Credit Card Segmenrtation using Kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Card Segmentation using Kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit = pd.read_csv('C:/Users/Lenovo/Downloads/Python-DataScience-Aanalytixlabs/DSP Class 23-24 files/CC_GENERAL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
       "      <th>INSTALLMENTS_PURCHASES</th>\n",
       "      <th>CASH_ADVANCE</th>\n",
       "      <th>PURCHASES_FREQUENCY</th>\n",
       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_TRX</th>\n",
       "      <th>PURCHASES_TRX</th>\n",
       "      <th>CREDIT_LIMIT</th>\n",
       "      <th>PAYMENTS</th>\n",
       "      <th>MINIMUM_PAYMENTS</th>\n",
       "      <th>PRC_FULL_PAYMENT</th>\n",
       "      <th>TENURE</th>\n",
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       "  </thead>\n",
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       "      <td>201.802084</td>\n",
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       "      <td>3202.467416</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>4103.032597</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>622.066742</td>\n",
       "      <td>627.284787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C10004</td>\n",
       "      <td>1666.670542</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>205.788017</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C10005</td>\n",
       "      <td>817.714335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>678.334763</td>\n",
       "      <td>244.791237</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
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       "      <td>28.493517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>291.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>291.12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>325.594462</td>\n",
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       "      <td>6</td>\n",
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       "      <td>300.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>275.861322</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
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       "      <td>23.398673</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>144.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>144.40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>81.270775</td>\n",
       "      <td>82.418369</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
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       "      <td>13.457564</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>52.549959</td>\n",
       "      <td>55.755628</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
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       "      <td>C19190</td>\n",
       "      <td>372.708075</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1093.25</td>\n",
       "      <td>1093.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>127.040008</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>63.165404</td>\n",
       "      <td>88.288956</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
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       "  </tbody>\n",
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       "<p>8950 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "     CUST_ID      BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0     C10001    40.900749           0.818182      95.40              0.00   \n",
       "1     C10002  3202.467416           0.909091       0.00              0.00   \n",
       "2     C10003  2495.148862           1.000000     773.17            773.17   \n",
       "3     C10004  1666.670542           0.636364    1499.00           1499.00   \n",
       "4     C10005   817.714335           1.000000      16.00             16.00   \n",
       "...      ...          ...                ...        ...               ...   \n",
       "8945  C19186    28.493517           1.000000     291.12              0.00   \n",
       "8946  C19187    19.183215           1.000000     300.00              0.00   \n",
       "8947  C19188    23.398673           0.833333     144.40              0.00   \n",
       "8948  C19189    13.457564           0.833333       0.00              0.00   \n",
       "8949  C19190   372.708075           0.666667    1093.25           1093.25   \n",
       "\n",
       "      INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "0                      95.40      0.000000             0.166667   \n",
       "1                       0.00   6442.945483             0.000000   \n",
       "2                       0.00      0.000000             1.000000   \n",
       "3                       0.00    205.788017             0.083333   \n",
       "4                       0.00      0.000000             0.083333   \n",
       "...                      ...           ...                  ...   \n",
       "8945                  291.12      0.000000             1.000000   \n",
       "8946                  300.00      0.000000             1.000000   \n",
       "8947                  144.40      0.000000             0.833333   \n",
       "8948                    0.00     36.558778             0.000000   \n",
       "8949                    0.00    127.040008             0.666667   \n",
       "\n",
       "      ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "0                       0.000000                          0.083333   \n",
       "1                       0.000000                          0.000000   \n",
       "2                       1.000000                          0.000000   \n",
       "3                       0.083333                          0.000000   \n",
       "4                       0.083333                          0.000000   \n",
       "...                          ...                               ...   \n",
       "8945                    0.000000                          0.833333   \n",
       "8946                    0.000000                          0.833333   \n",
       "8947                    0.000000                          0.666667   \n",
       "8948                    0.000000                          0.000000   \n",
       "8949                    0.666667                          0.000000   \n",
       "\n",
       "      CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
       "0                   0.000000                 0              2        1000.0   \n",
       "1                   0.250000                 4              0        7000.0   \n",
       "2                   0.000000                 0             12        7500.0   \n",
       "3                   0.083333                 1              1        7500.0   \n",
       "4                   0.000000                 0              1        1200.0   \n",
       "...                      ...               ...            ...           ...   \n",
       "8945                0.000000                 0              6        1000.0   \n",
       "8946                0.000000                 0              6        1000.0   \n",
       "8947                0.000000                 0              5        1000.0   \n",
       "8948                0.166667                 2              0         500.0   \n",
       "8949                0.333333                 2             23        1200.0   \n",
       "\n",
       "         PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n",
       "0      201.802084        139.509787          0.000000      12  \n",
       "1     4103.032597       1072.340217          0.222222      12  \n",
       "2      622.066742        627.284787          0.000000      12  \n",
       "3        0.000000               NaN          0.000000      12  \n",
       "4      678.334763        244.791237          0.000000      12  \n",
       "...           ...               ...               ...     ...  \n",
       "8945   325.594462         48.886365          0.500000       6  \n",
       "8946   275.861322               NaN          0.000000       6  \n",
       "8947    81.270775         82.418369          0.250000       6  \n",
       "8948    52.549959         55.755628          0.250000       6  \n",
       "8949    63.165404         88.288956          0.000000       6  \n",
       "\n",
       "[8950 rows x 18 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CUST_ID                             False\n",
       "BALANCE                             False\n",
       "BALANCE_FREQUENCY                   False\n",
       "PURCHASES                           False\n",
       "ONEOFF_PURCHASES                    False\n",
       "INSTALLMENTS_PURCHASES              False\n",
       "CASH_ADVANCE                        False\n",
       "PURCHASES_FREQUENCY                 False\n",
       "ONEOFF_PURCHASES_FREQUENCY          False\n",
       "PURCHASES_INSTALLMENTS_FREQUENCY    False\n",
       "CASH_ADVANCE_FREQUENCY              False\n",
       "CASH_ADVANCE_TRX                    False\n",
       "PURCHASES_TRX                       False\n",
       "CREDIT_LIMIT                         True\n",
       "PAYMENTS                            False\n",
       "MINIMUM_PAYMENTS                     True\n",
       "PRC_FULL_PAYMENT                    False\n",
       "TENURE                              False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit.MINIMUM_PAYMENTS.fillna(value=credit.MINIMUM_PAYMENTS.mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit.CREDIT_LIMIT.fillna(value=credit.CREDIT_LIMIT.mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CUST_ID                             False\n",
       "BALANCE                             False\n",
       "BALANCE_FREQUENCY                   False\n",
       "PURCHASES                           False\n",
       "ONEOFF_PURCHASES                    False\n",
       "INSTALLMENTS_PURCHASES              False\n",
       "CASH_ADVANCE                        False\n",
       "PURCHASES_FREQUENCY                 False\n",
       "ONEOFF_PURCHASES_FREQUENCY          False\n",
       "PURCHASES_INSTALLMENTS_FREQUENCY    False\n",
       "CASH_ADVANCE_FREQUENCY              False\n",
       "CASH_ADVANCE_TRX                    False\n",
       "PURCHASES_TRX                       False\n",
       "CREDIT_LIMIT                        False\n",
       "PAYMENTS                            False\n",
       "MINIMUM_PAYMENTS                    False\n",
       "PRC_FULL_PAYMENT                    False\n",
       "TENURE                              False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit['monthly_avg_purchases']=credit.PURCHASES /credit.TENURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit['monthly_cash_advance']=credit.CASH_ADVANCE /credit.TENURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2042"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit[(credit.ONEOFF_PURCHASES == 0) & (credit.INSTALLMENTS_PURCHASES == 0)].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2774"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit[(credit.ONEOFF_PURCHASES > 0) & (credit.INSTALLMENTS_PURCHASES > 0)].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1874"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit[(credit.ONEOFF_PURCHASES > 0) & (credit.INSTALLMENTS_PURCHASES == 0)].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2260"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit[(credit.ONEOFF_PURCHASES == 0) & (credit.INSTALLMENTS_PURCHASES > 0)].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(credit):\n",
    "    if (credit.ONEOFF_PURCHASES == 0) & (credit.INSTALLMENTS_PURCHASES == 0):\n",
    "        return 'none_one_ins'\n",
    "    elif (credit.ONEOFF_PURCHASES > 0) & (credit.INSTALLMENTS_PURCHASES > 0):\n",
    "        return 'both_one_ins'\n",
    "    elif (credit.ONEOFF_PURCHASES > 0) & (credit.INSTALLMENTS_PURCHASES == 0):\n",
    "        return 'one_off'\n",
    "    else:\n",
    "        return 'installment_withou_one'\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit['purchase_type']=credit.apply(func,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
       "      <th>INSTALLMENTS_PURCHASES</th>\n",
       "      <th>CASH_ADVANCE</th>\n",
       "      <th>PURCHASES_FREQUENCY</th>\n",
       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
       "      <th>...</th>\n",
       "      <th>CASH_ADVANCE_TRX</th>\n",
       "      <th>PURCHASES_TRX</th>\n",
       "      <th>CREDIT_LIMIT</th>\n",
       "      <th>PAYMENTS</th>\n",
       "      <th>MINIMUM_PAYMENTS</th>\n",
       "      <th>PRC_FULL_PAYMENT</th>\n",
       "      <th>TENURE</th>\n",
       "      <th>monthly_avg_purchases</th>\n",
       "      <th>monthly_cash_advance</th>\n",
       "      <th>purchase_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C10001</td>\n",
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       "      <td>0.818182</td>\n",
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       "      <td>7.950000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>installment_withou_one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C10002</td>\n",
       "      <td>3202.467416</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7000.0</td>\n",
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       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>536.912124</td>\n",
       "      <td>none_one_ins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C10003</td>\n",
       "      <td>2495.148862</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>773.17</td>\n",
       "      <td>773.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>622.066742</td>\n",
       "      <td>627.284787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>64.430833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>one_off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C10004</td>\n",
       "      <td>1666.670542</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>205.788017</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>124.916667</td>\n",
       "      <td>17.149001</td>\n",
       "      <td>one_off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C10005</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>6</td>\n",
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       "      <td>48.886365</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>48.520000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>8946</th>\n",
       "      <td>C19187</td>\n",
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       "      <td>300.00</td>\n",
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       "      <td>0.250000</td>\n",
       "      <td>6</td>\n",
       "      <td>24.066667</td>\n",
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       "      <td>installment_withou_one</td>\n",
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       "    <tr>\n",
       "      <th>8948</th>\n",
       "      <td>C19189</td>\n",
       "      <td>13.457564</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.558778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>52.549959</td>\n",
       "      <td>55.755628</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.093130</td>\n",
       "      <td>none_one_ins</td>\n",
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       "    <tr>\n",
       "      <th>8949</th>\n",
       "      <td>C19190</td>\n",
       "      <td>372.708075</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1093.25</td>\n",
       "      <td>1093.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>127.040008</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>63.165404</td>\n",
       "      <td>88.288956</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.208333</td>\n",
       "      <td>21.173335</td>\n",
       "      <td>one_off</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8950 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CUST_ID      BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0     C10001    40.900749           0.818182      95.40              0.00   \n",
       "1     C10002  3202.467416           0.909091       0.00              0.00   \n",
       "2     C10003  2495.148862           1.000000     773.17            773.17   \n",
       "3     C10004  1666.670542           0.636364    1499.00           1499.00   \n",
       "4     C10005   817.714335           1.000000      16.00             16.00   \n",
       "...      ...          ...                ...        ...               ...   \n",
       "8945  C19186    28.493517           1.000000     291.12              0.00   \n",
       "8946  C19187    19.183215           1.000000     300.00              0.00   \n",
       "8947  C19188    23.398673           0.833333     144.40              0.00   \n",
       "8948  C19189    13.457564           0.833333       0.00              0.00   \n",
       "8949  C19190   372.708075           0.666667    1093.25           1093.25   \n",
       "\n",
       "      INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "0                      95.40      0.000000             0.166667   \n",
       "1                       0.00   6442.945483             0.000000   \n",
       "2                       0.00      0.000000             1.000000   \n",
       "3                       0.00    205.788017             0.083333   \n",
       "4                       0.00      0.000000             0.083333   \n",
       "...                      ...           ...                  ...   \n",
       "8945                  291.12      0.000000             1.000000   \n",
