NEWS PUBLISHING COMPANY PROJECT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NEWS PUBLISHING COMPANY PROJECT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the required modules\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from sklearn.preprocessing import Imputer,StandardScaler,LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import datetime\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_auc_score,average_precision_score,accuracy_score,confusion_matrix,f1_score\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis,QuadraticDiscriminantAnalysis\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "import xgboost\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Data\n",
    "data1= pd.read_csv('Data/complaints.txt',sep=';',encoding='utf-8')\n",
    "data2= pd.read_csv('Data/credit.txt',sep=';',encoding='utf-8')\n",
    "data3= pd.read_csv('Data/customers.txt',sep=';',encoding='utf-8')\n",
    "data4= pd.read_csv('Data/delivery.txt',sep=';',encoding='utf-8')\n",
    "data5= pd.read_csv('Data/formula.txt',sep=';',encoding='utf-8')\n",
    "data6= pd.read_csv('Data/subscriptions.txt',delim_whitespace=True,encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "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>ComplaintID</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>ProductID</th>\n",
       "      <th>ComplaintDate</th>\n",
       "      <th>ComplaintType</th>\n",
       "      <th>SolutionType</th>\n",
       "      <th>FeedbackType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>181932</td>\n",
       "      <td>3464</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14/10/2010</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>168698</td>\n",
       "      <td>3464</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19/07/2010</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>163694</td>\n",
       "      <td>4107</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11/06/2010</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>111664</td>\n",
       "      <td>4870</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25/04/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>139482</td>\n",
       "      <td>4870</td>\n",
       "      <td>8.0</td>\n",
       "      <td>05/12/2009</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ComplaintID  CustomerID  ProductID ComplaintDate  ComplaintType  \\\n",
       "0       181932        3464        8.0    14/10/2010              7   \n",
       "1       168698        3464        8.0    19/07/2010              1   \n",
       "2       163694        4107        8.0    11/06/2010              1   \n",
       "3       111664        4870        8.0    25/04/2009              1   \n",
       "4       139482        4870        8.0    05/12/2009              1   \n",
       "\n",
       "   SolutionType  FeedbackType  \n",
       "0           2.0           NaN  \n",
       "1           2.0           3.0  \n",
       "2           2.0           3.0  \n",
       "3           3.0           3.0  \n",
       "4           3.0           3.0  "
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4125 entries, 0 to 4124\n",
      "Data columns (total 7 columns):\n",
      "ComplaintID      4125 non-null int64\n",
      "CustomerID       4125 non-null int64\n",
      "ProductID        3711 non-null float64\n",
      "ComplaintDate    4125 non-null object\n",
      "ComplaintType    4125 non-null int64\n",
      "SolutionType     3033 non-null float64\n",
      "FeedbackType     1623 non-null float64\n",
      "dtypes: float64(3), int64(3), object(1)\n",
      "memory usage: 225.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ComplaintID</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>ProductID</th>\n",
       "      <th>ComplaintType</th>\n",
       "      <th>SolutionType</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4125.000000</td>\n",
       "      <td>4.125000e+03</td>\n",
       "      <td>3711.000000</td>\n",
       "      <td>4125.000000</td>\n",
       "      <td>3033.000000</td>\n",
       "      <td>1623.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>116667.705939</td>\n",
       "      <td>5.490283e+05</td>\n",
       "      <td>5.818647</td>\n",
       "      <td>2.332848</td>\n",
       "      <td>1.721728</td>\n",
       "      <td>3.350585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44447.131851</td>\n",
       "      <td>3.192640e+05</td>\n",
       "      <td>2.305329</td>\n",
       "      <td>2.748423</td>\n",
       "      <td>0.770314</td>\n",
       "      <td>1.110292</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>37653.000000</td>\n",
       "      <td>3.464000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>80230.000000</td>\n",
       "      <td>2.304690e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>112595.000000</td>\n",
       "      <td>6.572770e+05</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>153540.000000</td>\n",
       "      <td>7.781450e+05</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>202605.000000</td>\n",
       "      <td>1.167188e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ComplaintID    CustomerID    ProductID  ComplaintType  SolutionType  \\\n",
       "count    4125.000000  4.125000e+03  3711.000000    4125.000000   3033.000000   \n",
       "mean   116667.705939  5.490283e+05     5.818647       2.332848      1.721728   \n",
       "std     44447.131851  3.192640e+05     2.305329       2.748423      0.770314   \n",
       "min     37653.000000  3.464000e+03     1.000000       1.000000      1.000000   \n",
       "25%     80230.000000  2.304690e+05     4.000000       1.000000      1.000000   \n",
       "50%    112595.000000  6.572770e+05     7.000000       1.000000      2.000000   \n",
       "75%    153540.000000  7.781450e+05     8.000000       1.000000      2.000000   \n",
       "max    202605.000000  1.167188e+06     8.000000       9.000000      4.000000   \n",
       "\n",
       "       FeedbackType  \n",
       "count   1623.000000  \n",
       "mean       3.350585  \n",
       "std        1.110292  \n",
       "min        1.000000  \n",
       "25%        3.000000  \n",
       "50%        3.000000  \n",
       "75%        4.000000  \n",
       "max        6.000000  "
      ]
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     "metadata": {},
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   "source": [
    "data1.describe()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SubscriptionID</th>\n",
       "      <th>ActionType</th>\n",
       "      <th>ProcessingDate</th>\n",
       "      <th>CreditSource</th>\n",
       "      <th>Amount</th>\n",
       "      <th>NbrNewspapers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002057</td>\n",
       "      <td>EN</td>\n",
       "      <td>18/05/2009</td>\n",
       "      <td>COM</td>\n",
       "      <td>1.0400</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002089</td>\n",
       "      <td>EN</td>\n",
       "      <td>06/10/2010</td>\n",
       "      <td>COM</td>\n",
       "      <td>0.8783</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002089</td>\n",
       "      <td>EN</td>\n",
       "      <td>22/12/2010</td>\n",
       "      <td>COM</td>\n",
       "      <td>0.8783</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002089</td>\n",
       "      <td>EN</td>\n",
       "      <td>23/12/2010</td>\n",
       "      <td>COM</td>\n",
       "      <td>0.8783</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1002089</td>\n",
       "      <td>EN</td>\n",
       "      <td>24/12/2010</td>\n",
       "      <td>COM</td>\n",
       "      <td>0.8783</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "   SubscriptionID ActionType ProcessingDate CreditSource  Amount  \\\n",
       "0         1002057         EN     18/05/2009          COM  1.0400   \n",
       "1         1002089         EN     06/10/2010          COM  0.8783   \n",
       "2         1002089         EN     22/12/2010          COM  0.8783   \n",
       "3         1002089         EN     23/12/2010          COM  0.8783   \n",
       "4         1002089         EN     24/12/2010          COM  0.8783   \n",
       "\n",
       "   NbrNewspapers  \n",
       "0            1.0  \n",
       "1            1.0  \n",
       "2            1.0  \n",
       "3            1.0  \n",
       "4            1.0  "
      ]
     },
     "execution_count": 480,
     "metadata": {},
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   "source": [
    "data2.head()"
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   "execution_count": 481,
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1940 entries, 0 to 1939\n",
      "Data columns (total 6 columns):\n",
      "SubscriptionID    1940 non-null int64\n",
      "ActionType        1940 non-null object\n",
      "ProcessingDate    1902 non-null object\n",
      "CreditSource      1940 non-null object\n",
      "Amount            1937 non-null float64\n",
      "NbrNewspapers     1408 non-null float64\n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 91.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
       "      <td>1.940000e+03</td>\n",
       "      <td>1937.000000</td>\n",
       "      <td>1408.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.860147e+05</td>\n",
       "      <td>6.297127</td>\n",
       "      <td>0.752841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.188891e+05</td>\n",
       "      <td>24.909583</td>\n",
       "      <td>1.667872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.933700e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.320825e+05</td>\n",
       "      <td>0.819100</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.131360e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.824305e+05</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.354916e+06</td>\n",
       "      <td>263.486700</td>\n",
       "      <td>40.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "       SubscriptionID       Amount  NbrNewspapers\n",
       "count    1.940000e+03  1937.000000    1408.000000\n",
