqWfsrIo5sVyw273JLJ/sDO5A+m1cAp4UPHjbEnKBKll8b/jlgLVJr36fn1rHNzBZovgdVvglADyk57QT8oNxwhlZ+yZ91EElbSZok6UFJD0t6RNLDZcdl3c/3oMq3XuG106cDHfvcUz+59l7nOR34KjCZ1CqIWVs4QZXv1VpHRLzWTU3xSVo0Il6uK/59KcHY/HguIv5QdhC24PE9qJJJeh14sdYLLE5qWLXj2+KTdGtEbCLpnIj4VNnx2LyRdBwwHPgtfh+UtZHPoErW5a+dXkTSeOBdkj5UPzAifltCTDZwm+f/78z/RXoOyu+DsiHlMygbMpK2JrUv+FFgYt3giIj92h+V9Vfdc2uQktIM4K8R0egN0GaDygnKhpyk/SPi9LLjsIFp8hzbaGBH4IiIOL/NIdkCxgnKhkyjy3pFvsTXmSSNBv4UEZuUHYt1N9+DsqH0wRbDgnTT3TpMRMzs0jc/W8U4QdmQiYh9y47BBp+kbYFnyo7Dup8TlA05ScsD3wVWjIidJK0HbOn7UtUm6S7mbrV8NDAd+HT7I7IFje9B2ZCT9AfgTOBbEbFRfl3DbbUWNKya8lt0iwJ4OiJebDS+2WDzGZS1w7IRcYGkQ+HNFjPcZE7FRcQ/yo7BFmxuLNba4UVJb2H2yxi3AJ4rNyQzqzqfQVk7fI30oO6akv5GelnhR8oNycyqzmdQNmQkbSrprbnNtvcA3yS15XYlMK3U4Mys8pygbCidArySu98FfAv4P1IV5VPLCsrMOoMv8dlQGh4RM3P3x4BTI+Ii4CJJt5cYl5l1AJ9B2VAanquUA7wP+HNhmH8cmVlLPkjYUDoPuFbSU8C/gb8ASFoL1+Izsz74QV0bUrlK+QrAlbUHPCWtDSzlF96ZWStOUGZmVkm+B2VmZpXkBGVmZpXkBGVmZpXkBNXBJB0h6RtDOP89Jd0n6ep5mHaUpC8MRVxlkbSNpMvKjqMox/SueZhuY0k7D9Z486u4bSXtKumQeZxP133uFmROUNbK/sAXIuK98zDtKGDABwpJw+dhWQuybUitdAzUxkB/Ek9/xxs0ETExIo6bx8nn6XNn1eQE1WEkfUvSA5L+BKyTy9aU9EdJkyX9RdK6ufwsSSdJulrSw5LeI+mMfFZ0VmGeH5d0l6S7JR2fy74NbA2cLOn7kobn/7dIulPSgXm8pSRdJenWPI/d8myPIzUOe3uebo6zD0knStondz8q6duS/grs2WJ99swx3iHpuhbbqFms20i6VtIFkh6UdJykvSXdnGNfs7DdTs7LflDSBxosY7SkS/L8b5S0oaRhkh6SNCaPM0zSFEnLDmBf7CDphrw9fyNpqcI2OrKwndeVNBb4HPDVvJ3/q8n2mGO7SVoEOAr4WJ7uY5I2k3S9pNvy/3WajDfHWXue71hJS0r6fV7G3ZI+1mL/bJqXcUfe9iPqhu8j6cTcPUbSRXlf3iJpq1x+RN5+1+Tt+eU8ef3nboW8zrfnuBpuI6uoiPBfh/wB7wTuApYARgJTgG8AVwHj8jibA3/O3WcB5wMCdgOeBzYg/TCZTPp1vCLwGKmF8YVIrT3snqe/BujJ3QcAh+XuRYFeYPU8zchcvmyOScBY4O5C7NsAlxX6TwT2yd2PAv9bGNZsfe4CVsrdo1psp2axbgM8S3oua1Hgn8CRebyDgJ8Uttsf83YaR2rYdrHiOgA/Aw7P3dsCt+fuw4Gv5O4dgIsGsC+WBa4DlszTHAx8u7CNvpS7vwCclruPAL7Rx+dmru0G7AOcWBhnJLBQ7t6uEHf9eHMsD7g77+sPA78olC/dJJZFgIeBTYvLrdu2by4T+BWwde5eFbivEMf1eT8uCzwNLMzcn7uvk16UCTAcGFH299h//f9zSxKd5b+AiyPiJQBJE0kHzncBv5FUG2/RwjS/i4hQen33ExFxV572HtKXeTXgmoiYkcvPBd4NXFK37B2ADSXVXpOxNLMP3t+V9G7gDWAlYPl5WLdf5+Uv1WJ9/gacJekC4Lct5tUs1leAWyLi8bysv5NaVod0EC9eyrwgIt4AHpL0MLBu3TK2Jh2UiYg/S3qLpKWBM4BLgZ8A+5HeJFzT175YGVgP+Fte90WAGwrT19Z5MvChFutfrz/bbWlggqRxpPd2LTyA+UPafj9QOgO/LCL+0mS8dYDHI+IWgIh4HqCwr+ttB6xXGD6ycMb1+4h4GXhZ0pM0/tzdApwhaWHgkohwG5AdxAmq89Q/WT0MeDYiNm4y/sv5/xuF7lr/QsBr/VyuSL/gr5ijMF2mGwO8MyJelfQoKWnWe405LynXj1N7jXjT9YmIz0naHNgFuF3SxhHx9ABi3Ya5t0Fx+xS/D/Xbub6/0RE1ImKqpCckbUs6+9u7MLyvffE6MCkiPt5g3sXpX2cA391G263BaEcDV0fEHvnS4TVNZtdwP0bEg5LeSbpfdaykKyPiqAbTi7m3ZSvDgC0j4t9zzCQlrOI2bLhNIuK6/ONpF+AcSd+PiLMHsHwrke9BdZbrgD0kLZ5/RX4QeAl4RNKeAEo2GsA8bwLek++TDAc+DlzbYLwrgM/nX6JIWlvSkqRf3k/m5PRe0hkZwCygeG/hH6RfwovmM433NQom/6JuuD6S1oyImyLi28BTwCpN1qlZrAOxp9I9pDWBNYAH6oZfR04+OfE9VTsbAE4Dfkk6CxvIq+1vBLZSaqsQSUsoNQvVSv12nkuT7VY/3dKkS56QLrE1m/+jwCZ5vpuQLp0iaUXgpYj4JfCD2jgN3A+sKGnTPN0IzW5QuJErgf8urEuzH2IN45W0Gunz+Qvg9BZxWQU5QXWQSG3X/Rq4HbiI3Pgq6UC5v6Q7gHtI9zj6O8/HgUOBq4E7gFsj4tIGo54G3AvcKulu0rueFgLOBXok9eY47s/zfZp0qeru/Kt1KnABcGee5rYWYTVbn+8rV+YgJYg7mkzfLNaBeICUqP8AfC4i/lM3/AjSet9JujE/vjBsIrAUc17e61O+zLoPcF6e743MfWmx3u9IP1qaVpKg8Xa7mvSD4fZcoeF7pDOfv5Hu1dTUj3cRMFrpdSmfBx7M420A3JzLvwUc02QdXyG9euVnef9OovEZd82XydtZ0r2kSiFN1X/uSPe2bpd0G+mS7AmtprdqcVt8ZnWUatVdFhEXzuP0PcCPI8I1xszmg+9BmQ0ipQdMP8+c957MbB74DMo6lqQdgePrih+JiD3KiKdskr4F7FlX/JuI+E5J8VxMvkdVcHB95RWzZpygzMysklxJwszMKskJyszMKskJyszMKskJyszMKun/Axj+IpwoTzdbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_bars(Customers, cols):\n",
    "    for col in cols:\n",
    "        fig = plt.figure(figsize=(5,5)) # define plot area\n",