       "8946                  300.00      0.000000             1.000000   \n",
       "8947                  144.40      0.000000             0.833333   \n",
       "8948                    0.00     36.558778             0.000000   \n",
       "8949                    0.00    127.040008             0.666667   \n",
       "\n",
       "      ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  ...  \\\n",
       "0                       0.000000                          0.083333  ...   \n",
       "1                       0.000000                          0.000000  ...   \n",
       "2                       1.000000                          0.000000  ...   \n",
       "3                       0.083333                          0.000000  ...   \n",
       "4                       0.083333                          0.000000  ...   \n",
       "...                          ...                               ...  ...   \n",
       "8945                    0.000000                          0.833333  ...   \n",
       "8946                    0.000000                          0.833333  ...   \n",
       "8947                    0.000000                          0.666667  ...   \n",
       "8948                    0.000000                          0.000000  ...   \n",
       "8949                    0.666667                          0.000000  ...   \n",
       "\n",
       "      CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT     PAYMENTS  \\\n",
       "0                    0              2        1000.0   201.802084   \n",
       "1                    4              0        7000.0  4103.032597   \n",
       "2                    0             12        7500.0   622.066742   \n",
       "3                    1              1        7500.0     0.000000   \n",
       "4                    0              1        1200.0   678.334763   \n",
       "...                ...            ...           ...          ...   \n",
       "8945                 0              6        1000.0   325.594462   \n",
       "8946                 0              6        1000.0   275.861322   \n",
       "8947                 0              5        1000.0    81.270775   \n",
       "8948                 2              0         500.0    52.549959   \n",
       "8949                 2             23        1200.0    63.165404   \n",
       "\n",
       "      MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  monthly_avg_purchases  \\\n",
       "0           139.509787          0.000000      12               7.950000   \n",
       "1          1072.340217          0.222222      12               0.000000   \n",
       "2           627.284787          0.000000      12              64.430833   \n",
       "3           864.206542          0.000000      12             124.916667   \n",
       "4           244.791237          0.000000      12               1.333333   \n",
       "...                ...               ...     ...                    ...   \n",
       "8945         48.886365          0.500000       6              48.520000   \n",
       "8946        864.206542          0.000000       6              50.000000   \n",
       "8947         82.418369          0.250000       6              24.066667   \n",
       "8948         55.755628          0.250000       6               0.000000   \n",
       "8949         88.288956          0.000000       6             182.208333   \n",
       "\n",
       "      monthly_cash_advance           purchase_type  \n",
       "0                 0.000000  installment_withou_one  \n",
       "1               536.912124            none_one_ins  \n",
       "2                 0.000000                 one_off  \n",
       "3                17.149001                 one_off  \n",
       "4                 0.000000                 one_off  \n",
       "...                    ...                     ...  \n",
       "8945              0.000000  installment_withou_one  \n",
       "8946              0.000000  installment_withou_one  \n",
       "8947              0.000000  installment_withou_one  \n",
       "8948              6.093130            none_one_ins  \n",
       "8949             21.173335                 one_off  \n",
       "\n",
       "[8950 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit=pd.concat([credit,pd.get_dummies(credit['purchase_type'])],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit.drop(['purchase_type','CUST_ID'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit['limit_usage_ratatio']=credit.BALANCE /credit.CREDIT_LIMIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit['min_payment_ratio']= credit.PAYMENTS/credit.MINIMUM_PAYMENTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
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       "      <th>CASH_ADVANCE</th>\n",
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       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
       "      <th>...</th>\n",
       "      <th>PRC_FULL_PAYMENT</th>\n",
       "      <th>TENURE</th>\n",
       "      <th>monthly_avg_purchases</th>\n",
       "      <th>monthly_cash_advance</th>\n",
       "      <th>both_one_ins</th>\n",
       "      <th>installment_withou_one</th>\n",
       "      <th>none_one_ins</th>\n",
       "      <th>one_off</th>\n",
       "      <th>limit_usage_ratatio</th>\n",
       "      <th>min_payment_ratio</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>95.40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>7.950000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.040901</td>\n",
       "      <td>1.446508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3202.467416</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6442.945483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>536.912124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.457495</td>\n",
       "      <td>3.826241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2495.148862</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>773.17</td>\n",
       "      <td>773.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>64.430833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.332687</td>\n",
       "      <td>0.991682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1666.670542</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>205.788017</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>124.916667</td>\n",
       "      <td>17.149001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.222223</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>817.714335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.681429</td>\n",
       "      <td>2.771075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8945</th>\n",
       "      <td>28.493517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>291.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>291.12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>48.520000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.028494</td>\n",
       "      <td>6.660231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8946</th>\n",
       "      <td>19.183215</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019183</td>\n",
       "      <td>0.319208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8947</th>\n",
       "      <td>23.398673</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>144.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>144.40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6</td>\n",
       "      <td>24.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.023399</td>\n",
       "      <td>0.986076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8948</th>\n",
       "      <td>13.457564</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.558778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.093130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.026915</td>\n",
       "      <td>0.942505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8949</th>\n",
       "      <td>372.708075</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1093.25</td>\n",
       "      <td>1093.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>127.040008</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.208333</td>\n",
       "      <td>21.173335</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.310590</td>\n",
       "      <td>0.715439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8950 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0       40.900749           0.818182      95.40              0.00   \n",
       "1     3202.467416           0.909091       0.00              0.00   \n",
       "2     2495.148862           1.000000     773.17            773.17   \n",
       "3     1666.670542           0.636364    1499.00           1499.00   \n",
       "4      817.714335           1.000000      16.00             16.00   \n",
       "...           ...                ...        ...               ...   \n",
       "8945    28.493517           1.000000     291.12              0.00   \n",
       "8946    19.183215           1.000000     300.00              0.00   \n",
       "8947    23.398673           0.833333     144.40              0.00   \n",
       "8948    13.457564           0.833333       0.00              0.00   \n",
       "8949   372.708075           0.666667    1093.25           1093.25   \n",
       "\n",
       "      INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "0                      95.40      0.000000             0.166667   \n",
       "1                       0.00   6442.945483             0.000000   \n",
       "2                       0.00      0.000000             1.000000   \n",
       "3                       0.00    205.788017             0.083333   \n",
       "4                       0.00      0.000000             0.083333   \n",
       "...                      ...           ...                  ...   \n",
       "8945                  291.12      0.000000             1.000000   \n",
       "8946                  300.00      0.000000             1.000000   \n",
       "8947                  144.40      0.000000             0.833333   \n",
       "8948                    0.00     36.558778             0.000000   \n",
       "8949                    0.00    127.040008             0.666667   \n",
       "\n",
       "      ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "0                       0.000000                          0.083333   \n",
       "1                       0.000000                          0.000000   \n",
       "2                       1.000000                          0.000000   \n",
       "3                       0.083333                          0.000000   \n",
       "4                       0.083333                          0.000000   \n",
       "...                          ...                               ...   \n",
       "8945                    0.000000                          0.833333   \n",
       "8946                    0.000000                          0.833333   \n",
       "8947                    0.000000                          0.666667   \n",
       "8948                    0.000000                          0.000000   \n",
       "8949                    0.666667                          0.000000   \n",
       "\n",
       "      CASH_ADVANCE_FREQUENCY  ...  PRC_FULL_PAYMENT  TENURE  \\\n",
       "0                   0.000000  ...          0.000000      12   \n",
       "1                   0.250000  ...          0.222222      12   \n",
       "2                   0.000000  ...          0.000000      12   \n",
       "3                   0.083333  ...          0.000000      12   \n",
       "4                   0.000000  ...          0.000000      12   \n",
       "...                      ...  ...               ...     ...   \n",
       "8945                0.000000  ...          0.500000       6   \n",
       "8946                0.000000  ...          0.000000       6   \n",
       "8947                0.000000  ...          0.250000       6   \n",
       "8948                0.166667  ...          0.250000       6   \n",
       "8949                0.333333  ...          0.000000       6   \n",
       "\n",
       "      monthly_avg_purchases  monthly_cash_advance  both_one_ins  \\\n",
       "0                  7.950000              0.000000             0   \n",
       "1                  0.000000            536.912124             0   \n",
       "2                 64.430833              0.000000             0   \n",
       "3                124.916667             17.149001             0   \n",
       "4                  1.333333              0.000000             0   \n",
       "...                     ...                   ...           ...   \n",
       "8945              48.520000              0.000000             0   \n",
       "8946              50.000000              0.000000             0   \n",
       "8947              24.066667              0.000000             0   \n",
       "8948               0.000000              6.093130             0   \n",
       "8949             182.208333             21.173335             0   \n",
       "\n",
       "      installment_withou_one  none_one_ins  one_off  limit_usage_ratatio  \\\n",
       "0                          1             0        0             0.040901   \n",
       "1                          0             1        0             0.457495   \n",
       "2                          0             0        1             0.332687   \n",
       "3                          0             0        1             0.222223   \n",
       "4                          0             0        1             0.681429   \n",
       "...                      ...           ...      ...                  ...   \n",
       "8945                       1             0        0             0.028494   \n",
       "8946                       1             0        0             0.019183   \n",
       "8947                       1             0        0             0.023399   \n",
       "8948                       0             1        0             0.026915   \n",
       "8949                       0             0        1             0.310590   \n",
       "\n",
       "      min_payment_ratio  \n",
       "0              1.446508  \n",
       "1              3.826241  \n",
       "2              0.991682  \n",
       "3              0.000000  \n",
       "4              2.771075  \n",
       "...                 ...  \n",
       "8945           6.660231  \n",
       "8946           0.319208  \n",
       "8947           0.986076  \n",
       "8948           0.942505  \n",
       "8949           0.715439  \n",
       "\n",
       "[8950 rows x 25 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr_log=credit[credit.columns.difference(['both_one_ins','installment_withou_one','none_one_ins','one_off'])].applymap(lambda x : np.log(x +1)).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE</th>\n",
       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_TRX</th>\n",
       "      <th>CREDIT_LIMIT</th>\n",
       "      <th>INSTALLMENTS_PURCHASES</th>\n",
       "      <th>MINIMUM_PAYMENTS</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
       "      <th>...</th>\n",
       "      <th>PRC_FULL_PAYMENT</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>PURCHASES_FREQUENCY</th>\n",
       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
       "      <th>PURCHASES_TRX</th>\n",
       "      <th>TENURE</th>\n",
       "      <th>limit_usage_ratatio</th>\n",
       "      <th>min_payment_ratio</th>\n",
       "      <th>monthly_avg_purchases</th>\n",
       "      <th>monthly_cash_advance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.735304</td>\n",
       "      <td>0.597837</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>4.568506</td>\n",
       "      <td>4.945277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.568506</td>\n",
       "      <td>0.154151</td>\n",
       "      <td>0.080042</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>0.040086</td>\n",
       "      <td>0.894662</td>\n",
       "      <td>2.191654</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.071989</td>\n",
       "      <td>0.646627</td>\n",
       "      <td>8.770896</td>\n",
       "      <td>0.223144</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>8.853808</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.978531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200671</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>0.376719</td>\n",
       "      <td>1.574068</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.287695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.822504</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.922792</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.442994</td>\n",
       "      <td>6.651791</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.651791</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>0.287197</td>\n",
       "      <td>0.688979</td>\n",
       "      <td>4.180994</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.419183</td>\n",
       "      <td>0.492477</td>\n",
       "      <td>5.331694</td>\n",
       "      <td>0.080042</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>8.922792</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.762968</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>0.080042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.313220</td>\n",
       "      <td>0.080042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>0.200671</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.835620</td>\n",