       "mean     4.860147e+05     6.297127       0.752841\n",
       "std      3.188891e+05    24.909583       1.667872\n",
       "min      5.933700e+04     0.000000       0.000000\n",
       "25%      2.320825e+05     0.819100       0.000000\n",
       "50%      4.131360e+05     1.000000       1.000000\n",
       "75%      6.824305e+05     2.100000       1.000000\n",
       "max      1.354916e+06   263.486700      40.000000"
      ]
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     "execution_count": 482,
     "metadata": {},
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   "source": [
    "data2.describe()"
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  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>DOB</th>\n",
       "      <th>District</th>\n",
       "      <th>ZIP</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>908289</td>\n",
       "      <td>F</td>\n",
       "      <td>24/01/1957</td>\n",
       "      <td>5</td>\n",
       "      <td>3660</td>\n",
       "      <td>42555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55837</td>\n",
       "      <td>M</td>\n",
       "      <td>31/01/1948</td>\n",
       "      <td>1</td>\n",
       "      <td>2620</td>\n",
       "      <td>24910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>493436</td>\n",
       "      <td>M</td>\n",
       "      <td>11/07/1977</td>\n",
       "      <td>5</td>\n",
       "      <td>3570</td>\n",
       "      <td>39586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>913684</td>\n",
       "      <td>F</td>\n",
       "      <td>01/01/1900</td>\n",
       "      <td>5</td>\n",
       "      <td>3500</td>\n",
       "      <td>37484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36632</td>\n",
       "      <td>M</td>\n",
       "      <td>18/08/1952</td>\n",
       "      <td>5</td>\n",
       "      <td>3910</td>\n",
       "      <td>46296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID Gender         DOB  District   ZIP  StreeID\n",
       "0      908289      F  24/01/1957         5  3660    42555\n",
       "1       55837      M  31/01/1948         1  2620    24910\n",
       "2      493436      M  11/07/1977         5  3570    39586\n",
       "3      913684      F  01/01/1900         5  3500    37484\n",
       "4       36632      M  18/08/1952         5  3910    46296"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 6 columns):\n",
      "CustomerID    2356 non-null int64\n",
      "Gender        2257 non-null object\n",
      "DOB           2356 non-null object\n",
      "District      2356 non-null int64\n",
      "ZIP           2356 non-null int64\n",
      "StreeID       2356 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 110.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>District</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>StreeID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.356000e+03</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.349043e+05</td>\n",
       "      <td>3.455008</td>\n",
       "      <td>3432.750424</td>\n",
       "      <td>36592.521647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.821129e+05</td>\n",
       "      <td>2.045992</td>\n",
       "      <td>1272.641443</td>\n",
       "      <td>17942.070858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.407000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1070.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.308502e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2640.000000</td>\n",
       "      <td>25083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.165090e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3530.000000</td>\n",
       "      <td>38524.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.522835e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3700.000000</td>\n",
       "      <td>43773.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.167808e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9800.000000</td>\n",
       "      <td>146620.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CustomerID     District          ZIP        StreeID\n",
       "count  2.356000e+03  2356.000000  2356.000000    2356.000000\n",
       "mean   5.349043e+05     3.455008  3432.750424   36592.521647\n",
       "std    3.821129e+05     2.045992  1272.641443   17942.070858\n",
       "min    3.407000e+03     1.000000  1070.000000       0.000000\n",
       "25%    1.308502e+05     1.000000  2640.000000   25083.000000\n",
       "50%    6.165090e+05     5.000000  3530.000000   38524.500000\n",
       "75%    8.522835e+05     5.000000  3700.000000   43773.500000\n",
       "max    1.167808e+06     8.000000  9800.000000  146620.000000"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SubscriptionID</th>\n",
       "      <th>DeliveryType</th>\n",
       "      <th>DeliveryClass</th>\n",
       "      <th>DeliveryContext</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>EndDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000024</td>\n",
       "      <td>MD</td>\n",
       "      <td>NOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18/01/2010</td>\n",
       "      <td>17/04/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000082</td>\n",
       "      <td>MD</td>\n",
       "      <td>ABN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/02/2010</td>\n",
       "      <td>13/02/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000082</td>\n",
       "      <td>MD</td>\n",
       "      <td>NOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/01/2010</td>\n",
       "      <td>11/02/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000082</td>\n",
       "      <td>MD</td>\n",
       "      <td>NOR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14/02/2010</td>\n",
       "      <td>12/03/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000082</td>\n",
       "      <td>MD</td>\n",
       "      <td>ABN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14/01/2010</td>\n",
       "      <td>14/01/2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SubscriptionID DeliveryType DeliveryClass DeliveryContext   StartDate  \\\n",
       "0         1000024           MD           NOR             NaN  18/01/2010   \n",
       "1         1000082           MD           ABN             NaN  12/02/2010   \n",
       "2         1000082           MD           NOR             NaN  15/01/2010   \n",
       "3         1000082           MD           NOR             NaN  14/02/2010   \n",
       "4         1000082           MD           ABN             NaN  14/01/2010   \n",
       "\n",
       "      EndDate  \n",
       "0  17/04/2010  \n",
       "1  13/02/2010  \n",
       "2  11/02/2010  \n",
       "3  12/03/2010  \n",
       "4  14/01/2010  "
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26494 entries, 0 to 26493\n",
      "Data columns (total 6 columns):\n",
      "SubscriptionID     26494 non-null int64\n",
      "DeliveryType       26494 non-null object\n",
      "DeliveryClass      23106 non-null object\n",
      "DeliveryContext    11346 non-null object\n",
      "StartDate          26494 non-null object\n",
      "EndDate            26494 non-null object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data4.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SubscriptionID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.649400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.709016e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.549329e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.382000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.520410e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.888250e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.728482e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.361402e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SubscriptionID\n",
       "count    2.649400e+04\n",
       "mean     6.709016e+05\n",
       "std      3.549329e+05\n",
       "min      5.382000e+04\n",
       "25%      3.520410e+05\n",
       "50%      6.888250e+05\n",
       "75%      9.728482e+05\n",
       "max      1.361402e+06"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FormulaID</th>\n",
       "      <th>FormulaCode</th>\n",
       "      <th>FormulaType</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10011</td>\n",
       "      <td>2051</td>\n",
       "      <td>CAM</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10013</td>\n",
       "      <td>2051</td>\n",
       "      <td>CAM</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10014</td>\n",
       "      <td>2051</td>\n",
       "      <td>CAM</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10017</td>\n",
       "      <td>3</td>\n",
       "      <td>CAM</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10019</td>\n",
       "      <td>71</td>\n",
       "      <td>CAM</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FormulaID  FormulaCode FormulaType  Duration\n",
       "0      10011         2051         CAM        12\n",
       "1      10013         2051         CAM        12\n",
       "2      10014         2051         CAM         6\n",
       "3      10017            3         CAM         3\n",
       "4      10019           71         CAM         3"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 997 entries, 0 to 996\n",
      "Data columns (total 4 columns):\n",
      "FormulaID      997 non-null int64\n",
      "FormulaCode    997 non-null int64\n",
      "FormulaType    997 non-null object\n",
      "Duration       997 non-null int64\n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 31.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FormulaID</th>\n",
       "      <th>FormulaCode</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>997.000000</td>\n",
       "      <td>997.000000</td>\n",
       "      <td>997.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6704.903711</td>\n",
       "      <td>973.668004</td>\n",
       "      <td>5.764293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4544.819045</td>\n",
       "      <td>1514.764332</td>\n",
       "      <td>4.046865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2551.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6004.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9585.000000</td>\n",
       "      <td>2046.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17444.000000</td>\n",
       "      <td>9000.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FormulaID  FormulaCode    Duration\n",
       "count    997.000000   997.000000  997.000000\n",
       "mean    6704.903711   973.668004    5.764293\n",
       "std     4544.819045  1514.764332    4.046865\n",
       "min       23.000000     1.000000    0.000000\n",
       "25%     2551.000000     8.000000    3.000000\n",
       "50%     6004.000000    82.000000    3.000000\n",
       "75%     9585.000000  2046.000000   12.000000\n",
       "max    17444.000000  9000.000000   24.000000"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "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>SubscriptionID</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>ProductID</th>\n",
       "      <th>Pattern</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>EndDate</th>\n",
       "      <th>NbrNewspapers</th>\n",
       "      <th>NbrStart</th>\n",
       "      <th>RenewalDate</th>\n",
       "      <th>PaymentType</th>\n",
       "      <th>...</th>\n",
       "      <th>PaymentDate</th>\n",
       "      <th>FormulaID</th>\n",
       "      <th>GrossFormulaPrice</th>\n",
       "      <th>NetFormulaPrice</th>\n",
       "      <th>NetNewspaperPrice</th>\n",
       "      <th>ProductDiscount</th>\n",
       "      <th>FormulaDiscount</th>\n",
       "      <th>TotalDiscount</th>\n",
       "      <th>TotalPrice</th>\n",
       "      <th>TotalCredit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000024</th>\n",
       "      <td>1150045</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>=VLOOKUP(D2;$E$15462:$F$15473;2;0)</td>\n",
       "      <td>18/01/2010</td>\n",
       "      <td>17/04/2010</td>\n",
       "      <td>76</td>\n",
       "      <td>25</td>\n",
       "      <td>17/03/2010</td>\n",
       "      <td>BT</td>\n",
       "      <td>...</td>\n",
       "      <td>27/01/2010</td>\n",
       "      <td>8552</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>1.039.474</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000082</th>\n",
       "      <td>1150046</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>14/01/2010</td>\n",
       "      <td>12/03/2010</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>3/03/2010</td>\n",
       "      <td>BT</td>\n",
       "      <td>Paid</td>\n",
       "      <td>...</td>\n",
       "      <td>10017</td>\n",
       "      <td>519.737</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0</td>\n",
       "      <td>223.737</td>\n",
       "      <td>223.737</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100012</th>\n",
       "      <td>89085</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>1/01/2008</td>\n",
       "      <td>30/12/2008</td>\n",
       "      <td>304</td>\n",
       "      <td>10</td>\n",
       "      <td>21/11/2008</td>\n",
       "      <td>BT</td>\n",
       "      <td>Paid</td>\n",
       "      <td>...</td>\n",
       "      <td>875</td>\n",
       "      <td>249</td>\n",
       "      <td>249</td>\n",
       "      <td>0.811842</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>246.8</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100013</th>\n",
       "      <td>89085</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>2/01/2007</td>\n",
       "      <td>31/12/2007</td>\n",
       "      <td>304</td>\n",
       "      <td>25</td>\n",
       "      <td>1/12/2007</td>\n",
       "      <td>BT</td>\n",
       "      <td>Paid</td>\n",
       "      <td>...</td>\n",
       "      <td>874</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>0.773026</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100016</th>\n",
       "      <td>89087</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>1/01/2008</td>\n",
       "      <td>30/12/2008</td>\n",
       "      <td>304</td>\n",
       "      <td>10</td>\n",
       "      <td>21/11/2008</td>\n",
       "      <td>BT</td>\n",
       "      <td>Paid</td>\n",
       "      <td>...</td>\n",
       "      <td>875</td>\n",
       "      <td>249</td>\n",
       "      <td>249</td>\n",
       "      <td>0.811842</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>246.8</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         SubscriptionID  CustomerID  ProductID  \\\n",
       "1000024         1150045         8.0  1111110.0   \n",
       "1000082         1150046         8.0  1111110.0   \n",
       "100012            89085         8.0  1111110.0   \n",
       "100013            89085         8.0  1111110.0   \n",
       "100016            89087         8.0  1111110.0   \n",
       "\n",
       "                                    Pattern   StartDate     EndDate  \\\n",
       "1000024  =VLOOKUP(D2;$E$15462:$F$15473;2;0)  18/01/2010  17/04/2010   \n",
       "1000082                          14/01/2010  12/03/2010          50   \n",
       "100012                            1/01/2008  30/12/2008         304   \n",
       "100013                            2/01/2007  31/12/2007         304   \n",
       "100016                            1/01/2008  30/12/2008         304   \n",
       "\n",
       "        NbrNewspapers    NbrStart RenewalDate PaymentType     ...      \\\n",
       "1000024            76          25  17/03/2010          BT     ...       \n",
       "1000082            25   3/03/2010          BT        Paid     ...       \n",
       "100012             10  21/11/2008          BT        Paid     ...       \n",
       "100013             25   1/12/2007          BT        Paid     ...       \n",
       "100016             10  21/11/2008          BT        Paid     ...       \n",
       "\n",
       "        PaymentDate FormulaID GrossFormulaPrice NetFormulaPrice  \\\n",
       "1000024  27/01/2010      8552                79              79   \n",
       "1000082       10017   519.737              29.6           0.592   \n",
       "100012          875       249               249        0.811842   \n",
       "100013          874       235               235        0.773026   \n",
       "100016          875       249               249        0.811842   \n",
       "\n",
       "        NetNewspaperPrice ProductDiscount FormulaDiscount TotalDiscount  \\\n",
       "1000024         1.039.474               0               0             0   \n",
       "1000082                 0         223.737         223.737          29.6   \n",
       "100012                  0               0               0         246.8   \n",
       "100013                  0               0               0           235   \n",
       "100016                  0               0               0         246.8   \n",
       "\n",
       "        TotalPrice TotalCredit  \n",
       "1000024         79         0.0  \n",
       "1000082          0         NaN  \n",
       "100012        -2.2         NaN  \n",
       "100013           0         NaN  \n",
       "100016        -2.2         NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15471 entries, 1000024 to 100\n",
      "Data columns (total 21 columns):\n",
      "SubscriptionID       15471 non-null int64\n",
      "CustomerID           15459 non-null float64\n",
      "ProductID            15459 non-null float64\n",
      "Pattern              15459 non-null object\n",
      "StartDate            15459 non-null object\n",
      "EndDate              15459 non-null object\n",
      "NbrNewspapers        15459 non-null object\n",
      "NbrStart             15459 non-null object\n",
      "RenewalDate          15459 non-null object\n",
      "PaymentType          15459 non-null object\n",
      "PaymentStatus        15453 non-null object\n",
      "PaymentDate          15138 non-null object\n",
      "FormulaID            15138 non-null object\n",
      "GrossFormulaPrice    15138 non-null object\n",
      "NetFormulaPrice      15138 non-null object\n",
      "NetNewspaperPrice    15138 non-null object\n",
      "ProductDiscount      15138 non-null object\n",
      "FormulaDiscount      15138 non-null object\n",
      "TotalDiscount        15138 non-null object\n",
      "TotalPrice           10595 non-null object\n",
      "TotalCredit          1 non-null float64\n",
      "dtypes: float64(3), int64(1), object(17)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data6.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "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>SubscriptionID</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>ProductID</th>\n",
       "      <th>TotalCredit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.547100e+04</td>\n",
       "      <td>15459.000000</td>\n",
       "      <td>1.545900e+04</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.890274e+05</td>\n",
       "      <td>6.209522</td>\n",
       "      <td>1.082186e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.463220e+05</td>\n",
       "      <td>2.394066</td>\n",
       "      <td>1.757144e+05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.276830e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.111110e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.051710e+05</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.111110e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.713630e+05</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.111110e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.167808e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.111110e+06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SubscriptionID    CustomerID     ProductID  TotalCredit\n",
       "count    1.547100e+04  15459.000000  1.545900e+04          1.0\n",
       "mean     4.890274e+05      6.209522  1.082186e+06          0.0\n",
       "std      3.463220e+05      2.394066  1.757144e+05          NaN\n",
       "min      1.000000e+00      1.000000  1.000000e+01          0.0\n",
       "25%      1.276830e+05      4.000000  1.111110e+06          0.0\n",