    "        ax = fig.gca() # define axis    \n",
    "        counts = data5[col].value_counts() # find the counts for each unique category\n",
    "        counts.plot.bar(ax = ax, color = 'blue') # Use the plot.bar method on the counts data frame\n",
    "        ax.set_title('Number of Customers by' + ' ' + col) # Give the plot a main title\n",
    "        ax.set_xlabel(col) # Set text for the x axis\n",
    "        ax.set_ylabel('Number of Customers')# Set text for y axis\n",
    "        plt.show()\n",
    "\n",
    "plot_cols = ['demofeatures_level_of_education', 'demofeatures_residence_state', 'demofeatures_loan_purpose', 'demofeatures_employment_status_clients']\n",
    "plot_bars('Customers', plot_cols)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#analysis with boxplot\n",
    "def plot_box(Incomes, col, col_y = 'Income'):\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.boxplot(col, col_y, data=data5)\n",
    "    plt.xlabel(col) # Set text for the x axis\n",
    "    plt.ylabel(col_y)# Set text for y axis\n",
    "    plt.show()\n",
    "    \n",
    "plot_box('Incomes', 'income_source')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#analysis with violin plot\n",
    "def plot_violin(income, cols, col_y = 'Income', hue_col = 'demofeatures_gender'):\n",
    "    \n",
    "    for col in cols:\n",
    "        sns.set_style(\"whitegrid\")\n",
    "        sns.violinplot(col_y, col, data=data5, hue = hue_col, split = True)\n",
    "        plt.xlabel(col_y) # Set text for the x axis\n",
    "        plt.ylabel(col)# Set text for y axis\n",
    "        plt.show()\n",
    "num_cols = ['income_source', 'demofeatures_marital_status', 'demofeatures_gender']\n",
    "\n",
    "plot_violin('Income', num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEBCAYAAACuZQjdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XlcVPX+x/HXmRmGAYYdZBEXEE1ccMHlulea1zRbLE1zuVm/Fm9l1zaX6pplmnV9tLnVTa+ppaZ2y0TL1JTUsq7llvsGIqggiyzDMjPn9wcwiSKOygEZPs/H4zyAmTPnfM4B5j3n+/2ecxRVVVWEEEIIDelqugAhhBCuT8JGCCGE5iRshBBCaE7CRgghhOYkbIQQQmhOwkYIIYTmDDVdwM1q586dNV2CEELUSnFxcZc9JmFTiYp2WG104MABYmJiarqMGlPXtx9kH9T17Yfq2wdX+qAuzWhCCCE0J2EjhBBCcxI2QgghNCdhUwcVFhby96eeYsqUKWRmZtZ0OUKIOkDCpg76z3/+w769e9n4ww8MHzGSjRs31nRJQggXJ2FTxxw8eJBly5ZRHNyM/Jb3kaOamDJlComJiTVdmhDChUnY1CHFxcVMmz4d1c2DooadUT39sUT3BuDHH3+s4eqEEK5MwqYOWb16NSdPnMDSqCsY3AFQ3b1QzcEkJEjYCCG0I2FTh3y/YQOqVyA2/0blHi/2a8TBgwdIT0+vocqEEK5OwqaOSE9PZ/8ff1Ds3/iy56yl4bNt27ZqrkoIUVdI2NQRCQkJAFgDGl/2nOrhBx6+jnmEEKKqSdjUET9s3gye/qge/pc/qSgU+TXkt99+Iy8vr9prE0K4PgmbOiAnJ4c9u3dTVEETWhmbfyNsNhs///xz9RUmhKgzJGzqgF27dqGqKrZKwsZurodi9GTdunXVV5gQos6QsKkDfvvtN/Dwwe4ZcOWZFB2FIS355Zdf5F4+QogqJ2Hj4rKzszl48CBFfo1BUSqdtzi0JZi8+XDWbGw2W/UUKISoEyRsXNzmzZux2+3YAqOuPrPOQEH9Dhw/dpT169drX5wQos6QO3W6uO83bED18MPuGejU/LbAKNSzfzB33jxSU1Px8/Ojffv2NG7cWNtChRAuTcLGhZ07d469e/ZQHN7uqk1oDopCQaMuZB/bxMKFCwFwMxp5bfJkevTooV2xQgiXJs1oLmzTpk2oqoo1qMk1vc5uDia3zYPkdXqE/DYPUmT04+VXXuGLL75AVVWNqhVCuDIJGxf2/fcbUM3BqCbf61uAokM1eZPfvD9W/0bMmjWLmTNnYrVaq7ZQIYTLk7BxUadOneLIkcMUBTgxMOBq9AYKo3tTFBbL6tWreeHFF8nJybnx5Qoh6oybvs/m448/Zvv27eh0OhRFYdy4cXz99deMHj2a8PBwp5ezY8cOli1bxrvvvqthtTcHu93u6G9xahSaMxSF4oadUD38+O33rdx11134+PoRElKPFjExtG7dmlatWhEaGopS2j+Ul5dHRkYG3t7eeHt7o9frq6YWIUStc1OHzdGjR9m0aRNLly5FURQOHDjA+PHjWb16dU2XdtOy2WzMmDGD77//nqL67VCNXn8+qaoYzh1An5mEYi9tCrMVoViLUA1G0BsvW56qM2Dzb4i1XgwoCtbgZtg9/NFnJZFelEdGag5Hjq3lq6++AsDbx4em0dGcOXuOlNPJjuUoikKbNm25664B9OrVC3d3d033gxDi5nJTh01AQAApKSmsXLmSnj17EhMTw8qVKxk5ciSvvfYaa9euJTk