       "      <td>2.898616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.707735</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.090910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.504483</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>0.080042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>0.080042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>0.519644</td>\n",
       "      <td>1.327360</td>\n",
       "      <td>0.847298</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8945</th>\n",
       "      <td>3.384170</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>5.677165</td>\n",
       "      <td>3.909748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>5.677165</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>0.028095</td>\n",
       "      <td>2.036042</td>\n",
       "      <td>3.902377</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8946</th>\n",
       "      <td>3.004851</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>5.707110</td>\n",
       "      <td>6.762968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.707110</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>0.019002</td>\n",
       "      <td>0.277031</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8947</th>\n",
       "      <td>3.194529</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>4.979489</td>\n",
       "      <td>4.423869</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.223144</td>\n",
       "      <td>4.979489</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>0.510826</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>0.023129</td>\n",
       "      <td>0.686161</td>\n",
       "      <td>3.221539</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8948</th>\n",
       "      <td>2.671218</td>\n",
       "      <td>0.606136</td>\n",
       "      <td>3.625907</td>\n",
       "      <td>0.154151</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.216606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.038755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.223144</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>0.026559</td>\n",
       "      <td>0.663978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.959127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8949</th>\n",
       "      <td>5.923475</td>\n",
       "      <td>0.510826</td>\n",
       "      <td>4.852343</td>\n",
       "      <td>0.287682</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.090910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.491878</td>\n",
       "      <td>6.997824</td>\n",
       "      <td>0.510826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.997824</td>\n",
       "      <td>0.510826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.178054</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>0.270477</td>\n",
       "      <td>0.539669</td>\n",
       "      <td>5.210624</td>\n",
       "      <td>3.098890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8950 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       BALANCE  BALANCE_FREQUENCY  CASH_ADVANCE  CASH_ADVANCE_FREQUENCY  \\\n",
       "0     3.735304           0.597837      0.000000                0.000000   \n",
       "1     8.071989           0.646627      8.770896                0.223144   \n",
       "2     7.822504           0.693147      0.000000                0.000000   \n",
       "3     7.419183           0.492477      5.331694                0.080042   \n",
       "4     6.707735           0.693147      0.000000                0.000000   \n",
       "...        ...                ...           ...                     ...   \n",
       "8945  3.384170           0.693147      0.000000                0.000000   \n",
       "8946  3.004851           0.693147      0.000000                0.000000   \n",
       "8947  3.194529           0.606136      0.000000                0.000000   \n",
       "8948  2.671218           0.606136      3.625907                0.154151   \n",
       "8949  5.923475           0.510826      4.852343                0.287682   \n",
       "\n",
       "      CASH_ADVANCE_TRX  CREDIT_LIMIT  INSTALLMENTS_PURCHASES  \\\n",
       "0             0.000000      6.908755                4.568506   \n",
       "1             1.609438      8.853808                0.000000   \n",
       "2             0.000000      8.922792                0.000000   \n",
       "3             0.693147      8.922792                0.000000   \n",
       "4             0.000000      7.090910                0.000000   \n",
       "...                ...           ...                     ...   \n",
       "8945          0.000000      6.908755                5.677165   \n",
       "8946          0.000000      6.908755                5.707110   \n",
       "8947          0.000000      6.908755                4.979489   \n",
       "8948          1.098612      6.216606                0.000000   \n",
       "8949          1.098612      7.090910                0.000000   \n",
       "\n",
       "      MINIMUM_PAYMENTS  ONEOFF_PURCHASES  ONEOFF_PURCHASES_FREQUENCY  ...  \\\n",
       "0             4.945277          0.000000                    0.000000  ...   \n",
       "1             6.978531          0.000000                    0.000000  ...   \n",
       "2             6.442994          6.651791                    0.693147  ...   \n",
       "3             6.762968          7.313220                    0.080042  ...   \n",
       "4             5.504483          2.833213                    0.080042  ...   \n",
       "...                ...               ...                         ...  ...   \n",
       "8945          3.909748          0.000000                    0.000000  ...   \n",
       "8946          6.762968          0.000000                    0.000000  ...   \n",
       "8947          4.423869          0.000000                    0.000000  ...   \n",
       "8948          4.038755          0.000000                    0.000000  ...   \n",
       "8949          4.491878          6.997824                    0.510826  ...   \n",
       "\n",
       "      PRC_FULL_PAYMENT  PURCHASES  PURCHASES_FREQUENCY  \\\n",
       "0             0.000000   4.568506             0.154151   \n",
       "1             0.200671   0.000000             0.000000   \n",
       "2             0.000000   6.651791             0.693147   \n",
       "3             0.000000   7.313220             0.080042   \n",
       "4             0.000000   2.833213             0.080042   \n",
       "...                ...        ...                  ...   \n",
       "8945          0.405465   5.677165             0.693147   \n",
       "8946          0.000000   5.707110             0.693147   \n",
       "8947          0.223144   4.979489             0.606136   \n",
       "8948          0.223144   0.000000             0.000000   \n",
       "8949          0.000000   6.997824             0.510826   \n",
       "\n",
       "      PURCHASES_INSTALLMENTS_FREQUENCY  PURCHASES_TRX    TENURE  \\\n",
       "0                             0.080042       1.098612  2.564949   \n",
       "1                             0.000000       0.000000  2.564949   \n",
       "2                             0.000000       2.564949  2.564949   \n",
       "3                             0.000000       0.693147  2.564949   \n",
       "4                             0.000000       0.693147  2.564949   \n",
       "...                                ...            ...       ...   \n",
       "8945                          0.606136       1.945910  1.945910   \n",
       "8946                          0.606136       1.945910  1.945910   \n",
       "8947                          0.510826       1.791759  1.945910   \n",
       "8948                          0.000000       0.000000  1.945910   \n",
       "8949                          0.000000       3.178054  1.945910   \n",
       "\n",
       "      limit_usage_ratatio  min_payment_ratio  monthly_avg_purchases  \\\n",
       "0                0.040086           0.894662               2.191654   \n",
       "1                0.376719           1.574068               0.000000   \n",
       "2                0.287197           0.688979               4.180994   \n",
       "3                0.200671           0.000000               4.835620   \n",
       "4                0.519644           1.327360               0.847298   \n",
       "...                   ...                ...                    ...   \n",
       "8945             0.028095           2.036042               3.902377   \n",
       "8946             0.019002           0.277031               3.931826   \n",
       "8947             0.023129           0.686161               3.221539   \n",
       "8948             0.026559           0.663978               0.000000   \n",
       "8949             0.270477           0.539669               5.210624   \n",
       "\n",
       "      monthly_cash_advance  \n",
       "0                 0.000000  \n",
       "1                 6.287695  \n",
       "2                 0.000000  \n",
       "3                 2.898616  \n",
       "4                 0.000000  \n",
       "...                    ...  \n",
       "8945              0.000000  \n",
       "8946              0.000000  \n",
       "8947              0.000000  \n",
       "8948              1.959127  \n",
       "8949              3.098890  \n",
       "\n",
       "[8950 rows x 21 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr_log=pd.concat([cr_log ,credit.loc[:,['both_one_ins','installment_withou_one','none_one_ins','one_off']]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ca95c18910>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xoxv8teVoQgTmB4FvFRKwwCXAO4RYGLcSvvzbAMQYQN2BybHKMOBnQI/cbE2GPxIMtHx+DlxoZi/FNqvN7Mq1H1ZxzKwKmEb0KGlm1cBPCLE+/kDYeT+3uAbHcZx1TE1N+lWBuPFSWfQBni1SloslMQnYIXoyhBBh+WxJT0q6QFKvTJ122aUV4PyEPhwF3BzbKjRTkuU5YEcz+wB4huAyGsKsy81mZpK2Brqa2TPALVF/lluA/pK2z8uv71nAmkETHys5qnqQtDnQi4xTJTN7AngR2J9gwBSru9rt9rVTXm1INxzHcdLxmRenQhgK3GRmtcDtRNf7ZjYN+AIwCugETIlLPwBV2aUV4Lz6GpC0B/Cemb0BPEIwKjavr0rmPrt0lF0yGkowUCBEgs43iGpi339RX98KkF02Glxm3Rz7SJpBmEG6x8zeyRXEfUMDgFZA52IKzGyMmQ0wswEn7NGrmJjjOE7j0ohRpTdE3HipLGYDu+dnSupLmBl4SNI8gkGw2ggws6VmdruZ/QS4AfjGWrY/DNgxtvEasAlweD3yuxFmJwDuBPaT1B9oZ2bPZXQOjzrvBnbNmx2CsEy1L/D5TF7BZ9HITDKzvsAuwMmS+mXKfkN4lhcClzRxPxzHccrDZ16cDYhHgTaSfpjLiLMhlwEjzaxnvLoDW0naJm4q3TzKtgZ6A2+U27CkFoTZnL65dggh0essHSnwP4T9NvdDMKCAiYRlrPFRbgegg5ltldH5O/I295rZKoKBcHomexTwy8xJqxaSzih3XCmY2SuxX2fHtnYBvgn8nhDMbBtJBzRF247jOGuFWfpVgbiflwoi7hE5DLhU0jnAcmAeMAg4OU/8DoIRsBC4Kp5UagHcy5qhylPZF1hgZgsyeY8DvSV1i+lRkv4XaE84PTTYzFZm5McTlrRyxsmw2M8stxGWj36bl/93wmZkAMxshqTTgfEKIdgtji3HCEnfy6S/bWbzkkZamKuBMyVtC1wFjDCz5QCSfgJcL6lf3njXYMWzbxUrWoPFH2yc3Kll3744SU5K/4Aqx/HcFyZfkSw7fK9Tk+SqXvyktFDkm/3TZVPZaLP0j8UhC9Lb79JtSbLsJ4vbJMktq2qVrHPL7unt16xK+117y9g0J4kAgzsvSpY9cJP0A5ELvpsW33XfHYr+adbhg9d6JMs+9o+012ozrfUhz7WjQmdUUnHjpcKI0TmPTJDLzkJcX0SmY156LHmunTNlE4E98/JqCLMrAMMT+nQHmX0wZjaygMwMwuwQcSYml7+CcEIpK3sPcE8BHSOBOrpL9G1QXnoiYaYol64injYCvpInOzXXZ8dxnA2CRg4PsKHhxovjOI7jNDOstjKXg1Jx48Wpg6RziaeVMvzLzNLmZzdAooff3+dlv25mhxWSdxzHqWh82cj5rBGNlIo1VAphZg8Qwrk7juM0fyr0CHQqbrw4juM4TnPDl40cx3Ecx6koqn3DruM4juM4lUSF+m9JxY0Xx1lHPPJUmu+IPh0/StZ52fJ0nzCdEv/cV5H+oZfquwVg+ydHJ8k9snOhOJyF2aHrB8myG7VO3APwTrqfk213TX+t2n8/3Y9h527bJsmp8zbJOmvffiVZ9vUT01xBfantRzyysr4IIZ/SslX6HoxNTz8wWfaiM6Ynyf3+tPyYr8X53qvzkmUPGZfuP6e10r9yG7xBzzfsOo5T6aQaLk664eKQbLg45RkujUIz3/Pif6WO4ziO09xo5MCMkg6S9LKkOdHDe375IEmLJU2L13mpddcGN14SkbQ0/t9Tkkn6aaZstKTh8X5PSU/HF+9FSSMlfT/zgq6UNDPeXxzrjJC0XNKmGZ2DJNXxHitpoqQBeXmDYp9OzOTtFvPOjOmxkl7P9OOJmD9cUm0M7pirOyuOMzeONyW9l6nbU9IJcRwzovyh9Ty7bNvPSdqr0Fii3lmZMS2W9LyklyT9MSPXUdJfJb0mabakxyV9Kfs6ZWSHSxqdlzdd0vi8vDqvW6Z+duzTJPWOsZQuj2OfKWlKDB3gOI6z/qm19KsEkloCVwAHE7yJD5NUyKv4JDPrF6/zy6xbFj6XvHa8C5wm6a8FYtn8AzjSzKbHF20HM3sBuA5AIXryYDN7P1NnGDAFOIwi7vkTmAkcRYgBBCF+UP5i8FlmdmuBuvOBc2P91ZhZziAYDgwws1NjukeU729miyV1BDqX6N9ZZnarpAOBvwJ9S8hD+EMYIqkd8LykO8zsP8A1wOtALzOrlfQFYKcEfUjaiWC07yupg5nlgtPUed0y1W7OjT2jZxghXEHf2IceQOMH2nEcx1kLrLqmMdUNBOaY2VwASTcRAvO+0MR1i+IzL2vHe8AjwPEFyrYkBEPEzGqi4VIUSdsBHQlBB+tEaC6DN4G2krpIEnAQcF9i3XuAnRWiPKewJbAEWAohYrSZvZ5Y93Fg+0RZov4qYBohUvZ2wJeAX5mF+U4zm2tm99anI8PRwD+BB4FvZfLLet0IMZ0WZvow38zq7N6UdJKkqZKmPrxsTmIXHcdxGkjjLhttBWQjy87n01hvWfaKM9v3Sdq5zLpl4cbL2nMx8LP4Kz3LJcDLku6Q9CNJbUvoGUaItjwJ2EHSlg3o060Et/5fBp4DVuSVj8osfYzL5NcCfwBSj3lMBxYBr0u6TtIhZfTxEMIsUTKSNgd6EQyfnYFpMShkIdpll3iA8/PKjwJuJjzzrLFY3+t2VN6yUTvgFuCQmP6TpN0KdcbMxpjZADMbsH/7smw2x3GctaeMZaPsj6x4nZSnrVCo+fz1pueAbcxsV+AvwJ1l1C0bN17WkjjT8Azhl3w2/3xgAOGX/dHA/SVUDQVuir/gb6duTKFyuCXWzxlE+ZyVWY88Jq/sRmDPlH0b0XA4CDgCeAW4JLdHpB5GRWPiJCC3N6fQGzibt4+kGcA7wD1m9k6pvgFVmTH2A7KbxvYA3jOzNwgzZ/2jYVTqdbs5q9PMqsxsPmFp6RcE4+8RSfsl9M9xHKfpqa1NvrI/suI1Jk/bfGDrTLoH8HZWwMw+NrPcbPwEoJWkLVLqrg1uvDSMi4CzyXuOZvaamV0F7AfsKulzhSrHTbK9gIfiXpihNGDpKH65rwIOIHw5l1O3GvgTYTwp8mZmz5jZ7wj9PrxElZzhdICZzYp5HwDZs5adgOxeoElm1hfYBThZUj9gNuGZrs17dxiwY3zWrwGbZPud+rpl5FeY2X1mdhbhvfDtteiT4zhO49OIG3YJezJ7SdpWUmvCZ/7dWQFJXeOWBSQNJHwvfpBSd23wDbsNwMxekvQCMIQwC4OkbwITzMwIhkkN8N8iKoYBI6MBQKz/uqR0z1N1OQ/Y0sxq4vuoHMYCPwfq9XwmqTvQ1cyei1n9gDfKbQyYCHxP0sPxeR0PPJYvZGavSPodcLaZDZM0FfiNpPPMzCT1Anqb2V319LkFYVaqr5ktiHmDCXuNrinzdUNSf+AdM3s76u4LzKhvsEP+WnBlqQ4rxqdu34Hfb542+1pbtSpZZ4sOrZNlq15M36Oc6nxuv9kXJeuc3u+MZNma2jR7t2vXj5N1/np212TZb5wyq7RQpEazk+RaleFFtb3SN3B275TmWv4Ho9IPjRx++ofJsj87Oc3xHMCFh6e9Xi+dvzxZ57SadOePf91kWZLcGx9ukqyzUWjEwIxmVi3pVILvvJbAtWY2W9KPY/nVhJn4kyVVA1XA0Ph5WrBuQ/vkxkvDuRB4PpM+lrCMsgyoBo6pZ3/GUMLxsSx3xPyngf0kzc+U5ZaU7pWU+zZ6knAMDQAze6Kevo6S9KtMemC20MxWSrocuKweHQCtgD9GI2Y5YQPzj0vUKcQYYEdguiQDphKWYQpxNXBmXNb6AWGWaE58zh8AZ5Voa19gQc5wiTwO9JbUjSKvWzQAj5L0lUy9nxBmbf4mqU3MewZIcyHrOI7TxDTyaaPcUtCEvLyrM/ejKfIZWKhuQ3HjJREz6xj/nwf0yeRPJ7NsZGZDS+jpmbmvs7/EzLI/JdsVUDGoiOqJBXSNzNwPL1JvLJnj2WZ2OXB5np58mTeArxXRV4dibcdj5gX9y5vZRDJjiieOsjvUf1ikXse89Fg+7fueeWU1hFNDEAzGQvqy9fMptZ/JcRxn/dDMPey68eI4juM4zQ03XhwnDUlXAHvnZV9mZtetj/44juN8ZmnEPS8bIm68OI2GmZ2yvvvgOI7j4DMvjuM4juNUFlbtMy+O4ziO41QStW68OI7jOI5TSfiykeM4jcF756fFyVy5LN1J3MNL6nUCvJqNy3D5UFOGb8Nv9k93UrdD1w+S5MpxPLfrtD8ny9bMmZIkt/TXVybrPH5RflD54vS5Yvdk2eqHJyfJvfVQfmi14vQ8o2Tkj9XM/VNVktyjJ6eHKbuud/p7ZZMRyZ4YuP4n05Lkjhu1XbLOs37+VLLsbZ+kfY2OP2odB51348VxHMdxnErCyvC+XIl4bKMKQ1IPSXdJelXSa5Iuk9Ra0iBJlo3wLOkeSYPi/URJL2ciI9+akTtJ0kvxeibrTbZAvSNifk1epOWeRfo7SNJiSc9LelHSr2P+cEmj82QnShoQ7+dJmilphqT/y4ZMkHScpFmSZkt6QdKZMX9srn8Z2aV56RGSlkvaNJPXXtK42N4sSZMldSwyznNi/pA4pumxDz9KePkcx3HWDY0b22iDw2deKogY9Op24CozO1RSS4KL/QuBewnRO88F/l1ExTFmNjVP5xDgR8BXzOz9GLPnTkkDM1Gc69QjRm9O7PokMxsiqQMwTdI9ifUGxz79hhCD6IeSDgZOBw6McYXaElz7pzKMECjsMD71nHsasMjMdgGQtAMhwCUUGKekVoTnPtDM5scQAT3L6IPjOE6T0txPG/nMS2XxNWB5zulbdG8/AjgBaA9MBxZLOqAMnWcTIj6/H3U+B/wDaHSfLWb2CfAskL74HHiST0MD/AI408zejjqXm9nfUpRI2g7oSDCEstG7uwGrYx6Z2ctmtqIeVRsTDP8PovwKM3s5cSyO4zhNTzOfeXHjpbLYmfDlvxoz+xh4E9g+Zl1A+HIuxLjM8seoYjoJARJ3LlIvt0O0XSbvjpTOx7p7AuVGFD0IuDPe9ynQ3yyjsss8eWXDgPHAJGAHSVvG/GuBsyU9KekChSjVObLjnCbpKDP7kBDS/Q1J4yUdoxBZug5xSW6qpKk3vregkIjjOE7jU1vGVYH4slFlIaCQmbw638wmSULSPgXkCi3/pLTT0GWjfSQ9T/gzuTiGUh9QRDbb7mOSugDvUtwgy+csM8vu58nueRkKHGZmtZJuJ0TpvsLMpkn6AnAgsD8wRdJeZvYiRcZpZj+QtEuUPxM4ABheQG4MYYmJNwfsV5k/cRzHqTisQmdUUvGZl8piNrDGl76kTYCtgdcy2RcS9r6k8AKQf4azf8xvLCaZ2W5mtnsmhPoHwOZ5cp2A9zPpwcA2hHGfH/NmF+hvSST1BXoBD0maRzBkVi8dmdlSM7vdzH4C3AB8o5ROM5tpZpcQDJfDy+2T4zhOk+HLRs4GxCNAe0nHAcQNu38ibDxdlhMyswcJhsGuCTr/APw+txwkqR9hBiHd2cXaMQXYW1LX2O4AoA3wVlbIzKoIG3SPk9QJ+B3wh0y9NpL+J6G9YcBIM+sZr+7AVpK2kbS3pM2jvtZAb+CNYookdVQ8xRXpV5+84zjOOseXjZwNBTMzSYcBV0r6X4LxOQH4JbBXnviFwF15eeMk5bxPvW9m+5vZ3ZK2Ap6QZMAS4HtmtrDpRgJmtkjSacCEuF9kKTDMrG4oVDNbKGk8cIqZ/TYuJT0cT18ZYc9KKYYCB+fl3RHzFwJXRX0tCCe3bosy7fL2ztxPeLY/l/RXoAr4hAJLRvnUrEr7rdB241WlhSL6OE2uRcHVxsLUUIaXujLYqHXap2RNbfpvqlTHcwAtt98jTbCMn3SdNl9WWihii94pLZTrQo8tkuTatHsvWafat0+Wra1New9UK/290jLd9yJ88G6yaOrLVc7z79ly42TZVok9WDYjfUwQTgU0BKuuzBmVVNx4qTDM7C3gkAJFE+OVk7sbPv0WMrNB9ei8CriqSFnBembWMaG7mNka/coru4u6BlaurGde+qeZ++uA65OW47EAACAASURBVArUGV6sn2ZWx72omWVduV5fpB/FXJiWXFZyHMdZXzT3PS9uvDiO4zhOc6NCl4NS8T0vTqMg6et5R4qTj1A7juM4jYvVpl8pSDpIwdv6nJyn8bzyYxQ8os+Q9ISkXTNlOY/p0ySlnHgtic+8OI2CmT0APLC+++E4juPQqDMv8XDIFYSTlfMJ7iTuNrPsqdTXga+a2UfRE/oY4EuZ8sE5Z6iNgRsvjuM4jtPMsOpGVTcQmGNmcwEk3QQcSsalhpk9kZF/CujRqD3Iw5eNHMdxHKeZ0cjLRluxphuL+XwasqUQJwL3ZbsDPCjpWUknlTuWQvjMi+M4juM0M1L3skAIYwJkjYox0Tv4apFCTRTRNZhgvHwlk713DKS7JcFR6Etm9nh6D+vixovjrCM679cuSa564ZJknQfXpLnjadGyaY5NbrRZ+kdIzVtpE71duyY6rwGW/roMX4qJ88yb/rPOKfyiLB50crLsRzekO61eVVXshP6a1NSkyQEsueHpZNktuqc9rI0/WZ6ss/3Xv5gs+8lN6X09eKu0Z/DJvTXJOveo3jpZdvtVaX6Zpr7YLVknwDfLkq5LOcZLNoxJEeYTPLnn6AG8nS8UPZlfAxxsZh9k9OcC6b4bD3IMBBpkvPiykeM4juM0N0zpV2mmAL0kbRu9kA8lBKddjaTPA7cDx5rZK5n8DpI2zt0TYsjNaujw3Hhx1juSauIRulmS/iWpfczfSNL7kn4X0wfGyM+K6Zax3pcljZRkkrbP6B0R8wbEdPa43jRJl8f8sZIWSGoT01tE2V0ysh9Kej3ePyyphaTLY59nSpoiqY4jPMdxnPVBY+55MbNq4FTCidIXgVtigN0fS/pxFDsP+BzBA3z2SHQXYLKk6cAzwL1mdn9Dx+fLRs6GwOrIzZLGAT8G/kyw0F8GjpT0SzN7UNIJhPXUa4CfAlPM7AlJBwIzCb8ILoh6j6BugMlix/VqgBPIeBo2s5mEuEVIGgvck4tYLWkY0B3oG6NU9yCECXAcx1nv1FY3bpgPM5tACEeTzbs6c/8D4AcF6s0lLc5eWfjMi7OhMQnIzZ4MAy4D3gT2jHkjgF9I2pnwS+DsTN07Ccf3kPQFYDGQGvzlUmCEpFSDvhuwMBeLyczmm9lHiXUdx3GaFDMlX5WIGy/OBkM0HA4GZkpqB+wH3AOMJxgyxICRlwJPAheY2YcZFR8Db0nqE+VvLtDMY5mloBGZ/DeBycCxid29BTgk6vmTpN2KjOkkSVMlTb12hgeedhxn3dDYHnY3NNx4cTYEcpGbpxKMiL8DQ4DHzGwZIcLzYdHLIwRPjy3NbGwBXTcRlo6+TYganc9gM+sXr0vyyi4CziLh78LM5gM7AL8g+LJ8RNJ+BeTGmNkAMxtwQt9tSql1HMdpFKxWyVcl4ntenA2B1XtecsQ9JXtLmhezPgcMBh6Oe0yKnf39NzAKmGpmH8e9vUmY2ZxoRB2ZKL+C4IjpPkmLCAbTI8kNOo7jNBHWvINKu/HibHhI2oTg4GjraCAg6fuEpaCH66trZlWSzgZeqU+uHi4E7k3oY3/gneh4qQXQF5ixlm06juM0KpU6o5KKGy/Ohsh3gEdzhkvkLuAPktrk5dfBzG6qp/gxSTlvVTPM7Li8urMlPQf0L9HHLYG/5Y5XE44Ajq6vwum3ty6hMnDI8nRnVk+1TZPbxNJXiFcUdpxZkCEL0g9Ybbtr2n7mX8/umqzz+EUrk2U7bb4sSa4cx3Ofn3hVaaHIf4d9P1m2fY+0jQg1H6ePv5xNAq/M2iJJbnTbev8U12DswkaLybcGVy9Ke78c9W6688chW9fxv1aUO+Z3T5Lrvmrdbi6prXHjxXGaFDPrmJceC4zNy/sQ6FxPnZFFdA/K3PcsIjM8L/2dBJn7gQb7KnAcx2kKfObFcRzHcZyKolKPQKfixovjOI7jNDMq9Qh0Km68OI7jOE4zo9ZnXhzHcRzHqSRqa5q3Gzc3XhzHcRynmeF+XhzHcRzHqSj8tJHjOI7jOBWF73lxHKdROHR5qyS57TosTtb5z+qWpYWA7mqXrLOK9GMKXbqlO/5q//0DkuS+ccqsZJ19rtg9WdYWvZMk99ENLyTrLMfx3Gbjr0uWrZkzJUnOZj2drLPF3ocky3b51qgkuYErOyXrXPbcW8mym984Jln2tYFnlxYCtjulc2mhyLvjlifLnr7osSS5V3fqnayzMWjuR6Wb944eZ4NAUk2MvjxL0r8ktZfUU9KsPLmRks6M92MlvR7rTc8GPZQ0UNLjkl6W9JKka6LO4ZJG5+mcKGlAJr2bJJP09Ty5cyXNljQjtvmlTP2XM5Gob435O8SyaZJelJT+aes4jtPEmKVflYjPvDjrgtWBFyWNA34M3J5Q7ywzu1XSYGAM0EtSF+BfwFAze1Ih8uLhwMaJfRkGTI7/PxD7tBchinV/M1shaQsg68v/GDObmqfncuASM7sr6tglsX3HcZwmp6a2ec9NuPHirGsmEYIYlsOTwFbx/hTgH2b2JICZGZCbDalXSTR0jgAOACZJamtmy4FuwPu5mElmlhKEpRswP5cws5nlDMhxHKcpqdQZlVSat2nmbFBI2gg4GCj3i/4g4M543wd4th7ZozJLPNOAAZmyvYHXzew1YCLwjZj/ILC1pFckXSnpq3k6x2V05jYDXAI8Kuk+SSMkbVaoM5JOkjRV0tT7l81JHrDjOE5DqDUlX5WIGy/OuqBdNCSmAm8Cf4eioYuz+aMkzQVuAC5KbOtmM+uXu2KbOYYBuYjTN8U0ZrYU2B04CXgPuFnS8Ey9YzI6z4p1rgN2IixhDQKeykSY/nQwZmPMbICZDTio/faJQ3Acx2kYZkq+KhE3Xpx1QVXmy/+nZrYS+ADYPE+uE5BdsjkL2B74FfCPmDebYGiUhaSWhL0x50maB/wFOFjSxgBmVmNmE83s18CpUbZezOxtM7vWzA4FqgmzQo7jOOudxp55kXRQPLwwR9I5Bcol6fJYPkNS/9S6a4MbL856Ic52LMydIpLUibA8NDlPrha4DGgRTwiNBo7PnQaKdb8nqWuJJvcHppvZ1mbW08y2AW4Dvh1PDvXKyPYD3qhPWfxjbBXvuwKfAxaUHLjjOM46wMq4ShF//F1BWPbvDQyTlH/2+2CgV7xOAq4qo27Z+IZdZ31yHHCFpD/F9G/ifpQ1MDOTdAHwczPbT9JQ4I+StgRqgccpfXppGHBHXt5twMnAC8Bf4r6VamAO4Y8vxzhJVfH+fTPbHzgQuExSziHEWWZWryOR5Ur7rbBZl2VJcgDtFuRPXhWmdRP9TvlkcZ2VsqJ07rZtklyNZifrrH54cmmhSIseWyTJrapK850D0L5Huk+cVN8tAC233yNNZ9sOyTppXYavn0SfRC3LWHFYuTT9uda8Pi1Ztp3SvsasuiZZ56bbrUqW7fX2VqWFgAc+7JKsE8KRzIbQyKeNBgJzzGwugKSbgEMJn505DgWuj4conpK0maRuQM+EumXjxovT5JhZxyL5LwCDi5QNz0vfRjA2iCeN9ilQbWy8svUGxdvh5GFmdwN3x+SXi/RjUJH8M4AzCpU5juOsb9LN6nCwgDV/sI0xs6zvqq2ArJfB+cCXWJNCMlsl1i0bN14cx3Ecp5lhpE+LRUOlPkebhZTlrzgVk0mpWzZuvDiO4zhOM6O2cf28zAe2zqR7AG8nyrROqFs2vmHXcRzHcZoZtSj5SmAKwcP5tpJaA0P5dMk9x93AcfHU0Z7AYjNbmFi3bHzmxXEcx3GaGTVlLBuVwsyqJZ1KCKnSErjWzGZL+nEsvxqYQHD8OQdYBny/vroN7ZMbL47jOI7TzChnz0uSPrMJBAMlm3d15t4I4VuS6jYUN14cx3Ecp5lRzmmjSsSNF8dxHMdpZrjx4jhOo5D6YbLik/Q/y05qnSS3iaXvzV9ZIjp3lmVVac7MANR5myS5VmWEw33roXTHZ23avZckV1NThjO1j1cmy9qsp9P1Jjqfa9kj3VFp9cxHk2VbtEh7DTqU8Q254pP094rNTnfot1nq19iK9Neq1RfSnD8CfHFKQTdWdXhES5J1QsOd1DX2stGGhhsvjuM4jtPMqG3etkvpo9KSaiRNkzRL0r8ktZfUU9KsPLmRks6M92MlvR7rTc/Fr4llAyU9HoM0vSTpmqhzuKTReTonShqQSe8myWKMm6zcuZJmx2BQ03Jxb2L9l2PeNEm3xvwdYtk0SS9KKuqcR9IgSffE++GSaiX1zZTPktQz3p8gaWbsxyxJh0q6IrbzgqSqTF+OiHXukvRksWeZl7+0QN7I+Ey2z+SNiHkDYnpe7Feu7cszr9MCxWjIkraIsrtkZD/MvJYPS2qhEHxrVtQ5RVJRv+8F2v5yfP/knsULkq7Xp3GCBklanJGfJmn/WNYjPq9XJc2VNDrT93rfP+U+g4yOL0qaoBBQ7EVJt0j6qqRXJLXLyN2rELbAcRxnvVODkq9KJGXmpcrM+gFIGkeYzSoVRwZCrJdbJQ0meO7rJakL8C9gqJk9KUmE6L0bJ/Z3GCFw3zDCsSsk7QUMAfqb2QpJWxCc4uQ4xsym5um5HLjEzO6KOnZJbB+CI55zgaOymZJ6xPz+ZrZYUkegc6aNnsA9uWcZ8zYD+gNLJW1rZq+X0Y8sMwln5y+I6SOoGzdisJm9T11qgBOIQbQAzGwmITghksbGfucMv2FAd6CvmdXGcX9Son9rtB2fxWtm1k8haNdDwJHAuCgyycyGZBXE98rtwFVmdmisNwb4A3BaifYL9iNDnWcQ22wL3AucYWb/jnmDCRGxbye83r+S9G2glZndlNgPx3GcJqW573kp10ndJGD7klJr8iQhtgGEY1T/iLFpsMCtZraolJL45XUEIUbNgfGLBaAbIVjeiqjzfTMr5b2vG8EIIdaZWcZ47gF2lrRDXv6WwBJgadS5NMEYORz4N3ATwfhYW+4kBLpC0heAxUDaAj9cCoyQEqObhWe3MEZ7xszmm9lHZfZ3NWZWAzzDp++RYnwNWG5m12XqjSA4RUpbdC5OsWdwNPBkznCJ7T5mZrOA84HvSuoHXEyRI4KO4zjrg1op+apEko2X+MF+MOFXfjkcRPhyBegDPFuP7FHZ5QJgQKZsb+D1GHV4IsEZDsCDwNZxGv9KSV/N0zkuo3NUzLsEeFTSfXGJZbMyxlNL+LX/y7z86cAi4HVJ10k6JEHXMGB8vIaV0Yd8PgbektQn6rm5gMxjmecwIpP/JmE269jEtm4BDol6/iRpt4Q6ubbr7FiMRuiXgPsz2fvkLRttB+xM3nvHzD4G5pFuUJf7DIq+X81sGXAmIaL1TWb2aiE5SSdJmipp6sPL5iR203Ecp2FYGVclkmK8tIuGxFTCh/zfKT7ebP4oSXOBG4CLEvtzs5n1y12xzRzDCDMUxP+HQZjhAHYnRMR8D7hZ0vBMvWMyOs+Kda4DdiIsYQ0ihO9uk9hHgBuBPbN7PeJMwEGE2aFXgEskjSymIC6hbQ9MNrNXgOpofKwtudmbbwN3FCgfnHkOl+SVXQScRcL7wczmAzsAvyAYco8os6epCLm2s5FEt4vvqw+AN81sRqZsUvZ9EA1WUfh9l/vZkPKebJRnsFpxmJH5L3BlPTJjzGyAmQ3Yv325k5aO4zhrR20ZVyWS8kFdlfnA/6mZrSR84eSfJesEZPcTnEX4cv4V8I+YN5tgaJRF3N9wOHBe3Ez5F+BgSRtDMBzMbKKZ/Ro4NcrWi5m9bWbXmtmhQDXhV3YSZlYN/Ak4Oy/fzOwZM/sdwZCorx9HEZ7h63FMPWnY0tG/CTMHb8YZiWTMbA4wjbDvJEV+hZndF43BiwgGU7m8Fg3U7QmG4LdKyM9mzZk4JG0CdAFeJu09WZQizyDl/VrJf/+O4zRTqqXkqxJZq8CMcbZjYe4Xt6ROhFmHyXlytcBlQAuFE0KjgeMVTwPFut+T1LVEk/sD081sazPraWbbALcB31Y4OdQrI9sPeKM+ZZIO0qenW7oCnwMWlBz4moyN/eoc9XSX1L+MfgwDDorj6Un4klxr48XMqgjG1IVrqeJCwjJIvUjqL6l7vG8B9KXE866PGLjrHMJMTn08ArSXdFxsuyXBgBwdxz4F2Dv3XoqnjNoAb5XRnfxncCPwZUnfzGXE9045G7wdx3HWOc192aghfl6OA66Q9KeY/k2c3l8DMzNJFwA/N7P94nHSP0rakvCL9XFKn14aRt2lkNuAkwmnav4S961UE4JCnZSRGyepKt6/b2b7AwcCl0laHvPPMrN3EsacHddKheO2l8WsVnFc3YHlhCWsgn6G4mmbzwNPZfS9LunjjGH3K0mnZ8p7EL6852dU/TmvT/WddnlMUk28n2Fmx+XVnS3pOcLpp/rYEvhbZpntGYJR2hDuBEZK2iem94lLSjkuiCfXDiO85/6XYDTebGYXxv4vknQaMCEaVUuBYbmNxZGynoGZVUkaAlwq6VJgFTCD9NNNa9C5dlWSXKu2NaWFIksWVyfJtVf675QVZUwkbdk93fFW7duvJMm1V/r4e55R9JR+HdS+fZLckhvSncmV8/Ovxd4p2+AirduVlqE8x3Mb7fK1ZNmOHf6VJliG37WOnZeXFoqU86yWJH78tNgm/b3yzqXPJ8vO+OTdJLlRrRuyK6B8mrufl5LGi5kVPMlhZi8Ag4uUDc9L30YwNognjfYpUG1svLL1BsXb4eRhZnfzaVjtLxfpx6Ai+WcAZxQqKyA7kbBBGDNbo49mdjnh2HWOop8OZjaPuDQV7+ucrjGznOHwNDCyQHk5+zEGZe57FpEZnpf+ToLM/ay5ubZUP+q0nX0WMW3ArhmRTYvoegv4FoCkLwPjJe1uZs/G8ruAu1L7EfOH56W/k5d+iTCrWJBieh3HcdYnzX0t2z3sOhWJmT0BpPmbdxzH+YxRqctBqbjxEol7cn6fl/26mR22PvpTacRj0Pknto618nzoOI7jOI3AZ37Z6LOCmT1A9NrrlE/eMWjHcRxnPZK2G65ycePFcRzHcZoZ5jMvjuM4juNUEr5h13Ecx3GciqK5Gy8Kp1Qdx2lqZvQ8JOmPrVWrdD8nLy5JD8tV1SLtpH3H2vSPvX7d03xcAHyyOC0CR+s26av1y5e3SpatTdzBuEX3pck6X3lti2TZLpuUCr7+KVWJ42rRIv3zu2OHFcmy2zx+VWkh4Nm+Jf1arqZtq/TX1cpY83iheuMkuf4dPkzWufDj9FivSxJj2m5s5e1C+dqiWxq08POXrb+X/Ob46Vs3rHVb0UntzQQv8fOAIy0vWK+krYHrga4Eu2qMmV0Wy0YCP+TTYMK/NLMJpdpdKw+7juNUFqmGi+M4zYNapV8N5BzgETPrRfCEfk4BmWrgZ2a2E7AncIqk3pnySzJhiEoaLuDGi1NBSPpcJir0O5IWZNKWF4n6nFhnoqSpGR0DJE2M98Mljc5rY2IMLYCkeZJmSpoh6f8kbZORqynUnuM4zoZAdRlXAzmUT+MX/oMCse7MbKGZPRfvlwAvUsBRazn4nhenYjCzDwgxo3JTjUvN7I8xvTQGeizElpIONrP71qLZwWb2vqTfEIKM/jDmV9XTnuM4znplHW4I6RJj1GFmC2Pon6LE8Di7ETzJ5zg1xq2bSpih+ahA1TXwmRfns8AoguHREJ6kgb8UHMdx1hXlLBtJOknS1MyVjQ+IpIclzSpwHVpOnyR1JIQKOt3MPo7ZVwHbEX6YLiQE3C2Jz7w4zYV2ecEcf2dmN8f7J4HDJA2mrFBya3AQIYBkSnuriR8CJwH8b6ddOGJjj2jgOE7TU85pIzMbA4ypp3z/YmWSFknqFmddugEFd/FLakUwXMaZ2epgzGa2KCPzN+CelD678eI0F0ot41xAmH05O5NXbGY1m/+YpC6EP8js7E3SslH2QyH1tJHjOE5DWYcfNncDxwMXx//rBMeVJODvwItm9ue8sm65ZSfgMGBWSqO+bOR8JjCzR4G2hJ3uOT4ANs8T7QS8n0kPJgSAnA2c35R9dBzHaSyqseSrgVwMHCDpVeCAmEZSd0m5k0N7A8cCX8sccvhGLPtD7mAE4fN2REqjPvPifJa4ELgamBvTU4DRkrqa2TvxlFEb4K1sJTOrknQ6MFPSBWaW7jDCcRxnPbCuZl7iQYr9CuS/DXwj3k8GCh7KNrNj16ZdN16c5kL+HpT7zWyN48tmNkHSe5n0IkmnARMktQCWAsPMrM5ycVzPHQ+cAvw2pb18el3zzaSB1Dz0YJIcwOdbpx10tKp0B2Vq0zpZ9paxXZNllybO8/5gVO/SQpFHT04PWl6tNIcWG3+yPFnn6Lbpz3Xgyk7Jsi0TfW90KGdjQxm7vfolOp/bfcYfk3Uev/vPkmW/RIdk2ROPXZYkN3nsJsk6X22d/tV4dL+3SgsBs6fUewin0WnuHnbdeHEqEjMbmZduWURuUF5697z0XRRYo41lPfPSPy3VnuM4zoZAIzif26Bx48VxHMdxmhm163LL7nrAjRfHcRzHaWY0b9PFjRfHcRzHaXY0wimiDRo3XhzHcRynmdG8TRc3XhzHcRyn2eGnjRzHcRzHqSh8w67jOI7jOBVF8zZd3HhxnHXGFSdMTpLrWt2+0dtuSatk2VWJztwABndeVFoo14dWaRPZh5+e7sD4ut6fpLef6Huv/de/mKxz7ML3SwtFlj2X5swMYOXSNDdCKz5Jf107dk53vrdw7qZJcuU4nvvHs0nBggFYcuL3k2X//c8eSXL79FxYWiiy6o0uybIPPpvWfhdblayzMfBlI8dxHMdxKoqaZj734oEZP6NI+paket3ZVyqSTpfUKNMXkoZL6p5JXyMp3X+94zjOeqAWS74qETdePqOY2d1mdvH67kcTcTqQbLxIqm+Ofjiw2ngxsx+Y2Qtr3zXHcZymx8q4KhE3XpohknpKeinOEsySNE7S/pL+I+lVSQPjjMLoKD9W0uWSnpA0V9IR9egeJOlxSXdIekHS1TGoIZKukjRV0mxJv4l5+0m6I1P/AEm3x/ulkn4v6VlJD8d+TYx9+FaUaSlplKQpkmZI+lGmHxMl3RrHOk6B/yEYG49JeqyecSyVdL6kp4G9JJ0X25glaUzUdQQwABgXQ7i3i20OiDqGxVDusyT9vkEvmuM4TiPiMy9OpbI9cBnQF9gROBr4CnAm8MsC8t1i+RCg1IzMQOBnwC7AdsB3Yv65ZjYgtvlVSX2BR4GdJHWOMt8Hrov3HYCJMVjiEuAC4ADgMOD8KHMisNjM9gD2AH4oadtYththlqU38AVgbzO7HHgbGGxmg+sZQwdglpl9KYZrH21me5hZH6AdMMTMbgWmAseYWT8zq8pVjktJvwe+BvQD9pD07fxGJJ0UDbqpTy19tZ7uOI7jNB61ZVyViBsvzZfXzWymmdUCs4FHzMyAmUDPAvJ3mlltXBIptdX+GTOba2Y1wHiC0QNwpKTngOeBnYHesc1/At+TtBmwF3BflF8J3B/vZwL/Z2ar8vp4IHCcpGnA08DngF6ZfsyPY5xWZFzFqAFuy6QHS3pa0kyCQbJzifp7EAyv98ysGhgH7JsvZGZjzGyAmQ3Ys2OvOkocx3Gaghos+apE/LRR82VF5r42k66l8OuelS91Vjb/3W5xNuRMYA8z+0jSWKBtLL8O+DewHPhX/LIHWBWNmzX6aGa1knJ9FPBTM3sg26CkQXl9rikyrmIsj8YXktoCVwIDzOwtSSMzfS9GMw847zhOJWMVapSk4saLszYMjMbKG8BRwBhgE+ATYLGkLsDBwEQAM3tb0tvArwjLQuXwAHCypEfNbJWkLwILStRZAmwMpDrhyBkq70vqCBwB3JqnK5+ngcskbQF8BAwD/lJfIwNXrqiveDXdO32cJAfw22Udk2W7kOjoBFieOJl84Cbpvis2Pf3AJLnbgCdOnp4ku8mIryW3zwfvJot+ctPT6XoT2fzGMcmyNa9PS5a12VOS5FrsfUiyzrcP/HOS3FnU8njBP4+6XNn/PI7dLc3XzcZ/v660UOSRAT9Pk1vUict/tmWS7LaXvJnc/r7vvZws+5+u6272tVKXg1Jx48VZG54k7IvZBXgcuCPOljxPWKKaC/wnr844oPNanNS5hrAc9JwkAe8BdfaW5DEGuE/SwhL7XgAws/9K+hthuWoekP02GAtcLamKsOSVq7NQ0i+AxwizMBPM7K7UQa1rmsJwaSpSDZemoikMl6Yi1XBpKlINFyDZcGkqUg2XpmJdGi4AteYzL06FYWbzgD6Z9PAiZWPzy2O61M/5ZWZ2VIF2hxeQzfEV4G/F2jGzkYXK4n6WX1J3k/HEeOXkT83c/4USsyD5YzSzXxFmhvLlbmPNvTGDMmU3AjfW147jOM76oHmbLm68OOsASc8SlpTSfYk7juM4a826OgItqRNwM2GGfB5wpJl9VEBuHmEZvgaojidTk+vn46eNnIJI2iX6NsleT5vZRDMbUo4uM9vdzPY1s7RNH41IPEGUP45d1nU/HMdx1iXr8LTROYTTrL2AR2K6GIOj24kBa1l/NT7z4hTEzGYS/JdUNGb2pfXdB8dxnHXNOnQ+dyifLqf/g7Ccf3ZT1/eZF8dxHMdpZlgZ/7LONON1UhlNdTGzhRAOMgDFdkYb8GD0qJ7Vn1p/DXzmxXEcx3GaGeWcGTSzMYRTmgWR9DDQtUDRuWU0s3d0m7El8JCkl8zs8TLqr4EbL47jOI7TzLBGPCptZvsXK5O0SFK36D6iG1DQoZKZvR3/fzfGuxtIcLWRVD8fN14cZx0xYESaQ7l3blicrPOaZ9ICg9fMS3d6pk3S/WEs+O6FybIXnZHmv+XCw9Od9F3/k/Rxpa6RH7xVfUHG1+TqRYV+jBbmtYHp2wDaKe2jebMyPsKXMDpZdlB1mv+WE49dlqzz3//skSyb6ngO4Kqpf0iSG7/reck65260abLsW7/8cpLcpEvLNF3iKgAAIABJREFUO6+wU1nSdVmHe17uBo4n+P46Hqjj70pSB6CFmS2J9wfyafy6kvUL4XteHMdxHKeZsQ5PG10MHCDpVYIH9YshBK+VNCHKdAEmS5oOPAPca2b311e/FD7z0gyJARCPNrMrY3oQcGahI86SJsayqeu4j0X71ACdPYF7YmRox3GczyzraubFzD4A9iuQ/zbwjXg/F9i1nPql8JmX5slmwE/Wdyccx3Gc9YOZJV+ViBsv6xlJPSW9JOkaSbMkjZO0v6T/SHpV0kBJnSTdKWmGpKck9Y11R0q6VtJESXMl/U9UezGwXXTINirmdZR0a2xrXIwTlO3HiZIuyaR/KKlodDZJx8X+TJf0z5h3SHQK97ykh2OARiR9NeMg7nlJuQX1evuU1955kqbEZzQmJytp99iHJ4FTMvJPS9o5k54YZQdKeiL24wlJO8Ty4ZJul3R/fO5/yNQ9SNJzsZ1HYl6H+OynRF2HFuu74zjOuqa2jKsSceNlw2B74DKgL7AjcDQhFtCZhJg+vwGeN7O+MX19pu6OwNcJO7d/LakVwUPha9GT4VlRbjfgdKA38AVg77w+3AR8K9YH+D5QMLRrNArOBb5mZrsCp8WiycCeZrZb1JfbdXcmcIqZ9QP2AaoS+5RltJntEZeE2gG55abrgP8xs73y5G8Cjoz97QZ0N7NngZeAfWMfzwMuytTpR4iSvQtwlKStJXUmxGQ6PI71u1H2XOBRM9sDGAyMihvR8p/Vav8J1z6dHn32/9k783jfpvKPvz/ma3YlQ4aLRMhMlBQiUsoUF6FEwy8hRCqhqEgSRSgkmUkqQ2ZC3Gu45hLKlKEyZHbv5/fHs/Y96+yzv9+zv+dcd2B9Xq/9Onuv/exnrb3XPt/17GcsKCgoGA56yfMyLaIIL1MHHrR9RypCeBeRKtlEleNRhCBzKoDtK4B5JVXu8H+w/Yrtp4kQs/k79HGT7UdSH7clvhNh+wXgCuBjkpYBZkxZdpuwHnBO6hPb/0ntCwOXSLoD2AeoNB9/Bn6UNENz2369zZhqWDdpU+5I/S+XnsHctq9ONKdm9GfRJ2h8Cjg77c8FnC3pTuDIbIwQz/1Z2y8DdwOLAWsC19h+sHavGwL7SbqNyAg5C7BofdC2j7e9mu3VPvvepbvcXkFBQcGkw3hPaL1NiyjCy9SBPIZuQnY8gXCqbjKnVOJyfu14Ojtht6E7EdiJLlqXBNFctPRoQkPyHuDzxIKO7e8DnyM0Jjcm4aj12CXNAvwM2DLxPiHx7jQObD8K/DuZ2LYmNDEA3wGuTBqcj1dj7DKeTn2I0MaslLZFbd/TNJaCgoKCyY0JuPU2LaIIL9MGrgG2g4lROk/b7pYM43mgXaKGDLb/AixCmK1O70J6OfApSfOmMY1M7XMBj6b9HStiSUsmzdIPgDGEqasXVALG05JmB7ZM430GeFbS2un8drXrKtPVXJkWKR/jTi36vgH4oKTF071U93oJsFvme7NyT3dUUFBQ8AbizW42KqHS0wYOBE6SNA54kUwwaILtfyeH3zuBi4A/9NDXWcBK3UqS275L0iHA1ZLGA7cSgsCBhEnmUeBGYPF0yR6S1iW0GXenMdV9VLrdzzOSTiDMaA8BN2enPwP8UtKLhECR4xzCl+g7WdthwCmSvkqYyQbr+ylFHY7zJE1HmOY2SDx/DIxLAsxD9PnhNOKJ3zwyWHcAzDhLezXu6Ssf1Ipu1gntf6B6+SlbZ+lXW9P+YPd3tKK79+CXW/Pc4fAlW9P6iX+1onvhD+Nb89z6yedb0y75f/O1pvXrLcfwSvvnP91iiw9OlPDQoXe2orvu5Dlb8/zAqMdb035i+/bfN22Tz42+/eDBiRL2XO3rrWm/+fN2czXjiI7xCI3YqCfqgZgwjUYRtUURXqYwbD8ELJ8d79Th3IBoFtsH1o5zPtvWyK/Kzn052/9QjW5twhdksHGfQlQAzdsuoCE7ou3dGlhc1WlMHfr7JvDNhvax9M8fcGB27glq77jtG4B3ZU3fSu0nAydndB/L9i8iBK6cz0uEaaygoKBgqsObW3QpZqOCBElzS/or8JLty6f0eAoKCgoKho43u89L0bwUABP9R3KNBMmnpUmQWT9lRZzkUBTsquu397VdNwkVFBQUFHTAtBpF1BZFeCnoiCSgrDSZ+9xscvZXUFBQ8GbEtKpRaYsivBQUFBQUFLzJMK1GEbVFEV4KCgoKCgreZJhWaxa1RRFeCgoKCgoK3mQoZqOCgoKCgoKCaQpvdoddvdlVSwUFUwt2H7VNq3+2NV5t/01xz0ztfqDmdPusCK/08MU234T2ibe23/bFVnRn/3rW1jxPm+6p1rSjpm+XdHr112cZnCjhY4s81pr2tZemb00715KvtaKbcYl5WvN86tL/taZ98F/t+N4100yteY56rX3yv8VnbZ/875zxcw1OBDyt1wcnSjhyzPda0x642oD0U42Y0b0lqTvwH6f1dkENKyywVut/5HH/umFYfU0JFM1LQUFBQUHBmwxv9gy7JUndFIKk64d43SclLduC7kBJe6f9kyVtOZT+ehjXTpIWGiaPgyV9OO3vIWnW7Fz7z8aCgoKCtzje7LWNivAyhWD7fUO89JPAoMLLFMBOwLCEF9sH2L4sHe4BtLcfFBQUFBRMxAS79TYtoggvUwiVJkHShyRdJekcSfdKOi2rVPx9SXdLGifph5LeB2wKHC7pNklLStpF0s2Sbpd0bq6t6NDvQ5IOlXSDpDGSVpF0iaS/S/pCRrdP4jtO0kGpbZSkeySdIOkuSZdKGpG0OqsBp6VxjWjodw1J56X9T0h6SdJMkmaR9EBqP1nSlpK+QghCV0q6MuNxSLrPGyXNn9oWk3R5GuflkhbNedWfd4dnIkmHS7pT0h2Stm4xN6tKulrS2PT8FuzAe9f0nMfc+fzfu01NQUFBwSTD5NK8SBop6U+S/pb+DnCYkrR0Whuq7TlJe6RzB0p6NDv30Tb9FuFl6sDKhKZhWWAJ4P2SRgKbAcvZXgH4ru3rgd8B+9heyfbfgfNsr257ReAeYOcW/T1sey3gWqIY4ZbAmsDBAJI2BJYC1iAy7K4qaZ107VLAT20vBzwDbGH7HGAMsF0a10sNfd6S7hPgA8CdwOrAe4G/5IS2fwI8Bqxre93UPBtwY7rPa4BdUvsxwK/SMzoN+EmL+69j83SfKwIfJoTDShhpmpsZgaOBLW2vCvwSOKSJse3jba9me7Xl52hfAbmgoKBgOBjvCa23YWI/4HLbSxHlZParE9i+L60NKwGrAi8C52ckR1bnbf+xTafFYXfqwE22HwGQdBswCrgReBk4UdIfgN93uHZ5Sd8F5gZmB9rUAPpd+nsHMLvt54HnJb0saW5gw7TdmuhmJ4SWfwIP2r4ttY9NYx0Utl+XdL+kdxNC0Y+AdYDpCSFqMLxK3zMYC2yQ9tcihA+AU4HD2oynhrWB022PB56QdDUhWD1H89w8Q1T7/lNSxEwPPD6EfgsKCgreEExGc9AngA+l/VOAq4B9u9CvD/zd9j+G02kRXqYOvJLtjwdmSIv9GsREbwN8GViv4dqTgU/avl3STvS9RG36m1DrewLxTgj4nu2f5xdJGtUw1gEmoi64FtgYeA24LI19emDvFte+5r64/vF0fncrmtdJmsVk6ukW09ktTHDA3CT6u5L2qqCgoGCqw2R0xJ3f9uMAth+X9PZB6LcBTq+1fVnSDoQGfy/b/x2s0yK8TKWQNDswq+0/SroRuD+deh7IE1bMATyeTBnbAY9Ogu4vAb4j6TTb/5P0DkLg6Ib6uJpwDfArwszzlKJq9QLAXV34PT0Iz+uJf4ZTifu/LrU/RKgnzyK+DGYcZFyfl3QKMJLQCO0DLNOB/j5gPklr2b4hPft32W66j4nYb+EnBrmVwAP3z9uKDmCzL8/dim78Q+3zkWi29nlOrjxl5ta0Hz+tXe6On8/ZLh8MwLkvtP8Jm7Gllfydr7XLsQJw/iPtfdT3eOLKwYkSlnrsHa3o3nXz7K15jnvhyda0P55xvlZ02670cGuel45duDXtOk/d15r24f3bxT588+ft88y0zd0CcOCY77ai+/6q32rNc1LAPZiDJO0K7Jo1HW/7+Oz8ZcRvdR3f6GVMkmYi/Da/njUfC3yH+PD8DnAE8NnBeBXhZerFHMAFkmYhvvT3TO1nACckp9YtgW8RPiP/IMxA7TJxdYHtS5N554ZkFvkfsD2heeiEk4HjJL0ErNXB7+UvwPyEsAAwDngy06jkOB64SNLjmd9LE74C/FLSPsBTwGdS+wnE87uJsMO+0IXH+YT56XbiH+hrtv8lqVF4sf1qcgb+iaS5iP+jH9MshBUUFBRMdvRSHiAJKsd3Of/hTuckPSFpwaR1WRDoJiVvDNxie+KXXL4v6QQ6u0j0QxFephBsz57+XkXYCKv2L2dkazRc92f6h0ofm7Y63YHZ/k7Z/qhs/2RC6Gg6dxRwVMPQl89ofpjtnwuc20Cfj+klYObseNfa+XycRxNOsdXx7Nn+OcA5af8hGsxp6R9izazp63WajNaEpmWfWvtVdJib5PezDgUFBQVTISZj9vzfATsC309/L+hCO5qayagSfNLhZkQwx6AowktBQUFBQcGbDJOxttH3gbMk7UwEdWwFoEhaeqLtj6bjWYlAi8/Xrj9M0kqE1vuhhvONKMJLwSSHpPOBxWvN+9puEwn1hkHSewjfmByv2H7vlBhPQUFBwRuFyRVtZPvfRGBJvf0x4KPZ8YvAAIc+258eSr9FeCmY5LC92ZQeQxNs30HkcykoKCh4U2NaTfvfFkV4KSgoKCgoeJNhMvq8TBEU4aWgoKCgoOBNhl6ijaZFFOGloKCgoKDgTYbxEyabw+4Ugd7sqqWCgqkFV82/Vat/tmfULZ9ef8zUMqJg5h6+wsZ3TTjcHyP0emva787YLdVOH/Z+dc7WPNfcuh1PgBfHdazN2Q9j7mmssdmIV9T+Wa08/1OtaS/5z/yt6C6frl3iP4CtX22f0G7khHaJ+mZQ+/fqdbd/VgvO2W6uAP7xXLv35YoR7fuf1e3L/k3fkm6/sd9pzRNgxrct0X7ADZhn9ne2npz//u/+YfU1JVA0LwUFBQUFBW8yvNnNRqWqdEFBj5A0n6S/SLpV0gckbSXpHknt878XFBQUvIGw3XqbFlE0LwUFvWN94F7bOwJIuhj4ku0ivBQUFEwVmIxVpacIivBSMM1D0lfpK+R1IvBb4CKiSOP7iGKVn7D9kqQlgZ8C8wEvArvYvrcD38WAXybaqm7SSOAwYISk24i6SGsDi0v6ne19mngVFBQUTE682fO8FLNRwTQNSasSQsV7iVpGuwDzAEsBP7W9HPAMsEW65HhgN9urAnsDP+vC/hiiAvYKwGnAT1JNowOAM22vZPsgooz7dk2Ci6RdJY2RNObClx6YBHdcUFBQMDjGT5jQepsWUTQvBdM61gbOt/0CgKTzgA8ADyZBA2AsMErS7IQm5mz1RYnMTGesBWye9k8lNC49Ia/W2jbaqKCgoGC4eLNrXorwUjCto1OI3yvZ/nhgBKFpfMb2UEsEvLl/DQoKCt40mFYdcduimI0KpnVcA3xS0qySZiNKql/bRGj7OeBBSVXVU0lasQvv64Ft0v52hA9NQUFBwVSPN3u0UU83WLayTY0b8FXgzrTtAYwC7szO7w0cmPYXBy4GbgfuBg7owncUcAUwDrgcWDS17wQck9FdBaw2xLHvOiVp3+r9T0tjndL9T0tjndL9v1FjLVvfVjLsFhRMQUgaY3u1KUX7Vu+/F9q3ev+90L7V+++FtheeBX0oZqOCgoKCgoKCaQrFYbfgLQ9J3wC2qjWfbfuQKTGegoKCgoLuKMJLwVseSUiZUoLK8VOY9q3efy+0b/X+e6F9q/ffC20vPAsSis9LQUFBQUFBwTSF4vNSUFBQUFBQME2hCC8FBQUFBQUF0xSK8FJQUFBQUFAwTaEILwUFUyEkrdkD7Zxdzi3ahq4XSJqhdvzlScG3Zd/zKCtMNTWgl7lquHZGSStLenvDudbzJelTQx1DxmOGbP9r2f5WNbpDh9vXGwlJIyQtPaXH0QRJl6e/P5jSY5nWUYSXgoLJBEnrZfuL185tXiM/VtLPJc3dgvVVGZ/La+d+m+3fKmkbWkDShZIWa2j/MHBbrfmzbXim6xeTNFd2vK6koyR9VdJMNdoDJC2T9meWdCXwd+CJNI6KbhdJS6V9STpJ0nOSxklapcbzj5JGTcpx0r0yeZ3vcZKWS/tzEZmef0XMzegaeev5AnaQdLGkJQbpv+285v1+vUa+URf+c0k6sqqkLumI/DnWaDeV9MO0fbwLz8MkzZkEvcslPS1p+w60H0/3cXE6XknS74bJc35Jv5B0UTpeVtLOHWhnkzRd2n9XuscZM5IFJX0Q2DQJravkW6dnUNCAKZ3it2xle6tswC1N+x2OpyNKHfwV+PQgfG9t2m84txhwPvAn4J2D8NwOeAD4BjAjsBBwFnAjsGq3sQ/C9y/AQml/JeBpYC/gFODEGu1d9EVE7gpcCUwPvBu4KaO7E5gx7W9LVBGfF/gwcG2N56fSM/1Gdc0kGGcv939Xtr8H8Nu0v0DD3LWer0T/SeAe4FvA24CR1dbrvLZ9pxrGcC5wELBE2r4NnNdA9z2i5MZn0/Yn4HsdeN6W/m6Wnv9I4PYOtGOBuWrjHzdMnhel9+b2dDwDcEeX/mcF3gE8nObvtOz8lonf80TpkSuz7Yq271HZXPK8FBRMRqjD/oBj2xOAH0u6FLhB0s+IqtaK085NCu6w3+/Y9j+AzSRtBPxZ0s3AhOz8ptn+aZJ+DxxGLIgzErlwTnD6Fc6wgqTnOtxvfawjbD+W9rcHfmn7iPS1WtfovJr19RHgDNvjgXtqpqvXbb+W9j8G/Mr2v4HLJB3W72HYZ0n6A3AAMEbSqbVn8KMhjHOJpq/7jOem2eGr2f4GwNmJ5l91a1gv85WOfyvpQaJY6c70zb0JQaKXeW31TjVgSdtbZMcHSao/L4BNgJXSe46kU4BbGajlIY0R4KPA6bb/08Vy+LrtZ7ucHwrPt6X35usAtl+XNL4DrWy/mDQzR9s+TNKt2fnHbW8s6QDbBw82yILOKMJLQcHkQ08LQvoB3I/4Sv5pg9BQ4e2SvkoIC9U+6Xi+Gs+lga8Rlbd/SrYYNmBZYA3gJmA1YH7iN+O1Gt0dtlfuwqffELL99UiLle0JDYvHK5KWB54A1iUKbFaYNdufIGlB4L/A+vRPODiiYQyvAS8AMwNz0PwMehnnU8ARDTya8IykjwGPAu8nhIzK32TAWNvOl6SZgW8SX/bb2f59lzG0mdcVk0AqYEQmnAqYpQvvlyStbfu6NK73Ay91oJ0b+E/abzQtJVwo6d7E50uS5gNe7kB7p6RtgemTKfErRHX44fB8QdK8pP9RhY/Tsx1oJWktQsNVmZbydfYnwKqElqwIL8NAEV4KCiYfqi900f9rXUS164mQdD3wEPAB2/8ahO8JxCJc3wc4MeP5fWBTYC/bF3VjKOlEYBXgS7ZvkDQbYQ64XdIeti8dZEydcIWks4DHgXkI1TlJ+Hi1Rrs7cA4hgB1p+8FE+1HiK73CAcAYwqT0O9t3JboPEiaS/L42An4E/A5YxfaLHcZ5ZQ/j/J/tq1vdPXyeWMAWAPbI5nZ94A+1sbaeL6Ly+bnEPXUSFlrPq+3pW95PHV8ETkl+LiKEk50a6L5H+PRcmejWoVnrgu39FA6uz9keL+lF4BMd+t+NEPZfAX4DXAJ8twXPF7rw/Crxviwp6c/E+7hlB9rd032cb/uu5IN0ZXb+NUknAe+Q9JOGcX2lA9+CGkqG3YKCyYS0mHZEvgAmx8MbbT9V4/F24ge301dit/4PAQ62/UoL2j2BnyQzTd7+HuBntj+Qte1vu1UEikJtsTWwIHCW7UdT+8rA221fktFubvu8lnxnAOaw/d+sbTbiN+5/Wdu1wOdt3z0Jx3me7brD9bDR43ytAzxdvy+Fc/CT1XvUy7x26Gdu4P88SN0vpUgp203mxIpmQWB1Qnj5SychXdKshACxqO1dk0Zl6UG0S4NC0vuAUWQf8bZ/1YF2BmDpNNb7MjNlr32+jfDF+gEhdPeD7VOGwvetiCK8FBRMJiTV9HyDLTCp7Xjg4vriLWk7YG3bX8zaDgMesH1cjXZPYAHb+6bjvYBnbP+iRrcbML3tH7e8j8WSP0Z1fDRd/CDyr0lJy9i+N+3PnC/Mkta0fWN2fIvtQSMwJH3N9mFpfyvbZ2fnDrW9f3a8ETC77XNqPLYj5uBP6fhS2xsO1nei/SDd7/+ajPYs259K+z+o5qapz17mS9IZwLF1DZCkjwA72t62xX1MnFdJixCOvwsREWu/Ab4D7AD8xvbutWu3t/3rzGRZfwY/SnTL2L5XHSJrbN/SMK4zCUfYHWwvL2kEcIPtlRpo/wRsZfuZdDwP4Sv1kRrdqcCShP9SJci5k+ajraAj6V2EebNOu16NbkXbtzf1VdAORXgpKJhM6GWBkXS37WU78LnL9nI5LbB85fyYtU9HRFosn47vJMwKr9boZgZutr1CrX0tImriGttPSlqB8MH5gO1FMrodu913/jWZCyR14WSw407okeeNwMcbNFoLEKr+tdLxrW39eCRd2NBsYEVg4dwEk/NtGFu/PnuZr/o7UaO/s3oH0