       "50%      5.051710e+05      8.000000  1.111110e+06          0.0\n",
       "75%      7.713630e+05      8.000000  1.111110e+06          0.0\n",
       "max      1.167808e+06      8.000000  1.111110e+06          0.0"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exploring different data sets with visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "data1.groupby(\"ProductID\").ComplaintID.count().sort_values(ascending=False).plot.bar()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "data1.groupby(\"ComplaintType\").ProductID.count().sort_values(ascending=False).plot.bar()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "data3.groupby(\"District\").CustomerID.count().sort_values(ascending=False).plot.bar()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "data5.groupby(\"Duration\").FormulaID.count().sort_values(ascending=False).plot.bar()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "data3.groupby(\"Gender\").CustomerID.count().sort_values(ascending=False).plot.bar()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exploring data6 and preparing it as base table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SubscriptionID', 'CustomerID', 'ProductID', 'Pattern', 'StartDate',\n",
       "       'EndDate', 'NbrNewspapers', 'NbrStart', 'RenewalDate', 'PaymentType',\n",
       "       'PaymentStatus', 'PaymentDate', 'FormulaID', 'GrossFormulaPrice',\n",
       "       'NetFormulaPrice', 'NetNewspaperPrice', 'ProductDiscount',\n",
       "       'FormulaDiscount', 'TotalDiscount', 'TotalPrice', 'TotalCredit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=data6.columns\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SubscriptionID', 'CustomerID', 'ProductID', 'Pattern', 'StartDate',\n",
       "       'EndDate', 'NbrNewspapers', 'NbrStart', 'RenewalDate', 'PaymentType',\n",
       "       'PaymentStatus', 'PaymentDate', 'FormulaID', 'GrossFormulaPrice',\n",
       "       'NetFormulaPrice', 'NetNewspaperPrice', 'ProductDiscount',\n",
       "       'FormulaDiscount', 'TotalDiscount', 'TotalPrice', 'TotalCredit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6=data6.drop('TotalCredit',axis=1)\n",
    "data6.reset_index(inplace=True)\n",
    "data6.columns=cols\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(4,len(data6.columns)-1):\n",
    "    data6.iloc[0,i]=data6.iloc[0,i+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(data6)):\n",
    "    if (data6.iloc[i,7]=='BT'):\n",
    "        for j in range(len(data6.columns)-3,6,-1):\n",
    "            data6.iloc[i,j+2]=data6.iloc[i,j]\n",
    "        data6.iloc[i,7]=np.nan\n",
    "        data6.iloc[i,8]=np.nan\n",
    "    \n",
    "    if (data6.iloc[i,8]=='BT' or data6.iloc[i,8]=='DD'):\n",
    "        for j in range(len(data6.columns)-2,7,-1):\n",
    "            data6.iloc[i,j+1]=data6.iloc[i,j]\n",
    "        data6.iloc[i,8]=np.nan\n",
    "    \n",
    "    if (data6.iloc[i,10]=='Not'):\n",
    "        data6.iloc[i,10]='Not Paid'\n",
    "        data6.iloc[i,11]=np.nan\n",
    "    \n",
    "    x=str(data6.iloc[i,7])\n",
    "    \n",
    "    if (x.rfind('/')!=-1):\n",
    "        data6.iloc[i,8]=data6.iloc[i,7]\n",
    "        data6.iloc[i,7]=np.nan\n",
    "    \n",
    "    x=str(data6.iloc[i,12])\n",
    "    \n",
    "    if (x.rfind('/')!=-1):\n",
    "        data6.iloc[i,12]='99999999'\n",
    "    \n",
    "    if(type(data6.loc[i,'NetNewspaperPrice'])!=float):\n",
    "        x=str(data6.loc[i,'NetNewspaperPrice'])\n",
    "        if((data6.loc[i,'NetNewspaperPrice'].count('.')>1)|(x.rfind('-')!=-1)):\n",
    "            data6.loc[i,'NetNewspaperPrice']=np.nan\n",
    "    \n",
    "    if(type(data6.loc[i,'FormulaDiscount'])!=float):\n",
    "        if(data6.loc[i,'FormulaDiscount'].count('.')>1):\n",
    "            data6.loc[i,'FormulaDiscount']=np.nan\n",
    "    \n",
    "    if(type(data6.loc[i,'TotalDiscount'])!=float):\n",
    "        if(data6.loc[i,'TotalDiscount'].count('.')>1):\n",
    "            data6.loc[i,'TotalDiscount']=np.nan\n",
    "    \n",
    "    if(type(data6.loc[i,'ProductDiscount'])!=float):\n",
    "        if(data6.loc[i,'ProductDiscount'].count('.')>1):\n",
    "            data6.loc[i,'ProductDiscount']=np.nan\n",
    "    \n",
    "    if(type(data6.loc[i,'TotalCredit'])!=float):\n",
    "        if(data6.loc[i,'TotalCredit'].count('.')>1):\n",
    "            data6.loc[i,'TotalCredit']=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=['NetNewspaperPrice','FormulaDiscount','TotalDiscount','ProductDiscount','TotalCredit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in x:\n",
    "    data6[i]=data6[i].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [],
   "source": [
    "data6=data6.drop(data6.index[15459:15471])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "data6.FormulaID=data6.FormulaID.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [],
   "source": [
    "data6.RenewalDate=np.where(data6.RenewalDate.isna()==True,'01/01/2099',data6.RenewalDate)\n",
    "data6.PaymentDate=np.where(data6.PaymentDate.isna()==True,'01/01/2099',data6.PaymentDate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=['StartDate','EndDate','RenewalDate','PaymentDate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in x:\n",
    "    data6[i]=pd.to_datetime(data6[i],format='%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [],
   "source": [
    "data4.StartDate=pd.to_datetime(data4.StartDate,format='%d/%m/%Y')        \n",
    "data4.EndDate=pd.to_datetime(data4.EndDate,format='%d/%m/%Y')    \n",
    "data3.DOB=pd.to_datetime(data3.DOB,format='%d/%m/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining base table as \"data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.merge(data6,data5,how='left',on='FormulaID')\n",
    "data=pd.merge(data4,data,how='left',on='SubscriptionID')\n",
    "data=pd.merge(data,data3,how='left',on='CustomerID')\n",
    "data=pd.merge(data,data2,how='left',on='SubscriptionID')\n",
    "data=pd.merge(data,data1,how='left',on=['CustomerID','ProductID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing columns with high correlation\n",
    "data=data.drop(['FeedbackType','SolutionType','ComplaintDate','ComplaintID','ComplaintType','TotalPrice','NbrNewspapers_y','NbrStart','Amount','CreditSource','ProcessingDate','GrossFormulaPrice','ActionType','Duration','FormulaType','FormulaCode','DeliveryContext','NetFormulaPrice'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.NbrNewspapers_x=data.NbrNewspapers_x.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining Churn in reference to Renewal Date, Start Date and End Date\n",
    "data['Churn']=np.where((data.RenewalDate > data.StartDate_y) & (data.RenewalDate < data.EndDate_y),0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining Quantiles for treating outliers later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>1%</th>\n",
       "      <th>5%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "      <th>99%</th>\n",
       "      <th>Q3-Q1</th>\n",
       "      <th>LB</th>\n",
       "      <th>UB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SubscriptionID</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>6.364140e+05</td>\n",
       "      <td>3.486594e+05</td>\n",
       "      <td>53820.000</td>\n",
       "      <td>1.361402e+06</td>\n",
       "      <td>68477.43</td>\n",
       "      <td>135641.15</td>\n",
       "      <td>3.286830e+05</td>\n",
       "      <td>6.010370e+05</td>\n",
       "      <td>9.205265e+05</td>\n",
       "      <td>1224895.00</td>\n",
       "      <td>1337725.55</td>\n",
       "      <td>5.918435e+05</td>\n",
       "      <td>-5.590822e+05</td>\n",
       "      <td>1.808292e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>5.126550e+05</td>\n",
       "      <td>3.277738e+05</td>\n",
       "      <td>3407.000</td>\n",
       "      <td>1.167808e+06</td>\n",
       "      <td>7182.00</td>\n",
       "      <td>44026.00</td>\n",
       "      <td>1.691560e+05</td>\n",
       "      <td>6.486740e+05</td>\n",
       "      <td>7.804760e+05</td>\n",
       "      <td>980298.00</td>\n",
       "      <td>1142892.00</td>\n",
       "      <td>6.113200e+05</td>\n",
       "      <td>-7.478240e+05</td>\n",
       "      <td>1.697456e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductID</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>6.018975e+00</td>\n",
       "      <td>2.301290e+00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>1.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pattern</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>1.085896e+06</td>\n",
       "      <td>1.631701e+05</td>\n",
       "      <td>10.000</td>\n",
       "      <td>1.111110e+06</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1111110.00</td>\n",
       "      <td>1.111110e+06</td>\n",
       "      <td>1.111110e+06</td>\n",
       "      <td>1.111110e+06</td>\n",
       "      <td>1111110.00</td>\n",
       "      <td>1111110.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.111110e+06</td>\n",
       "      <td>1.111110e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NbrNewspapers_x</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>2.094454e+02</td>\n",
       "      <td>1.189960e+02</td>\n",
       "      <td>2.000</td>\n",
       "      <td>6.080000e+02</td>\n",
       "      <td>12.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>7.600000e+01</td>\n",
       "      <td>3.040000e+02</td>\n",
       "      <td>3.040000e+02</td>\n",
       "      <td>306.00</td>\n",
       "      <td>329.00</td>\n",
       "      <td>2.280000e+02</td>\n",
       "      <td>-2.660000e+02</td>\n",
       "      <td>6.460000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FormulaID</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>2.531198e+07</td>\n",
       "      <td>4.347748e+07</td>\n",
       "      <td>23.000</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>290.00</td>\n",
       "      <td>869.00</td>\n",
       "      <td>8.950000e+02</td>\n",