5mfPnz5OSksLEiRPp0aMHP/zwAx988AFmsxlfX19uueUWOnXq5FjuunXrWLhwITqdjri4OF544YUa3MqqNX36dNavX09RRBzF9dthSDuCIe0QAEpRPrrCC+Xm9/DwYMDdA4iPj8eSk1HhMg3Zybil7kM1elb4vNUjAGzFKLZisi3F7Ny1F3Q6VKMZdHpsXkGoRi92HTzGrl1TeWvGDEJDQ6kfHo63tzdGoxGDwYC7uztGoxGr1YrFYsFisVBQUEBhYSF6vR4fHx98fHwwmUyYTCbc3NzQ6XTo9XrHwAXlolF3drsdi8VCamoqO3fuxGg0OuZVnB2dp4GKatXa2bNn2b9/f7Wt72bjitvv7u5O79698fDwqOlSnHLTh83cuXNZsmQJs2fPxmQyMW7cuHLzGI1GPvnkE7Zt28aCBQvo2rUrU6dOZfny5QQFBfH888+Xmz8rK4sPP/yQVatW4eHhwYsvvsi2bdvo1q1bdW6aJnJycli/fj0271CK67e7fAZb0WUPDRgwgLFjxwKwcuXKKy/cVgRUHDYA6N1Q9W4AXDpeze4bgTW4KcUNOmI4+wck/kzyqVMknzp1lS0SQlTm8OHDPPfcczVdhlNu6rBJTEzEbDYzffp0APbu3cvjjz9OUFCQY56YmBgAQkNDKSoqIiMjA7PZ7JinQ4cO5e5AmZSUREZGBo8//jhQ0q9wykXe9MxmM126duWn7dsxnNmPNbQF1uCmWIObAmA4ux/3k9vLvSY+Pr7c1yspjojDGhJzxeeVwlx0uefQ5aWjy89ANXph9wpCdTeDtRBD6h6MGSdQctMA8Pb2ITw8DB8fH9zc3DAajRiNRtzc3LDb7eTn55cc2RQWYsm3YHAz4Ofri7e3NyaTCYPB4DhCuXg49sWPlR05ZGRkEBAQcNkRzaXzObO8i79XVRVVVdHpdJcNCXdm2Zeu40r1X/z9xeu99Mjo0tdcXFdmZib+/v6XzXdpzVfbD87WcOl+unjZldWglYu3vzo48/u50vZf7e+hjNlsZvDgwRpUr42bOmwOHTrE0qVLmTdvHu7u7kRGRl7W0XzpLyAwMNDRMR0QEMDu3bupX7++4/mIiAjCwsJYsGABbm5ufPnll47Aqu0UReGN11/nn5Mns33bNsCONbSV43lrvZLtvLjPJtdWxBerv0U1eID35UOk/+yzaf7neizZ6LOTUYry0BXm4JafjlpQMjpNrzfQoGED0tJSyCttvivTODKSgaOH0qdPn2r9xz9w4IDL/I6vV13fB3V9+28GN3XY9O3bl2PHjjF48GA8PT1RVZWXXnqJTz/99Iqv0el0vPrqqzz22GN4e3tjt9tp1KiR4/mAgAAefvhhRo4cic1mo379+tx5553VsTnVwmg0lgTOP//Jtu3bsQY2AbfSNl1FwRrSAmtIi+tevj4jEY/jm1Ftxej1BgICA2n5lzhiY2Np1aoVUVFRGI1GVFXlzJkzpKen4+Pjg6+vL76+vjXaVyKEqDmK6oKnhH/00UeMHj0ao9HICy+8QPfu3bn33nuvaRk7d+4kLi5Oowq1d+zYMUaPHk1h4643FC4Oqopb6h6Mp/5Hs1uaMeW11wgLC0Onu/lP1ZJPtbIP6vr2Q/Xtgyu9d97URzbXy8vLiyFDhmAymahfvz79+/ev6ZKqXZMmTWjUqDEnzh+/8bCx2zGe3Ipb2mFuvfVWXn75ZRm6LIS4Ji4ZNiNGjGDEiBE1XUaN69v3Dv7973+jFOaWdNRfD2sRpqMb0WefZuTIkTz66KO14mhGCHFzccmwESV69+7Nv//9bwznj1Ec3sbp1yn5mXieSEApykEtKkCn1/Pi+PEMGDBAw2qFEK5MwsaFhYeHExPTgv1Jx50PG1XFPXE7Xli4o38//P396dSpEy1bttS2WCGES5OwcXF33NGHAx98gJKfiep59eHG+qwk9BdS+b9nn+X++++vhgqFEHWBNL67uNtuuw1FUTBkHL/6zHY7plO/Uj+iAffcc4/2xQkh6gwJGxcXGBhIdHRTjJknrzqv4dxBsGTx1N/HYDDIQa8QoupI2NQBcXHtIT8TxZJ55ZlUFdOZPcTGxrrEdeKEEDcXCZs6oG3btgAYMk5ecR5dXjpqYS533XWXnOUvhKhyEjZ1gL+/Py1atMStkqY0fWYiOp2OLl26VF9hQog6Q8KmjrjttltR8s6jFFyo8HljViKxsW3w9b38YpxCCHGjJGzqiJ49ewIVN6UpBdmQn0nPnj2quSohRF0hYVNHhIWF0bRpM9yyTl72nD4zEYDu3btXc1VCiLrimsPGbrdrUYeoBn369EbJOYcuO6Xc426ZiUQ1aUJoaGgNVSaEcHVOhc26deuIj4/nv//9L926dWP+/Pla1yU0cN999xEWHo7Hya1gKy55sNiCLucsvUqb2YQQQgtOhc2CBQvo2rUrq1evZsuWLfzwww9a1yU0YDKZmDhhAhRcwJi8E6UwF9OxzQD06CH9NUII7TgVNmX3LvHy8sJoNJKXl6dpUUI7bdu25Z577sHtzD689n2JR0E648aNIzo6uqZLE0K4MKfCJiIigvvvv5/777+fWbNmERsbq3VdQkNjxowhokED2reJZdGnn3LffffVdElCCBfn1AWw3nrrLfLy8vDy8qJVq1YEBwdrXZfQkKenJ58tWSJXChBCVBunwmbixImXPTZ9+vQqL0ZUHwkaIUR1cips+vfvD4Cqquzfv59z585pWpQQQgjX4lTYXDxSqWfPnjzyyCOaFSSEEML1OBU2W7dudXyflpZGenq6ZgUJIYRwPU6FTXx8vON7o9Eo/TVCCCGuiVNh0759ewYPHuz4edGiRbRs2VKzooQQQriWSsNmzZo1bNq0iR07dvDzzz8DJddGO3z4MKNGjaqWAoUQQtR+lYZNjx49CA4OJisriwcffBAAnU5HgwYNqqU4IYQQrqHSsPH19aVz58507tyZc+fOYbVaUVWVlJQUQkJCqqtGIYQQtZxTfTaTJk1i165dWCwWLBYLDRs25IsvvtC6NiGEEC7CqWujHT9+nPj4eLp3787atWsdF+YUQgghnOFU2Hh5eaEoCvn5+QQEBFBcXKx1XUIIIVyIU2HTsmVL5s+fT7169Rg3bhxWq1XruoQQQrgQp/psxo4dS0FBASaTiYSEBFq3bq11XUIIIVxIpUc2aWlpnDhxgoceeoj09HROnTpFo0aNGDNmTHXVJ4QQwgVUemSze/duPv30U06cOME///lPVFVFp9PRvXv36qpPCCGEC6g0bPr06UOfPn3YsmULvXr1qq6ahBBCuBinBgh4enqSkJDAli1b6NOnD998843WdQkhhHAhToXNO++8Q+PGjVm0aBFLly5l2bJlWtclhBDChTg1Gs3d3Z3AwEAMBgPBwcEUFRVpXZfQmKqq/Pzzzxw/fhxVVVFVlZiYGNq1a4der6/p8oQQLsapsDGbzYwePZqHHnqIzz77jLCwMK3rEhrat28fc+