vGg85rMOVcDNxCh0esT0V97NmlIJH3e9s8lfbvpWTg5p0o6PmlPruxAt169UdIY26vVnt3ttldsoB0wbx3a7gGWdYsFsBdBR9LtwHGEsDVRu2V7bI1uYeBowu/JwHXA7rYfGWw8BYHi81JQMPnwnrrgAmD7Ekl1h89u4RL1/EyuCy6pcUIyf+R0dX8NbL9So0PS4UTkzm3AvooIlS8Bh1LL69Kjqrt1xBWwjKRxHXg4W7x74TlrXXCBidE+s2VNc2lg7p2c/rxsv1+OEklrE34XjwP1BH7dFssBTtxt54u+6JkmTDzXw7yOtH1g2r9E0hPA6u5gwrL987R7me0/5+cUTrsV3a5pd2PXTJ+SOjkCv5q0LZXD7JKET0sTJkha1PY/E+1iND/zOwm/o8c78MmxGi0FHSLa6dgWdCcR2qwqAeD2qW2DFtcWUISXgoLJiVYLTMKTktawfVPeKGl1Irolx4uSlrL9txrtUtQiPSTNb/uJelvDeDYBVrb9clK9PwasUO8jXd8xTBgGhPT2EnH1INAxedkQec4iaQbbr+eNikRiebTPXMQi3yREGhjgiyNpfcLUYuDQygRVw6wKv5npiCielVMfojnaqO18/U3SR23/sUa7Mf2dlnuZ13nou/9/pbHPBmD7P3X6hKMJh+DB2q5v2QaRK+ZiYBFJpxHaip069P8N4DpJ1UfCOkSOoDreBtwt6SYyQaj2rlboRdC5UNKXiPwuOd/683q77ZOy45Ml7dGCf0FCEV4KCiYf2i4wAPsAZ0k6mVBBQ3wB7kD/7KcQjn8XSfpujfbrRCK0CocDf0i+FJVvwapEzo8f1ni+VH0Z2/6vpPuaFriEtYiEXKcTyd26aY0WVkRZKNsnHb+jRvuqM9+aLuglrPc84ARJX7b9AlA59v6E/gLJP2y3yhwsaRNi0XwW+EZd81DDv4hop/p+dZyjl/naE/i9okxA/g6sRQhhFdrO61yJTz6X1Rgm5o2pkExR7wPmq/m9zElEgVV0CxDznAtuFV0e/j4Rtv8k6RZgzUS/u+2nO9BenPxpKto9O9Ae2HR9B/Qi6FQm1H3yYVF7XsBTioy+p6fj0cC/exjTWx7F56WgYDIhOfP9nvjCHLDA2P5rjX5+QqVf+SvcBRxj+8kG3ssTP5g57eG276jRbUz4N1R0dwLfdy0UV9IzRLKzCuvkx/kPt6TpCXX3aGAFIuT3dKeQ5RrfXvxjjrE9SesmKaJGvgt8DvgHscAtAvwC+JZTFEmPPi8TgEeIVP8DflA7LHJtx9tqvhLtzESG4fwd+E1unullXnsc5weBDwFfIHw+KjwPXFgJSGn+dyLe+zE1upPdEF2WzE632X4hLfirAEd1EmwlvYPITpw7zF7TRNvDvQ1Akwm4B56LAscQ//smfhN2bymsF1CEl4KCyYo2C8zUgE4/2BU6/XCn+xtNaA0Otn10i77mIaJqXGvfke5RPL9KdLMCr2WCx9JE5tSHbJ/foc8RwDvT4f2u5UaRtFyT8NWBVy8h8KsDD1dOr5J2ALYgBKkDu5hjJgnajlUDo4FMhGI/PAj/xdoswJK2sH3uYHSJdhzh/LwCUQfql8DmtgfciyJ3y9bE/1XlB+ZKKJN0ne21JT1P/3erKRt0znd+IqwbojTFk7Xz69m+opOfVJNQ1g2Svm77e71c81ZDEV4KCqZCpGiMTv+ctr1+RnvSILRVFtcBeSVqdN/JeJ5se6cexjsz4U8xmggT/R2RUv/RGt0BRN6Ue9M1FxG1g14HtrV9WUbbJPiI8IN5h+0ZEt01wM62/ybpnUTm2NOITLI3294v47lOt/uovtAVafYHLG59ZF4y4zmnO+QzyZ1H0/EtwIcd6ejXAc4gEqutBLzb9pYZbS/zVR9vnXbJRNdqXtUcDTQSmAkYbbsp5T+KdABfA5YjM9m5OYpokwa6AVlnlaKy0vN41PYv1CESTdJ9hA/PoLlxukHSPE45g5Ip7nCiAKqADwD7OAu3l3SQ7W+n/8U63NYEmfFrFWn3VkbxeSkomExou8Ak7N1AsyaxMNTNRk3JuhYl/F3yTKkvNNDNRqQxn5fI41FhhQbaRijq0ixPCCIH2b6zC/nWWT87wsQSBu8iCuRNFF5s75b1ISLl+r5EEcE8Sdo8md/GjoTJajdF9eexhNmlQu6LMLErUlgzfc9rtRrNdERxvr3pn90XYlGrQrUvzwVLIkdKvghNn2lXtgaOTxqIczWwBlAv89V2vK3m1fYA5zKTAAAgAElEQVS6Te2SViP8gzoJgacBZxJ+Nl8g5mNAdJek4wgfl3WJLNBbEkJnE55X1BXaHlgnmSk7Ob8/kM4NS3ghikZW8/YNItLqyTT2+Yj3dKLwYvvb6e9nujGVtKPbRecNWpzprY4ivBQUTD60XhCd5YVIqv5vEbV4vlD3d8jV74p05PsTi8v3CV+Oiu6IjG4OIpX5Z4iv/3qodhUV0/gj6v7JxD5NLLTvAr6ivijeJlV822KL1ThnIHwk9iKcgbe0fV99ONn+esRXMrZfTf4o+bhbhTU7CjuiyJXzaULouQ3YxAOz8+bPaGSXcxA1d6pop/XpHwnT7/57ma8extvLvDadHyNp9i4k8ybNyO7JBHW1+iJ/crzP9gqSxtk+SJEqoJNpZWvC1LqzI6R9UdIcN+BF4DZJl9PfubbXtPv585muZib6NwPTFbTF7oSQPhiKSWQQFOGloGAyoccFEUXyum8RBeMOsd2kyq9o300swisTP+xfcC0cONGNJFKtb0f8iK7iLKV+hncQC2SnUOGJZgDbvfyQty22iKT/I37sLwc26uJLMU7SD4lih+8ELk3Xz91pEBokrFkROv1ZIornOuATtv/egV0vodqnEwv600QY+7Wpv3fSUOyv7Xz1MN7W89qE5PvRbWGt0uY/nsxCjxEarToqH68XJS1ECASLN9CR/IN+lB3/k/B9acLv0jZc5Pd4saRL6IsM2hr448BLWqGtRqVoXgZBEV4KCiYTelkQJd1MmFMOJ7Kc9nOizL+QJZ1NaHV+mHiPB+asNCCVmUKRoGxz4HgiYd7Emj8NuL/JT6HDWOvahn6oOaG2LbYIkR/kSWBtIn/GxC7pn6Rul8R3FLCh+4otLkstpFjtw5ofJPxwfgz8kwjHnpjRteaA2bqqt+1DklZgQeDSTAs1HbWEdj3OV9vxtppXNZd8GEmEQ+8+8IqJ+K6iKONexPzNSf9w/QoXJuHycCIE20RR0aaxrJl4vZvwuZmeKIY5oBJ1S5NMT7C9T3LEXZuY0+PdwRG8DbuWdGcPTvLWRnHYLSiYTJD0CP0XmH7IF0RJV9HdP2a9jPahjLb6q4x2iUQ3gVClv17jPcC8o95ChStfnsav+ar/Fnz6JWRTZEftiC6amOr6RYBtbB+etbUKa1bk1+n2/Cc6YKo5JX5OfNAg45wN2IxwhN2kNta289VqvG3nVQND2k1oR252Q6h+dt376wJhvS1pHte0fX06nhmYxfYAzVM6P4bIbVQJ6TsASzmrWZXRLkVUrF6W/o7Ard7BjE8v7/8NTmUl2vJVpE04FpjfUa9pBWBT2wMqYBc0o2heCgomHy6jzzm0XpelX9ZW2x9qy9T2qJZ0vZh39h2cBCSda7tR3Z/O1xPP1c/PRYQKb0t8Wef0I9yliCMRXlzn9zYi5froxKv+hdzoiFqHe4i0Gkw4aUJyJv4ocd8bAefSPz9KT/PVw3hbzSuxkG7Rtv8Mg2bYdZStOILIcUKa164OtrbvlzR98o86SdL1HUhPIjLyHknM9WfoYIJJ/k5L2T4pOeHOXmkCCX+ktuhU1qAJlRB3AmE2/jmA7XGSfkPkICpogSK8FBRMJnRbYFRL+a4eKiV34Lck8bU62n2FGdezfUXaXzz7oUbS5u5fr+fSlrc12BftDUTkUz62EcCmxMK9CjAH8En6J0+DqP1SLXo30H9R/Bl9ET5zEJqLbQmn4fOBJWw3+Vrc6i5hzdn+j23vkfZ3t31Udq5fuLH6sgQ3wv2ralfJ/D4CXAmcCqzhhiiVXuar7Xh7mNcNJV3R+Zb6RVShlhl2M1wqaQvgvMx01gkvJmHvNkUF9ceJqKsmjLB9uSQlzdyBkq4lBJp8vN8mtDhLEwLPjMCvidID3cofNGHi+NP/8aHAQrY3lrQssJZTZXD3JV2c1fZN6l+iaoCPWkFnDNVjuqCgYJiQNJekz0q6jL7U6xXyEgBfr53bqAO/BSXtoUhjfhfxcTI6I8n9P+oJwr7ZfuT9MNjCUy/4eBrwV2BDIsPoKOC/tq/ywOKSbQsuPkmEDx8CLGl7L2BAQcOEq7KxXF4799tsPw8FrptQ6uHGXyD8IR4jssaOrW05LiEqFK9te3vbF9KXTK2OXuarl/G2wSOEZiDfziXG3pTIbSZgduKdmyPbniPCoOv4KmEGekXSc5KeV19Zhzo+TZ9P0AtERuROWqGXk1nqb5K+LGkz4O0NdJsRAvQLALYfS+MdLk4m5nihdPxXmn1+nk4fGFWxyS1pVzupIKFoXgoKJiN60Dq0rpQsaRdCSFkYOItIfX9Bgzmjl+rLkwp14WZ54L/APcC9tsdL6uir0YVPfrw/IewdC/xG0pldxtM2rLnbs6pjQcJUtTXx9XwmcG6HKK5V01gvk/QAEfbcpJkYbAzdjifFXL7kFK6vQUL1YWJm3quTlmfQDLu2uwoKyjIcZ/xeBgYz0e1BRK19hciDsy4DhTlIIfvVu6f+FcV7Rf6832b7LEVeGmy/Lml8wzX/RzhiLyPpUcLhevthjOEthyK8FBRMJiStwzpEKO8xwBVE9MdVDeS9hN/+lDCrbGt7TOqrSSDohWdbqENkCsSPer9wZdsrSlqGEN4uk/QkMIekBZxS5mdoVcTR9pHAkYocN6MJDcpCkvYFznf/mlFtn8F0irIF02X71SLVT9hwhMAfBxyXfHxGA3dJ2tf2qTXaW4moqn0VNXtGAzNJuiiN9fghjLWn8baE1EOofoYXFVFSg2bYHQSn0mcWfD9RSLFer6jJZPl6isr6H+Hv0glnSfo5MHcS/j9Lh2inFvh0tv+CpHnp06isSUMIvO0HgA8noWk6288Pse+3LEq0UUHBZIKk24kF5VfAmbYflvRA049w+lp7IdGPIJJvkY5nsT1jRps7qc5PaF92sr1IjWdVlK9KcX5NxnNt2/NktK1S3kvakNA8dIS7hK8qMrZuS5gWHrH9vuxc6yKODXzfk/h+yv1T+T9C5AwREVZe5Q8RsEf1zBQRXBPoIYJKEco+mihSORY4wg35exqumy5ds03u+9LjfPU83nTdjIQ27NE8ikiRZn9mslD9GsPGZHaSLiU0T3uTZdi13dZRuOIzMdpH0r3EXI0l0gBUYxhQhVlR1mBBwiR1hrvUp0r+RxsSz+wS13L9ZHR5HaSZCP+YF9xQBym9A0cTz/ROIlR+S9vjanQzE6avUfQXyAaURyhoRhFeCgomIzKtw9aEr8YyRA6PutZhqPwXSbxHE+rz8yvnXvVWQHBibRXVUt5riHVXJB3tLOV/7ZyAdTyMSr0txzCssOYOPA8i0uHfQ5iBLnZDgsBE2/W5uX/+niEVxxxkrMcBR9u+SxHpdQMhEIwE9rZ9eqK7ipah+jX+Y22vqsicu0Jqu9oNRRQHGWf+/v3F9nt7uHYBInP11oR/zpmehCHIkj5JOFk3Os0rskIvTQhF9zkVDK3RXExoZOoCWT3TdUEHFOGloGAKIWkdRhNak7rWoe6PYRoqLyfaY4jK1NfX2pcmvuaHsiDnX779cl7Uj3vgeQsRKtqtUnQemdM1U6r7crLUKwRPZEEtH0oPY+1FyJhA1NSpKlPnuXbsvmR6lWagwqr0d+jtKBQMYbyNVaAl3WV7ubS/B/Ah259MC/5FQ5nXGv8bba+pyEj7E8KJ+Rz3r9vVhs8thO8WhCAyPZFKIE/537WUQdK+fQ3Y2vZMtXObAz8gnHlFj+9KdZ8dzr2PgRqVX9Vo7nSKAiwYGorPS0HBFELyTxkjaR/iRzbHWAYmfptDUbzvc7Yfytr/BhwhaUFCZX+67dscNYAmCi6S7mCgH8XTRMjuD22/XDvXtN903AvGZPsHUQthrWEt4GEiLftf6OCImjt/DiZYaWBY88RnYPu62jjvoq+ooGrX5EJGxzw3DWOdmGcmjbVj3pke56vpi32kIsQ4rwKdR2FtQMrk6qgZlPc91FD9pgy7e3a6xy54teGe8tpgjaUMFGUytibMkP8mNGF7NfA/DPi47XsGG0gSdCpMl8bR+D8g6VQiIus2+jQqZmA5g+slvcf2HYP1X9CMonkpKJgKIOmfthdtQbc5sKvtAeHSioy026RtFmLRP92p4rKaM9aOJPwSZrO9S8arlW9IL6ibm1oIGtMTC+xoItz3D+l+uvkxdDVpdfCjGUl83Z9p+8eJbk/CJ+FZYgE83x3S80v6GbBfJx+hYYy19Xx14bEa8CPb66TjKwmh4FFCCFomCS4zAHfaXqY+toZ5axx3mq+vOByoBxtXvfp2Y1uvkHQj8d6f7Qh/7kT3Z9vvb8nzpOzwdeAh4AQ3ZBqWdA+wbJOGtEZ3N7AUobF7hQYtXUF3FOGloGAqgKSH2woEbXxOFJWDfwmsYHvQaJMG09Ab4RtS76O170xycBxNOJAebPvoDnRD9ccZAVxfF6YkLZ76/QSR0ffQTItR0XyNqK/0bdu/6aHPIY01XdtL+vpcEHkXYc5ZAPix7ZNT+0eIulB71fn3YjaUdOUg2qRZCF+sK4EP0afRmpMwW7274ZpDgcNsP5OO5wH2sj3U3ERIOop4Br+lvymqU2XrtnzPJgS4rjlbkmA6D+GIDeGM/YxbhJkXBIrZqKBg6kCrrwhJs9MhuaQicmQjQvOyPnA1g+fFqNCPZy/CySBmhBxHDU4ygPfMwCaEADGKWHjPq9Hkav25a8etFiTbL0kDrVK2H5R0ARHx9Wkig+9tNZrDFGHwP5K0M5FvZkJ2Ps+Em4eV5+HfFe1XaIdWCUZVqwLtCBsfoLWzfQmRXG1iU4f9puMc1ycfrDNJCeAS/8o/5fNELpaFCNNo9dCfI0L+m7Bx/n7Z/q+ikOdE4aXBxNZ/wAM1GnMSEXwb5mTU3q3Ee2HCBPb+RHMdsLvtRxq6ehtwtyJRZC4UbVqj+yTh03Me8QxOJUK1G4XygoEompeCgskESRfS2bF0PduzZbRfbaCbh0hwd4ztEzLayrTyMcI35Azgt7ZfyC/u4IQ6D5Ec63/OIoEUadgfsN2v3k4ypyzgLPS1Rw1K7lw7K/1DwPs5TEo6hQg5vYgIe72zA8+TmtoT7KyIYofrZyAEk81tfzy1LUEIgZ8g/G7OAH5f8zOp89mByPJ7BX3CS7/+O5it8sGektH2Ml9dq0A7Mvki6Szbn0r7P6jN46W2N0z7E4hcKYOG6teeQVMuGLvmiCxpt07aswae44DVnWpbJS3ZGCfH49RWmdj+L/2t8utsB7zoYYQgS/oTUaqi4rk9sJ3tDRpoG6OqXIsMS/e0VvU/qsj3ckMxG7VHEV4KCiYTOv2wVXD/UOW62aaq6ntN3ckvLRinE1EdHWuyNCwsFc+rgOOdhXQmm/zyrqXsV+QkGecsUkKRv+ZDdHao7aVOTN7XBPq+3rtWVe6BZyU85WN9kdBS7VH5SaS+xwEXEFqBfj+UtisfICQtR2hbHgP27GYySGaTOWw/VWt/O/BcLhz1OF+tqkDXzEEdfZB6MUv1Akk7VgKaWkTlJLqvEUL7Sem+Pgv8zsmhuEY7wJelQ1trbYqk22yvNFhbdm5+YPV0eFMH35g7CIHs5XQ8CzFX72niWTAQxWxUUDCZUP/6qqDIzbINsYBWtHmU0JyprdEh1Pa6SXuwsSKPDETOkX75Rrr5IjSzHVBrqKoIXBdSlqG/CaDfJWTFGyWtTqRQ75diXtLHgcecUtKnvtqaRr4KPOtU/C5r3w2Y3skJN/FsW7/mYPoEltkHoT2HEHxys0u1IH3cWaQOYfa6mIHmiQ2I+khfzMbaer4ygWBW4J2p+T5nlbgr0m5sWtINB7sDp6hlVE56104nBMkPE+/Yd+rPOsNsktZ2ihxLAlJT6v+TCG3KVul4+9Q2QJtC1CHaPo0DQss5IEFe6u9ThF/WVWmsR0vax/Y5Df3/RVJV9fyTwC8oaI2ieSkomAJQ/6y47yCiWfau0exOhFDPQvwQPg0cYPsMSYs45fCQtBDhAPk4kXpewMqEQ+K6zqIuFI68ewHLpqYxhDPk/ZJmqIQdSTcT5Qb+VhvTUkTEz2pZWy/Oo1cR2X8fqrW/k9AmrJe1/RH4Up22geedwCq2X621z0x8za5Qax9BmBPyZ3BOfr2kNW3f2PKeZs5MGtMTfhRV5ehrbW+Z0d5te9kOfCbmYMna2s7XjMSi+WkiGmY6IofJ0ba/L2ll27cqstWOTud/TSRMrPKc/NrJYVZ90WaNyDVPvaB6V9QyKiddM9b2qi35r0o4qs+Vmp4BPutaTphetCmKauPHEKH7Bq4ntDQDnGuTFnKDStsiaT7gMtsrNtCuQgisIjSqt7a5x4JA0bwUFEwmSJqDqGa7LeH4eT6whO2FG2gPBNYAPuCog1L5YRyV7Pu70PeFfShwbK5hSPRfAb5HKkwnaQsiMdehRJ4LEYnSzpH0ReC7hKMvwAHARZK+S18itdWICtdNVXLbYt4mYSQtxvPWmk8GLk2+L4e5IVNp3+X9BZfU+EpdS6RIXHYhoeWqtEUfAfZU+A7t7YhiOVbhdPk12wNq0zT0sw4xr5sANxHmiMVtv1gj71Y0sZ+mqcf5OoLwIRrlVCcnaex+KOlYwkl3ceBf9Akl+X51XGF6QuM0qQt2VsLKnYRw3aaS8o2SVrd986DMQ3O3Yrp3dZm71toURymMusNtJ0xXMxP9mw7O1Umg6ppor6AzivBSUDD58CSxsH0TuM62JW3WgXY7omzARB8I2w8ktfRTxEJZYU3bO9UZ2P6JokZNhW8DH64JD7dLugK4l2whs32RIg36PkDlGHoXsEXd54beoohGdDnXT73vqM77B0KQGpNMDXkUT+53Mr/tJ/Lrk+9BHT8BdnGtjo2kDxMLapVDZlWiMvHNkr7jWoHF2rWPAP8k/F72sf28pAcbBBeAJyWtYfumGo/V6UuIV6H1fAEfBZbKNRm2n0tCztPAxqntQ53uo4bH/cbU2amEobZRORCVoT8v6R/01ftyXaM2sQNpE1JhyEp2bbiXzxLalCPp06Y0OnYn7ckuDPTPaaK/WJFduBKKtgb+2MS3YHgowktBweTD/oRvy7HAbySd2YV2ghsiWxwhvY/azlPnv1Sny5AvoDN00Ho8JOkfroU7O6J7ukbHJGyhWnhyjU++IF0m6RDgm/lCq6gPdEXD5a8RC9bMwBxkwkuGw4E/SNqLvi/ZVQltRT1L64J1wSWN8TJJrxGaMZK/z48VhQZvUCSiqxx97f7OwucSPgtbA+MVodWdzCH7EBWNT6a/RmsH4t3I0ct8TWgywdgeL+mpygSWhKSHnWppKSKktiBy2BzoPufqSa1xqfDn9PfAHq7ZuC2honbTrITAcyKRafemOt1g2hRJX7f9vXR4AXAtcBlZHaIm2N4n/S9U5qDjbZ/f7ZqCoaH4vBQUTGYk889oYrFaivjCPt+Rg6OiuZxIiHZ57dr1iIU/9w15gKjiO6ArwtyyZKK7nXAg/WeN52LAhe5fg6eK7GiCbe+c0fYSRTUbsaisQV++lBUJX47POctiK2kjQrvwOyIxXZMmo6LdGNiPCK02oUH5vgc6Bv+V0Gi9UmufhYiielfWtnPieRTw027+Gck8tS4xrx8l8ojsDPzRtcy8SSP0pTRW0liPqZkbep2v3wLneWANne2BrWx/Ih3fQmhz/pNMXWcQmrWVgHdX/jmSRnoIUWLp3g4FFrK9saRliZDgAc6o6T6WSoLjrIRz9fMNdI2Zp+vPJdGOs71C9nd24rls2MCi233kif06Rhb1Ckk32F5rUvB6q6MILwUFUxDJB2Nb4FPOitcpwm8vIEI4qzpHqxO+FJvavjuj7ZbnBNufSXSfJLQRh9Z47gfsa/u3Gc8tGlgtSvi7TN/kp5Oumy/1WTeB1OmWIFT7AHclk9hiuROkpGuBL7hWDkApOsv24YP0MTfwf7YPydq+CawJfLnSakgaRZiTbrb9ndR2PeH4+lXXKn5LmrGL/03lPLsRIchsaPtt3caZrtmA8K/ZIGvrZb7eQUQwvVSjHQFsZvvRRHe7k/OopJ8CT9k+MB0Pe5GWdBERSfMN2ysqouBudS0EWNIuwK7ASNtLKhzBj3NDeQD1JaAT4by+OBFJtVwD7V9sv1dRJmBzwufkTttL9Xgfedj4d4nsy8M2/+gNCkF/S8J22cpWtim0EbZ/dTg3C2GHP4LQQOxMJAjrhf/8teMViXDUsYSJ5VfAioPwWILQlvyVCOWdqYHm24Rvxb+B/xL+Gwd04LcWoc5/ezpegQhbfXiQ5/RFIo36A0RhwurcIsDPgd+nZzQr8EPCx+ioBl5fJnxUnk7bP4DdajQb1I5FFAI8EXiidm5N4HYiqdsNhAajOjeiRrteeo7/I6J9liW0TmOJJHn1sfY0X4n/boS/zvoN5+8kzFEQfjPr5Ocmwft8c/p7a9Z2WwPdbcBMNbo7WvaxCvDzDue+BcxNmML+RTgEHzyE+7gFeJ7I8fM8Ya58KTt+bojP55bhPuOypWc5pQdQtrK9Vba0yF1FfCGvnBaSf6VFdqMa7TLZ/sx1PoP0Mxch9FwGPNpybDM0tL07LbB3ATs10SS6PYE/EdE1VdsSRLr5PWu0hxM5aE4HbiaEnieI/B+z1GjnIHxBLiYEliOARxr6v5LwofgI4YA5LvFfYJB7noNIGNeN5r2E2eifhMCxIzBPjWYMkR9kZiL8/ZIu/G4lEvrNTPjJPEeE3fb6Ls1QO14v21+8dm7zbP8bhN/JBWkslfb9ncCfJ8E7fhUwb7VIp3f+6ga6v1TPo7ofwmzXtp9BhYD0jOeqtW3Qkv+tbcfS4/MpwsukepZTegBlK9tbZUuL3IZpgftvJYQQSd5urdHe0rTfdJzaRhAOoxcQ6eyfSYvkdBnNddn+qd14AmcDDxLp1ucjUs1P3Gq0txKJ5+pjmq/hvu4mCSlEqvuXCL+Hpuf1EhHS/IFskX2gge722vET1AS+7NyPs/3da+dOzvYPAf4GXE7UoJkXeLADz0Hnpwvt37vQ9jJfrd+XJFBsRlSmrtreBaw8Cd7xVQjh6Nn0969EcdA63WGEA/u9hOB3PnBIB55fzba9CS1dRwFxkPG1Eh6A/bP9yxvOD2hryfcNEYreiluJNioomHyYwfalAJIOdooAsX2vBhYFVIf9AceKooDrAJcS4Z9XAPfbvqp2XR6KvHztXL2P1Qk/g72JJGk5jcmy5gIz2n66fgO2n0r+HzlecoqichTYu8+1RHgZWkdnKSoNV+P7FzBrcg7G/R1P18n2d6R/mHceersrcF/q+/e2X5bUyUGwXgyy37H7F4as06oLbS/z1fp9cS35XnpOaxA+OpswDNi+JTlwL536vc/N/kH7ESa+O4hijX8kTHJNyLMivw78gYjwGgoEg4c/2z40OXHPBryt9n7NSRSWbO6gvyPyCOL/vnJE/vQQx11QQxFeCgomH/Iw33p4c31hdMtzEAvbfwlzzL2O8NimhbY1T9ujGq7vhAEJ4rqcW1JSHuY9Kj92FlZt+0jgyCw667fAQpL2pX901lwMLE9QhUzXBa1ui3yOBejLlPtjRZ2hEcqy2ma4Gvh4h2PTvxRAL7S9vAO90CJpJiIqalvCufhc4Lg6Xa9oCJl/l6RnCX+WidFUjlD0E9LWFU6lMhSJ5+yGiKQeUD2LNuHPeQXsPJlcxwrYuSMyUf5gYeK5rp/upbG4aEHvKMJLQcHkw4qSniMWzRFpH/qiKHIsLOkn6Vy1X9G+Iyd0RHUsQyxEl0l6EphD0gLuHykztyIp3nT01wCIvnTqHSFpSUITMtpZYcbsvgZc0nBfn6gd1/OwNOE5R8TQISk6azRRaXpJ6FnQmi59RU+X7VdCzPQVke3xqY+L0hf4xwhH4EclXW5724z2M206VhQlbEWb0Mt8LZGEQGX7Fe3i2RiqCuQfIXyFTgXW6HFc3bAz4ZB9ZTr+EHAjIcQc7JTsT9LHgO8AixHrUMdim5JWIyKY5kjHzxIp/8fWaXvArM4qajfB9lFERuvWFbAJM+saRHV3bP9NUXSzYBKjhEoXFEyF0MAqwf3gVIivw7WrEQvUVoSD6/tS+0mD8BywgElakPCl2ZYwq3yPyJtRz7I7yaEo1vhLwlQwnggnv76BbpVak4GnnWo/1WgfIjRgnSpgL57oZgG+QDiyjgN+afv19PW/Wbfn3+V+biEcoAeMlfBvebBG33q+1DLXjqJa9rVEfakHU9sDtpfodn1bSLqQyNfzRDqenzC9fY6o37N8ar+fCGW+w4MsQpLGESHv16bjtYGfuUOG3UF4nWd78zbhz5LWs31FgzYJGGDiq66pQrWrGk4zEH42PY+1oDuK5qWgYCrEIMLJYoNcO4ZIp783/X08/uq+rKFdkdTfowm191nE4nOBs2rXGe16tq9I+4vni7CkzfMf+Sxnx8ThEov3lUT4c55V+BCittO9kt5LOHk2LdJN2puRyTQy2naVDK8XLc0pRHbfawnzyrJE5ejn0rmhQPT336gwCviGpANtn5GNtbU2xB0qlgNIen92uCqhPbtMkdzwDDKN0yTAKPcv0/Ak8C5HUrzc9+VhIjS7zdfz85XgAmD7OkkdTUeKStKj6O/LUiXv2yDTfu4v6RVinps0Px8k/MdyM99ElgysDA5wtaT9Cc3qBkQywgsHv8WCXlE0LwUFUykkrUWYiK6x/aSkFQhHxw/YXmQI/G4hHHHnqRxs0wK/ExHS/O6M9lUiZ8leSRjq+IWu/tlIJ+53OG4SvEYSzrOz2d6ly7X9jlvc72rAj2yv04J2aaIo4y7p+A6nxGrp6/mmXvru0EfH8UsaSVQfXqXWPj3t5mt64FPE+3Kx7TuTaWZ/ItfMgMRoSagZTeREuY3wIzp+mPf4MyKZ4dmpaQvgEaIswu9tr5voVifMRlfTv7bRgGrVko4kTHanE0LD1oSP17npmlsy2lMJc+Jt9Pmy2PZXhnNfbSFpOsJ0tiEhEF0CnEuvXlIAACAASURBVNhSSCvoAUXzUlAwFULS4YSfxW3AvpJ+T3zFHUqHAnItMA/wH+AFSX8jcqOcSuRb2a5GuxBhdvpRUv2fBdQjhyYOt8P+gGNnGXQz/AO4VdKttfa3S/pqp+Omha7W1xhFevi+wYQA+EPi/n4LHA38jMjnkmtwXsv4vK6B0WBDQUcmSTNRjyLbhki+12a+fkEk67sJ+ImiiOFawH7OMvHW+vwz8GdF9fENCI3MsIQXwudjCyITtIikeuemxXvdjO4QIm/OLESyum6osv5+u9b+PkKYWS9rWw1YtoUp6nLXsvk2taX2vxN+O9cSHxJ312kq9OKIXDA8FOGloGDqxCZE3o2Xk1PpY0S+jE5hxW2wAFHX5/7kJ3IDkWZ/QOG49KV/LHCsIh3/1kRF5HuIL/S8KGBPkS5dMF3t+AT6m1nqx12RhK56/ycQ93UDEWVzC5E3ZLuaySp3Qs4drDs6lnYYwxa2q7DeP3ehW4/QJuT4JrBqm/kiFu0VbE9I/jpPA+/0wNIGnbRHTxGC3LCQhIZz0tYNI92y3lClremE5AhdmfLuJN7zxzvQDiX8eVlCuP0A8EOFc/zttgdUhE/arAMZ6Ig8SXyKCvpQzEYFBVMhJI21vWp2PCnqzrxoe9bs+F7by3SgPQb4Td1BNplXtsl9XyQ9Q6TtF/EDf011Cljb9jwZbdPiOQ+wPfA/27sN4b6uJxIA5hhJfJnvbvvCjLbfc5T0MOGn0bVa8FAh6Z+2F82O6z4/1VgfA3awfW9GWzebdZuvViY2Rch3hVXpq2wNsciuxzAgaU1CCHo3oVGZHnihLuxJ+j5whVPeo2H2eQvwKPFc5yA0NTfR3xy1aaLdnb7w58cyNs8BJ9g+poH/DIS59YNEteh5iWzAn2+gvZfIOD2WLATb9r+HdZMFA1CEl4KCqRCZQFBhnfzYWT6UHng+C+QOt18laiZVPH+U0e5OmBEWBM4ETs8dX2t8W0W6JNor66eJekhXAce7S8HDTlBEEOUmhYrnzR5Yqflews+j+uI+jYikUhprns9j2JD0cO6f1ODzY+Dftl9ouPYRsvmh+3y9CNxfHRJ+H/fT9+U/INpFb0CRQEljiPfmbEIbtAOhAfpGje55QgPSzWG2bZ+3EgJJR9QdmtVD+HN6tncQz/6yboJIFW3Uhm/B8FCEl4KCqRA9CgRH08U8UzkrSqr7DNTpmiKJFiMWo20I/4TTCUGmlflK0vuTb8UbhrR4fZZYsO+yfU8X2qvo/KyGrXlo6K+ueclDsO8AfuGBSe8q2tbz1cEROqcd4GvUqwN0G0gaY3s1SeMqgUnS9U7h+m8Eag7jsxFZnCdIehdReuOiulCcHJ+/QF803lVEsccBwrOkTxAalzWIpIvXE74vlzfQfp/QNp1Hf83PJBWKC4rwUlAwVULSorb/2ZI2zwlzEDXHRg8hJ0mHflYm8q6sYHv6rL11pIsi5Pl4QtC4g0g21lHYaDmuxwjnz7GEb8L3bE82h8kOpiAIbcLStmfKaM+kLwR7Y+AftnefBGNY07W0/y2ueSOEl2uADxOp/quqzjvZXrFGdw7xLl2cnFyH0+dEDZKksYTpch7CyXYM8KLt7WrXnEg4oFf/G58Gxtv+XJd+liHmbA+iIvqIBpq6ZhHeAKG4oAgvBQVTJWpfk+fa3qLldR1NAZIurZwkJX3dLXK+KGoTbURoXtYnQltPdxbBIulk+iJd3ktEDzVGuiSzwtcJE9imREKzj7S5ty5jfAmY1/aLkuYlFsTVO9AeWjkbS9rA9p+G03fi01rroR5CsHuZr9r7coPttTrQ5Vq6bYg8L/lYhxVSnJ7FE4S/y55EJuCf2b6/Rvdh4DNEkciziaKY9zIESDrG9pfT/i22V5G0GyE4H9bkLybp9gaBakBbaj+X8KO5nxA6ryWqYr9cpy2YfCjRRgUFUyfysNleIhW6fY3Ml+1vRWTLbe68L438x4hU52cAuzb5ZtAy0iVhukxgOFvS17uMty3+Y/tFCMdIRa6NTtiI0AgB/AAYtvDSZJKpIOnPRNhwhV5CsFvPF/3fl3pJhhy5Y/Nw0us3InsWLytKWixSF1wS3WVEory5iPfsT8l5+gTg17n5Rv3D5Ss8C4y1fVsluPSRay0ilHzn1NaUhG+8pCVt/z1dtASdaxx9n8iS23g+F4LT/XybPnPU1cDBtp/twLtgiCjCS0HB1Ilu4ceTgudg2J/wb9nb/asyN+HVSvXvCO3+awfBBXqowKy+ek6NyLQEs6l/LZ9+xR+H4tw8CbFo7biXEOyewszVuWZTXln7TGAO20/lFyvq7zTVp+oJyadoU2JtuQ14StLVtgcIIElLtj1hsrmVcJ5em0hY+KGMdLW0VVFjmxC5br4g6Wzbh2W0uxOavfNt35WEkiZTzj7AlYoswxAZeRszGtu+uftd9xOCf0mEa38qHX+aqMvUWGKgYOgoZqOCgqkQksYDL5AWOODF6hS1RS5FblT/yLMm2mrhmkirziHNFWG/RT6ZNTYmnB4hqlZfXHcw7RLpUvFdIaPtVq/Htj+b0b5KLARnEWGt9YR3pyS6XpybqwgeEWaNH9Vouya+6xV1h90er209X+pes8lOeUYkHU/MYb/U9pK2I8LavziUsWZ8qpo+nyO0Lt/OnXczuvOI9+pUwmT0eHZujO3VsuNLgC1s/y8dz07kkdmM0L4sO4RxzgLsRar2TAgfRw7FFFTzuWkyUQ07zUHBQBTNS0HBVIjcIbYbJM1ju23itryi8w8H4bsQ8cX6OPFVLMKEdISkdW3nOTJWBOYn6tXkWIz+uTR6qsBMhGlvRSTIe53QGpxru18yN3ep61PjeS79E931lPSuC99OX9WV4DlUtJ4vt6/ZtL7tXRuuP01Rk2e4mEFRzPNTwDe60B3jVA+rYSyr1ZoWJaJ8KrwGLGb7JUVtoomQNB/wNWA5MvNZg8PsrwhN03fS8WhCkNqqy5g7IdcAvCRpbdvXpfG8H3hpCDwLBkERXgoKpm1crqiy+1rlJ6BIJPdR4CFn2Vh7XOSfB461/ePaua8Qvhd5hNORwP5134+0kBxJc2G7wbB70qwcBxwn6R3EAnOXpH1tnzoEnkv04PjcyqE5odv9/b4ljwHoZb7a3heds8jCwAzHQ8HBRD2f62zfnMw2A8LqHdWalyey1+ZCxq/qtEQG5BslXZCOPw6crgiLrqfqP40Qcj9GhELvSGQPrmPpmnPulZJub3ODg+CLwCnJ9wUia3LXCvEFQ0MxGxUUTMNQ5Dh5HtjZ9t8kvZOI+jmNWBhutr3fEHiOcOdsrvfZXjo7vtP28h1oJ0bX9DqGTBW/CiG4bEA4mR7hLvVluvBsHRrcC+2URrcIswba5wnty0219tWJ5zpoEctJAUUOmw8R7+gfCfPkdba37EC/KuEPo0RXz6hc0Y21var655m52vYHa3QnA8c5hZcrQvh3tP2lBp4z265reCa2STrP9uZVO7AlYTqdm3Astu2DWzyWgh5QNC8FBdM2TFQdrr5udyRCmXdTJOIaS1Si7pVnN1X3i7XjbtEtQzWbWNJBxBf0PUS009fr/jZvIFpXYmyIhjERcXWd7Qcn6aia0csX6KPAWWnxrqKNqky42wx3IMmnacB4cl+mhC0Jc+Ottj+jqEN1YgeeRwFn2j6qxRCqKKXHJW1CmC0XznhVOXlmBHaQ9M90vBgDtTgVbgDqguzEtkpwSbgAeIaomfVoi/EWDBFFeCkomPaRLxbrAYcD2H5V0lATgM3VwZdDRBG7HDdL2sW1xHCSdmbo4bgCvgU8QCxyKwKHptDijinvW/Bsi14Egia/mVHANyQdaPuMhvNTCi8SNXq+BOyU2u4C3utaKYUhIjeTzUI41T7WQFdlwX1d0pzAk3ROCXAL8E1FxtzzCUGmUfMCfDeZbPYiaizNSThmV/hY2xuRtACReHGEIkFjXsRx1g6XLWx7o7Z9FAwdRXgpKJi2IWCcpB8SX3rvBC4FkDT3MHheTWdfjmtqx3sA56eIlfxrfiZi8RoK/gx8sg2h2mcj3reH/lsLOm4oqwAgaSRwGbVEcG8AehHKXrX9BLUszJI2kPQ12xsMZyDuq6Bd8T2deAZ1jEnv5wnEO/M/wtzZxPMUwo9kJLAF8IM050s10FbC07PAgGrUdb+sQfARQsBbmP5Rac/Tlyuojuslvcf2HT30UzAU2C5b2co2lW3Aetn+4rVzm2f7IwnTzH7AUcCK2bn3AZ/Ojufs0t+i2f6Gg4xt/g7t6wK7pW29DjQfJyJFquMDgNuB39Xvs4dndUu2f+4kev77TyI+t07Cd2JGYGUiNX3eviFh9pirNhdHEcUcZ8rfK+CvhLDwa8LnZAwhQGw+qcaa9bc0cP8gNKOIJIeD8VoDOAL4O3BhB5pTgLmz43mAXw7zHrbogfZuIjLqPmAcUQJj3KR+rmVzEV7KVrapcastyLd0OjcIj0WAfTrwvLwXnkSa988SX9GPDuO+xgGzpv2PpYV0VeBzwCU12ueJcNZqezYtXCcS5QAqulub9jv0vwuwVNoXkUDsuTSuVSbxHK4HXDGM648Dlsue/91pMXwUGF2j/QuwUNpfifC52Sst5ifmz4dwlJ2Z0Gw9R0R2Tap7rs/ZXzst/kTith8lgWSzLjx/QEQsXZzewbm70A6Y/8HeiRb3NDNReXx/Qtg+ADigA+1iTdukfK/KFlsxGxUUTJ1Qh/2m474T0tuIXBWjCXv9+fnpbH/kYDwljSCypW5LOCfOQSx4dbNRL7BTKn9i8fqF7bHAWElfqhEO8CVJmWN3Ihb2KidHL9mIdwdOTvujgRWAxQmNxlFEMrieoObCjCMJX48deuWX4QO2v5D2PwP81fYnky/GRUQG5Aoj3Jd7Z3tC23CEolTCbRmdbV+V9n8r6Sm3c4RthaY5yyFpOUfm258RJs7qHj4v6cO2/6/hsgeBtWw/3WII06XcR/9N/Y1k+O4RF5DKEZBVim6CezNLFQwDRXgpKJg60W1B7ncsaQ7Ct2RboHJqXML2wl2uG4znaUR9lkuBY4ArCPX/Ve1v4f/bO/NwyarqfL8fDdqAzaRCEGRsZkSmBiNEQmOLA1GBKDQqCgSjEkHUwE9QERWiiEJAo2JUBGVShohGZEZRWuapu3ECERACggqCyvT9/ti7+p46t6puzcOt9T5PP5w6Z5+9V9W93LNq7bXWVxPlDqlPkLqb/lfhWqOqpWRkeiidIOlthdOVlvvFdvtQu+X+057QzdkNOM32wySdnWKb+VYoJ4EaeNi1daBaodiYbR5JwBDbD2iyJlLxxFxSi3yckmKL48ryDFIdeYYecTrJEd4J2Nw5XCHpG6So0iRsf0nSypK2o7onTC0n+rOkvJPvkH4ObwaO6dDmSMIdQsJ5CYLhZD0lfR4Vjsmv1y2NfZCU7PhhUnmuJdVKlF01l/WqcFyZ84WlsZuTGmwtBu6w/YykbjSFOpEUCXgUWOxcNZKrOe5vdGMFJaXrJX+73GQ34syzuQPsH0jOU/HB1m5Z9zaVh37xW38X+KOk3UjbRDuQhQaVZBvKtl4h6RzSZ7gyydkkv9eiE1ROxC6+NtBr56XiSf2c1Dm3Eql4MWnrbvINSWrgEFLi7M0kJeprSE5aFbZPU1Iun5vX2sOFnkBt/nwiCXcIiSZ1QTCEqDW9nkNJPTqWJ3UjPRu4xFnPpjCuqsKkxpxVVTOSNiZFc/YiOUgbAy9xfdHFKZG0Fkm9d1XgFmdBx/yQXcaFqqE6pdorZ3uudm78JWmPZp2H7Ax8maQ0fKHtA/P5nYDDbL+ujfe0pKFdN5vb5dLgk4C/A060fWo+vyspqfoDhbEifS6rA+fYvi+fryT4/rAbNnVK5fORdBUwh4kKozkkh6SiDl7UbbotX19ge8v8e3m07b3aXb/FexaRtrjuIm0btVuqH3SRcF6CYAhRaq3/Qpc6yUraDHjQJVXgfG09Uh7H3sAGpMTCC2z/ogv2bJvnfhNwr+2XtzlPK11uyyKOBh4GrrT9/VpzNjN/jlzMKjo5Sq3m5Sz+1wqq7gbcdLfbbiLpYtuvamLcQBvqFZyXVpzz62zPkXQzqR/N39Sm2GE7Px9Ja9exMfJbBkg3tCyCIOg+JzN5KwdS6LwqwVLSbEk72L7T9jFO7fi3A15N2vapjDtO0rsoIelQSZ9uZIzt6/M3/bXJ+RT53g/Vv6smrfRP2a/0b3/b/150XGrM2XD+3Mvkadt/kLREhC/np7QrTLispK2UWtjPzMdbV/61OSd5G6hy/OnStYtLw2v9rtRiVunfCqSePD+Q1HGH3SZ4EpJz0uifpGsK99ybe8JcAFyipHFUq/FdM7T8bT07KS8mlf/fTYoOxbNzwETkJQiGEEkLbW9W51qVlpCk75H6ktxaGjcHOMr2bvn1IlKS5LOlcUuRelHU1Ceaws6WwvCSHqRB0zbbBxfGntRorspYSXeQokJLkXqX7EPBibF9Yy17y7a3u+Uj6YrGZk5SNG523mJEp2xrVQRB0p3ABxsY0TCXJVflXNrplpeky2zvMtW5JuapGSHJEZsVgYtsP5nPNZ3H0ua20VEkB28j2xsqKa5/2/YOrcwTdJdI2A2C4WSZFq6tU3ZcAJxUfdeuPuVJcgG5IqWVLq1FWr3vLzQvGfAu4HbgHNI37Xpr3c9EB9QHqO6GaqoTO9sqQW+E7UmdXGshaZ7tS1qZuoVrK5Kqnmq9hykTcW0/0sHvAJJmklrmv0CpnL3YSr+RknVdk2qerK20fRmTtYfq0c573J1USn9jtuF3ShV+wQAJ5yUIhpNfSnqt7f8tnpT0GpLeT5FmhRGfkLSBJ0QcK3NuQGMhxka0Grp92KndezOsTsqx2Qt4mpSIfG75W3azzkNleJ3jWq+7zaeBVpyX5XLC7VJU6+uIydVGd3uy+GHTSJpLqsBql38lyUS8iPyQzzwKfKGDeZthiUOiJJPxddsL64xtKQKUedK2K9V2OT8qGDDhvATBcHIo8D1Jb6ZaL+jvmdxXpFlhxI+Schs+WZrzQ6QHTzu0+k32yamHJJz6r3wJ+JKkNUhbQwslHW779CojpFWBg4DNSE7IIuALniw22KgnzJR9Zjqk1c+qGEUqR5TKFV9Nza0eNdRzanT3n5Lea/vkducp0K6I5h3AKTkp++skhfU/Fex8pA1bzpH0ZVKPnANJXX6/MsU9QY+JnJcgGFIkVdqSV3JRFgJn2P5radxqpMZ0T1JDGLFY2ixpc+DfS3N+xm32sJB0hO1jWxi/VqPrriGwmJNe55Matd0AfNbVvTt2IJWIn5qvi7SN8HbgLbZ/0qx9vaSbZdQ15t6sQbShOK5cOdOVhnqS5tq+XLXL21tufidpc9u3Nzl20ucqaSNSV+L5JJHPr9hulJs01RrzSBpSIslYtBJBC3pAOC9BME2QtDMFp8T25R3MdTINtlGKibUtzlv55l/8Zm1StcyqLjSck3Q0Kcq0mJTke5Htp2vMuQB4t+2bSue3BL5se/vCueWAp5y77OaH3GuB39guSil0nTaSm+cA91ScT0n7klSV7wY+VowiSLqL6p+XCq9te/08biYpl2g2qaPtV2t9pq0i6WjbR2lyeXtl/f1L4/cgbaOtysRWmF3dDbnZtcvJyzNIvzf7kaqEzgF2BB633XZFlaQVqG6O2E4UJ+gS4bwEwRBS42FUZMnDqMU5vz7FnAcUxr69cO1o4KjS4GbzVqayaR3gcOCVwEnFLQdJz5Lyeyr5OBXbq5qESVpke9M681ddk/Qj4ADbv5Q0m9Qk7VskdeXrbP+/bryvOracZ7tmZKLO+BuBV+Zk2leQHLj3koQXN7H9z4Wxzy/dvhSpNf4HSaKbe+ZxZwNPAT8GXkPKlTmkg7fVFpJ+BfyT7cVNjG2YxyJplYojIelzJD2uy0iO2bWFcT+3vVEbtv4r8HHS7+GzTPz+rdfwxqCnhPMSBENIsw+jFuesdc9apHyXGZ6shVS5r+uN13KS8JHA9iQ9mm94QnOoMqZmc7AKzk3CJC0GXl5O5M3lvz+1vXHh3G1OfXCQ9AlgFdsHSXoOcEPlWovv5a2kv6XlPJwDSd/2z2h1znz/LbZfmo+/ADxk+2P5dc0mbbns/W2krcGbgWNLW2zF9780cG03t7KU+rHsC6xDdZTi4NK4nzRbaqwkD7Bfnm9SHktp7P7AWZ4Q/yxeW7HefVOs/0uaF4YM+kQk7AbBEJKTVWs9jF7nUtfdFuY8t3Ks1I33CJL44qeArza6tZ31apFzbo4kJdYeR4qCPFPH3mY7mJ4AXCzpg0xUumxD2pY4oTxt4Xgu8Jm81pM50tMOHyB9jmXOAq4k5eO0wwxJS+dtnV2AdxauVf3tVtJ72p+U6H018Abbv64x5xIH0fbTHVRH1+N/gQWkLalGn+f1OQp0AQWl5lq5Mbb/G/jvQh7LrZKq8lg00QzwZmDj8vuyfWM7jkvm12TZgmB4COclCIaQFh5Grc67Ccl52Ir04H5XN3IeWuAW4B7g+6QuwNsVHzSublL3GLUdp6r8CNunSPod8Amqq40+afvC0r235m2I+0h5HxfntVbq4D3NsP1Y+aTtx/LPsV3OBK6S9HvSlsWPAfJ2V/lBfBepnPxE4LekqqqXFmypOAWVaiuorrhqO+ekxEzbZQmCWqxAcgiKkgZ1+9HkPJaN87/fk36P3i/pX3Mey2cbrFXu9dMqHyKJM/6MakerrbyvoDvEtlEQDCGS7qX6YVRFq9Ubec5vk6qQjiclMVZFPEoJoEXHYTnSg0YTQ9t7yEl6Bw0iOd3KpWmw/rIkheLVga/ZviWffzmwfnnrp8k5FwPblit2lBqZXVfctmpj7pdlWy+uzK8k2Lh8MUFZ0qk0zmdquwdMKyiJhP4Z+B7VD/q2k1t7kcfS4vrXkr5AVEWTev27GjQmnJcgGEJ68TCS9JvCnMXk18qcA01ALGyRtHrfObbfnI8/bfvwwrVmBQtfDOxt+zNtrP9B0rbOu23/Jp9bh9Sc7cp25myw1vKkjq/z3YYCdq+RdBBwDPBHqqud1svXD7N9XL1qtlrRjFbzWLIjug7VOTendfCefuo2hUiD3hHbRkEwhNh+R71rSn1d2plznWbH9qqkWNLVtnfMx6fbflvh8rUU2rwXoj/lsuqlgefYrvz92qBwfR6peqlCXcFCSS8gdfCdD6xB6pXTMraPl/Rn0hbP87KNjwOfsv3FduYs2fkc0me/D0ls81xS877imBNtvy8fH+LUNK5y7dRGv09d5v3A7AbJrZXqouuZIpeqnTwWSacD6+d7KpFFA207L8AVkt4JXEiXoklB54TzEgQjgKQVST0+9gE2IT1suzHv+sDepG/yRWHGi4ADSDIFs4FrSCXFu0navoOS4mJr9bLwZNXTyXaVfkzehnkPqRV90dFoWgMoz7E76XPcMM+zXr1Kq2axXekE/DxSRPsxSTMkvcX2t9qZU6kx2nxgV+AK4HRgO9v71RheTBh+O9XK41u0s36bLKRBcmshB2kRKWF8HSaeQ2Uno508lm2BTd3dLYV98n+LCuoGolR6gITzEgRDSs7PeD3pj+fWwCzgjcCPOpx3dZJe0D6kB9t/kB6SRVb2hAbS20nlqe+tlBQD7TovrYgNVuxdiVTOvS+pcmdOpRor04oG0IOkCM+HgattW9Lubb2TCftWIEkTrAH8D3CppH9jokKsLecF+CEpSXdH23fltf6zzthGgpP95BngZiWl7UbJrd8kfT51q5LcmmZVhduBvyOJdXYF2+t2a66ge4TzEgRDiKRvkb5NXwx8Hrgc+JXtKzuY80CSk7ImKWH3X4D/sX10jeG9KCmGpA+zO8nRWEkT7eRFUkYu2vsCUhnyXsDXgK3qlLu2ogF0BCnS9EXgjFyu2ymnk0QNrwEOBA4jSTO8wfbNHcy7DcnWSyXdSSq9nlFn7FJKas5LFY4rTky9e3rBBfnfVDxk+7vNTjpVHoukC0m/s7OARTnJtug8vb7ZtQpzNmwo2E7SfNA9ImE3CIYQSbeQHj6nAWfbvkfSnZ0k1Up6kvSA/YDt6/O5mnNK+ibpwX8fKcqyru0nchTkKufmaW3YUKt9/BKKWyKSHgceIjUmq1WK/LnyuRbsWI/kyO1Nypk5Cjjf9i/amKvY+G0GqZR3rVrl0x3Yu0O2d09SNOd826cUrv+Gie6vZQaejF1G0i6k93MZU/R5qZfHUiqr36nReravasPGRr+rfavgCmoTzksQDCmSNiZt7exF2u7YGHiJC0KLLc5XTFBdjRR9eYftF9cY2/WS4jbs/RiNy6qPzuNqNYgrjmu4zSbpJaTP+c1uT3ahSrOo/LqbKDUtnEeqjKqV+zIQKhVfqq1ajbOUQ2H8N0m/zwuZ2Daq6RDkUvSm8ljK1Wb1zgWjTzgvQTACSNqW5HS8Cbi309LNXBq8V55zOdI3+SOavK+tkuJ8f8MGZu1EU/KWwaSpgJcCa7og9tgLJD1Dqi6qRD2WZaIvTic9cRo6QLZvbGdsL5C0uu37VUfSwaVuycVoVRNzfxs42PaUeSy1HEdJt5adpybX7frvatA9IuclCEaAvM1zvaR/J+VUtIykzwNn2P6p7XtIzeqOz2XQddV2u1VSnDmeFP7/AWm7oG5yqaSTGk1U2Taw/U+l+3YkdRG+H/i30rWmuva2Qg+do2K1zTakROkly1JdbXM9KYrxUH5dLi/vpMPslFQci7KT0oAFkjZ1A6mLVvJYJL2bVIm2nqRbC9PMAn7a9BupZtbUQ4JBEZGXIBgxJP3W9lpt3HcIyUlZHTibVEFUM6G0TknxXp2WFEvaMtvwatLD+EzgslpbAqpWtp6ESx1Ocx7FR0gPvGNtXzKFLV0RnJQ0E3gXSW7gVtI2W1clF6ayVamz7Z4k2YCzSJG0P3fThinsa8kpzFtB65NkDf5WGLdFYUzTeSy5lcDKpMq5mGnskQAAFqJJREFUYiXcY9GPZXoSzksQjBiS7qmVp9LC/WuTHIi9gZkkB+LMQmk0kv7C5JLijhKGa9jxclI055XA4a1Un5TmeR0p0vInkp7RT5q8ryu5Kbli6SlSWfNrgLttH9LpvKU1mrJV0rqkz/QNwN0kJ66Tiqee0Oz2Uh7bUh5LTppejerKpEkSGy3YOpPU82gz0v8vlTkjYXeAhPMSBCNGu5GXOnNtRSpD3qK4/ZG/ye9Naip3BilSc0m3nBdJLwTeTNqOegr4iO0FpTENnZnKtkEu3b6XJNZX/INW+TZfs0y2i85LsdpoaeDabifstmKrpM1IP7u3AYfZPqebtvSbVvJYcn+djwH/R3UicNuN+nLOzR2kKOTHgbcAi7vtoAatETkvQTCEFPb7J10Cnt/h3MuQtm32JmnyXAVU9XqxfQJwQqGk+ALgRZIOAy5op6Q4r70fKVF4JvAdUoXPg3WG/z1JgfpM4GfUz4+pNDNbllT2/Czwa5ISc3n9Yu+OlUqv2+3d8VTh/qelumk8LaFq/Z81yzlApVLh9Ug/zzeQPrOzgGNs/7UrxgyANvNY3gds5Oomhp0y2/abJL3B9jcknUFqIBgMkIi8BMEQ0qO+FZV287uRnIGzSI7I4zXGzgZWK27BSNqCpHK9U7tJqjlKchsTStlVf4BKSZgzSGXB80mdgL9P2t5aWJpzaeBYYP88r0iN+E4FjnDWZ8pju967o1BtRF67W9VGTef85M/1VlKH30eZ/LmOXGVMO3ksSp1953Uz50jStba3k/QjkjP1ACm6NlS9c8aNcF6CYITopFQ5/2E/E/jOVEmMkr5HevDfWjo/BzjK9m6trp/vb8spk/RckhPzGeDjtk8uXDsBeB7wfufGcEot+48HnnAWLBw1cq7FLNsPlc6vCjxajKpM0RPHtj/eM0P7QLN5LJK+CmxEcnSLlUmdNDT8F5IY5ktIDvHzSNucX253zqBzwnkJgiGnVqmy7Q+2OdfSpKTSjfOpxcBF5W+qkm53tVBj8VrTPTpatO1s23uVzj0XeB3pva8DfJdUzXNfYcwvgQ3LFUv5gXeH7Q0K594P/Mn2V0tj3wvMsH1id99V+0g6hfSzOa90/i0kvaN3NznP+4bpfbVKK3ksko6qNYdrS2A0u/66ztpSjc4F/SWclyAYQnpRqizpRSR14vuBm0jbGluRhOx2tv27wthf2Z5dZ5661zqhnIgs6RvA5qSeMGfZvr3Ofb+wvWEz1yTdDmxt+8nSuOcC13WS2NltJC2yvWmdawttl1W5683TtQTvQSDpV8D2reSx5P9/3I1y8ToJwzfY3qbTuYP2iYTdIBhOuq5+TMoL+WL5W7ikg0l5BcUci+skHWj7K6WxB1DdLK2XvI2US7IhcHAhEbacS7JI0r4uCPVlW99KqhIp4rLjkk/+Td3KtO0ejexZqkvzjAL3kMrgp0TS5iShzFXy698D+5bzpJqca2NSefSKpcTuFSiUTAeDIZyXIBhOeqF+/DLb7yiftH2SpJ+XTr8POD9vUVSclW1JasltO1Gq38ZewDIlu5p9QB8EnCdpf5KtBuaQEmcn2SppNdv/Vz7X5Fr95EFJ29m+tngy5x09VOeeWox6eP1O4EpJzeSxnELKfboCQNI/Al8B2pHT2IiU3L4SUOzi/BhJPTwYILFtFARDjLqrfly3S2u9a5J2Jm3dACy0fXmr65bmu6LRdds7N7o+xdxzSd+URbL1shpj9gUOBj4AVPR+tgGOA/7L9qntrt9tJG1HEs88lWoHcl9S0vbPCmMbdbhdrt3qsGGglTwWSbe4pHhe61yL6/+97WvavT/oDeG8BMGIoM7Vj+8EaiX6CjiunTm7iaTtiw/kHq7zGlLp7eakB/5C4FO2f9DrtVslR4TeQ8GBBD7foDfOtKWZPBZJ55Oc0orq+VuBbW2/sYN1jwM+SeobdBFJ8PN9tr/Z7pxB54TzEgQjQq46erhcVdPC/Y16nGB7v7YM6xKDTCyVtBJwkO1jBrF+K+R+PYfZntfk+FFP2K3KYwHq5rFIWpnUcHEHklP+I+Bjtv/Ywfo3294y55y9ETgUuKKTaE7QOZHzEgRDiKSXAZ8CHgE+Qfrj/QJgqZycelGrczZyToYk56PniaW5T86HmVDHPpPU8n3ffDw05G2wLwEvInU4PhY4jfQ5teJkjXrCbit5LOsDLyYlNC9N6iA9l9TksF0quVivJTVJfGT4crvHj3BegmA4+TwpaXdF4HLgNbYX5AqIM0nh647IHUz3JG1FbUJ6oA+SfoSBTyPJIZxHkkhYQNqK2cL2A31YvxU+C7wTuIbUm2cBqTnaf7Y4z6iH15evOC4Atq+UtHydsd8ibY3ezkRPmE65UNIdpG2j9yjpco2s7MJ0IbaNgmAIqYSq8/Fi25sUrtVNvG1i3mWB15Mclq1JOjFvBH5ku1t/7But30izaa7teg+lbq1flbwp6f+AtWz/rcFtA6HcX0TSr+vlJeXmezUvAUfaXqXO9aGnlTwWSVfb3rEHNqxM6mr8jKTlgBWG0NkdKyLyEgTDSdGRKAsMtpvz8i3gFcDFpMjO5cCvbF/Zznxtcnyb17pGfhBV4v4PAMtVvsl7CtmEPlMWjlTxdanz7qwG87QaqRk29iflsZzLRB7LO+qMPUrSfwOXUV1W3Y7gZpFNgHVyh+oKp9UbHPSeiLwEwRCiCbG/otAf+fVM28vUu7fBnLfk+08DzrZ9j6Q7PQQCc+pAs6nFdX5DcgxrJS14GD6LClMkWNtNikhKmmP7ui6Z1XckbQscSZKHqDgP9eQBvkmSvlhItZRAy4KbhTlPJ+XS3Aw8U5jz4Pp3Bb0mnJcgGCNyzsw+wF6kLr4bAy8ZRAhcXdRsCqqRtCmpN9B8kpbTtgM2qW1yA8VJeSy2764xtuu6W5IWA5u2W+UX9IbYNgqCMcL2HcBHgY/mb7TzgWsl3Wu7nS6kLVFHs2m9TjSbWly/3OHXwO9t39OP9VuhRh6LSWXCV7uGKKCktUk/z/nA08DapNyQ3/TY1F7zkO0Lmxy7QNKmthd1cf3bSfpf93dxzqBDIvISBGNO1vR5he2r8usP2f6PHq31FyZrNvVt66pOh99VSLIH823f3A87mqFOZ9lVgF1JvUvOKoz9Kaky7SySiOUvJd1le93+WNs7JO1CcsimzGPJUZL1gbvy2IoOVtul0vl3ZkvS721x/de3O2fQOeG8BEFQRS0V3S7OfShpO2N54AzgbOCSQeea5CjU52y/YpB2NIOkVYBLS5VI/0NSCP8ucIbtnw5LPlOntJLHkqNPk6i1xdTC+jvVmfOqducMOieclyAIquikFLuFNbqm2dRFm3rmtHWbWj+jQt+e+cBskqDgri4JO44avchjCUafVmTVgyAYD3r2jUbSsQC277R9TH4ozSFteQxMWyh3GB6Jb3K58+4fSuf2sP0n21/LsgHbk3KbTpQ0dPk8LbIgJyD3FUlX5/8+JunRwr/HJD3ab3uCaiLyEgRBFb2MvAw6uiHpZCY7KauQWs0f0kJiaM+RdBu1bf0dSdvnjsLYup+rpLU72TYZNL3IYwlGn6g2CoKgzLd7OPeMUpO4KvrQJO768pLAwyTtnGFTat6t9NokYc7HW5lklB2XzKsHbUAwfETkJQjGhDpRhyX0o+mWpL8B9zHAJnGStiJ9k19oe3Gv12sXSTOBd5HyV24Dvmr76TpjnwB+VesSEaUIpiEReQmC8aEYdTialCTbbxb1Ohm4EZI+ArwNuAE4TtJ/2P7KoOyZgm8ATwE/JgkzbgocUmfsXcA/9cmuIBg4EXkJgjGkHxVFw7RuYf2FwBzbT0h6PnCR7TmDsqcRxSqbrKlzbYO8loF+rkHQb6LaKAjGk0F9a5kkEihp5dworx/81fYTALYfZrj/Bj5VOai3XVTgJz22JQiGitg2CoKgn6wlaWPbd0h6LnAR8FLgaUn72L60x+uvL+m7+Vil18PWNfWlhZJcAcvm15U8lhUKY39YrCqS9FFSz5e7SVVUk+QEgmCUiW2jIBgTJD3GRMRlOZJSdSXiUX4Y9sqGhcDmWRbgnaSGaq8k6Rx9w/Z2PV6/ZrfUCqPaNVXSrcDL8nbYbsDnSJ/tVsCbbO86UAODoMtE5CUIxgTbswZtA/BkQZ13V5IOzzPA4pzX0VOadU4knWt7z17b00Vc2Q4D9iBVJt0A3CDpPQO0Kwh6wjDv9wZB0EUkLSdpmcLrjSQdKmn3PprxN0mbS3ohsDNwceHacn20YypGTRNIkp4naSlgF5KIYYWZA7IpCHpGOC9BMD5cBKwDIGk2cA3pIf1vkj7VJxveB3wHuAM4oZKLIem1wE19sqEZRm0//UTgZlI5/GLb18OSnjb3D9KwIOgFkfMSBGNCqfT2E8Aqtg+S9BzghhC/m2DQMgbtIGkNYFXgFtvP5nOrA8vY/u1AjQuCLhM5L0EwPhS/qcwFPgNg+0lJz/bDAEn7Nrhs26f3w44m6FfpdleQtFY+fBhYs3+V50EwGMJ5CYLx4VZJx5Pa888m55tIWqmPNtRqCCdSd9g1gJ46L5LWajIKcXgv7egB3yc5p0WvxcALSdGYGYMwKgh6RWwbBcGYIGlZUnv51YGv2b4ln385sH6/ox65Md1bSI7CIuAY27f2eM0l20EjWFHUNJLWIX2urwROsn3yQA0Kgi4TkZcgGBNs/wWolZh7D7BDv+zIJdHvAD4A/Az4Z9s/79fyheNRqyiaEkkbAEcC2wOfBQ62/VTju4Jg9AjnJQjGEEkvAN5EamS2BnB+n9Y9iBT9uQx4daUjbB9xneORRtLmJKdlM+A44IDcPycIpiWxbRQEY4KkWcDuwD6kjrbnA3vZXrOPNjwLPAg8RLXzUGl5v0WP138GeDyvtyypy3Bx/Z53Ge4F+X3dQ8p9meS02D6470YFQQ+JyEsQjA8PAtcCHwauzi36+9mgDmDdPq9Xhe3pmrh6ANMokhQEUxGRlyAYEyQdCuwNLA+cAZwNXGJ72uV+1EPSHrbPy8cr2/7DoG0KgqB1wnkJgjFD0nqkXJe9gQ2AjwIX2P5FH9YuikNWXaIP2zalaqORa0RXD0kX0iDyMmRq2UHQMeG8BMGYkCUBVrP9k8K5LUit5XeaxlsqS5B0k+2tysejznRVyw6CekTOSxCMDycCRxRP2L5V0uHAUYMxqe8sm/V+lgJm5uMl5dO2bxyYZR0wjdWyg6AmEXkJgjFB0u22N69zbYnu0XRG0hUNLtv23L4ZMwCmU7QpGG8i8hIE48PMBteW7ZsVA8T2zoO2YcDEt9VgWhDOSxCMD9dJOtD2V4onJR0A3DAgm/qOpFWBg0gN3UySJviC7QcHalgQBE0T20ZBMCZIWo3UmO5JJpyVbYHnALvbfmBQtvULSTuQysRPJX0GArYG3g68pZjMPB2JbaNguhDOSxCMGZJ2Biq5LwttXz5Ie/qJpAXAu23fVDq/JfBl29sPxrLuImkZ0s/4vmJESdKrbF88OMuCoDuE8xIEwdggaZHtTVu9NuxI+hJwsu2FklYEriHJBKwCfND2mQM1MAi6zFKDNiAIgqCPSNLKNU6uwmj/PfwH2wvz8X7AL3L12DbAYYMzKwh6wyj/zxoEQdAqJwAXS9pJ0qz87x+BH+Rro8qTheN5wAUA45DHFIwnUW0UBMHYYPsUSb8DPkF1tdEnbV84UOM644+SdgPuA3YgCTUiaWnGpAw+GC8i5yUIgmDEkbQhcBLwd8CJtk/N53cFXmX7AwM0Lwi6TjgvQRCMDZLOsf3mfPxp24cXrl1s+1WDsy4IgmaJnJcgCMaJDQrH80rXXthPQ7qJpHMKx58uXYvS6GDaEc5LEATjRKNQ8yiHoaelUxYE9YiE3SAIxonlCqrSFYVp5X+jnNg6XZ2yIKhJOC9BEIwTDwCfq3FceT2qTFenLAhqEgm7QRAEI46kK2kQYQk17WC6Ec5LEARjg6RXNLpu+0f9siUIgvYJ5yUIgrFBUq1GdAZeCqxpe0afTeoKkuYA91Q66kraF9gTuBv4mO1HBmlfEHSbcF6CIBhbJO0IHAmsDBwzql12Jd0IvNL2Izm6dBbwXmBLYBPb/zxQA4Ogy0TCbhAEY4ekXYCPkKIux9q+ZMAmdcqMQnRlL+AU2+cC50q6eYB2BUFPCOclCIKxQdLrSJGWPwFH2v7JgE3qFjMkLW37aWAX4J2Fa/F3Pph2xC91EATjxIXAvcDDwOGSKucF2PbrB2VYh5wJXCXp98BfgB8DSJpNctSCYFoROS9BEIwNknbKh8uSutI+C/ya9MDH9lUDMq1jJL0MWB242Pbj+dyGwPK2bxqocUHQZcJ5CYJgbJC0NHAssD/wW1LEZU3gVOAI208NzrruIml5YHdgvu3XDdqeIOgmoW0UBME48RlSZdG6tre2vRWwPrBivjbSSHqOpDdmocb7SfkvXxqwWUHQdSLyEgTB2CDpl8CGLv3hkzQDuMP2BrXvHG4kzQPmA7sCVwBnAyfbXmeQdgVBr4jISxAE44TLjks++QyjLWD4Q1IEaUfbb839ap4dsE1B0DPCeQmCYJxYlLvPViHprcAdA7CnW2wDLAAulXSJpAOAkewWHATNENtGQRCMDZLWAM4jVRfdQIq2zCFVH+1u+74BmtcVJO1A2kLaE7gZON/2KYO1Kgi6SzgvQRCMHZLmApuRqo0W2r5swCZ1HUlLAfOAvW3vN2h7gqCbhPMSBEEw4kjautF12zf2y5Yg6AfhvARBEIw4kq4ovNyGtCVWwbbn9tmkIOgp4bwEQRBMIyTdlPvXBMG0JaqNgiAIphfxjTSY9oTzEgRBEATBSBGq0kEQBCOOpJOZiLisKemk4nXbB/ffqiDoHeG8BEEQjD7XF45vqDsqCKYJkbAbBEEw4kiaCcyy/VDp/KrAo7b/OhjLgqA3RM5LEATB6HMS8A81zs8DTuizLUHQcyLyEgRBMOJIWmR70zrXFtrerN82BUEvichLEATB6KMG1+LvfDDtiF/qIAiC0edBSduVT0qaAzxUY3wQjDSxbRQEQTDiZMflHOBUJqqNtgX2JQkz/mxApgVBTwjnJQiCYBogaTXgPcDm+dRC4PO2HxycVUHQG8J5CYIgmKZImgccZnveoG0Jgm4SOS9BEAQjjqS5kn4h6c+SvilpU0nXA58Cvjho+4Kg24TzEgRBMPp8Fngn8HzgO8AC4HTb29g+b6CWBUEPiG2jIAiCEUfSjba3Lrz+te31B2lTEPSS0DYKgiAYfVaStEfhtYqvI/oSTDci8hIEQTDiSPp6g8u2vX/fjAmCPhDOSxAEQRAEI0VsGwVBEIw4kt5fOmXg98DVtu8agElB0FOi2igIgmD0mVX6twKpw+4PJO09SMOCoBfEtlEQBME0RdIqwKXFSqQgmA5E5CUIgmCaYvsRGitOB8FIEs5LEATBNEXSXOAPg7YjCLpNJOwGQRCMOJJuIyXpFlkF+B1JWToIphWR8xIEQTDiSFq7dMrAw7YfH4Q9QdBrwnkJgiAYcSTNBN4FzAZuA75q++nBWhUEvSOclyAIghFH0tnAU8CPgdcAd9s+ZLBWBUHvCOclCIJgxJF0m+2X5OOlgWujPDqYzkS1URAEwejzVOUgtouCcSAiL0EQBCOOpGeASnKugGWBJ/Kxba8wKNuCoBeE8xIEQRAEwUgR20ZBEARBEIwU4bwEQRAEQTBShPMSBEEQBMFIEc5LEARBEAQjRTgvQRAEQRCMFP8fXiuUO7Bii8oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns \n",
    "sns.heatmap(cr_log.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=StandardScaler()\n",
    "cr_scaled=sc.fit_transform(cr_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.20521818, -0.14875746, -0.93073294, ...,  1.72051649,\n",
       "        -0.54369045, -0.514625  ],\n",
       "       [ 0.94891762,  0.17961568,  1.52878819, ..., -0.58122082,\n",
       "         1.83928189, -0.514625  ],\n",
       "       [ 0.82499258,  0.49271003, -0.93073294, ..., -0.58122082,\n",
       "        -0.54369045,  1.94316249],\n",
       "       ...,\n",
       "       [-1.4738341 , -0.09290575, -0.93073294, ...,  1.72051649,\n",
       "        -0.54369045, -0.514625  ],\n",
       "       [-1.73377525, -0.09290575,  0.08603831, ..., -0.58122082,\n",
       "         1.83928189, -0.514625  ],\n",
       "       [-0.11830096, -0.73437135,  0.42995349, ..., -0.58122082,\n",
       "        -0.54369045,  1.94316249]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_ratio={}\n",
    "for i in range(2,26):\n",
    "    pc=PCA(n_components = i)\n",
    "    cr_pca=pc.fit(cr_scaled)\n",
    "    var_ratio[i]=sum(cr_pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 0.5285032469306609,\n",
       " 3: 0.636885922205627,\n",
       " 4: 0.7208236615633933,\n",
       " 5: 0.7751698120243122,\n",
       " 6: 0.8235139586689121,\n",
       " 7: 0.8642619839435957,\n",
       " 8: 0.8952185517489375,\n",
       " 9: 0.9205977691021563,\n",
       " 10: 0.9412777193271273,\n",
       " 11: 0.9561258246224533,\n",
       " 12: 0.9661150698087927,\n",
       " 13: 0.9747037127009305,\n",
       " 14: 0.9815021181321357,\n",
       " 15: 0.9867633329816737,\n",
       " 16: 0.9903073086740675,\n",
       " 17: 0.992973570012939,\n",
       " 18: 0.994882499038601,\n",
       " 19: 0.9966224479739915,\n",
       " 20: 0.9983368810579597,\n",
       " 21: 0.9994603617928873,\n",
       " 22: 0.9997215195039324,\n",
       " 23: 0.9999666293820364,\n",
       " 24: 0.9999999999999999,\n",
       " 25: 0.9999999999999999}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ca9a282640>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(var_ratio).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_final=PCA(n_components = 9).fit(cr_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=9)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.48698511, 4.72707249, 2.70986966, 2.09867797, 1.35880558,\n",
       "       1.20873872, 1.01881447, 0.77400068, 0.63455133])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_final.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.01751601707255"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc_final.explained_variance_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "cr_reduced=pc_final.fit_transform(cr_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd=pd.DataFrame(cr_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.225606</td>\n",
       "      <td>-3.361888</td>\n",
       "      <td>-0.859278</td>\n",
       "      <td>-0.881257</td>\n",
       "      <td>-0.152581</td>\n",
       "      <td>-0.235840</td>\n",
       "      <td>0.073323</td>\n",
       "      <td>0.793763</td>\n",
       "      <td>0.127862</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.497135</td>\n",
       "      <td>0.740006</td>\n",
       "      <td>-0.162801</td>\n",
       "      <td>1.736986</td>\n",
       "      <td>-1.306137</td>\n",
       "      <td>-0.508172</td>\n",
       "      <td>-0.312046</td>\n",
       "      <td>-0.748732</td>\n",