       "      <td>4.242000e+03</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>99999999.00</td>\n",
       "      <td>99999999.00</td>\n",
       "      <td>9.999910e+07</td>\n",
       "      <td>-1.499978e+08</td>\n",
       "      <td>2.499987e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetNewspaperPrice</th>\n",
       "      <td>56330.0</td>\n",
       "      <td>8.104224e-01</td>\n",
       "      <td>1.736459e-01</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.120000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>7.557760e-01</td>\n",
       "      <td>8.190790e-01</td>\n",
       "      <td>8.831170e-01</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.273410e-01</td>\n",
       "      <td>5.647645e-01</td>\n",
       "      <td>1.074129e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductDiscount</th>\n",
       "      <td>62301.0</td>\n",
       "      <td>9.811619e-01</td>\n",
       "      <td>1.548281e+01</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.760000e+02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FormulaDiscount</th>\n",
       "      <td>59440.0</td>\n",
       "      <td>3.874906e+00</td>\n",
       "      <td>2.008498e+01</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.394740e+02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>20.48</td>\n",
       "      <td>116.55</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalDiscount</th>\n",
       "      <td>59397.0</td>\n",
       "      <td>4.906844e+00</td>\n",
       "      <td>2.543805e+01</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.394740e+02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>20.48</td>\n",
       "      <td>129.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCredit</th>\n",
       "      <td>62310.0</td>\n",
       "      <td>-3.252668e+00</td>\n",
       "      <td>1.424699e+01</td>\n",
       "      <td>-974.704</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>-42.40</td>\n",
       "      <td>-21.53</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>3.139965e+00</td>\n",
       "      <td>2.114964e+00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>-5.000000e+00</td>\n",
       "      <td>1.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIP</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>3.434977e+03</td>\n",
       "      <td>1.514414e+03</td>\n",
       "      <td>1070.000</td>\n",
       "      <td>9.800000e+03</td>\n",
       "      <td>1820.00</td>\n",
       "      <td>2070.00</td>\n",
       "      <td>2.600000e+03</td>\n",
       "      <td>3.454000e+03</td>\n",
       "      <td>3.690000e+03</td>\n",
       "      <td>9032.00</td>\n",
       "      <td>9190.00</td>\n",
       "      <td>1.090000e+03</td>\n",
       "      <td>9.650000e+02</td>\n",
       "      <td>5.325000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreeID</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>3.660882e+04</td>\n",
       "      <td>2.173582e+04</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.466200e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12460.00</td>\n",
       "      <td>2.388600e+04</td>\n",
       "      <td>3.734300e+04</td>\n",
       "      <td>4.379400e+04</td>\n",
       "      <td>71039.00</td>\n",
       "      <td>112114.00</td>\n",
       "      <td>1.990800e+04</td>\n",
       "      <td>-5.976000e+03</td>\n",
       "      <td>7.365600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>62344.0</td>\n",
       "      <td>2.943667e-01</td>\n",
       "      <td>4.557612e-01</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.500000e+00</td>\n",
       "      <td>2.500000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean           std        min  \\\n",
       "SubscriptionID     62344.0  6.364140e+05  3.486594e+05  53820.000   \n",
       "CustomerID         62344.0  5.126550e+05  3.277738e+05   3407.000   \n",
       "ProductID          62344.0  6.018975e+00  2.301290e+00      1.000   \n",
       "Pattern            62344.0  1.085896e+06  1.631701e+05     10.000   \n",
       "NbrNewspapers_x    62344.0  2.094454e+02  1.189960e+02      2.000   \n",
       "FormulaID          62344.0  2.531198e+07  4.347748e+07     23.000   \n",
       "NetNewspaperPrice  56330.0  8.104224e-01  1.736459e-01      0.000   \n",
       "ProductDiscount    62301.0  9.811619e-01  1.548281e+01      0.000   \n",
       "FormulaDiscount    59440.0  3.874906e+00  2.008498e+01      0.000   \n",
       "TotalDiscount      59397.0  4.906844e+00  2.543805e+01      0.000   \n",
       "TotalCredit        62310.0 -3.252668e+00  1.424699e+01   -974.704   \n",
       "District           62344.0  3.139965e+00  2.114964e+00      1.000   \n",
       "ZIP                62344.0  3.434977e+03  1.514414e+03   1070.000   \n",
       "StreeID            62344.0  3.660882e+04  2.173582e+04      0.000   \n",
       "Churn              62344.0  2.943667e-01  4.557612e-01      0.000   \n",
       "\n",
       "                            max        1%          5%           25%  \\\n",
       "SubscriptionID     1.361402e+06  68477.43   135641.15  3.286830e+05   \n",
       "CustomerID         1.167808e+06   7182.00    44026.00  1.691560e+05   \n",
       "ProductID          8.000000e+00      1.00        2.00  4.000000e+00   \n",
       "Pattern            1.111110e+06     10.00  1111110.00  1.111110e+06   \n",
       "NbrNewspapers_x    6.080000e+02     12.00       25.00  7.600000e+01   \n",
       "FormulaID          1.000000e+08    290.00      869.00  8.950000e+02   \n",
       "NetNewspaperPrice  1.120000e+00      0.00        0.56  7.557760e-01   \n",
       "ProductDiscount    2.760000e+02      0.00        0.00  0.000000e+00   \n",
       "FormulaDiscount    4.394740e+02      0.00        0.00  0.000000e+00   \n",
       "TotalDiscount      4.394740e+02      0.00        0.00  0.000000e+00   \n",
       "TotalCredit        7.900000e+01    -42.40      -21.53  0.000000e+00   \n",
       "District           8.000000e+00      1.00        1.00  1.000000e+00   \n",
       "ZIP                9.800000e+03   1820.00     2070.00  2.600000e+03   \n",
       "StreeID            1.466200e+05      0.00    12460.00  2.388600e+04   \n",
       "Churn              1.000000e+00      0.00        0.00  0.000000e+00   \n",
       "\n",
       "                            50%           75%          95%          99%  \\\n",
       "SubscriptionID     6.010370e+05  9.205265e+05   1224895.00   1337725.55   \n",
       "CustomerID         6.486740e+05  7.804760e+05    980298.00   1142892.00   \n",
       "ProductID          7.000000e+00  8.000000e+00         8.00         8.00   \n",
       "Pattern            1.111110e+06  1.111110e+06   1111110.00   1111110.00   \n",
       "NbrNewspapers_x    3.040000e+02  3.040000e+02       306.00       329.00   \n",
       "FormulaID          4.242000e+03  1.000000e+08  99999999.00  99999999.00   \n",
       "NetNewspaperPrice  8.190790e-01  8.831170e-01         1.04         1.10   \n",
       "ProductDiscount    0.000000e+00  0.000000e+00         0.00         0.00   \n",
       "FormulaDiscount    0.000000e+00  0.000000e+00        20.48       116.55   \n",
       "TotalDiscount      0.000000e+00  0.000000e+00        20.48       129.00   \n",
       "TotalCredit        0.000000e+00  0.000000e+00         0.00         0.00   \n",
       "District           3.000000e+00  5.000000e+00         7.00         7.00   \n",
       "ZIP                3.454000e+03  3.690000e+03      9032.00      9190.00   \n",
       "StreeID            3.734300e+04  4.379400e+04     71039.00    112114.00   \n",
       "Churn              0.000000e+00  1.000000e+00         1.00         1.00   \n",
       "\n",
       "                          Q3-Q1            LB            UB  \n",
       "SubscriptionID     5.918435e+05 -5.590822e+05  1.808292e+06  \n",
       "CustomerID         6.113200e+05 -7.478240e+05  1.697456e+06  \n",
       "ProductID          4.000000e+00 -2.000000e+00  1.400000e+01  \n",
       "Pattern            0.000000e+00  1.111110e+06  1.111110e+06  \n",
       "NbrNewspapers_x    2.280000e+02 -2.660000e+02  6.460000e+02  \n",
       "FormulaID          9.999910e+07 -1.499978e+08  2.499987e+08  \n",
       "NetNewspaperPrice  1.273410e-01  5.647645e-01  1.074129e+00  \n",
       "ProductDiscount    0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "FormulaDiscount    0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "TotalDiscount      0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "TotalCredit        0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "District           4.000000e+00 -5.000000e+00  1.100000e+01  \n",
       "ZIP                1.090000e+03  9.650000e+02  5.325000e+03  \n",
       "StreeID            1.990800e+04 -5.976000e+03  7.365600e+04  \n",
       "Churn              1.000000e+00 -1.500000e+00  2.500000e+00  "
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc=data.describe().T\n",
    "desc['1%']=data.quantile(.01)\n",
    "desc['5%']=data.quantile(.05)\n",
    "desc['95%']=data.quantile(.95)\n",
    "desc['99%']=data.quantile(.99)\n",
    "desc['Q3-Q1']=desc['75%']-desc['25%']\n",
    "desc['UB']=desc['75%']+(desc['Q3-Q1']*1.5)\n",
    "desc['LB']=desc['25%']-(desc['Q3-Q1']*1.5)\n",
    "desc=desc.loc[:,['count','mean','std','min','max','1%','5%','25%','50%','75%','95%','99%','Q3-Q1','LB','UB']]\n",
    "desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating dummy variables and handling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=pd.get_dummies(data['DeliveryClass'],prefix='DeliveryClass')\n",
    "data=pd.concat([data,cols],axis=1)\n",
    "data=data.drop('DeliveryClass',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.dropna()\n",
    "data.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.NetNewspaperPrice=data.NetNewspaperPrice.fillna(data.NetNewspaperPrice.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.TotalDiscount=data.TotalDiscount.fillna(data.TotalDiscount.mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.FormulaDiscount=data.FormulaDiscount.fillna(data.FormulaDiscount.mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handling Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['NbrNewspapers_x']=np.clip(data['NbrNewspapers_x'],desc.loc['NbrNewspapers_x','1%'],desc.loc['NbrNewspapers_x','99%'])\n",