fMZu++Py57LigwgL5/7cd9990n178TQlQZp8Lm/fffJykpiejoaA4fPuy4t83u3btp06aNpgWKqmO323n//ff573//i58JRjfPo1toEXod2FTYne7Gj6nFLFv6OSu+WE7/AXcxYsQICR0hxA1zKmyMRiPR0dEANGvWzPH4zJkzWbRokTaViSpltVqZNm0aGzZsoF+DAh6ItmC6qLXMDegcUkznkGLSLTq+OWlizTdfE79mDX/t14/hw4cTERFRY/ULIWo3p8LmSlRVrao6hIaKiop4+eVJ/PTTzzwYnc/AxoWVzh/kYWd0TD53R1pYc9LE+nXxrFu7lltvu41BgwbRunVrFEWppuqFEK7ghsJG3nBufhaLhVmzPuTwocOMbp5H7wjnB3cEmlT+1tzCPZEFrEsysenHTWzatInIxo0YcNdAunfvTnh4uIbVCyFcxQ2Fjbi55eXl8dJLL3L48GGeaJlH97DrG0Xo564yrKmF+6Is/HTGyMbTJ5g1axazZs2iUcMGtGnbjiZNmhAVFUVoaKhj5KIQQpSRZjQXlZGRwcSJEzh88CBPtcrlLyE3flsIkx5uq1/EbfWLOJuv4/d0N3aln2DjumRWF//5t6AoCv5+PoSGhRMeXp+IiAhiYmJo0aIFvr6+N1yHEKL2uaGwGThwYJUUsWPHDkaNGsW7775L//79yy2/ZcuWvPXWW9e0vI8//pi//OUvxMbGXnXef/3rX0RFRTFo0KBrrvtmtX37dt6aPo283As80zqXDvWq/v5DIZ52+jUspF/DQlQVMgoVknP1nC/QkVmoI6OwgLTUdHafPMBGC5R9LmnYIIKOnTrTsWNH2rRpg5eXV5XXJoS4+VQaNpVdcHPr1q0MGTKkygqJiopizZo1jrA5dOgQFovlupb1+OOPV1ldtcmpU6dYunQpa9asoaG3nQkdc4gw2684v6rChmR3dqW7UWi7/HmLVSHfquBpUPEwOH8U666HdsFF/F9MEYoCBTY4ccHAkWw9BzNP8M1Xp1m1ahWKotC4UUNatmpNgwYNqFevHkFBQXh5eeHu7l5uMhqN6HROXfBCCHETqjRstm7dWl110Lx5c06ePMmFCxfw8fFh9erVDBw4kNTUVJYsWcL69euxWq14e3vz4YcfsmbNGlatWoXdbmfs2LFMmjSJqKgooqKiyMnJoX///nTp0oXJkyeTmJiI3W7nH//4B507d+a7775j7ty5jruORkVFVdt2VhVVVUlPTycpKYmTJ0+yadNG9u7dh06B/g0LGBxtwU0HP6YY2ZJirHAZmYU6zlqufLUADw8PBtw1gPj4eCy51xb8u8+7sS7RhL/75WEX6V3kCLJzySdKfj9OZJlep0Ov16M36NHrDRgMBnQ6nWNq0qSJY4i+oiiOKTMzk927d2M0GvH09MTDwwMPDw9MJhMmkwmj0YibmxsGgwG9Xo+iKOh0unIDYC5eXtlzZc8riuK4CgNQ7vuLX6srq1+vl+AUdY5TzWi7du3iyy+/dNwO+ty5c8yfP7/Ki7njjjv4/vvvGTRoEHv27OGxxx7j9OnTZGVlsXDhQnQ6HY8++ih79+4FwMfHh7lz5wKQmprKl19+ib+/PxMmTABgxYoV+Pv7M23aNDIzMxkxYgTx8fG88847rFixAj8/v1p7FDR79my++OILx8/eRpWh0Ra6hRXh7+7cUYjFWvlowgEDBjB27FgAVq5cec01WqwK/u6XP64AngYVT8fRkg27Cla7QrEKdlVBVcFOydGXvfR7u92OTbViKwZbkUKBXcF60aaeO3eOn3766ZrrFEKU9/DDD/PII49U6TKdCpupU6fy8MMP891339GsWTPNro02cOBAXnvtNRo0aECHDh0A0Ol0uLm58dxzz+Hp6cmZM2cct6WOjIx0vNbf3x9/f/9yyzt8+DA7d+5kz549QMmJjenp6ZjNZse87dq102RbtHbpkON8q8LxCwYamG34Gq3oSnOkR3gRPcIr/n1tSDay8OCV+0zi4+PLfb1W9zexXHWotapCeoGOcxYdGaX9PQU2KLIpFNoVim1QZFcotCnkWXXk2/TkWRVyi1RsMj5FCE00bNiwypfpVNj4+Phw1113sW3bNp555hlGjBhR5YUANGjQgPz8fBYvXsxzzz3HqVOnyM3NZcOGDaxYsQKLxcKgQYMcTRQXN0VU1CxRNhT3ySefpKCggLlz5+Lj40NOTg4ZGRkEBASwd+9eQkNDNdkeLQ0aNIhBgwaRm5vLiRMn2Lx5M999u45fduXSwt/KEy1zCTRV/m7cu35JEPyeZrxCn42VLWuWU8+g4uF37X02t9e/PGjsKiTl6NmXYeBQloFjOe5cKCy/bJ1Oh8ndiNFoxFTaZ2Py8MDPx5cG3t6YzWZ8fHzw9vbG09PT0a9T1px2cfOWTqcjNTWVJk2aOJrRTCaToxnNaDTWifPFDhw4QExMTE2XUWPq+vZDze8Dp8JGURSOHDmCxWLh+PHjpKWlaVZQ//79+frrr4mMjOTUqVPo9Xo8PDwYNGgQRqOR4OBgzp0759Syhg4dyiuvvMKIESPIzc3loYcewmg0Mn36dB599FF8fX1r/fkgZrOZ1q1b07p1a5544gnWrVvH7FmzmLTDwOjmlQ95VhToE1FEn2s40fNaqCrkFCmk5us4mm3gSLaBQ9l/hkuDiPp06xhLixYtaNiwIcHBwQQFBWEymaq0jpr+JxNCgKI6cbLMkSNHOHLkCCEhIbz55pvcfffdPPzww9VQXs3ZuXMncXFxNV3GdUlOTuaN16dw4OAh7o+ycG9kAVp9eLerkGbRkZSr53SunvOFpc1hRXrSLHosF3WqhIXUo3WbtnTo0IEOHToQFBSkTVGXkLCRfVDXtx+qbx9c6b3T6QECZVd6/vLLL+Ximze5iIgIZs+Zy9tvv82qb7+l2A6Dm1Rd4FisJaPNfk9zY0+GiZyiPwPF38+XoKAgIuqF0CE0lLCwMCIiImjevDmBgYFVU4AQotapNGzWrFnDpk2b2LFjBz///DNQMiLo8OHDjBo1qloKFNfHYDAwYcIEDAYDq9esodiu8FBTyw0FTmKOno3J7mw7a6LQquLjbabrbd1o06YNUVFRREZG4uHhUXUbIYRwGZWGTY8ePQgODiYrK4sHH3wQKOm8bdCgQbUUJ26MTqfjxRdfJC8vj3U//ECBTWF083zHSDVnqCr8kWlg9QkP9mcaMLq5cXuf3gwYMIBWrVrJXT2FEE6pNGwsFgudO3cmODi43Iid/Px8zQsTVUNRFB588EEiIiJYvHgxFqvCky3zMFzlnEJVhd3nDXx1wpOj2XoCA/wZM2YoAwYMwMfHp3qKF0K4jErD5j//+Q8TJ05k8uTJjrOkoeQNTPptag9FUXjssccwm83MnTuXC0U6/nZLHvUruJSNXYXf0934+oQnxy/oCKkXzPPPj6Jfv364u1dwhqYQQjih0rCZOHEiAL169eL//u//qqUgoZ1hw