       "      <td>-0.084370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.440968</td>\n",
       "      <td>1.141236</td>\n",
       "      <td>2.735377</td>\n",
       "      <td>-2.283795</td>\n",
       "      <td>-0.390269</td>\n",
       "      <td>-0.947020</td>\n",
       "      <td>-0.124069</td>\n",
       "      <td>-1.129244</td>\n",
       "      <td>-0.471080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.072342</td>\n",
       "      <td>-0.443385</td>\n",
       "      <td>2.215807</td>\n",
       "      <td>-3.031340</td>\n",
       "      <td>1.798279</td>\n",
       "      <td>0.115346</td>\n",
       "      <td>-2.499140</td>\n",
       "      <td>-0.565960</td>\n",
       "      <td>-1.175867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.255497</td>\n",
       "      <td>-1.356607</td>\n",
       "      <td>1.795194</td>\n",
       "      <td>-2.003665</td>\n",
       "      <td>-1.103167</td>\n",
       "      <td>-0.717548</td>\n",
       "      <td>1.414714</td>\n",
       "      <td>0.613457</td>\n",
       "      <td>0.343376</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>8945</th>\n",
       "      <td>2.281242</td>\n",
       "      <td>-3.350852</td>\n",
       "      <td>-1.637311</td>\n",
       "      <td>0.888633</td>\n",
       "      <td>2.629677</td>\n",
       "      <td>0.637509</td>\n",
       "      <td>3.085441</td>\n",
       "      <td>-2.112704</td>\n",
       "      <td>0.840492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8946</th>\n",
       "      <td>1.551327</td>\n",
       "      <td>-2.505018</td>\n",
       "      <td>-2.399246</td>\n",
       "      <td>-1.415214</td>\n",
       "      <td>2.740167</td>\n",
       "      <td>0.935566</td>\n",
       "      <td>1.717320</td>\n",
       "      <td>-2.255900</td>\n",
       "      <td>1.364902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8947</th>\n",
       "      <td>1.462775</td>\n",
       "      <td>-3.696080</td>\n",
       "      <td>-1.692651</td>\n",
       "      <td>-0.471294</td>\n",
       "      <td>2.980649</td>\n",
       "      <td>1.279952</td>\n",
       "      <td>2.046741</td>\n",
       "      <td>-2.127638</td>\n",
       "      <td>0.766658</td>\n",
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       "    <tr>\n",
       "      <th>8948</th>\n",
       "      <td>-3.242988</td>\n",
       "      <td>-3.967156</td>\n",
       "      <td>0.373441</td>\n",
       "      <td>0.584977</td>\n",
       "      <td>2.185475</td>\n",
       "      <td>3.099068</td>\n",
       "      <td>2.548628</td>\n",
       "      <td>-1.672025</td>\n",
       "      <td>-0.119279</td>\n",
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       "    <tr>\n",
       "      <th>8949</th>\n",
       "      <td>0.054924</td>\n",
       "      <td>0.057244</td>\n",
       "      <td>2.502896</td>\n",
       "      <td>-1.368239</td>\n",
       "      <td>4.908191</td>\n",
       "      <td>1.376892</td>\n",
       "      <td>1.879566</td>\n",
       "      <td>-1.240276</td>\n",
       "      <td>0.435248</td>\n",
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       "<p>8950 rows × 9 columns</p>\n",
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       "             0         1         2         3         4         5         6  \\\n",
       "0     0.225606 -3.361888 -0.859278 -0.881257 -0.152581 -0.235840  0.073323   \n",
       "1    -4.497135  0.740006 -0.162801  1.736986 -1.306137 -0.508172 -0.312046   \n",
       "2     1.440968  1.141236  2.735377 -2.283795 -0.390269 -0.947020 -0.124069   \n",
       "3    -1.072342 -0.443385  2.215807 -3.031340  1.798279  0.115346 -2.499140   \n",
       "4    -1.255497 -1.356607  1.795194 -2.003665 -1.103167 -0.717548  1.414714   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "8945  2.281242 -3.350852 -1.637311  0.888633  2.629677  0.637509  3.085441   \n",
       "8946  1.551327 -2.505018 -2.399246 -1.415214  2.740167  0.935566  1.717320   \n",
       "8947  1.462775 -3.696080 -1.692651 -0.471294  2.980649  1.279952  2.046741   \n",
       "8948 -3.242988 -3.967156  0.373441  0.584977  2.185475  3.099068  2.548628   \n",
       "8949  0.054924  0.057244  2.502896 -1.368239  4.908191  1.376892  1.879566   \n",
       "\n",
       "             7         8  \n",
       "0     0.793763  0.127862  \n",
       "1    -0.748732 -0.084370  \n",
       "2    -1.129244 -0.471080  \n",
       "3    -0.565960 -1.175867  \n",
       "4     0.613457  0.343376  \n",
       "...        ...       ...  \n",
       "8945 -2.112704  0.840492  \n",
       "8946 -2.255900  1.364902  \n",
       "8947 -2.127638  0.766658  \n",
       "8948 -1.672025 -0.119279  \n",
       "8949 -1.240276  0.435248  \n",
       "\n",
       "[8950 rows x 9 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC _0</th>\n",
       "      <td>0.339441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC _1</th>\n",
       "      <td>0.189062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC _2</th>\n",
       "      <td>0.108383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC _3</th>\n",
       "      <td>0.083938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC _4</th>\n",
       "      <td>0.054346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC _5</th>\n",
       "      <td>0.048344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC _6</th>\n",
       "      <td>0.040748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC _7</th>\n",
       "      <td>0.030957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC _8</th>\n",
       "      <td>0.025379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0\n",
       " PC _0  0.339441\n",
       " PC _1  0.189062\n",
       " PC _2  0.108383\n",
       " PC _3  0.083938\n",
       " PC _4  0.054346\n",
       " PC _5  0.048344\n",
       " PC _6  0.040748\n",
       " PC _7  0.030957\n",
       " PC _8  0.025379"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pc_final.explained_variance_ratio_ ,index =[' PC _' + str(i) for i in range(0,9)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "KMeans()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.22560637, -3.36188787, -0.85927766, ...,  0.07332301,\n",
       "         0.79376292,  0.12786188],\n",
       "       [-4.49713513,  0.74000558, -0.16280114, ..., -0.31204601,\n",
       "        -0.74873184, -0.08437045],\n",
       "       [ 1.44096751,  1.14123583,  2.73537733, ..., -0.1240695 ,\n",
       "        -1.12924449, -0.4710799 ],\n",
       "       ...,\n",
       "       [ 1.46277522, -3.69608013, -1.69265077, ...,  2.04674092,\n",
       "        -2.1276383 ,  0.76665849],\n",
       "       [-3.24298833, -3.96715643,  0.37344056, ...,  2.54862753,\n",
       "        -1.67202485, -0.11927909],\n",
       "       [ 0.05492422,  0.0572436 ,  2.50289586, ...,  1.87956579,\n",
       "        -1.24027567,  0.4352478 ]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cr_reduced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster error technique for finding  n_clusters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "errors=[]\n",
    "for i in range(1,21):\n",
    "    clusters=KMeans(i)\n",
    "    clusters.fit(cr_reduced)\n",
    "    errors.append(clusters.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[205983.75083683545,\n",
       " 147366.6929756694,\n",
       " 121479.94911711775,\n",
       " 100539.66739824996,\n",
       " 87940.97434565716,\n",
       " 81192.09801877297,\n",
       " 75124.21595244326,\n",
       " 70578.12122580432,\n",
       " 66612.92183013086,\n",
       " 63409.29721811834,\n",
       " 60676.92034516758,\n",
       " 58303.10207028883,\n",
       " 56178.679043276396,\n",
       " 53775.26972115531,\n",
       " 52228.16973646581,\n",
       " 49793.21887008362,\n",
       " 48524.30021303038,\n",
       " 46067.559095376535,\n",
       " 45621.79027862876,\n",
       " 44317.33084902092]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_df =pd.DataFrame({'Cluster_no':np.arange(1,21),'Cluster_errors': errors})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ca929808e0>]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "plt.plot(cluster_df.Cluster_no,cluster_df.Cluster_errors,marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Silhouette technique for finding n_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "score=[]\n",
    "for i in range(2,21):\n",
    "    km=KMeans(i)\n",
    "    km.fit(cr_reduced)\n",
    "    score.append(sklearn.metrics.silhouette_score(cr_reduced,km.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.2852553628512301,\n",
       " 0.26356830661452607,\n",
       " 0.31255117545862254,\n",
       " 0.3109619953387744,\n",
       " 0.30620015396936184,\n",
       " 0.3066823186186726,\n",
       " 0.29056775769792287,\n",
       " 0.2920639749471146,\n",
       " 0.27121687286508134,\n",
       " 0.26554774193510355,\n",
       " 0.26102097372247,\n",
       " 0.27035737787437464,\n",
       " 0.25402341996301664,\n",
       " 0.24572498998329007,\n",
       " 0.2438707089756043,\n",
       " 0.26947913986158767,\n",
       " 0.24351689945507127,\n",
       " 0.2528773061014423,\n",
       " 0.25286635735331753]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ca9b624850>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(score).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "km=KMeans(n_clusters=4,random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4, random_state=123)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.fit(cr_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, ..., 1, 2, 3])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2715\n",
       "1    2225\n",
       "2    2151\n",
       "3    1859\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(km.labels_).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit['clusters']=pd.Series(km.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>BALANCE</th>\n",
       "      <th>BALANCE_FREQUENCY</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>ONEOFF_PURCHASES</th>\n",
       "      <th>INSTALLMENTS_PURCHASES</th>\n",
       "      <th>CASH_ADVANCE</th>\n",
       "      <th>PURCHASES_FREQUENCY</th>\n",
       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
       "      <th>...</th>\n",
       "      <th>TENURE</th>\n",
       "      <th>monthly_avg_purchases</th>\n",
       "      <th>monthly_cash_advance</th>\n",
       "      <th>both_one_ins</th>\n",
       "      <th>installment_withou_one</th>\n",
       "      <th>none_one_ins</th>\n",
       "      <th>one_off</th>\n",
       "      <th>limit_usage_ratatio</th>\n",
       "      <th>min_payment_ratio</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.900749</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>95.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>95.40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>7.950000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.040901</td>\n",
       "      <td>1.446508</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3202.467416</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6442.945483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>536.912124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.457495</td>\n",
       "      <td>3.826241</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2495.148862</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>773.17</td>\n",
       "      <td>773.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>64.430833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.332687</td>\n",
       "      <td>0.991682</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1666.670542</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>205.788017</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>124.916667</td>\n",
       "      <td>17.149001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.222223</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>817.714335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.681429</td>\n",
       "      <td>2.771075</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8945</th>\n",
       "      <td>28.493517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>291.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>291.12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>48.520000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.028494</td>\n",
       "      <td>6.660231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8946</th>\n",
       "      <td>19.183215</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019183</td>\n",
       "      <td>0.319208</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8947</th>\n",
       "      <td>23.398673</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>144.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>144.40</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>24.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.023399</td>\n",
       "      <td>0.986076</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8948</th>\n",
       "      <td>13.457564</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.558778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.093130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.026915</td>\n",
       "      <td>0.942505</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8949</th>\n",
       "      <td>372.708075</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1093.25</td>\n",
       "      <td>1093.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>127.040008</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>182.208333</td>\n",
       "      <td>21.173335</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.310590</td>\n",
       "      <td>0.715439</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8950 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
       "0       40.900749           0.818182      95.40              0.00   \n",
       "1     3202.467416           0.909091       0.00              0.00   \n",
       "2     2495.148862           1.000000     773.17            773.17   \n",
       "3     1666.670542           0.636364    1499.00           1499.00   \n",
       "4      817.714335           1.000000      16.00             16.00   \n",
       "...           ...                ...        ...               ...   \n",
       "8945    28.493517           1.000000     291.12              0.00   \n",
       "8946    19.183215           1.000000     300.00              0.00   \n",
       "8947    23.398673           0.833333     144.40              0.00   \n",
       "8948    13.457564           0.833333       0.00              0.00   \n",
       "8949   372.708075           0.666667    1093.25           1093.25   \n",
       "\n",
       "      INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
       "0                      95.40      0.000000             0.166667   \n",
       "1                       0.00   6442.945483             0.000000   \n",
       "2                       0.00      0.000000             1.000000   \n",
       "3                       0.00    205.788017             0.083333   \n",
       "4                       0.00      0.000000             0.083333   \n",
       "...                      ...           ...                  ...   \n",
       "8945                  291.12      0.000000             1.000000   \n",
       "8946                  300.00      0.000000             1.000000   \n",
       "8947                  144.40      0.000000             0.833333   \n",
       "8948                    0.00     36.558778             0.000000   \n",
       "8949                    0.00    127.040008             0.666667   \n",
       "\n",
       "      ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
       "0                       0.000000                          0.083333   \n",
       "1                       0.000000                          0.000000   \n",
       "2                       1.000000                          0.000000   \n",
       "3                       0.083333                          0.000000   \n",
       "4                       0.083333                          0.000000   \n",
       "...                          ...                               ...   \n",
       "8945                    0.000000                          0.833333   \n",
       "8946                    0.000000                          0.833333   \n",
       "8947                    0.000000                          0.666667   \n",
       "8948                    0.000000                          0.000000   \n",
       "8949                    0.666667                          0.000000   \n",
       "\n",
       "      CASH_ADVANCE_FREQUENCY  ...  TENURE  monthly_avg_purchases  \\\n",
       "0                   0.000000  ...      12               7.950000   \n",
       "1                   0.250000  ...      12               0.000000   \n",
       "2                   0.000000  ...      12              64.430833   \n",
       "3                   0.083333  ...      12             124.916667   \n",
       "4                   0.000000  ...      12               1.333333   \n",
       "...                      ...  ...     ...                    ...   \n",
       "8945                0.000000  ...       6              48.520000   \n",
       "8946                0.000000  ...       6              50.000000   \n",
       "8947                0.000000  ...       6              24.066667   \n",
       "8948                0.166667  ...       6               0.000000   \n",
       "8949                0.333333  ...       6             182.208333   \n",
       "\n",
       "      monthly_cash_advance  both_one_ins  installment_withou_one  \\\n",
       "0                 0.000000             0                       1   \n",
       "1               536.912124             0                       0   \n",
       "2                 0.000000             0                       0   \n",
       "3                17.149001             0                       0   \n",
       "4                 0.000000             0                       0   \n",
       "...                    ...           ...                     ...   \n",
       "8945              0.000000             0                       1   \n",
       "8946              0.000000             0                       1   \n",
       "8947              0.000000             0                       1   \n",
       "8948              6.093130             0                       0   \n",
       "8949             21.173335             0                       0   \n",
       "\n",
       "      none_one_ins  one_off  limit_usage_ratatio  min_payment_ratio  clusters  \n",
       "0                0        0             0.040901           1.446508         1  \n",
       "1                1        0             0.457495           3.826241         2  \n",
       "2                0        1             0.332687           0.991682         3  \n",
       "3                0        1             0.222223           0.000000         3  \n",
       "4                0        1             0.681429           2.771075         3  \n",
       "...            ...      ...                  ...                ...       ...  \n",
       "8945             0        0             0.028494           6.660231         1  \n",
       "8946             0        0             0.019183           0.319208         1  \n",
       "8947             0        0             0.023399           0.986076         1  \n",
       "8948             1        0             0.026915           0.942505         2  \n",
       "8949             0        1             0.310590           0.715439         3  \n",
       "\n",
       "[8950 rows x 26 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['BALANCE', 'BALANCE_FREQUENCY', 'PURCHASES', 'ONEOFF_PURCHASES',\n",
       "       'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'PURCHASES_FREQUENCY',\n",
       "       'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY',\n",
       "       'CASH_ADVANCE_FREQUENCY', 'CASH_ADVANCE_TRX', 'PURCHASES_TRX',\n",
       "       'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT',\n",
       "       'TENURE', 'monthly_avg_purchases', 'monthly_cash_advance',\n",
       "       'both_one_ins', 'installment_withou_one', 'none_one_ins', 'one_off',\n",
       "       'limit_usage_ratatio', 'min_payment_ratio', 'clusters'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_kpi=['BALANCE','PURCHASES','monthly_avg_purchases','monthly_cash_advance','limit_usage_ratatio','min_payment_ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_df_4=pd.concat([credit[col_kpi],pd.Series(km.labels_,name='Clusters')],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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>BALANCE</th>\n",
       "      <th>PURCHASES</th>\n",
       "      <th>monthly_avg_purchases</th>\n",
       "      <th>monthly_cash_advance</th>\n",
       "      <th>limit_usage_ratatio</th>\n",
       "      <th>min_payment_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clusters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>1900.065835</td>\n",
       "      <td>2330.891436</td>\n",
       "      <td>197.960939</td>\n",
       "      <td>72.629989</td>\n",
       "      <td>0.365345</td>\n",
       "      <td>7.361299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>627.258999</td>\n",
       "      <td>532.179771</td>\n",
       "      <td>46.643789</td>\n",
       "      <td>21.297819</td>\n",
       "      <td>0.243291</td>\n",
       "      <td>13.387034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>2242.790392</td>\n",
       "      <td>5.905235</td>\n",
       "      <td>0.518158</td>\n",
       "      <td>190.703216</td>\n",
       "      <td>0.579175</td>\n",
       "      <td>9.714633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>1411.230532</td>\n",
       "      <td>781.877816</td>\n",
       "      <td>69.342043</td>\n",
       "      <td>76.155067</td>\n",
       "      <td>0.377336</td>\n",
       "      <td>5.489526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              BALANCE    PURCHASES  monthly_avg_purchases  \\\n",
       "Clusters                                                    \n",
       "0.0       1900.065835  2330.891436             197.960939   \n",
       "1.0        627.258999   532.179771              46.643789   \n",
       "2.0       2242.790392     5.905235               0.518158   \n",
       "3.0       1411.230532   781.877816              69.342043   \n",
       "\n",
       "          monthly_cash_advance  limit_usage_ratatio  min_payment_ratio  \n",
       "Clusters                                                                \n",
       "0.0                  72.629989             0.365345           7.361299  \n",
       "1.0                  21.297819             0.243291          13.387034  \n",
       "2.0                 190.703216             0.579175           9.714633  \n",
       "3.0                  76.155067             0.377336           5.489526  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_4=cluster_df_4.groupby('Clusters').apply(lambda x: x[col_kpi].mean())\n",
    "cluster_4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1cab3f07250>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 7200x7200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m8dFHHwGwZ88edu3aRZMmTahTpw7fffcddevWZceOHURERPD666/TsWNHd2G2u+++O8dj0q5jVwAaNongyJEjOSbRmjVr0qRJEyC1ami/fv0oX748AFdeeaW7X1rxuYYNG5KUlARASkoKQ4cOJSEhAU9PT3bu3AlAaGgo/fv3JyUlhU6dOhEcHMxXX33Ftm3biIiIAOD06dOEh4dzxRVX4OXlxYABA7jrrruy/A5Fio+qbEqJkTYVIaSWS0577uHhwZkzZ7jssgvfrmn396Vf19PTkzNnzuT4Gln1GzBgAOPGjaNevXr069cvy3jj4uJYunQpq1evpnz58kRGRnLq1CkgdaKWOXPmUK9ePTp37owx5oKhpNw4//5FYwyXXXZZhvH6tNeE1F9aaay1WU5Ek3Yc0h+DV199lauvvprvv/+ec+fOuauDtmjRgq+//ppPP/2UXr16MWLECCpXrsztt9/unmQmvfj4eHel0ylTprB8+fI877cUHiV9uUBJvcSyRYsWfPDBBzzzzDPExcXh6+vLFVdckWX/ihUr5noy9PTCwsLYs2cPGzZsyHau3sOHD1O5cmXKly9PYmIi333395XKXbp0YezYsdSsWZOXXnoJgGbNmvHAAw8watQozpw5w6effsrAgQOzjeWLTz6kcdPmbIhfjY+PDz4+PtSqVYtFixYBsGHDBn766adM123Tpg0vvPAC9913n3t4J/3Zfmb7U6NGDTw8PIiJieHs2dRR2Z9//pnq1aszcOBAjh8/zoYNGxg9ejRDhgxh9+7d3HjjjZw4cYLk5GSqVavGiRMnuPPOO2nSpAk33nhjtvsnRU9JX0qNMWPG0K9fPwIDAylfvjwxMTHZ9q9SpQoRERH4+/tzxx13cNddd+X6te655x4SEhKoXLlyln3atWvH1KlTCQwM5Oabb3YPqwBUrlyZ+vXrs23bNho3bgykDpN06NCBoKAgatasSaNGjfDxyaaUN3CFTyV6d2rDsaNHmfle6v527dqVGTNmEBwcTGhoKDfddFOW8aVNB3n55Zdz5513Mm7cuCxfa/DgwXTt2pW5c+fSsmVL918NcXFxTJgwgTJlyuDt7c2MGTOoWrUq0dHR9OjRwz0F5IsvvkjFihXp2LEjp06dwlrLq6++mu3+SdFzZD39WiM/zXJZktd9Wa+oevqO0b59e4YPH07r1pl+bZVvx44dw9vbmxMnTtCiRQumTZtGSEiIe3n6evr3d2vPY0//H35BDQDV05e8UT19kVw4dOgQN910E+XKlSvwhA8waNAggoODCQkJoWvXrhkSvkhR0PCOSDqVKlVyX7WS5uDBg5n+Ali2bBlVqlTJ0/Znzpx5QduQIUNYtWoVAKdSUsfR7+v/IO/MXZSnbYvkhpK+SA6qVKnivta/MLzxxhvux9lNlyhSEDS8IyLiIEr6IiIOoqQvIuIgGtOXC2yvV7CX092SuL1Atyci+aczfblklYZ6+tnFlF9JSUn4+6vElWROSV8uWU6upy+SFSV9KRFKaz39GTNmEBgYSFBQEL169QLgk08+ISwsjAYNGnDbbbe5K35+9dVXBAcHExwcTIMGDdx1gdLHNGrYwGwLs73wwguEhobi7+/PoEGD3H3Xr19PUFAQ4eHhGS4BDQsLY+vWre7nkZGRrF+/nvj4eJo2bUqDBg1o2rQpO3bsAFJr/3fp0oV27dpRt27dDPMCLF68mJCQEIKCgtz3LRw/fpz+/fsTGhpKgwYN+PhjTZFR0inpS4mxe/duHnnkETZt2kRiYqK7nv7EiRMZN24czz33HA0aNGDTpk2MGzeO3r17u9dNTEzkiy++ID4+nueff56UlBTGjx/PDTfcQEJCAhMmTABg48aNvPbaa2zbto0ff/zRfVNUmu7du7Nw4UJSUlIAmD59epaVNrdu3crYsWNZvnw533//Pa+//jqQWljtu+++Y+PGjXTv3t1dV3/ixIm88cYbJCQk8M0337hLLKePKfmXn9m49rtMXw9g6NChrF27li1btnDy5El34bV+/foxadIkVq9efcH+zJkzB0idDGXv3r00bNiQevXq8fXXX7Nx40ZeeOEFnnrqKfc6CQkJzJ49m82bNzN79mz27NnDgQMHGDhwIPPnz+f7779n7ty5AIwdO5ZWrVqxdu1aVqxYwYgRIzh+/Hh2/81SzJT0pcRIq6fv4eGRaT39lStXus+ms6qn7+vrm6t6+h4eHu56+umlr6efmJiYbT395cuXExUVha+vL/B3vfrk5GTatm1LQEAAEyZMcJ9pR0RE8NhjjzFp0iQOHTrkLhWdPqab6/uzN/mXLI/RihUrCAsLIyAggOXLl7N161YOHz7MoUOHuPXWWwHcxwhSC8elJeg5c+bQrVs3ILWiZrdu3fD392f48OEZ/hpo3bo1Pj4+eHl5Ub9+fX7++We+++47WrRoQe3atTPs65dffsn48eMJDg52l5b+5Zes45fip6QvJUZO9fQzG/YorHr60dHR2Z7lQ9b16ocNG8bQoUPZvHkzb731lrve/ciRI3n77bc5efIkTZo0ITEx8cL99vTk7JmzF2wTUuvmDx48mHnz5rF582YGDhzormaZVd386tWrU6VKFTZt2sTs2bPp3r07AM888wwtW7Zky5YtfPLJJxlq8md2jLJ6DWst8+fPJyEhgYSEBH755RcVUyvhdMmmXKCkXmJZ0urpt27dms6dOzN8+HCqVKnirld/+PBhqlevDpCh/PMPP/xAQEAAAQEBrF69msTERCpVyn3lzLTE7Ovry7Fjx5g3bx5RUVFUqlQJHx8fVq5cSbNmzfjggw8yrJc2xHT48GH3Xy3pY4yOjs7xtcPDwxkyZAg//fQTtWvXdu9r27ZtmTx5MpMnT8YYw8aNG2nQoEGu90mKns70pdQYM2YM69atIzAwkJEjR+apnn7aF7m5dc899xAREZFtPX0/Pz9Gjx7NrbfeSlBQkPsL3zFjxtCtWzeaN2/uHvoBeO211/D39ycoKIhy5cpxxx135CmmSpUqMXDgQAICAujUqROhoaHuZdOnT2fIkCGEh4e7vytIExUVxaxZs7jnnnvcbf/85z8ZNWoUERER7slSslO1alWmTZtGly5dCAoK4t577wVS/2JISUkhMDAQf39/nnnmmTztkxQ91dM/j+rpCxRePf2cZFdwTfX0JS9UT18kFwq7nr5IcdOYvkg6hV1PP7ceHfAP9u75OUPb669MpG3btoXyeuIcSvoiOSjsevqZee3t9y9ou1SGd6R4aXhHRMRBlPRFRBxESV9ExEE0pi8XeOPB5QW6vSFTWxXo9kQk/3SmLyWCt7c3AHv37iUqKipP6z777LMsXboUSL0B6sSJEwUeX3Fbu3ol3377bY794uLiMvSbOnUqM2bMKMzQpJTRmb6UKNWqVWPevHl5WueFF15wP37ttdf4xz/+Qfny5Qs6tEKXVb0ggHWrV/K/ar40bdo0223ExcXh7e3t7vfggw8WaIxS+inpS4mSlJRE+/bt2bJlC9HR0SxYsICzZ8+yZcsWHn/8cU6fPs17771H2bJl+eyzz7jyyivp27cv7du3Z+/evezdu5eWLVvi6+vLihUrMn0Nb29vjh07BsC8efNYtGgR0dHRzJ07l+effx5PT098fHz4+uuvSUpKolevXu5ywVOmTKFp06acO3eOoUOH8tVXX1G7dm3OnTtH//79iYqKYv369Tz22GMcO3YMX19foqOjufbaazONJTIykqZNm7Jq1So6dOhAmSur8d9J/yYl5TSVKl/JvyZN49SpU8x9fzply1zG+++/z+TJkzl06BAvvvgip0+fpkqVKnzwwQecPHmSqVOn4unp6e63bNkyvL29eeKJJ0hISODBBx/kxIkT3HDDDbz77rvZlpmQS5OGd6RE27JlCzNnziQ+Pp7Ro0dTvnx5Nm7cSHh4+AXDFg8//DDVqlVjxYoVWSb87Lzwwgt88cUXfP/99yxcuBCAq666iiVLlrBhwwZmz57Nww8/DMCHH35IUlISmzdv5u2333bXsU9JSWHYsGHMmzeP9evX079/f0aPHp3t6x46dIivvvqKxx9/nJDQcN5fuIQ5i7+mXYcuTH9zEtWvu55u/+jH8OHDSUhIoHnz5pnW7K9VqxYPPvhghn7p9e7dm5deeolNmzYREBDA888/n+djJKWfzvSlRGvZsiUVK1akYsWK+Pj4cPfddwMQEBCQbQXM/IiIiKBv377cc889dOnSBUhN4kOHDiUhIQFPT0/33borV66kW7dueHh4cM0119CyZUsAduzYwZYtW7j99tsBOHv2bJZn+WnSipcB7N/3KyMG9+eP338jJSWF6tfVzHSd5ORk7r33Xvbt28fp06fdde6zcn7N/T59+rhr64uzKOlLiZZTjf38SF8XPn0d+alTp7JmzRo+/fRTgoODSUhIYPLkyVx99dV8//33nDt3Di8vL4AspzS01uLn53fBDFbZqVChgvvx+GefpNfAwUS2uZO1q1cy9ZXxma4zbNgwHnvsMTp06EBcXBxjxozJ9euJsynpywVK8yWWaTX005c0Pt/VV1/N9u3bufnmm/noo4+oWLEikFrvPiwsjLCwMD755BP27NnD4cOH3bNaxcTEuMsQN2vWjJiYGPr06cOBAweIi4vjvvvu4+abb+bAgQOsXr2a8PBwUlJS2LlzJ35+frmK/+jRI1x1TTUAFs6NdbeXr+CdYW6ArGr2V6xYkSNHjlywXR8fHypXrsw333xD8+bNee+999xn/eIsGtOXS8qgQYO444473MMtmRk/fjzt27enVatWGYZeRowYQUBAAP7+/rRo0YKgoCAGDx5MTEwMTZo0YefOne6z8q5du1KjRg38/f154IEHCAsLw8fHh8svv5x58+bx5JNPEhQURHBwcK4utUzz0PCRPPFQX/p2uYPKrikJAW69vR0fffQRwcHBfPPNN1nW7L/77rsz9EsvJiaGESNGEBgYSEJCAs8++2yu45JLR4719I0x7wLtgd+ttf6utjHAQOCAq9tT1trPXMtGAfcDZ4GHrbVfuNobAtFAOeAz4BGbi2L+qqdf+FTfPH+OHTuGt7c3Bw8epHHjxqxatYprrrnmorapevpSULKqp5+b4Z1oYApw/h0er1prJ573IvWB7oAfUA1Yaoy5yVp7FngTGAR8R2rSbwd8nsf9ECkx2rdvz6FDhzh9+jTPPPPMRSd8kaKQY9K31n5tjKmVy+11BGZZa/8CfjLG7AYaG2OSgCustasBjDEzgE4o6UshCgsL46+//srQ9t5777nnib1YcXFxue47ZMgQVq1alaHtkUceyXbidZHCcDFf5A41xvQG1gGPW2v/B1Qn9Uw+TbKrLcX1+Px2kUKzZs2a4g7B7Y033ijuEESA/H+R+yZwAxAM7AP+7Wo3mfS12bRnyhgzyBizzhiz7sCBA1l1ExGRPMpX0rfW7rfWnrXWngP+CzR2LUoGrkvXtQaw19VeI5P2rLY/zVrbyFrbqGrVqvkJUUREMpGvpG+MSX+LYWdgi+vxQqC7MaasMaY2UBeIt9buA44aY5qY1DtjegMfX0TcIiKSDzmO6RtjYoFIwNcYkww8B0QaY4JJHaJJAh4AsNZuNcbMAbYBZ4Ahrit3AB7i70s2P0df4pZY/763fYFu7/HZiwp0eyKSfzme6Vtre1hrr7XWlrHW1rDWvmOt7WWtDbDWBlprO7jO5NP6j7XW3mCtvdla+3m69nXWWn/XsqG5uUZfJL2FCxcyfnzmZQlKu4KcByA6Opq9e/8ePR0wYADbtm0rkG1L6ac7cqXU6NChAyNHjizuMApFXpN+WjmIzJyf9N9++23q169/UfHJpUNJX0qEpKQk6tWrx4ABA/D396dnz54sXbqUiIgI6tatS3x8PNHR0QwdOhSAvn378vDDD9O0aVPq1KmT7cQrcXFxtGjRgs6dO1O/fn0efPBBzp07B8BDDz1Eo0aN8PPz47nnngNg2bJldO7c2b3+kiVL3FU3vb29efLJJ2nYsCG33XYb8fHxREZGUqdOHXc55rNnzzJixAhCQ0MJDAzkrbfecscRGRlJVFQU9erVo2fPnlhrmTRpknsegPvvuTvL/fD29ubZZ58lLCyM1ZVX3UMAABBfSURBVKtX88ILLxAaGoq/vz+DBg3CWsu8efNYt24dPXv2JDg4mJMnTxIZGUnaXe2xsbHuUhNPPvlkfv+7pBRTwTUpMXbv3s3cuXOZNm0aoaGhzJw5k5UrV7Jw4ULGjRtHp06dMvTft28fK1euJDExkQ4dOmQ7zWJ8fDzbtm2jZs2atGvXjg8//JCoqCjGjh3LlVdeydmzZ2ndujWbNm2iVatWDBkyhAMHDlC1alWmT5/uvonq+PHjREZG8tJLL9G5c2eefvpplixZwrZt2+jTpw8dOnTgnXfewcfHh7Vr1/LXX38RERFBmzZtANi4cSNbt26lWrVqREREsGrVKh5++GFeeeUVVqxYwd5TWX8kjx8/jr+/v3umsPr167vr5/Tq1YtFixYRFRXFlClTmDhxIo0aZbwDf+/evTz55JOsX7+eypUr06ZNGxYsWHDBcb2UZFtyZfxdRRhJyaEzfSkxateuTUBAAB4eHvj5+dG6dWuMMQQEBJCUlHRB/06dOuHh4UH9+vXZv39/tttu3LgxderUwdPTkx49erBy5UoA5syZQ0hICA0aNGDr1q1s27YNYwy9evXi/fff59ChQ6xevZo77rgDgMsvv5x27doBqTX9b731VsqUKZMhxi+//JIZM2YQHBxMWFgYBw8eZNeuXe440qp2BgcHZ7pfWfH09KRr167u5ytWrCAsLIyAgACWL1/O1q1bs11/7dq1REZGUrVqVS677DJ69uzJ119/nevXl0uDzvSlxMhr7fz0/XNROPCC5z/99BMTJ05k7dq1VK5cmb59+7rr6/fr14+7774bLy8vunXrxmWXpX5UypQp495WVjFaa5k8eTJt27bN8JpxcXEZYvb09MzTnABeXl54enoCqfMADB48mHXr1nHdddcxZsyYDHMDZEbXTggo6UsmLsVLLOPj4/npp5+oWbMms2fPZtCgQRw5coQKFSrg4+PD/v37+fzzz4mMjARSJ2ivVq0aL774IkuWLMnTa7Vt25Y333yTVq1aUaZMGXbu3OmufZ+VtHkAKJO7OWvTEryvry/Hjh1j3rx57uEt97bOExYWxiOPPMIff/xB5cqViY2NZdiwYXnaNyn9lPTFEcLDwxk5ciSbN292f6nr4eFBgwYN8PPzo06dOkRERGRYp2fPnhw4cCDPV74MGDCApKQkQkJCsNZStWpVFixYkO06afMAVLyyKu/M+STH16hUqRIDBw4kICCAWrVqERoa6l7Wt29fHnzwQcqVK5dhBq9rr72Wf/3rX7Rs2RJrLXfeeScdO3bM075J6ZdjPf3ipnr6he9Sr28eFxfHxIkTWbQob3/BDB06lAYNGnD//fcXUmQXUj39guXkL3Ivpp6+iOM0bNiQChUq8O9//zvnziKliJK+XDI2b95Mr169MrSVLVuWNWvWuMfqc2v9+vUFGFne9Lz7NlJOZ5wHYOxrUwmsEZHFGiK5p6QvQOqVHedf4VLaBAQEkJCQUNxhXLQPPlla3CEUmpI+nOwEuk5f8PLy4uDBg/pASqGy1nLw4EG8vLyKOxRH05m+UKNGDZKTk9GENcVv//9OZrls+9FyRRhJ4fDy8qJGjRo5d5RCo6QvlClThtq1axd3GALc4eCrTaRoaHhHRMRBlPRFRBxESV9ExEGU9EVEHERJX0TEQZT0RUQcRElfRMRBlPRFRBxESV9ExEGU9EVEHERJX0TEQZT0RUQcRElfRMRBlPRFRBxESV9ExEGU9EVEHERJX0TEQZT0RUQcRElfRMRBlPRFRBxESV9ExEGU9EVEHCTHpG+MedcY87sxZku6tiuNMUuMMbtcPyunWzbKGLPbGLPDGNM2XXtDY8xm17JJxhhT8LsjIiLZyc2ZfjTQ7ry2kcAya21dYJnrOcaY+kB3wM+1zn+MMZ6udd4EBgF1Xf/O36aIiBSyHJO+tfZr4M/zmjsCMa7HMUCndO2zrLV/WWt/AnYDjY0x1wJXWGtXW2stMCPdOiIiUkTyO6Z/tbV2H4Dr51Wu9urAnnT9kl1t1V2Pz2/PlDFmkDFmnTFm3YEDB/IZooiInK+gv8jNbJzeZtOeKWvtNGttI2tto6pVqxZYcCIiTpffpL/fNWSD6+fvrvZk4Lp0/WoAe13tNTJpFxGRIpTfpL8Q6ON63Af4OF17d2NMWWNMbVK/sI13DQEdNcY0cV210zvdOiIiUkQuy6mDMSYWiAR8jTHJwHPAeGCOMeZ+4BegG4C1dqsxZg6wDTgDDLHWnnVt6iFSrwQqB3zu+iciIkUox6Rvre2RxaLWWfQfC4zNpH0d4J+n6EREpEDpjlwREQdR0hcRcRAlfRERB1HSFxFxECV9EREHUdIXEXEQJX0REQdR0hcRcRAlfRERB1HSFxFxECV9EREHUdIXEXEQJX0REQfJscqmiMglaYxPNssOF10cRUxn+iIiDqKkLyLiIEr6IiIOoqQvIuIgSvoiIg6ipC8i4iBK+iIiDqKkLyLiIEr6IiIOoqQvIuIgSvoiIg6ipC8i4iBK+iIiDqIqm3JRao38NMtlSePvKsJIRCQ3dKYvIuIgSvoiIg6ipC8i4iBK+iIiDqKkLyLiIEr6IiIOoqQvIuIgF5X0jTFJxpjNxpgEY8w6V9uVxpglxphdrp+V0/UfZYzZbYzZYYxpe7HBi4hI3hTEzVktrbV/pHs+ElhmrR1vjBnpev6kMaY+0B3wA6oBS40xN1lrzxZADFISjfHJZtnhootDRNwKY3inIxDjehwDdErXPsta+5e19idgN9C4EF5fRESycLFJ3wJfGmPWG2MGudquttbuA3D9vMrVXh3Yk27dZFfbBYwxg4wx64wx6w4cOHCRIYqISJqLHd6JsNbuNcZcBSwxxiRm09dk0mYz62itnQZMA2jUqFGmfUREJO8u6kzfWrvX9fN34CNSh2v2G2OuBXD9/N3VPRm4Lt3qNYC9F/P6IiKSN/lO+saYCsaYimmPgTbAFmAh0MfVrQ/wsevxQqC7MaasMaY2UBeIz+/ri4hI3l3M8M7VwEfGmLTtzLTWLjbGrAXmGGPuB34BugFYa7caY+YA24AzwBBduSMiUrTynfSttT8CQZm0HwRaZ7HOWGBsfl9TREQuju7IFRFxECV9EREHUdIXEXEQJX0REQdR0hcRcRAlfRERB1HSFxFxECV9EREHUdIXEXEQJX0REQdR0hcRcRAlfRERB1HSFxFxECV9EREHUdIXEXEQJX0REQdR0hcRcRAlfRERB1HSFxFxECV9EREHUdIXEXGQy4o7gNIkICYgy2Wb+2wuwkhERPJHSV/kEre93i1ZLrslcXsRRiIlgYZ3REQcRElfRMRBlPRFRBxESV9ExEGU9EVEHERJX0TEQZT0RUQcRElfRMRBlPRFRBxEd+QWEN31KOIMpf2zrqQvInKe7OpszSnCOAqDhndERBxEZ/pSLFSxVKR4FPmZvjGmnTFmhzFmtzFmZFG/voiIkxXpmb4xxhN4A7gdSAbWGmMWWmu3FWUcUrKV9i/KREqyoh7eaQzsttb+CGCMmQV0BByb9P99b/tM2x+fvaiIIyn9sjqWcIkczzE+2Sw7XOAvd8kfzyJWUo6nsdYW3YsZEwW0s9YOcD3vBYRZa4ee128QMMj19GZgR5EFmX++wB/FHcQlQseyYOl4FqzScjxrWmurnt9Y1Gf6JpO2C37rWGunAdMKP5yCY4xZZ61tVNxxXAp0LAuWjmfBKu3Hs6i/yE0Grkv3vAawt4hjEBFxrKJO+muBusaY2saYy4HuwMIijkFExLGKdHjHWnvGGDMU+ALwBN611m4tyhgKUakajirhdCwLlo5nwSrVx7NIv8gVEZHipTIMIiIOoqQvIuIgSvoiIg6ipC8i4iCqsplPxpirgeqk3ly211q7v5hDEnHT+7PgXGrHUlfv5JExJhiYCvgAv7qaawCHgMHW2g3FFVtpdql9sIqL3p8F51I9lkr6eWSMSQAesNauOa+9CfCWtTaoeCIrnS7VD1Zx0fuz4Fyqx1LDO3lX4fw3AYC19jtjTIXiCKiUiybrD9Z0oFR+sIqR3p8F55I8lkr6efe5MeZTYAawx9V2HdAbWFxsUZVel+QHqxjp/VlwLsljqeGdfDDG3EHqPADVSa0cmgwstNZ+VqyBlULGmEnADWT+wfrp/LLbkjO9PwvOpXgslfSl2F2KHyyRkkpJvwAZYwa55gIQKXH0/iw4pflY6uasgpXZJDGST64Z1KTg6P1ZcErtsdQXuflgjKnH38MRltSJYBZaa98q1sAuPaX2g1WcXO/P6sAaa+2xdIt+LqaQSi1jTGPAWmvXGmPqA+2AxNL8WdeZfh4ZY54EZpGakOJJnRjGALHGmJHFGdsl6HRxB1DaGGMeBj4GhgFbjDEd0y0eVzxRlU7GmOeAScCbxph/AVMAb2CkMWZ0sQZ3ETSmn0fGmJ2An7U25bz2y4Gt1tq6xRPZpccY84u19vrijqM0McZsBsKttceMMbWAecB71trXjTEbrbUNijXAUsR1LIOBssBvQA1r7RFjTDlS/4oKLNYA80nDO3l3DqjGhX8qX+taJnlgjNmU1SLg6qKM5RLhmTakY61NMsZEAvOMMTXRcFlenbHWngVOGGN+sNYeAbDWnjTGlNrPupJ+3j0KLDPG7OLv68qvB24EdE153l0NtAX+d167Ab4t+nBKvd+MMcHW2gQA1xl/e+BdIKB4Qyt1ThtjyltrTwAN0xqNMT6U4hM8De/kgzHGA2hMxuvK17rOCiQPjDHvANOttSszWTbTWntfMYRVahljapB6hvpbJssirLWriiGsUskYU9Za+1cm7b7AtdbazcUQ1kVT0hcRcRBdvSMi4iBK+iIiDqKkL45ijLnGGDPLGPODMWabMeYzY8xNxpgt+dxeX2NMtYKOU6SwKOmLYxhjDPAREGetvcFaWx94iou7NLQvqZfw5iUOXTUnxUZvPnGSlkCKtXZqWoO1NsF1ExOQeuYONEor6WyMWQRMBL4B3gEakVp6411SL9ltBHxgjDkJhAP1gVdIvXPzD6CvtXafMSaO1EtQI4CFxphfgOeAs8Bha22LQttrkXSU9MVJ/IH1+Vw3GKhurfUHMMZUstYeMsYMBZ6w1q4zxpQBJgMdrbUHjDH3AmOB/q5tVLLW3upafzPQ1lr7qzGm0sXslEheKOmL5M6PQB1jzGTgU+DLTPrcTOovliWpI0l4AvvSLZ+d7vEqINoYMwf4sFAiFsmEkr44yVYgKoc+Z8j4XZcXgLX2f8aYIFLvHh4C3MPfZ/BpDKn1l8Kz2PbxtAfW2geNMWHAXUCC6y7ag7neE5F80he54iTLgbLGmIFpDcaYUKBmuj5JQLAxxsMYcx2pd16n3YXpYa2dDzwDhLj6HwUquh7vAKoaY8Jd65QxxvhlFogx5gZr7Rpr7bOkjv1fV0D7KJItnemLY1hrrTGmM/Caqwz2KVKT/KPpuq0CfgI2A1uADa726sB0VwkOgFGun9HA1HRf5EYBk1z1WS4DXiP1L4zzTTDG1CX1r4NlwPcFsY8iOVEZBhERB9HwjoiIgyjpi4g4iJK+iIiDKOmLiDiIkr6IiIMo6YuIOIiSvoiIg/w/KxC07y1BUSYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(100,100))\n",
    "cluster_4.plot(kind='bar')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}