4bh5+fH+++9x8QdBnqGFdI1tAi9TsVmV9h93o3tZ01kFkBYaAgvPfk3+vXrV+uHhwshap5T7yIJCQmMHj1a2uddwJ133kmXLl1YtGgRX/33v2xO+fNoRa/X0bnzX+jXrx/du3eXkBFCVBmn3k0yMzPp0aMHERERjosKLlu2TOvahEb8/PwYO3YsDz74IMnJydjtdlRVpWnTppdd8kcIIaqCU2Ezb948resQNSAkJISQkJCaLkMIUQc4FTZWq5Vvv/223FWfX3/9dU0LE0II4TqcuqnG+PHjAfjtt99ITk4mKytL06KEEEK4FqfCxmQy8cQTTxASEsJbb71Fenq61nUJIYRwIU6FjaqqpKWlkZ+fT35+PtnZ2VrXJYQQwoU4FTZPP/0033//PXfffTe9e/emZ8+eWtclhBDChTg1QKBjx4507NgRgN69e2takBBCCNfjVNi8++67rFy5stz10bZu3apZUUIIIVyLU2GzefNmfvjhB4xGo9b1CCGEcEFO9dm0aNGCwsJCrWsRQgjhopw6smnatCndu3cnKCgIVVVRFIWNGzdqXZsQQggX4VTYrF27lo0bN8p9TIQQQlwXp8ImPDwcDw8P6bMRQghxXZwKmzNnznDHHXc4bgctV30WQghxLZwe+lyR3bt306ZNmyotSAghhOtxKmzq169f4eMzZ86U20MLIYS4KqeGPl+JqqpVVYcQQggXdkNhc/EVBYQQQogruaGwEUIIIZwhzWhCCCE0d81hk5qa6vh+4MCBVVqMEEII1+TUaLRFixZhMpm4cOECX375JT169GDixIkMGTJE6/qEEEK4AKeObOLj47n33ntJSEggPj6eAwcOaF2XEEIIF+JU2CiKQlpaGkFBQSiKIreFFkIIcU2cCpvOnTszYsQIRowYwbRp0+jbt6/WdQkhhHAhTvXZjBs3jnHjxpGdnc0LL7wgF+QUQghxTZwKm19//ZUpU6Zgs9no168f4eHhDB48WOvahBBCuAinmtHee+89lixZQlBQEE8++SRLly7Vui4hhBAuxKmw0el0+Pn5oSgK7u7ueHl5aV2XEEIIF+JU2DRs2JCZM2eSlZXFxx9/THh4uNZ1CSGEcCFO9dlMnjyZVatWERcXh4eHB2+88YbWdYlqcOrUKbZs2cKBAwfIysrCaDQSHh5Oy5Yt6dSpE0FBQTVdohDCRTgVNk8++SQLFizQuhZRTdLS0pg9ezabNm0CQPFRsLvbUWwKv+/7nW+++QZFUWjXrh0DBw6kZ8+euLm51XDVQojazKmw8fb2ZuPGjTRu3BidrqTlLTIyUtPChDZ++uknXn/jdfIL8rHH2FGbqODx5/M21QYXQElW+P3w7/w25Td8/Xy5e+DdDBgwQJpQhRDXxamwycjIYOHChY6fFUWRO3TWQl999VXJLb79wNrdCt4VzKQAvqD6qlhbWOEMZB3LYvHixSxevJjmMc3p1rUbbdq0oXnz5phMpureDCFELeRU2CxevFjrOoTGli1bxpw5c1DDVeyd7c795hUgDOxhdsgHJVHhYMpBDs4/CJSMUmwc2Zh2bdvRsWNH4uLicHd313IzhBC1lFNhc/vtt5e7K6e3tzdfffWVZkWJqrVhwwZWrFiBPcKO2lm9vrsYeYIao2KLsUEhcB7sGXaOnT/Gia9PsGrVKjw8Pfhr37/ywAMP0LBhw6reDCFELeZU2Hz77bdAyc3S9u3b5/hZ3PxWrlzJihUrUCPU6w+aS7kD4aCGq6io2G12SIO8xDy+/uZrvv76a2699VYeeeQRGjVqVAUrFELUdk699RiNRoxGI+7u7sTFxbF//36t6xJVYOnSpXzwwQeo9UubzrS6CbgeCAW1s4q1vxXbLTY2/7iZUaNGMWPGDNLT0zVasRCitnDqyGbmzJmOZrS0tDTHiDRxc7JarcyePZtVq1Zhb2BH7VRFRzTOMIHaWsXazIqyX2HturWs/349QwYPYejQofj6+lZTIUKIm4lTb0FRUVFERkYSGRlJjx49mDt37jWtZNSoUezZsweAoqIi4uLimD9/vuP5ESNG8NRTT1FUVFTudQkJCUyYMOGa1nUttF5+TTh58iRPP/10SdA0vYE+mhvlDmo7FetfrRSGFvLZZ5/xwOAHmD17NikpKTVQkBCiJjl1ZLN3717++c9/On5+6aWXePvtt51eSffu3fnf//5HbGwsO3fupHv37mzevJlHH32UwsJCUlNTWbJkybVXL4CSI5k9e/awdu1aNmzYgOpW0mymNlQrfoEKyjEFJVUB2xUWWlw6uZVOldGX9t9EqSUj2C5mLmleszW3YTlgYfkXy1m+fDktW7Wka5euxMTEEB63RD4uAAAbNUlEQVQejre3NzqdjoKCAnJychyTxWLBZrOh1+vx8PDA19eXoKAgAgMDMRic+vMVQtwEKv1v/eyzz5g7dy7Z2dmsX7/e8XiTJk2uaSVdu3Zlzpw5PPLII2zZsoXBgwfzr3/9i5ycHP744w86derE7bffzrp160hOTmbSpEl4eHg43lwAVq9ezaefforRaKRx48a8/vrrDBkyhE8++QQfHx86d+7MkiVLaNGiBffddx/Ll5e8qa1ZswZFUejfvz+jRo3i2LFjFS6/NrJarTz33HPs2rULAMVNwdbEhhqjlnTil1JOKignL0oBCyi5l6ZCeR4eHgwYMID4+HgsWZar1qKcUVAPlz9BtCKqvwoF8MfBP/hj3x9XXW6l69QpJcGpKBiNRp566inuueeeG1qmEEIblTawDB8+nK1bt/LUU0+xdetWx/Tpp59e00patGjB8ePHUVWVX3/9lU6dOtGlSxe2b9/OL7/8Qo8ePRzzvv/++4wdO5aFCxfSrl07ADIzM/nwww/59NNPWbp0Kd7e3ixfvpzevXvz448/snPnTiIiIti2bRtHjx6lcePGJCUlsXbtWj7//HM+//xzNmzYwPHjxytcfm1lsVgcQQNgi748aCpUfPVlDxgwgLFjxzJgwADnC3JiuegpGUbtqaK6X+HIq5SqqKh6FdWsovqpqKby86t2FVVVsdvtFBQUsHLVSudrFUJUK6faIYYOHcqaNWuwWq2oqsq5c+d44oknnF6JTqejefPmJCQkEBwcjNFopGfPnmzevJmDBw8yatQox7xHjhwhNjYWgPbt23P8+HFOnTpFdHQ0ZrMZgI4dO7J161aGDx/OvHnzCAsLY9y4cSxevBhVVenbty+HDx8mJSWFhx9+GIDs7GySkpIqXH5t5e3tzQcffMDvv//Ojl92sP+P/ShHFGy32FCb/9lXozZWURv/+UatHFNQfqv8yCY+Pr7cV2eoLdWSy99cSS4oRxT0SXrUIhWDm4Ho5tFERERgNpsdzWi5ublkX8gm+0I2lnwLVqsVg8GAZ4AnAf4BBAYGEhwcTFBQEEFBQfj5+eHj40NoaKjTtQohqpdTYTN27FgaN27M4cOHcXd3x8PjKm0lFejWrRsfffSR45NyXFwcc+bMcdwrp0xUVBS///47PXv2ZN++fQBERERw7Ngx8vPz8fT05JdffiEyMpJmzZqRnJxMWloazz//PB999BEbN25kwYIFjoD65JNPUBSFhQsX0qxZswqXX5u1bduWtm3bMnr0aE6ePMmCBQvYvHkzSoqCtYsVKrj1kBqlYseOknLlPpv84nxWrFtRcpRkvkoRF/fZVCQHlP0KulM69Ho9t992O3fccQft2rWTKw4IUUc43cP6+uuvM3HiRN58802GDx9+zSvq2rUrr7zyimNggdFoxNvbmxYtWpSbb/LkyYwbN4758+cTEBCAu7s7AQEBPPPMM4waNQqdTkfDhg154YUXgJKjnOTkZHQ6HR07duTo0aN4eXnRvHlzunTpwrBhwygqKiI2NpaQkJAKl+8qyvqyfvzxR96c9iaWTRas3awQcMmMCqhNrnIUUhUKSkPmuA6j0cj9Q+9nyJAhBAYGarteIcRNR1FV9arvOKNGjeLf//4348eP59133+Xee+/l66+/ro76aszOnTuJi4ur6TKuW1JSEs89/xxpGWklF92szlvTFIFyWEF/RI+iKtxz9z387W9/IyDg0tSrHgcOHCAmJqZG1n2zqOv7oK5vP1TfPrjSe6dTZ2AMHz6chQsX0q1bN3r16kVUVFSVFyiqVsOGDZk7Zy7hIeEYfjRAdZzEnwfKHgXDOgO6Azpu7XErixctZty4cTUWNEKIm4NTzWh//etfgZJO9jvvvNPRUS9ubsHBwXz4wYc8/sTjnN96HmsPK1RlC5YNyAIlTUGXqoP0kmHIvXr1YtSoUURHR1fhyoQQtZlTYfPrr78yZcoUbDYb/fr1Izw8nMGDB2tdm6gCQUFBvPD8C7z3/nuc3Xq2pA/nepvU7MA5UM4q6NJ1KFkKqr2kFTYyKpLb77mdfv36ERISUmX1CyFcg1Nh895777FkyRKeeeYZnnzySYYNGyZhU4v4+/sz68NZPPuPZ0lJSMHayQoR17CALFCOK+iT9aiFKgaDgZgWMbT6aytiYmJo3bq1dPoLISrlVNiUDU9WFAV3d3e8vCoYTytuavXq1WPunLlMmDCB/T/tx97EjtpKBeMVXmAruTW07pgOzoObmxu9evWid+/exMXFyR06hRDXxKmwadiwITNnziQrK4uPP/5Y7kNfS/n5+fHBBx/w8ccfs2LFCjgFtgY21HoqmCgJmBwF0kB/Vo9arBIWHsZ9f7+P/v374+PjU9ObIISopSoNmxMnThAZGcmUKVNYsWIFcXFxeHh48MYbb1RXfaKKGY1Gnn76afr168eSJUv48ccfKT5W/joz/gH+dPtrN3r37k27du3klhJCiBtWadhMnDiRZcuW8eyzzzJ79uzqqklUg+joaF577TUKCws5efIkWVlZGI1GwsPDqVevXrnbgAshxI2qNGwaNmxIt27dyM7Opnv37uWe27p1q6aFierh7u7OLbfcUtNlCCFcXKVhU3ZpmSlTpjB58uRqKUgIIYTrcWqAwHPPPcfbb7/tuHz/3//+93IXzxRCCCEq41TP78svv0xoaCjjxo2jfv36LncrZSGEENpy6sgmMzPTcc+ZmJgYvvvuO02LEkII4VqcOrIpLCwkLS0NgPT0dOx2u6ZFCSGEcC1OHdk8++yzDB06FLPZTF5enpxnI4QQ4po4FTbdunVj48aNZGRkyKXihRBCXDOnwmbZsmUsX76cwsJCx2Nr167VrCghhBCuxamwWbRoER9//DG+vr5a1yOEEMIFORU2t9xyC2FhYej1eq3rEUII4YKcCpu//OUv9OnThwYNGqCqKoqisGjRIq1rE0II4SKcCpvly5fz3nvv4e3trXU9QgghXJBTYRMSEkLr1q3lUvNCCCGui1NhU1RUxD333EPTpk0dl56fOXOmpoUJIYRwHU6FzRNPPKF1HUIIIVyYU+1iLVq0YNu2bXz11VdkZWUREhKidV1CCCFciFNhM2nSJBo0aMDJkycJCgri5Zdf1rouIYQQLsSpsMnKyuKBBx7AYDDQvn17VFXVui4hhBAuxOnhZceOHQPgzJkzMipNCCHENXEqNV555RVefvllDhw4wNixY5k4caLWdQkhhHAhlY5Gu/322x1DnVVVJSAggPT0dJ5//nnWrVtXLQUKIYSo/SoNm2+//RZVVZkyZQpDhw4lNjaW/fv38/nnn1dXfUIIIVxApWFjNBoBOHXqFLGxsUDJMOgTJ05oX5kQQgiX4dRJnd7e3rz33nvExsaya9cu6tevr3VdQgghXIhTAwT+9a9/ERwcTEJCAkFBQUyfPl3ruoQQQrgQp45sPD09GT58uNa1CCGEcFFywowQQgjNSdgIIYTQnISNEEIIzUnYCCGE0JyEjRBCCM1J2AghhNCchI0QQgjNSdgIIYTQnFMndQrXY7fbycnJIS8vD4vFQn5+PsXFxdjtdux2O4qi4ObmhsFgwGQy4enpiZeXF2azGb1eX9PlCyFqGQkbF5Wdnc3p06dJTU1l165drFmzhrS0NM6dPUvG+fNcyM3Fbrdf83IVRcHbyws/Pz8Cg4MJDg4mKCiIevXqERoaSkhICGFhYXh6emqwVUKI2krCphYrLi7m9OnTJCUlkZSURGJiIqeSkjiVlEROXl65ec06Hd6qio+qEg14Al6ACXAHjJT8MSiUtK2qgK10KgIKS6d8VSU/N5fc3FzOJydzUqfjgqpiu+RW4T5mM2Hh4YTXr09oaCjh4eGEhYURFhZGSEiI44riQoi6QbOw+fjjj9m+fTs6nQ5FURg3bhytWrXSanXlLF++nEGDBuHm5nbVeZcuXUp6ejrPPPNMNVR27ex2O2lpaaSkpJCcnExSUhKnTp0i6eRJUs6cKXd04qPTEWS30xwILJ38SyejvSwMlCouUMWOSh6QVTplAlm5uWQePszeo0dJqCCMAv39CQkNJSQ0lHr16hF80VFSYGAgAQEBmEymqq1VCFFjNAmbo0ePsmnTJpYuXYqiKBw4cIDx48ezevVqLVZ3mY8++oh77723WtZ1I+x2OxcuXCAjI4Pz58+Tnp5OWloaZ8+e5ezZs6SePs2Zs2cptlodr3FTFAIUhSC7nSZAEBBc+tXdrnItYaKi8gtwCCi+6PGC0slUOl2NG9Ac6Ag0uHT9pWGUQ0kIlU3ZmZlkZ2ay99AhslWV4kvCCMDD3R1fX1/8AwLw9fPD19cXb29vvL29MZvNmM1mPD09y00mkwkPDw9MJhNGoxGdTsbACHEz0CRsAgICSElJYeXKlfTs2ZOYmBhWrlzJoUOHmDp1KgB+fn5MmzaN/fv3M2/ePHQ6HWlpaTz44IMMHz6cX375hVmzZgFQUFDAjBkzcHNzY8yYMfj5+dGzZ0/atGlz2Tz/+9//SEtLY9y4ccyZM4eZM2fy66+/oqoqDz/8MHfeeSf/+9//mDZtGr6+vuh0Otq2bVul25+QkMDp06exWCwUFhaSn59PXl4eeXl55Fy4wIXsbC5cuMCFnBzsFbzJeul0+KkqvqpKIyCgdAoEfFQVnQrXEiq/o/JbBY9fADIqeNzDw4MBAwYQHx/PGYvFqXUcAbYBPly+PZUx2+2YATtg5c+mOxtgKywk59w57OfOka0oWBQFC1BwDX1N7hcNcjAajbi5uWF0d8doNGJ0d8dgMGA0GjEYDBgMBtzc3NDr9Y6fDQaD42e9Xo9er0en0zm+Xvp92ZF82deLJ+Cyn8seq+h7Z1xp/kvXmZyczPnz5x21lT1+8fdlk1rB36QWrnVbL3atNSYlJZFX2rR88a3una3HbrejqiqqqjpaE8oG05Qtq2y6lhor+ru4dLr0b+lqy7rSei/eB2Wio6Px8/O7ap1VQbOwmTt3LkuWLGH27NmYTCbGjRvH/PnzmTZtGtHR0axYsYJPPvmErl27cvbsWb766ivsdjsDBw6kX79+HDlyhHfeeYeQkBDmzZvHt99+y8CBA0lLS2PVqlUYjUY+++yzy+YZM2YMc+fO5d1332XLli0kJyezbNkyCgsLGTJkCN26dWP69OnMnDmTyMhIJk+eXKXbnpOTwyuvvHLV+YxAOFAPCAFCAT/AG3DTqsnrEoVXeHzAgAGMHTsWgJUrV97w8pyho2SfVKQ90E4FSv+JbPzZh1RQ+jUHyC2d8kq/WoD84mIsxcVkWSzYbqA+IVxRREQEn3/+ebWsS5OwSUxMxGw2O26ytnfvXh5//HEKCgqYMmUKUNK5HRkZCUC7du0cHcZNmzYlKSmJkJAQ3nzzTTw9PTl79izt27cHSnZO2bxXmqfM4cOH+eOPPxg5ciQAVquVlJQUzp4961h3+/btSUpKqrJtN5vNDBo0iJMnT2K32yksKMBSdmSTn09+6ZFCEZBcOkFJrHjrdJhLj2j8KQkff0qOavwBt+sMn3YotKvg8V9Q+aaCx+Pj48t9ddbtQKcbDMii0ia3i4MjC4hHxUJJgBQABTpdSdCoKoXX+Ulcpyi4XXQ0UzbUu9xU+pjeYHAcuUDJp8eyo4Cy7y89Kih7vExFnzwvnudaPmlX9NorHZkoikJeXh6enp6X1Xy15VQ2z418X1kNzmzvxfvHmfXm5uZiNpsBHEP7L16OM/ukshouXubVtkWLfXrpPqro95efn4+Xl1e5earzPmWahM2hQ4dYunQp8+bNw93dncjISLy9vQkJCWHGjBmEh4ezc+dO0tLSADhw4AA2m42ioiKOHj1Ko0aNGDNmDBs2bMBsNjN+/HjHDr24Df6VV16pcB5FUbDb7URFRdG5c2feeOMN7HY7c+bMISIiguDgYI4dO0aTJk3Yu3cvvr6+VbbtiqLwj3/844rPW61WcnNzuXDhAtnZ2WRmZpKZmenor0lPT+dMairHz56lsKio3Gv9dAoBdjtB4JiCAR9Adx1v8h1Lvx7kkj4bi4UNK1fiS8lR19Vc3GdTGRsq2fw5kCALyC6dcnQ6LgAF9oqDw9PDAx9vb7x9fAj29XX02Xh5eeHp6YmHh4ejz6as38bd3R2TyURycjIxMTElTWelU1nA1BUHDhwgJiampsuoMXV9+6Hm94EmYdO3b1+OHTvG4MGD8fT0RFVVXnrpJUJDQxk/fjw2W0mDxptvvsm5c+ewWq089thjZGVlMWbMGAICArjnnnsYMmQIPj4+BAUFce7cucvWc6V5OnTowOOPP86iRYv45ZdfeOihh8jPz6dPnz6YzWbeeecdxo8fj5eXF15eXlUaNldjMBjw8/O7ajupqqpkZ2eTkpLC6dOnHaPREhMT+SMxkbyL+lLcFIUgIFBVHSFUNhrNo5IQUlDoBHSqgu0CSgcCqCWj0Sg/ICBLpyPLrl7WoxPg50e90FCal45ICwwMdEz+/v74+/vj6+vr1MjCK9ZltxMeHn7drxdC3DhFra7ewCvYsWMHy5Yt4913363JMi6zc+dO4uLiarqMCqmqSlZWFomJiSXn1pw6VfL9iROcTUsrd1jtodPhX9o05wv4AmZKzrHxBDwo6Su5+DwbBQUVFTsl/SPF/Nk3kk9J01Y+Jf0k2WVfdTqyKxji7O/nR/369Qm76Dyb0NBQQkNDCQ4OrpbzbWr6E93NoK7vg7q+/VB9++BK751yUmctpCiK41P/pSPpCgsLSU1N5dSpU44joiNHjpB74QKJ6emOPqNKl8/lRyAVMRgMBPr7ExwSQlRICCEhIeWuIhAWFoa7u/t1bqUQwpXUeNh07tyZzp0713QZLsPd3Z3GjRvTuHFjx2MXf6LJy8sjIyODrKwssrKyyMvLIz8/n/z8fKxWK3a7HZvN5hjmWzZsuOycFh8fH/z8/PD398dsNst5LEIIp9R42IjqVdZP1aBBg5ouRQhRh8jHUiGEEJqTsBFCCKE5CRshhBCak7ARQgihOQkbIYQQmpOwEUIIoTkJGyGEEJqTsBFCCKE5CRshhBCak7ARQgihOQkbIYQQmpOwEUIIoTkJGyGEEJqTsBFCCKE5CRshhBCak7ARQgihOQkbIYQQmpOwEUIIoTkJGyGEEJpTVFVVa7qIm9HOnTtrugQhhKiV4uLiLntMwkYIIYTmpBlNCCGE5iRshBBCaM5Q0wUIbdjtdl577TUOHTqE0Whk6tSpNGrUqKbLui7FxcVMmjSJ06dPU1RUxJgxY4iOjmbChAkoikLTpk2ZPHkyOp2OWbNmsXnzZgwGA5MmTSI2NpbExMQbnvdmcP78eQYNGsSCBQswGAx1bvs/+ugjNm3aRHFxMcOGDaNTp051ah8UFxczYcIETp8+jU6n44033qhdfweqcEnfffedOn78eFVVVfX3339Xn3zyyRqu6PqtXLlSnTp1qqqqqpqRkaH26tVLfeKJJ9Sff/5ZVVVVffXVV9X169er+/btU0eOHKna7Xb19OnT6qBBg1RVVW943ptBUVGR+ve//13t27evevTo0Tq3/T///LP6xBNPqDabTc3NzVU/+OCDOrcPvv/+e3Xs2LGqqqrq1q1b1aeffrpW7YOaj2uhiZ07d9KjRw8A2rZty759+2q4ouvXr18/nn32WcfPer2eP/74g06dOgHQs2dPtm/fzs6dO+nevTuKohAeHo7NZiMjI+OG570ZzJgxg6FDh1KvXj2AOrf9W7dupVmzZjz11FM8+eST3HrrrXVuH0RGRmKz2bDb7eTm5mIwGGrVPpCwcVG5ubmYzWbHz3q9HqvVWoMVXT8vLy/MZjO5ubmMHTuWf/zjH6iqiqIojudzcnIu2+ayx2903pr25ZdfEhAQ4PjwANSp7QfIzMxk3759vP/++0yZMoUXXnihzu0DT09PTp8+zZ133smrr77KyJEja9U+kD4bF2U2m8nLy3P8bLfbMRhq7687NTWVp556ioceeoiBAwfyzjvvOJ7Ly8vDx8fnsm3Oy8vD29u7XFvz9cxb01atWoWiKPz0008cOHCA8ePHk5GR4Xje1bcfwM/Pj6ioKIxGI1FRUbi7u3PmzBnH83VhHyxcuJDu3bvz/PPPk5qayt/+9jeKi4sdz9/s+0CObFxU+/btSUhIAGDXrl00a9ashiu6funp6TzyyCO8+OKLPPDAAwC0aNGCHTt2AJCQkECHDh1o3749W7duxW63k5KSgt1uJyAg4IbnrWmfffYZS5YsYfHixcTExDBjxgx69uxZZ7YfSk4S/PHHH1FVlbNnz2KxWOjSpUud2gc+Pj54e3sD4Ovri9VqrVX/B3JSp4sqG412+PBhVFVl2rRpNGnSpKbLui5Tp05l3bp1REVFOR57+eWXmTp1KsXFxURFRTF16lT0ej0ffvghCQkJ2O12Jk6cSIcOHThx4gSvvvrqDc17sxg5ciSvvfYaOp3uhreptm3/22+/zY4dO1BVlXHjxhEREVGn9kFeXh6TJk0iLS2N4uJiRo0aRatWrWrNPpCwEUIIoTlpRhNCCKE5CRshhBCak7ARQgihOQkbIYQQmpOwEUIIoTkJGyFqQHJyMkOGDKnpMoSoNhI2QgghNFd7r18ihAsYOXIkzZs358iRI+Tm5vL+++9Tv3595syZw4YNG7DZbAwbNoyhQ4eyYMEC4uPjMRgMdOjQgRdffJEPP/yQxMREMjMzyc7O5qGHHmL9+vWcOHGCGTNm0LZtWxYvXsyaNWtQFIX+/fszatSomt5sUQfJkY0QNSw2NpaFCxfSrVs34uPj2b9/PwkJCaxYsYJly5Zx9OhRDh06xLp161i2bBnLli0jMTGRH374AQCTycT8+fPp27cvW7ZsYd68eTz++OPEx8dz9OhR1q5dy+eff87nn3/Ohg0bOH78eA1vsaiL5MhGiBrWokULAEJDQ0lPT+fEiRPExsai1+vx8PDglVdeYd26dbRp0wY3NzcAOnTowJEjR8q93tvbm+joaKDk2lmFhYUcPnyYlJQUHn74YQCys7NJSkoqd+kfIaqDHNkIcZOJiopi//792O12iouLGT16NJGRkezZswer1Yqqqvz6669ERkYCOC4Ff6VlRUdHs2jRIhYvXsygQYNq9UVZRe0lRzZC3GRiYmLo0aMHw4YNw263M2zYMJo3b86dd97peCwuLo4+ffpw8ODBSpfVvHlzunTpwrBhwygqKiI2NpaQkJBq2hIh/iQX4hRCCKE5aUYTQgihOQkbIYQQmpOwEUIIoTkJGyGEEJqTsBFCCKE5CRshhBCak7ARQgihOQkbIYQQmvt/CjHExnFZ/x4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_violin(income, cols, col_y = 'Income'):\n",
    "    \n",
    "    for col in cols:\n",
    "        sns.set_style(\"whitegrid\")\n",
    "        sns.violinplot(col_y, col, data=data5 )\n",
    "        plt.xlabel(col_y) # Set text for the x axis\n",
    "        plt.ylabel(col)# Set text for y axis\n",
    "        plt.show()\n",
    "num_cols = ['income_source', 'demofeatures_marital_status', 'demofeatures_gender']\n",
    "\n",
    "plot_violin('income', num_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot count plots in stacked bar forms\n",
    "\n",
    "\n",
    "Instead of your aggregate function, you can accomplish what you want with a groupby + pivot. \n",
    "df_plot = df.groupby(['class', 'survived']).size().reset_index().pivot(columns='class', index='survived', values=0)\n",
    "\n",
    "class     First  Second  Third\n",
    "survived                      \n",
    "0            80      97    372\n",
    "1           136      87    119\n",
    "From here you can just plot it as a bar with the stacked=True argument\n",
    "df_plot.plot(x=df_plot.index, kind='bar', stacked=True)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " plot two count plots beside one another\n",
    " \n",
    " import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "batData = ['a','b','c','a','c']\n",
    "bowlData = ['b','a','d','d','a']\n",
    "\n",
    "df=pd.DataFrame()\n",
    "df['batting']=batData\n",
    "df['bowling']=bowlData\n",
    "\n",
    "\n",
    "fig, ax =plt.subplots(1,2)\n",
    "sns.countplot(df['batting'], ax=ax[0])\n",
    "sns.countplot(df['bowling'], ax=ax[1])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@here this is key concept that I think should be made public.As we are developing pre-delinquency models for the final challenge, firstPaymentDefault would be known as would max_amount_taken and max_tenor_taken. But any feature that would occur after that on a loan timeline should be considered as feature leakage.This is very useful and should help everyone. I thought I would make this public as I have got a lot of questions surrounding it and I do not think my slides made it clear enough. (edited) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}