    "data['NetNewspaperPrice']=np.clip(data['NetNewspaperPrice'],desc.loc['NetNewspaperPrice','1%'],desc.loc['NetNewspaperPrice','99%'])\n",
    "data['TotalCredit']=data['TotalCredit'].clip_lower(desc.loc['TotalCredit','1%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [],
   "source": [
    "lecr=LabelEncoder()\n",
    "data['Gender']=lecr.fit_transform(data['Gender'])\n",
    "data['PaymentStatus']=lecr.fit_transform(data['PaymentStatus'])\n",
    "data['PaymentType']=lecr.fit_transform(data['PaymentType'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=pd.get_dummies(data['District'],prefix='District')\n",
    "cols=cols.drop(cols.columns[0],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.concat([data,cols],axis=1)\n",
    "data=data.drop('District',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=pd.get_dummies(data['DeliveryType'],prefix='DeliveryType')\n",
    "cols=cols.drop(cols.columns[0],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.concat([data,cols],axis=1)\n",
    "data=data.drop('DeliveryType',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop_duplicates()\n",
    "data=data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(data)):\n",
    "    data.loc[i,'DOB']=pd.datetime.toordinal(data.loc[i,'DOB'])\n",
    "    data.loc[i,'RenewalDate']=pd.datetime.toordinal(data.loc[i,'RenewalDate'])\n",
    "    data.loc[i,'PaymentDate']=pd.datetime.toordinal((data.loc[i,'PaymentDate']))\n",
    "    data.loc[i,'StartDate_x']=pd.datetime.toordinal((data.loc[i,'StartDate_x']))\n",
    "    data.loc[i,'EndDate_x']=pd.datetime.toordinal((data.loc[i,'EndDate_x']))\n",
    "    data.loc[i,'StartDate_y']=pd.datetime.toordinal((data.loc[i,'StartDate_y']))\n",
    "    data.loc[i,'EndDate_y']=pd.datetime.toordinal((data.loc[i,'EndDate_y']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data['Churn']\n",
    "data.drop('Churn',axis=1,inplace=True)\n",
    "data.index=data.CustomerID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=[\"CustomerID\",\"SubscriptionID\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RMQeY08XmPwNbldbfxhsvxzwR+PsU605JY4HNgA26054reV1riojGiNgVWAuc3EP/c0rLk4F/6cubqeDPw8zMzMysOtwLTJG0raTRwAeBGyv6/Ak4BEDSTsBY4LkNfWMnFb2zANgBQNJPJN0naXlHOVbS+UC9pMWSrgLOB7ZP619JfT4p6V5JSyWdl9oaJD0k6VJgEbCVpFcl/aekJZLukpT/3vZmZmZmZtWqvX3gXt2IiFbgFOAWiisDr4uI5ZL+Q9K7U7d/BU6StAS4BpgVGZ5x58s1eyBpFHA4cHNqOiEiXpBUT3Eb1Bsi4mxJp0REY9qnAdi1tH4YMIXiDjsCbpR0IEXm/nbg+Ij4l9R3PHBXRJwr6QLgJOALA3S4ZmZmZmaWSXrm3U0VbZ8pLT8I7Jf7fV3J61q9pMXAQopk7Hup/bSUad9FcY3tlF7EOiy97qeo2E0t7ffHiLir1Hct8PO0fB+dPCejPMHzpqY/9OmgzMzMzMyqWUTbgL2GKlfyutbUUYnrIGkmxW1N94mI1ZLmU1w32xMBX4qI71TEawBWVfRtKZVo2+jkMypP8Lz5zR/c4HKumZmZmZlVD1fy+mYj4MWU4E0F3lna1iKpLi2vBCaWtt0CnCBpAoCkt0rafEBGbGZmZmY2kgyROXmDyZW8vrkZOFnSUuARiks2O8wBlkpaFBHHSbpD0gPALyLik+luOXdKAngV+CeKSp2ZmZmZmVk2TvK6EBETOmlrprgJS2f9zwLOKq0fW7H9YuDiTnbdtav3jYgfAj/s08DNzMzMzEayGLoVtoHiyzXNzMzMzMyqiJM8MzMzMzOzKuLLNc3MzMzMrHoM4RuiDBQnecPcIYs/mz1m6z0/yx7z3o/emzXevF3OzRoP4LDl/5k9ZssV+Z9jf9tX83/bbj5qTfaY9eNassd8dMWk7DEPmv6X7DGXLcx/89wpO6zIHvOZpyb23KmPNt50dfaY0a7sMbfdvfLpNRvm/ps3yRoPoL42/725apQ/ZkT+z2f0qPzjXNtWmz1mXc3w+I9kf3xGY+pas8dcvbau5059VKv8T5rK/bXUlP9Usum4/L/XbXhxkmdmZmZmZtXDN17xnDwzMzMzM7Nq4kqemZmZmZlVD8/JcyXPzMzMzMysmriSZ2ZmZmZm1cNz8oZ/JU/SuZKWS1oqabGkvSWdLmncesSaJWnL0vp8SY+k2A9LukTS5F7EOaev721mZmZmZpbDsE7yJO0DHAG8IyKmAYcCTwGnA31K8iTVArOALSs2HZdiTwOagZ/2IpyTPDMzMzOzwdDePnCvIWpYJ3nAFsCKiGgGiIgVwFEUidptkm4DkPRtSQtTxe+8jp0lPSnpM5J+C3wImAFclSqC9eU3ioi1wL8BW0vaPe3/E0n3pbizU9v5QH2KcVVq+ydJ96S276SE8g0knSjpotL6SZIuzHOqzMzMzMxsJBjuSd48YCtJj0q6VNJBEfEN4K/AwRFxcOp3bkTMoKjGHSRpWinGmojYPyL+B1hIUblrjIimyjeLiDZgCTA1NZ0QEdMpksPTJG0aEWcDTSnGcZJ2Ao4B9ouIRqANOK6L4/kB8G5JHU8DPR64orKTpNkpaV14+f9c18tTZWZmZmY2AriSN7xvvBIRr0qaDhwAHAxcK+nsTrp+IFXaRlFU/3YGlqZt1/bxbVVaPk3Se9PyVsAU4PmK/ocA04F7JQHUA892cTyrJP0aOELSQ0BdRCzrpN8cYA5Ay9MPRR/Hb2ZmZmZmVWxYJ3nwWnVtPjBf0jLgI+XtkrYFzgT2jIgXJc0Fxpa6rOrte6XLLHcDHpI0k2IO4D4RsVrS/Iq4r+0G/FdEfKqXb3M5xZy+h+mkimdmZmZmZt3w3TWH9+Wakt4uaUqpqRH4I7ASmJjaJlEkci9LejNweDchy/tVvlcd8CXgqYhYCmwEvJgSvKnAO0vdW0qXXN4KHCVp8xRnE0nbdDWAiLiboip4LHBNN2M1MzMzMzN7g+FeyZsAfDM91qAV+D0wm+ImKr+Q9HREHCzpfmA58DhwRzfx5gKXSWoC9kltV0lqBsYAvwKOTO03AydLWgo8AtxVijMHWCppUZqX92lgnqQaoAX4GEUy2pXrgMaIeLFXZ8HMzMzMzApDeK7cQBnWSV5E3Afs28mmb6ZXR79ZXezfULF+A3BDqWlmN+/dTBdVwYg4CzirtH4tfZv7tz9wUY+9zMzMzMzMKgzryzWrjaTJkh6luDvnrYM9HjMzMzMzG36GdSVvOJN0N8UloGUfjogdB2M8ZmZmZmZVwTdecZI3WCJi7xxxWq68IEeY19Euu2WPObqmLWu88XnDAdByxReyx6w7/tPZY24355TsMZvW1PXcqY/+smJS9phNNfkvPojW7CGpVf4nm6x8vvJvQhuuvV09d+qjlS91dpPhoUfL1mSNV1+b/4dSTT98HdXU5I8Zkf/rqLUt//f62vb8Mfvje70/Pve2fviM+kN/jLK2Jv9/9mszn8/arNEKrzTl/51hw4uTPDMzMzMzqx6+8Yrn5JmZmZmZmVUTV/LMzMzMzKx6eE6eK3lmZmZmZmbVxJU8MzMzMzOrHp6TV32VPEltkhaXXmf3cf8nJW1WEWu5pCWSPiGp23MmqUHSsRtyDGZmZmZmZuurGit5TRHRmDuWpM2Bq4GNgM92s08DcGzqa2ZmZmZmA8mVvOqr5HUlVejOk7RI0jJJU1P7ppLmSbpf0nfo4jEtEfEsMBs4RYUGSQtSvEWS9k1dzwcOSBXAMyTVSvqKpHslLZX00W7G+N+SjiytXyXp3dlOgpmZmZmZVb1qTPLqKy7XPKa0bUVEvAP4NnBmavss8NuI2AO4Edi6q8AR8TjFOdsceBZ4V4p3DPCN1O1sYEFENEbERcCJwMsRsSewJ3CSpG27eIvLgeMBJG0E7AvcVNlJ0mxJCyUt/P7dj/R4QszMzMzMRoyIgXsNUSPtcs0fpX/vA96Xlg/sWI6I/5X0Yg/xOyp9dcAlkhqBNmDHLvofBkyTdFRa3wiYAjxR2TEifiPpW+nS0PcBN0REayf95gBzAFZ/+fih+9VlZmZmZmYDrhqTvO40p3/beP2x9ypRkrRd2vdZigrgM8DuFNW9NV3tBpwaEbf0coz/DRwHfBA4oZf7mJmZmZkZeE4e1Xm5Zl/dTpFUIelwYOPOOkl6E3AZcElEBEVF7umIaAc+DNSmriuBiaVdbwH+r6S6FGdHSeO7Gc9c4HSAiFi+nsdkZmZmZmYjVDVW8uolLS6t3xwR3T1G4TzgGkmLgN8Af+okVh3QSlFluzBtuxS4QdLRwG3AqtS+FGiVtIQiYbuY4o6biyQJeA54T1eDiYhnJD0E/KQXx2pmZmZmZmWu5FVfkhcRtV20N5SWFwIz0/LzFPPmOpzRU6y07TFgWqnpU6m9BTikovs56dUjSeMo5uxd05v+ZmZmZmZmZVWX5A1nkg4Fvg9cGBEvD/Z4zMzMzMyGnXAlz0neIJC0G8Wln2XNEbE33TzCwczMzMzMrCdO8gZBRCwDunrMQ58s/vorOcK8zqT6X2ePuaatPmu83WY8mzUewG1fzf/tsN2cU7LH3OHOS7LHXNL4iewxvza6KXvMT7W2ZY/5xAObZI9ZP+oNTz7ZYPe/tGn2mDuPzf/z48XVY7PHHF2T/y+yi/82OWu8d9T39PSdvlvZNCZ7zFG1+c9lU3Nd9pjPtub/OlLvbqTdJ/3wpcm4US3ZY65sHZ09Ztua/PfuGzc6/7HXj80fk8y/3iZN7OoG7evvty+/KXtMgP36Jar1Byd5ZmZmZmZWPXzjFT9CwczMzMzMrJq4kmdmZmZmZtUj8l+ePdy4kmdmZmZmZlZFXMkzMzMzM7Pq4Tl5G17JkxSSvlZaP1PS59LyXElH9SJGQ4pzaqntEkmzNnR8ZmZmZmZmI0mOyzWbgfdJ2mx9dpbUUU18Fvi4pPz3+R1EpeMzMzMzM7P+1t4+cK8hKkeS1wrMAc7oYvuhkhZIelTSEQCSZkm6XtLPgHmp33PArcBHKgNI2l7SzZLuS7GmSqqV9LgKkyW1Szow9V8gaQdJB0lanF73S5ooaaak2yX9WNKDki6TVJP2+7akhZKWSzqv9P5PSvqypHvSa4fU/iZJN0i6N732S+2fkzRH0jzgSkm7pP0WS1oqaUpnJ0rSnmn7WEnj0zh27ftHYmZmZmZmI1WuKtO3gKWSLuhkWwNwELA9cFtHggTsA0yLiBckNaS284FfSPp+RYw5wMkR8ZikvYFLI+LvJD0K7AxsC9wHHCDpbuBtEfF7SRcBH4uIOyRNADqeNrlX2u+PwM3A+4AfAuem8dQCt0qaFhFL0z6vRMRekv4Z+DpwBHAxcFFE/FbS1sAtwE6p/3Rg/4hokvRN4OKIuCpVKms7O4kRca+kG4EvAPXA/0TEA5X9JM0GZgP826Q9OHLctp2FMzMzMzMbeWLoVtgGSpYkLyJekXQlcBrQVLH5uohoBx6T9DgwNbX/MiJeqIjzhKR7gGM72lJyti9wvaSO5jHp3wXAgRRJ3peAk4DfAPem7XcAF0q6CvhRRPw5xbgnIh5P8a8B9qdI8j6QEqhRwBYUiWBHkndN6d+L0vKhwM6lcU2SNDEt3xgRHefiTuBcSW9L43isk9PY4T/S+NdQnM83iIg5FIkvv9vi/b5HrJmZmZmZvSbnIxS+DpwIjK9or0xCOtZXdRHni8BZrBtbDfBSRDSWXh3VsgXAARSVuZuAycBM4HaAiDgf+P8pqmJ3SepIMN8wJknbAmcCh0TENOB/gbFdHEfHcg2wT2lcb42IlZXHFxFXA++mSIBvkfR3XRw7wCbABGBixfubmZmZmVkPoj0G7DVUZUvyUlXuOopEr+xoSTWStge2Ax7pIc7DwIMUl0MSEa8AT0g6GiDNwds9db+bosrXHhFrgMXARymSPyRtHxHLIuLLwELWVRH3krRtmot3DPBbYBJFYvaypDcDh1cM7ZjSv3em5XnAKR0dJDV2dkyStgMej4hvADcC07o5BXOAfweuAr7cTT8zMzMzM7M3yP0w9K8BlXfZfITiEspfUMyrW/OGvd7oP4G3ldaPA06UtARYDhwJEBHNwFPAXanfAooK2LK0frqkB9J+TWkMUCRp5wMPAE8AP46IJcD9Kf73KS71LBuT5vt9nHU3mTkNmJFulvIgcHIXx3MM8ICkxRSJ5pWddUrz/VpT5e98YM8eqn5mZmZmZlbmu2tu+Jy8iJhQWn4GGFdan9XFPnOBuaX1J4FdS+tLKCWgEfEE8PddxDqgtHw1cHVp/dTK/mn+3OqIOKZyW1fjTb4VEeeVGyJiBesqfOX2z1Wsf4lizmC3IuJKUgIYEW3A3j3tY2ZmZmZmVuZnuJmZmZmZWfXw3TVHXpIXEfOB+X3cpyHnGCRtSvFMwEqHRMTzOd/LzMzMzMxGlhGX5A0FKZHr9CYtfY+lnjv10dqWTh/jt0E2HdebqZi9t2Thm7PGA9h8VN4xAjStqcsec0njJ7LH3H3xhdljnrHLWdlj/k35z2fjW57LHnPLm76ePeaKPT6fPebKNaOzxxxf15I9Zlt77unj8PYub/C8flb1w7kcNyb/ueyPcdYo/93lxtOaPeZm4yuf8LThmprz/0zqD5Nq838t9cfn3tae//80K1eN6blTHynzsT/zwsSeO/VR45hXsse04cVJnpmZmZmZVY8h/GiDgZL/z6NmZmZmZmY2aFzJMzMzMzOz6jGEH20wUFzJMzMzMzMzqyJDPsmT1CZpcXqo+c8kTR7sMXWQNFPSz9PyLEnPSbpf0mOSbpG0by9ivEfSzv0/WjMzMzOzEcAPQx/6SR7QFBGNEbEr8ALwscEeUDeujYg9ImIKcD7wI0k79bDPewAneWZmZmZmlsVwSPLK7gTe2rEi6ZOS7pW0VNJ5qa1B0kOSvitpuaR5kurTtu0l3SzpPkkLJE2VVCvpcRUmS2qXdGDqv0DSDpL2kvS7VKX7naS39zTQiLgNmAPMTrFOSmNdIukGSeNSpe/dwFdStXL7zsaY/SyamZmZmVWriIF7DVHDJsmTVAscAtyY1g8DpgB7UTxzbnpHcpbavxURuwAvAe9P7XOAUyNiOnAmcGlEtAGPUlTT9gfuAw5Qi3YEAAAgAElEQVSQNAZ4W0T8HngYODAi9gA+A3yxl8NeBHQkaT+KiD0jYnfgIeDEiPhdOp5PpmrlHzobY59OlJmZmZmZjWjD4e6a9ZIWAw0UCdgvU/th6XV/Wp9Akdz9CXgiIhan9vuABkkTgH2B66XXHrbZ8YTMBcCBwLbAl4CTgN8A96btGwH/JWkKEEBvn35afqrnrpK+AExOY73lDZ27H2O532xShfCTE/fgyHHb9XI4ZmZmZmZVbgjPlRsow6GS1xQRjcA2wGjWzckT8KVUAWuMiB0i4ntpW3Np/zaKZLYGeKnUvzEiOubLLQAOoKgK3kSRiM0Ebk/bPw/cluYF/iMwtpdj34OiagcwFzglInYDzusiRndjfE1EzImIGRExwwmemZmZmZmVDYckD4CIeBk4DThTUh1FJeyEVP1C0lslbd7N/q8AT0g6OvWXpN3T5rspKmjtEbEGWAx8lCL5g6KS95e0PKs345V0EEW17bupaSLwdBr7caWuK9O2nsZoZmZmZmY9aY+Bew1RwybJA4iI+4ElwAcjYh5wNXCnpGXAD0nJUjeOA06UtARYDhyZ4jYDTwF3pX4LUqxlaf0C4EuS7gBqu4l/TLqByqPAOcD7I6KjkvfvFMnkLynm+HX4AfDJdFOX7bsao5mZmZmZWW8M+Tl5ETGhYv0fS8sXAxd3stuupT5fLS0/Afx9F+9zQGn5aooEsmP9TmDHUvd/T+3zgflpeS7FJZldHce3gW930n4Hb3yEQqdjNDMzMzOzHoTn5A2rSp6ZmZmZmZl1b8hX8szMzMzMzHptCM+VGyiu5JmZmZmZmVURV/KGuY3HN2WP+UrTGx7Nt8GeXVufNd7bxr+aNR5A/biW7DH/smJS9phfG53/Mz9jl7Oyx9xv+Zezx3xoxsezx/zrM/k/oyf3+Fz2mPse8VL2mA/clP/Yn1/b2yfMDK7t3pT3fK5eNTprPICW1vx/h32+Lf84x5D/L+a15J9PM35ic8+d+mhtS3f3Yls/LW35Y262yarsMZet2DR7zC1q1mSP+Wpbbx9t3Hvja/P+f6HtdY9VzmOjyfn/r2DDi5M8MzMzMzOrGuGHoftyTTMzMzMzs2riSp6ZmZmZmVUP33jFlTwzMzMzM7NqMmQreZLagGUUY3wI+EhErB7cUa0j6ZyI+KKkTYFbU/NbgDbgubS+V0SsHZQBmpmZmZmNRH4Y+pCu5DVFRGNE7AqsBU4e7AFVOAcgIp5P42wELgMu6lh3gmdmZmZmZgNtKCd5ZQuAHQAk/UTSfZKWS5qd2k6UdFFHZ0knSbpQUoOkhyVdLukBSVdJOlTSHZIek7RX6j9e0vcl3SvpfklHpvZZkn4k6ebU/4LUfj5QL2mxpKu6GrSkL0n6WGn9y5L+JY3htnQsD0r6liSlPodLulPSIknXShqf/3SamZmZmVWp9hi41xA15JM8SaOAwyku3QQ4ISKmAzOA09Llkj8A3i2p42EoxwNXpOUdgIuBacBU4Fhgf+BMUjUOOBf4dUTsCRwMfKWUXDUCxwC7AcdI2ioizmZdpfG4boZ/OTArHUctcDRwTdq2N3B6irsTcKSkzYGzgUMi4h3AUiD/g8HMzMzMzKxqDdk5eaRKWVpeAHwvLZ8m6b1peStgSkTcJenXwBGSHgLqImKZpAbgiYhYBiBpOXBrRISkZUBDinMYRZJ4ZlofC2ydlm+NiJfT/g8C2wBP9eYAIuIPklZK2i3td09EvJiKdndFxJMp7g8oEk+AnYHfpT6jgd9Wxk0VzNkAn91sVz4waevKLmZmZmZmI5Ofkzekk7ymNM/tNZJmAocC+0TEaknzKRIyKKpm5wAPs66KB9BcWm4vrbez7vgFvD8iHql4v70r9m+j7+fsexTVvAbgO6X2yvpupHHcHBEf7i5gRMwB5gA8uP0/DN06sZmZmZmZDbghf7lmhY2AF1OCNxV4Z8eGiLiborJ3LOsuieytW4BTS/Pi9ujFPi2ly0O7cwPwjxSXff6q1P5OSVunyzg/QFGx+x1wkKTt0jjGS5rSh+MwMzMzMxvZPCdv2CV5NwOjJC0FPg/cVbH9OuCOiHixj3E/D9QBSyU9kNZ7Mif17/LGKwARsQa4Hbgm4nX3c/0d8DWKuYaPAjdGxDPAicC1kpakPjv28VjMzMzMzGwEG7KXa0bEhE7amiluwtKV/YHX7rKZ5rztWlqf1dm2iGgCPtrJ+80F5pbWjygtnwWcVdH/c5UxJNUAewHvqdi0KiKO7uQ9fwn88o2HZmZmZmZmPfJz8oZdJa9TkiZLepRiHt+tPe4wQNINV/5AMc/u8cEej5mZmZmZVb8hW8nri4h4iSF4WWO6q+e2nbT/itfPzzMzMzMzsxyG8Fy5gVIVlTwzMzMzMzMrVEUlbyR7bNVG2WOuKW4ymtU2tU1Z461tqc0aD+DRFZOyx2yqyf93lE+1tmWP+bde3Si2bx6a8fHsMXdaeHH2mPVbHpA95gMNu2ePufDnG2ePOalubfaYo2vzf33WKP9fZP+S+ft94uj857KlLf/PuU1r84+zLfL/zqiryT+f5q/P5P992R9f7/1Rf3jhxXHZY26u5p479VFtP3zuk5T/a16ZfyaNrWvNGg/gqWcmZ48JnVyeZkOWkzwzMzMzM6sa4Yeh+3JNMzMzMzOzauJKnpmZmZmZVQ/feMWVPDMzMzMzs2oyaEmepDZJiyU9IOl6SflnBW8ASedUrJ8rabmkpWnce6f203sz9t72MzMzMzOzDdAeA/caogazktcUEY0RsSuwFjh5EMfSmdeSPEn7AEcA74iIacChwFNp8+lAb5K33vYzMzMzMzNbb0Plcs0FwA4Akn4i6b5UNZud2k6UdFFHZ0knSbpQUoOkhyVdniqCV0k6VNIdkh6TtFfqP17S9yXdK+l+SUem9lmSfiTp5tT/gtR+PlCfKnZXAVsAKyKiGSAiVkTEXyWdBmwJ3CbptrTvtyUtTOM/L7V11u/V0vEcJWluWj46HcsSSbf32xk3MzMzM6tG0T5wrx5I+ntJj0j6vaSzu+l3lKSQNCPHKRj0JE/SKOBwYFlqOiEipgMzgNMkbQr8AHi39NrDvI4HrkjLOwAXA9OAqcCxwP7Amayrxp0L/Doi9gQOBr4iaXza1ggcA+wGHCNpq4g4m3WVxuOAecBWkh6VdKmkgwAi4hvAX4GDI+LgjveKiBlpPAdJmtZFv658Bvj/ImJ34N29O4tmZmZmZjaUSKoFvkWR6+wMfEjSzp30mwicBtyd670HM8mrl7QYWAj8Cfheaj9N0hLgLmArYEpErAJ+DRwhaSpQFxEdSeETEbEsItqB5cCtEREUSWND6nMYcHZ6v/nAWGDrtO3WiHg5ItYADwLbVA40Il4FpgOzgeeAayXN6uK4PiBpEXA/sAvFB9oXdwBzJZ0EdPokXEmzU7Vw4S2rf9/H8GZmZmZmVWzozMnbC/h9RDweEWspCldHdtLv88AFwJpcp2AwH6HQFBGN5QZJMynmu+0TEaslzadIyAAup6jMPcy6Kh5Ac2m5vbTezrrjE/D+iHik4v32rti/jS7OSUS0USSI8yUtAz4CzK2Ity1FBXHPiHgxXYI5ls6Vvype6xMRJ6dx/QOwWFJjRDxfMZY5wByAn77l2KE749PMzMzMbOR6K+vu4wHwZ2DvcgdJewBbRcTPJZ2Z640H/XLNChsBL6YEbyrwzo4NEXE3RWXvWOCaPsa9BThVkuC1k9mTlo7LQyW9XdKU0rZG4I9peSUwMS1PAlYBL0t6M0Vplk76ATwjaSdJNcB7OxolbR8Rd0fEZ4AVFMdsZmZmZma9EO0xYK/yFXbpNbs0FHU2vNc2FnnARcC/5j4HQ+1h6DcDJ0taCjxCcclm2XVAY0S82Me4nwe+DixNid6TFHfL7M6c1H8RcCHwTUmTgVbg9xSXbnb0+4WkpyPiYEn3U1w2+jjFpZd01g84G/g5RXb/ADAh9ftKSigF3Aos6eOxmpmZmZnZAChfYdeJP/P6gs3bKO7T0WEisCvFlYIAbwFulPTuiFi4IeNSMX1teJD0c+CiiLh1sMcyVPTH5Zpr1NkfHTbMNmrKGm90bVvWeABPtozvuVMfNdXkL5Zvz+rsMf/W3tVVxetvu7Ers8fcaeHF2WPWb3lA9pgPNOyePeZfX57Qc6c+mlS3NnvM/lCj/L+nXm0ZnTXexNH5z+Xa1k6nZQ85bZH/d0ZdTc93rOurtW35z2d//C7qj/NZ2w/fQy3t+X+/9cf5jH44n8p8PkfV5v96f6V5TPaYAAf+7fr8J7QfrDztiAFLcCZ+4+ddnpN0g8lHgUOAvwD3AsdGxPIu+s8HztzQBA+G3uWanZI0WdKjFPP4nOCZmZmZmdmQFhGtwCkUU8ceAq6LiOWS/kNSv95Ff6hdrtmpiHgJ2HGwx2FmZmZmZkNce/7q6PqKiJuAmyraPtNF35m53ndYVPLMzMzMzMysd4ZFJc+6tu9uf8keU/2Q+v/pwclZ4zW11GWNB3DQ9PznMlqzh+SJBzbJHrPxLc9lj/nXZyZlj9kf8+ea/roge8zbdjkne8xtNn45e8yXXqnPHrO2H+ZStfXD3J/NJ63KGu/5V8ZljQcwZlT+HyD9Mb9xTE3+mK1t+T/z8WPyz5ts6Yd5k/0yz68fvofGjW7JHrO5Jf9/S/vjaz73+WxuzX/cm4zLey8EG36c5JmZmZmZWfXo+SHlVc+Xa5qZmZmZmVURV/LMzMzMzKx6uJLnSp6ZmZmZmVk1GbJJnqQ2SYslPSDpekn5Z7VvAEnnVKx3jHe5pCWSPiF1fwsTSQ2Sju3fkZqZmZmZjRwRMWCvoWrIJnkUDz5vjIhdgbXAyYM9oAqVt8/rGO8uwLuA/wN8tocYDYCTPDMzMzMzy2YoJ3llC4AdACT9RNJ9qWI2O7WdKOmijs6STpJ0YaqUPSzp8lQRvErSoZLukPSYpL1S//GSvi/pXkn3Szoytc+S9CNJN6f+F6T284H6VLm7qnKwEfEsMBs4RYUGSQskLUqvfVPX84EDUpwzJNVK+koax1JJH+2/U2pmZmZmVoXaY+BeQ9SQT/IkjQIOB5alphMiYjowAzhN0qbAD4B3S+p4eNrxwBVpeQfgYmAaMJWicrY/cCbrqnHnAr+OiD2Bg4GvSBqftjUCxwC7AcdI2ioizmZd5e64zsYdEY9TnN/NgWeBd0XEO1Ksb6RuZwMLUpyLgBOBl9M49gROkrRt38+amZmZmZmNVEM5yauXtBhYCPwJ+F5qP03SEuAuYCtgSkSsAn4NHCFpKlAXER1J4RMRsSwi2oHlwK1RXEC7jOJySYDDgLPT+80HxgJbp223RsTLEbEGeBDYpg/HoPRvHfBdScuA64Gdu+h/GPDPaRx3A5sCU94QVJotaaGkhVf++ek+DMfMzMzMrMq5kjekH6HQFBGN5QZJM4FDgX0iYrWk+RQJGcDlFJW5h1lXxQNoLi23l9bbWXf8At4fEY9UvN/eFfu30ctzJmm71P9Zirl5zwC7UyTWa7raDTg1Im7pLnZEzAHmADz3roOG7leXmZmZmZkNuKFcyevMRsCLKcGbCryzY0NE3E1R2TsWuKaPcW8BTpUkAEl79GKfltLloa8j6U3AZcAlqWq4EfB0qiZ+GKhNXVcCEyvG8X874krasXTZqJmZmZmZ9SDaY8BeQ9VQruR15mbgZElLgUcoLtksuw5ojIgX+xj388DXgaUp0XsSOKKHfeak/ovSvLyOy0vrgFbgv4ELU99LgRskHQ3cBqxK7UuB1nT56VyKuYMNwKI0jueA9/TxWMzMzMzMbAQbskleREzopK2Z4iYsXdkfeO0umxHxJLBraX1WZ9siogl4w50sI2IuRfLVsX5Eafks4KzSei1diIjHKG780uFTqb0FOKSi+zm88fEMZmZmZmbWG0O4wjZQhtvlmp2SNFnSoxTz+G4d7PGYmZmZmZkNliFbyeuLiHgJ2HGwx2FmZmZmZoOsfbAHMPiqopJnZmZmZmZmhaqo5I1ki5e+JXvMsWrLHnPjcV09NWL9vLR2TNZ4AMsWbp49Zq3yXxNeP6o1e8wtb/p69phP7vG57DEfaNg9e8zbdsk/Bfbg5V/MHnPeLudmj7lpbXPPnfqoph++5ttCPXfqo5deHdtzpz4YXZv/5+aYuvwxVzd3elPoDVLTD3NfVrXm/+/JpnV5fw8BtGSPCG3tw+Pv7y2tXd6KYL3V1uQvv0Q//Pyoy/z93toPn3l/fD42vDjJMzMzMzOzqjGUH20wUIbHn4vMzMzMzMysV1zJMzMzMzOz6uFKnit5ZmZmZmZm1aSqkzxJbZIWl14NA/jeDZIe6G0fSTMlvSzpfkmPSLpd0hHd7W9mZmZmZhXaB/A1RFX75ZpNEdHY150kjYqI/Lcw7NmCiDgijaER+IkkP+DdzMzMzMx6raoreZ2RNFbSFZKWparZwal9lqTrJf0MmJcqa7+RdJ2kRyWdL+k4SfekfbdP+82VdFQp/qudvGeDpAWSFqXXvj2NMyIWA/8BnJLt4M3MzMzMqly0x4C9hqpqr+TVS1qclp+IiPcCHwOIiN0kTaVI6HZMffYBpkXEC5JmArsDOwEvAI8Dl0fEXpI+DpwKnN7LcTwLvCsi1kiaAlwDzOjFfouAT/byPczMzMzMzKo+yevscs39gW8CRMTDkv4IdCR5v4yIF0p9742IpwEk/QGYl9qXAQf3YRx1wCXpEsy20vv1pNMneEqaDcwG+PjE6fxD/fZ9GIqZmZmZWRUbwnPlBsqIu1yTLhKnZFXFenNpub203s66BLmVdB4lCRjdSdwzgGcoKoMzuujTmT2AhyobI2JORMyIiBlO8MzMzMzMrGwkJnm3A8cBpMs0twYe2YB4TwLT0/KRFFW7ShsBT0dEO/BhoLanoJKmAf8OfGsDxmZmZmZmNqJ4Tl71X67ZmUuByyQto6jCzYqI5qIIt16+C/xU0j3ArbyxGtjxnjdIOhq4rYs+AAdIuh8YRzGP7zTfWdPMzMzMzPqiqpO8iJjQSdsaYFYn7XOBuaX1+cD80vrMzrZFxDPAO0uhPpXanwR2TcuPAdN66DOfouJnZmZmZmbry3PyRuTlmmZmZmZmZlWrqit5ZmZmZmY2soQrea7kmZmZmZmZVRNX8oa5nRqeyx6ztbnHm3/22TPPTswab5Ty381oyg4rssdc+fyY7DHvf2nT7DFX7PH57DH3PeKl7DEX/nzj7DG32fjl7DHn7XJu9piHLf/P7DG/Ov0z2WO2k/9788+1LdljfmGXZ7LGe/GJsVnjAfzx+cnZY9bXtGaPubY9/9+L1/R8E+o+q6tryx6zrX29b9rWpeaW/P81Gz0q/7E3t+b/jBT5z2dbP8TMrbYmf9lpVK1LWSOdkzwzMzMzM6seznF9uaaZmZmZmVk1cSXPzMzMzMyqhm+84kqemZmZmZlZVekxyZMUkr5WWj9T0ud62GempH1L65+TtFrS5qW2V9dzzEOOpLmSnpC0WNIiSft00e9kSf880OMzMzMzMxsx2gfwNUT1ppLXDLxP0mZ9iDsT2LeibQXwr32IMSxI6ri91CcjohE4G/hOJ/1GRcRlEXHlgA7QzMzMzMxGlN4kea3AHOCMyg2S3iTpBkn3ptd+khqAk4EzUmXrgNT9+8AxkjbpJM4/Sbon9f+OpFpJH5B0Ydr+cUmPp+XtJf02LZ8v6UFJSyV9NbXNlXSZpAWSHpV0RGpvSG2L0mvf1D5T0u2SfpxiXSapJm07TNKdqf/1kiak9iclfSaN4+iKw7kd2CH1my/pi5J+A3w8VTTPTNt2kPQrSUtS/O1T+yfTuVwq6bxefD5mZmZmZpZE+8C9hqrezsn7FnCcpI0q2i8GLoqIPYH3A5dHxJPAZam9MSIWpL6vUiR6Hy8HkLQTcAywX6qEtQHHUSRLHQniAcDzkt4K7A8sSMnie4FdImIa8IVS2AbgIOAfgMskjQWeBd4VEe9I7/eNUv+9KKqMuwHbs65y+Wng0LTPQuATpX3WRMT+EfGDinPyj8Cy0vrkiDgoIr5W0e8q4FsRsTtF1fNpSYcBU9J4GoHpkg7EzMzMzMysl3p1d82IeEXSlcBpQFNp06HAztJrD5qcJKm7p15/A1hcnuMHHAJMB+5NceqBZyPib5ImpHhbAVcDB1IkfD8CXgHWAJdL+l/g56WY10VEO/BYqgBOBZ4ALpHUkUjuWOp/T0R0VAqvoUgk1wA7A3ekcY0G7iztc23FsX1F0qeB54ATu+lHOqa3RsSPASJiTWo/DDgMuD91nUCR9N1esf9sYDbA+Q1v57jNt6x8CzMzMzOzEWkoV9gGSl8eofB1YBFwRamtBtgnIsqJH6Wk73Ui4iVJVwP/Uu4O/FdEfKqTXe4EjgceARYAJwD7AP8aEa2S9qJIEj8InAL8XcdbVb41xeWmzwC7p3Gvqdhe2V/ALyPiQ50eDKyqWP9kRPywF/1IsTsj4EsR8YY5fa8bXMQcikto+fPef1c5djMzMzMzG8F6/QiFiHgBuI7XV6nmUSRXAKQqGcBKoKuK3oXAR1mXYN4KHNVx501Jm0jaJm27HTgz/Xs/cDDQHBEvp/lxG0XETcDpFJc3djhaUk2a57YdRZK4EfB0qvB9GKgt9d9L0rZpLt4xwG+Bu4D9JHXMrxsnqVz9W28R8QrwZ0nvSbHHSBoH3AKcUJr799byHUnNzMzMzKx7npPX9+fkfQ0o32XzNGBGuknIgxQ3XAH4GfDeihuvABARK4AfA2PS+oMUc9/mSVoK/BLYInVfQHGp5u0R0QY8RZGAQZFE/jzt8//Yu/M4uao6//+vd2/ZQwyLLDKGPQKGsA8KEgRm1GFEFEcEl8yoEUdAzERldNSMoz+DICgg5huYIcKIOqgooEhQCTAMWweysskSBEQwhCVk7eXz+6NOQ1Gp6iU5Vemuej951INb5577uedWVVfn02e5N/PahWEeTGXXA6em4ZAXAx+VdAeFoZrFPWy3A7OApRSGdV4dEX8BpgI/Sue4g8Kwz1w+DJyRYv8fsH1EzKMwLPV2SUuAn1I5WTYzMzMzM9tIn8M1I2J00fYzwMii5yso9HyVHvMQMKmo6NaS/dMpWsQkIn5CmblrEfEIRUMbI+JvirafprBASTm3RcRrVgONiD+UtKl4eOiaiCh3Hb8HDi5TPqHk+dRyjYiIKSXPZ5a05+0lhxAR36WwoI2ZmZmZmQ1UVJoZ1TgG2pNnZmZmZmZmg9hAFl4ZEir1qvVSfz4wvxptMTMzMzOz2hrMc+VqxT15ZmZmZmZmdaTuevIazYo/j+670gDFEBjHPLKlI3vMZ57Iv8ZNd3f+13Lv4S9lj7lqXVv2mEt/PTZ7zLGtG7LHfOGlEdljbt28PnvMcw/8SvaYMxZ8LXvMBZNmZI856cs7ZY/54DfzvkfPrx+WNR7A2Jb8n3cp/1132lq6sscc3pE/5str8n/PdXXn/1t5UxXeo86u/O0cVoX3vRrtbG7K36WT+99Jbc35X8t1G/xP/EbnT4CZmZmZmdWNqMIf2YcaD9c0MzMzMzOrI+7JMzMzMzOzuuGFV9yTZ2ZmZmZmVleqnuRJ6pK0UNJSSVdJGtn3URVjTZV00WYcu2PR8/mSHpS0WNIDki6SNK5o//9tajtzKG2vmZmZmZn1LUI1ewxWtejJWxsRkyNiX2ADcGrxThXUoh1TgdKk6ZSImARMAtYDv+zZERFvqUGbejOVjdtrZmZmZmbWq1oP17wV2F3SBEn3S7oYuAfYWdIHJS1JPX5n9xwg6R8lPSTpZuCtReVzJZ1Y9Pzlou3Pp1iLJM1K9Q4Cfph6FV+zZnpEbAA+D/yVpP2K40naQdItRb2RR6Tyd0i6J53jd6lsvKRfpN7BOyRNSuUzJc0oat/S9Br0vA6XSFomaZ6kEX2118zMzMzMyovu2j0Gq5oleZJagHcCS1LRXsDlEbE/0AGcDbwdmAwcLOk9knYA/p1CcncssHc/zvNO4D3AoRGxH/CtiPgp0E6h525yRKwtPS4iuoBFwMSSXScDN0TEZGA/YKGkbYFLgPelc7w/1f134N7UO/hF4PJ+vDR7AN+LiH2AF1LMPttrZmZmZmZWTi2SvBGSFlJIWv4I/Gcqfzwi7kjbBwPzI+IvEdEJ/BB4G3BoUfkG4Cf9ON8xwGURsQYgIlYOoK3lBtbeDfyjpJnAmyNiFfDXwC0R8VjJOQ4Hrkhlvwe2lrRVH+d8LCIWpu0FwIQ+GylNk9Quqf1nLz/eV3UzMzMzs4YR3arZY7CqxS0U1qZesFdIAlhdXNTL8VGhvJOUpKoQsK0oVqVjKpLUDLwZuP81J4+4RdLbgL8DrpB0DoUet3LnKHcdUdzWZHjR9vqi7S6gz6GZETEHmAOw8I3vHvC1mpmZmZlZ/Rost1C4EzhS0jYp2fogcHMqnyJpa0mtvDosEmA5cGDaPh5oTdvzgH/qWcVT0vhUvgoYU+7kKfY3gSciYnHJvjcCz0bEJRR6IQ8Abk/t3aXkHLcAp6SyKcCKiHgptfWAVH4AsEs/XpOK7TUzMzMzs/IiavcYrAbFzdAj4mlJ/wrcRKE37NcR8UsoLFpCIal6msIiLc3psEuAX0q6C/gdqWcwIn4jaTLQLmkD8GsK8+PmArMlrQUOSzF+KGk9MAz4LYVksdQU4HOSOoCXgY9ExF8kTQN+nlYGfZbCnMGZwGWSFgNrgI+mGD8DPpKGrd4NPNSPl+U17fW8PDMzMzMz6w/FYE5BrU/VGK5ZjXt+bOhs7rvSADQ35V/OqKU5f8zuKozVbm3J385V69r6rjQItDV3ZY/Z1Z1/QEM1vlVvat3kW4xWNGPB17LHXDBpRt+VBmjSl3fKHvPBb/4xa7zn1w/LGg9gTEtH9phS/k9nW0v+n8v1Hfn/Bl2Na6/G90dTFdpZDdX4ndnZlf/1rMb7nvvfSdX4N001PpsAB/p3vtQAACAASURBVD919eCdhFbk8QOOqdkP0hvv+e2gfE0Gy3BNMzMzMzMzy2BQDNc0MzMzMzPLYTCvelkr7skzMzMzMzOrI+7JG+Jet/Wa7DHXrWntu9IArX0x73yiYVWYC1CN13LVC8P7rjRAz6/JH3NUa/65P89tyN/OaszJq8ZciGrMqemuwky/asyfO3Dxudljdj26IHvM4cMuyxpv9Yb8cya3bcu/3ta6Dfl/7eeecw3w+h1eyh7z4Se2zh5zVHNn9pjV0FWFufZdVXjfq9H3Uo3ejO7Mr2dHZ/6fy+EtQ+OzadXjJM/MzMzMzOqG15X0cE0zMzMzM7O64p48MzMzMzOrG154xT15ZmZmZmZmdaUqSZ6kLkkLix4TqnGeCueeIGlpP+qslXSvpPsl3SXpo0X73y3prOq3tmL7xkn65y11fjMzMzOzoSpCNXsMVtUarrk2IiYP9CBJLRFRq+WAHomI/dN5dwV+LqkpIi6LiGuAa2rUjnLGAf8MXLwF22BmZmZmZkNQzYZrShou6TJJS1IP2lGpfKqkqyRdC8yTNEXSzZL+R9JDkmZJOiX1ti2RtFs6bq6kE4viv1zmnBMk3SrpnvR4S7m2RcSjwHTgjKI2XZS23y9pqaRFkm5JZc2Szk3tWSzp9FR+dLq2JZL+S9KwVL5c0jZp+yBJ89P2zFRvvqRHJZ2RmjQL2C31gp6zua+9mZmZmVmjiO7aPQaravXkjZC0MG0/FhEnAJ8GiIg3S5pIIaHbM9U5DJgUESslTQH2A94ErAQeBS6NiEMkfQY4HTizn+14Fjg2ItZJ2gP4EXBQhbr3ABPLlH8F+NuIeErSuFQ2DdgF2D8iOiWNlzQcmAscHREPSboc+BTwnT7aOBE4ChgDPCjp+8BZwL6b0htqZmZmZmaNrVo9eWsjYnJ6nJDKDgeuAIiIB4DHgZ4k78aIWFl0/N0R8XRErAceAeal8iXAhAG0oxW4RNIS4Cpg717qVhpUexswV9IngJ47fx4DzO4ZWpravheFhPahVOcHwNv60cZfRcT6iFhBISl9fV8HSJomqV1S+5UrnurHKczMzMzMGkN3qGaPwaqWt1Do7VVYXfJ8fdF2d9Hzbl5tcycpSZUkoK1M3M8Cz1DoGWwC1vXShv2B+0sLI+JUSYcCfwcslDQ5XUvpbRZ7u75X2goML9lXfK1d9OM9iYg5wByAxw84xrd7NDMzMzOzV9TyFgq3AKcApGGafwU8uBnxlgMHpu3jKfTaldoKeDoiuoEP82pP3Guk1T/PBS4ss2+3iLgzIr4CrAB2ptCzeKqkllRnPPAAMEHS7unQDwM3l2nr+/pxbasoDN80MzMzM7MB8OqatU3yLgaa09DJnwBT03DMTXUJcKSku4BD2bg3sOecH5V0B4WhocV1duu5hQLwP8CFEXFZmRjnpIVUllJIVBcBlwJ/BBZLWgScHBHrgH8ErkrX2A3MTjH+HfiupFsp9Nb1KiKeA25LC7544RUzMzMzM+s3RXi031BWjeGa69aU6xTdPM+9ODJrvJFtHVnjAbxu6zXZY656oXR07uZ7fk3+mKNa87+ez23I387XtW7O34Vqp0n5v1evbxqdPebRHWuzxzxw8bnZY3Y9uiB7zMdOKfc3vU338KqtssYDmDBio0WjN9u6DflnaagKn/fttl+VPebDT2ydPeao5vx3farG90fXIO5tKFaNVlbj9cw9D6sa78/wlurckeyAJ345JD5MD+z5rpolOBMf+vWgfE1q2ZNnZmZmZmZmVVbLhVfMzMzMzMyqygMV3ZNnZmZmZmZWV9yTN8RFd/5hwNWIORQMletua+rOHrOre2j8vacqc1WqcO3VmF/xZHP+eZOTvrxT9pjVmD/XvOuBfVcasLxz8rqqMJtoqPwluqW5Ct9JHfl/Lps2uvPR5hsib1FVNA+BuW5QnTmjZG5nNV5LMyd5ZmZmZmZWN4bKH+6raWj8+d7MzMzMzMz6xT15ZmZmZmZWN6ox9HeocU+emZmZmZlZHRl0SZ6krSUtTI8/S3qq6HlbmfrjJZ3aj7gtkl5I27tLWivpXkn3S7pT0oeL6p4g6XN5r6z/+ntNZmZmZmb2WhGq2WOwGnTDNSPiOWAygKSZwMsRcW4vh4wHTgVmD/BUD0bE/uk8uwNXSyIiroiIqwfe8qw29ZrMzMzMzKzBDbqevN5I+rykpelxeiqeBeyVevpmSRor6feS7pG0WNJxfcWNiIeBfwHOSOf5uKTvpO2T0vkWSboplbVIOj+VL5b0z6n82NSOJZIu6el5lPSkpHFp+68l/TZtf13Sf0q6WdKjkj5d7ppyvX5mZmZmZvUuonaPwWrQ9eRVIukQ4BTgEKAZuEvSzcBZwO4R0dP71wocHxGrJG0H3AZc149T3ANMLFP+VWBKRDzTk6gBnwJ2BPaLiK40vHIk8F+p7iOSfghMAy7q47x7AkcD44D7Jc0uvSYzMzMzM7P+Gko9eUcAP4uINRGxCvgFcHiZegLOlrQYmAfsLGmbfsSvNKj2NuBySR/n1dfrGGB2RHQBRMRK4E3AHyLikVTncuBt/TjvdRGxISKeBVYC2/bZUGmapHZJ7Vc+92Q/TmFmZmZm1hi6QzV7DFZDpiePyklYqY8AWwEHRESnpCeB4f04bn/g/jLlnwAOBY4DFkmalNpS2kHbW/s6eTVBLG3L+qLtLvrxnkTEHGAOwPLJxw7ijmIzMzMzM6u1odSTdwtwgqQRkkYDxwO3AquAMUX1tgKeTQnescBOfQWWtCtwDnBhmd27RsQdwJeB51O8ecCnJDWn48cD9wF7pFgAHwJuTtvLgQPT9vv6ca2l12RmZmZmZv3g1TWHUE9eRNwl6UfA3ano+xGxBCANXVwC/Ao4D7hWUjuFeXZ/qBByL0n3AiOAl4BvR8QVZeqdL2kXCj118yJiqaQHgT2AxZI6U1tmS/oY8POU/N0JXJJizAQukfRn4K5+XOszxdcUEWf1dYyZmZmZmRkM8iQvImaWPP8W8K0y9T5QUnRohZDjUv2HKSR3lc57adH2u8vs7wA+U6Z8HoVevtLy+RSSwtLyfyt5PrFou/SazMzMzMysD4N51ctaGUrDNc3MzMzMzIYMSe+Q9KCkhyVtNDpP0jBJP0n775Q0Icd5B3VPnpmZmZmZ2UAMllUv0xSu7wHHAk8Cd0u6JiLuK6r2MeD5iNhd0knA2cBmj+hzT56ZmZmZmVl+hwAPR8SjEbEB+DGFxSOLHQ/8IG3/FDha0mZnqe7JG+LG7rIhe8yRL+WPOWxFR9Z4Tz6zVdZ4ALvstzp7TC1Zlz3mwj+Pyx5zL/Jf+67bvpA95lMrxmaPud3Y/Nf+wsv9uWvLwHx9n2eyx3zwm+v7rjRAw4ddlj0m5I+5++0XZY33l32/kDUewMvr27LHHD9mbfaYrW1d2WOuXjUse8w3bp//O+m550Zlj9nVnf/v783KP0Fp+x1fyh5z+ROvyx5zRFN39pidmd+jliq0cbsdV2WPOZQMolUvdwKeKHr+JBuvHfJKnXR3gBeBrYEVm3Ni9+SZmZmZmZltAknT0qr4PY9pxbvLHNKfe21v9l9m3JNnZmZmZma2CSJiDjCnwu4ngZ2Lnr8B+FOFOk9KaqFwz++Vm9suJ3lmZmZmZlY3BsvCKxTu771Huuf2U8BJwMklda4BPgrcDpwI/D5i828C4STPzMzMzMwsszTH7jTgBqAZ+K+IWCbpa0B7RFwD/CdwhaSHKfTgnZTj3HWT5EnaGvhdero90AX8JT0/JK1oU1x/PPAPETG7j7gtwIqIGJeeTwTOB3YHOoFFwBkR8ewmtvvrKf53JH0D+G1E3CRpOnBxRORfucPMzMzMrE4NpnuhR8SvgV+XlH2laHsd8P7c562bhVci4rmImBwRk4HZwPk9z0sTvGQ8cOpAziFpBHAdcGFE7BERbwIuobACTnG9TUqeI+JLEXFTejodyL9En5mZmZmZ1bW66cnrjaTPAx9JT/9fRFwIzAL2krQQ+A3w/wG/AMZReF2+GBHXlYT6MHBLysgBiIjfpXN8HDgGGA0MA45Nd7V/L4Vk7acR8bVU9yvAKcAfKXTLrkjl/03h/hi7ANsBt0p6JiKOyfhymJmZmZnVrUE0J2+LqfskT9IhFBKqQyiMhb1L0s3AWcDuqecPSa3A8RGxStJ2wG0Ueu2K7Qss6OV0hwGTI+J5Se8C/orCvTAE/FrSWygM8XwfMBloAxZSmGj5iog4X9K/AEdExEY39klLs04DOG/yHnx0wg79fj3MzMzMzKy+1X2SBxwB/Cwi1gBI+gVwODCvpJ6AsyUdDnQDO0vaBhjI3VPnRcTzaftvgHcC96bno4E9gW1Se9YCayVdO9ALKl6qdeUJRw6mYcdmZmZmZlvUILoZ+hbTCElef9/lj1C4L8UBaSWcJ9l4TtwyNr5LfbHVJef9ekT852saI81gcM0HNTMzMzOzOlI3C6/04hbgBEkjJI0GjgduBVYBY4rqbQU8mxK8Y4GdysS6AjhS0jt6CiS9S9LeZereAHxM0qhU7w2pZ/AW4L2ShksaCxxXod2l7TMzMzMzsz501/AxWNV9T15E3CXpRxRuRgjw/YhYAiCpXdIS4FfAecC1ktqBe4A/lIm1RtLfA+dLuhDooDCn7jNl6v463W7hDklQSNpOTu25msKtF5ZTSPrKmQP8VtITXnjFzMzMzMz6qy6TvIiYWfL8W8C3ytT7QElRpaGY44qOuQ/42zJ1Li0T/zwKyWNp+deAr5Up/1DR9vkU7sdnZmZmZmb9FP2erVW/GmG4ppmZmZmZWcOoy548MzMzMzNrTN1e4tBJ3lC39PbtssdsUf5ppCNaO7PGG93WwZjR67LG/MvdLTz53NisMQFGNHdljbcHaxgzYn3WmACr17Vljbdmdd54AGPaNmSP+dxLI7PHbMv8ngM8/1jpYr8ZYq4flj3m6g35X8+uKgy7+cu+X8ge87ClZ2ePees+Z2WN96cXR9Nahe/4oTA0akNH85ZuQr80N+V/f1qa88d86qmtsscc1dqRPebLHUPjd5GUNytZ8efRrNnQmjUmwBuyR7RqcZJnQ1LuBA8YEgkeMCQSPLNGMxQSPKBhEzyzwSx3ggdUJcEbSrr9veQ5eWZmZmZmZvXESZ6ZmZmZmVkd8XBNMzMzMzOrGx5G7p48MzMzMzOzuuIkrwJJr5d0paRHJS2QdLukEzLEnSLpuhxtNDMzMzOz1+qu4WOwcpJXhiQBvwBuiYhdI+JA4CS2wMqxkjyk1szMzMzM+s1JXnlvBzZExOyegoh4PCIulNQs6RxJd0taLOmT8EoP3XxJP5X0gKQfpmQRSe9IZf8LvLcnpqRRkv4rxbpX0vGpfKqkqyRdC8yr6ZWbmZmZmQ1hgWr2GKyc5JW3D3BPhX0fA16MiIOBg4FPSNol7dsfOBPYG9gVeKuk4cAlwN8DRwDbF8X6EvD7FOso4BxJo9K+w4CPRsTbSxsgaZqkdknt16x5dHOu08zMzMzM6oyHAvaDpO8BhwMbgMeBSZJOTLu3AvZI++6KiCfTMQuBCcDLwGMR8YdU/t/AtHTs3wDvljQjPR8O/FXavjEiVpZrT0TMAeYA3LL9+/PfQdPMzMzMbIgazHPlasVJXnnLgPf1PImIT0vaBmgH/gicHhE3FB8gaQqwvqioi1df30qJmID3RcSDJbEOBVZvzgWYmZmZmVlj8nDN8n4PDJf0qaKyken/NwCfktQKIGnPoiGW5TwA7CJpt/T8g0X7bgBOL5q7t3+W1puZmZmZNSivrukkr6yICOA9wJGSHpN0F/AD4AvApcB9wD2SlgL/j156RCNiHYXhmb9KC688XrT7P4BWYHGK9R/VuB4zMzMzM2scHq5ZQUQ8TeG2CeV8MT2KzU+PnuNPK9r+DTCxzDnWAp8sUz4XmDuwFpuZmZmZ2WBe9bJW3JNnZmZmZmZWR9yTZ2ZmZmZmdaPbHXlO8oa6Ec2dW7oJ/fLM+hHZ423Tui5rzNGtHXR15+3cblL+O1ysWjsse8yRwzqyx+zozD9QoKOrOXvMYS35f4aGtXZlj/n4c+OyxxzbsiF7zG3b1maPGVW4UczL69uyxrv7zZ9jXXf+z+cRy2Zljbdg0oy+Kw1Qd+T/11Q1vjs3VOH7oyPz7wyAtqb8SzlU49qfi7w/QwA7NOX//hg/Mn/Ml9flv/Z1VXiPxrbl/463ocNJng1JuRM8IHuCZ2aNYygkeGY2ODnBy6/bc/I8J8/MzMzMzKyeOMkzMzMzMzOrIx6uaWZmZmZmdaMKU7mHHPfkmZmZmZmZ1REneZlJ6pK0UNIySYskTZfUVLT/cEl3SXogPaYV7Zsp6al0/AOSvl98rJmZmZmZ9a67ho/ByglEfmsjYnJE7AMcC7wL+CqApO2BK4FTI2IicDjwSUl/V3T8+RExGdgbeDNwZE1bb2ZmZmZmQ5qTvCqKiGeBacBpkgR8GpgbEfek/SuAzwNnlTm8DRgOPF+j5pqZmZmZDXndUs0eg5WTvCqLiEcpvM7bAfsAC0qqtKfyHp+VtBB4GngoIhaWxpQ0TVK7pParVy+vTsPNzMzMzGxIcpJXGyr6f7kFf4rLeoZrbgeMknTSRpUj5kTEQRFx0AmjJmRvrJmZmZnZUBU1fAxWTvKqTNKuQBfwLLAMOKikyoHAfaXHRUQH8BvgbdVuo5mZmZmZ1Q8neVUkaVtgNnBRRATwPWCqpMlp/9bA2cC3yhwr4C3AI7VrsZmZmZnZ0ObVNX0z9GoYkebUtQKdwBXAeQAR8bSkDwGXSBpDYfjmdyLi2qLjP5vqtAKLgYtr2nozMzMzMxvSnORlFhHNfey/BTi4wr6ZwMz8rTIzMzMzawzdg3fRy5rxcE0zMzMzM7M64p48MzMzMzOrG924K89J3hAn5V+8taU5/zTSQw56OnvMx+8dlzVek7qyxgNoahoa78/qdW3ZYz7XlT/m1s0bssdsqsLP0Jr1rdljjmjqzB6zGt8f6zYMjV8r48eszR7zLy+NzBpvwaQZWeMBHLj43OwxO2/5cfaY93/mzuwxq3HP4tVV+Fnfqm199pjVuPa99l6RPeYDC7bNHvPNv/5U9pgrPnl29phPPzE2e0xrbEPjt7FZidwJnpnZ5sid4JlZ43CCl99gvn9drXhOnpmZmZmZWR1xkmdmZmZmZlZHPFzTzMzMzMzqhm+h4J48MzMzMzOzuuIkr0YknSBpYcmjW9KnJC1NdaZIelHSvZLul/TVLd1uMzMzM7OhpLuGj8HKSV6NRMTVETG55wFcDNwK3FBS9daI2B84CPiQpANr3VYzMzMzMxu6PCdvC5C0J/AV4C1USLQjYrWkBcBuwIIaNs/MzMzMbMjyLRTck1dzklqBK4EZEfHHXuptDfw1sKzMvmmS2iW1/3z18qq11czMzMzMhh735NXefwDLIuLHFfYfIeleCsN8Z0XERkleRMwB5gC0v+E9/mOFmZmZmVni1TWd5NWUpCnA+4ADeql2a0QcV5sWmZmZmZlZvXGSVyOSXgdcBpwcEau2dHvMzMzMzOrRYF71slac5NXOqcB2wPel1/Qh/2jLNMfMzMzMzOqRk7waiYhvAt+ssPvsVGc+ML9GTTIzMzMzqzvuyfPqmmZmZmZmZnXFPXlmZmZmZlY3wqtrOskb6mKIfIpbdxmbNd7uu3TzyNV5r70ar2U1Yq5d35o9ZpPy34ljWBVuRdpVhddzWFP+djZ154+5oTv/wIu2lq7sMTd0NmeP2dKcf+BNa1vea99xm1U8tSLv91x3FT7vnbdUunvPpmt520nZYzY13ZE/ZhW+56ph+IiO7DG3mbg2e8xhe2+XPWb3gvzvUceP5mSPufLPo7LGG9baxboNef9J3lKF73cbWpzk2ZCUO8EzM9scuRM8M2scuRM885w88Jw8MzMzMzOzuuIkz8zMzMzMrI64f9jMzMzMzOqGh2u6J8/MzMzMzKyuOMmrQNKXJC2TtFjSQkmHSjpT0siM55gpaUbanivpMUmLJD0k6XJJO+U6l5mZmZlZI4gaPgYrJ3llSDoMOA44ICImAccATwBnAmWTPEk51g3/XETsB+wF3AvcJKktQ1wzMzMzM2sQTvLK2wFYERHrASJiBXAisCOFxOsmAEkvS/qapDuBwyQdKOlmSQsk3SBph1RvN0m/SeW3SprY28mj4Hzgz8A7q3idZmZmZmZ1pVu1ewxWTvLKmwfsnIZNXizpyIi4APgTcFREHJXqjQKWRsShwJ3AhcCJEXEg8F/AN1K9OcDpqXwGcHE/23EPsFFCKGmapHZJ7VevXr6Jl2hmZmZmZvXIq2uWEREvSzoQOAI4CviJpLPKVO0Cfpa29wL2BW6UBNAMPC1pNPAW4KpUDjCsn00p+/eBiJhDIXHk7p1OGMzDgc3MzMzMasqrazrJqygiuoD5wHxJS4CPlqm2LtWDQkK2LCIOK64gaSzwQkRM3oRm7A/8bhOOMzMzMzOzBuXhmmVI2kvSHkVFk4HHgVXAmAqHPQhsmxZtQVKrpH0i4iXgMUnvT+WStF8f55ekMyjMDfzNZl6OmZmZmVnD6K7hY7ByklfeaOAHku6TtBjYG5hJYYjk9T0LrxSLiA0UFmc5W9IiYCGFYZoApwAfS+XLgOMrnPecVOch4GAK8/825LssMzMzMzOrdx6uWUZELODVBK3YhenRU290yXELgbeVifcY8I4y5TOLtqducoPNzMzMzAwY3PevqxX35JmZmZmZmdUR9+SZmZmZmVndGMz3r6sVJ3lDXEtz/imfaza0Zo/5xLXrssZra4E16/O2s62lq+9KA9TZlb+z/NnO4dljjqIze8zmKkxHbm3KH7Ma79Hqzvxfretozh5zeEf+z/zrd3gpe8yujiq8R6v6eyebLadJ+Qcc3f+ZO7PHbGq6I3vMfRZ8J3vMfz3oS9ljfqAl7+82gBdfGpE95vN3jswec+zS1dljtjXl//227AfZQwL5/520oTvv99zYMfk/mza0OMmzISl3gmdmZma2JeRO8Gxwr3pZK/5UmZmZmZmZ1REneWZmZmZmZnXEwzXNzMzMzKxu+BYK7skzMzMzMzOrK+7JMzMzMzOzutHtvryB9+RJ6pK0UNIySYskTZfUaxxJEyQtTdsHSbpgUxs8gHa+U1K7pPslPSDp3FQ+U9KMGpz/BEkhaWJR2QRJa9Prt0jS/0naK+2bkur/fVH96yRNqXZbzczMzMysfmzKcM21ETE5IvYBjgXeBXy1vwdHRHtEnLEJ530NSRV7ISXtC1wEfCgi3gTsCzy6ueccoA8C/wucVFL+SHr99gN+AHyxaN+TQP4b+ZiZmZmZNYjuGj4Gq82akxcRzwLTgNNU0CzpHEl3S1os6ZOlx6Qeq+skNUlaLmlc0b6HJb1e0raSfpbi3C3prWn/TElzJM0DLpd0q6TJRcffJmkS8HngGxHxQGpnZ0RcXKYtn0jxF6XzjUzl75e0NJXfksr2kXRX6oVbLGmPSq+LpNHAW4GPsXGSV2ws8HzR80XAi5KO7eUYJE1LvZTtP3t5eW9VzczMzMyswWz2wisR8WiKsx2FpObFiDgYOBj4hKRdKhzXDfwSOAFA0qHA8oh4BvgucH6K8z7g0qJDDwSOj4iTU/nUdPyewLCIWEyh525BP5r/84g4OPWq3Z/aD/AV4G9T+btT2anAdyNiMnAQhV63St4D/CYiHgJWSjqgaN9uKVF8BJgOnFdy7NeBf+ut0RExJyIOioiD3jd6Qt9XaWZmZmbWIKKGj8Eq1+qaSv//G+AjkhYCdwJbAxV7vICfAB9I2yel5wDHABelONcAYyWNSfuuiYi1afsq4DhJrcA/AXMH2O59U2/gEuAUYJ9UfhswV9IngOZUdjvwRUlfAN5Y1IZyPgj8OG3/OD3v0TNcczfgTGBO8YERcSuApCMGeC1mZmZmZmabv7qmpF2BLuBZCsne6RFxQ0mdCRUOvx3YXdK2FHq/vp7Km4DDShMpSQCre55HxBpJNwLHA/9AoYcNYBmFHr9FfTR/LvCeiFgkaSowJcU9NfUs/h2wUNLkiLhS0p2p7AZJH4+I35d5PbYG3k4hgQwKSWJI+nyZ818DXFam/BsU5uZ19tF+MzMzMzMrMpjnytXKZvXkpeRsNnBRRARwA/Cp1LOGpD0ljap0fDrmagpDFu+PiOfSrnnAaUXnmVzm8B6XAhcAd0fEylR2DoVetz3T8U2Sppc5dgzwdGrvKUXn2y0i7oyIrwArgJ1TMvtoRFxAITmbVKE9JwKXR8QbI2JCROwMPAYcXqbu4cAjpYURMQ94HbBfL9dtZmZmZma2kU3pyRuRhlG2UuhpuoJX55VdCkwA7lGh2+0vFHroevMT4G7S3LrkDOB7khanNt5CYU7cRiJigaSXKOoRi4jFks4EfpQWUwngV2UO/zKFYaWPA0soJH0A56SFVQT8jkKP4FnAhyR1AH8Gvlbhej4IzCop+xlwMnA2aU5eir0B+HiFON+gMGfRzMzMzMz6qVt916l3A07yIqK5l33dFG4J8MWSXS9SWAyFiJgPzC86pp1X5/T1lK3g1bl6xeUzS8sk7UihR3JeSd3rgOt6ixER3we+X6bOe0vLgG+mR68iYkqZsuL7Ao6ocNx8Xvu6XEPJ62JmZmZmZtaXzZ6TtyVJ+giFHq/pKcE0MzMzM7MG1j2o172sjSGd5EXE5cDlW+LcaYGV35XZdXTR3MKqW7F+ePaYY5s7ssdsaevKGm9sWxfPPD86a8wNXRU7qTc9ZneuBWxfpSp8cW0zqrfFYjfNqDHrs8f80zNbZY85atiG7DG3bl2XPWZra96fIYCX17Rlj/nwE1tnj9lUhc/8G7d/IWu8DR1V+P6owneSqjA+pEn5359/PehL2WN+s/0b2WMu2f+z2WN2R/43Kf9vImhqzv++V+Of5dV4PYe35F0Xr7W5i9Gj8/7O3Pm8OViIhgAAIABJREFUd2WNZ0PPkE7ytqSUyPW2IIxVUe4Ez8zMzGxLyJ3g2eC+f12tVOOPO2ZmZmZmZraFOMkzMzMzMzOrIx6uaWZmZmZmdcOrMbonz8zMzMzMrOYkjZd0o6Q/pP+/rpe6YyU9Jemi/sR2kmdmZmZmZnWjm6jZYzOdBfwuIvagsGr/Wb3U/Q/g5v4GHlCSJ6lL0kJJyyQtkjRdUq8xJE2QtDRtHyTpgt7q5yDpnZLaJd0v6QFJ56bymZJmVPncIenbRc9nSJpZ9HxaatMDku6SdHjRvvmSHkyv7d2SvHqnmZmZmVl9Oh74Qdr+AfCecpUkHQi8HpjX38AD7clbGxGTI2If4FjgXcBX+3twRLRHxBkDPOdGJFWcSyhpX+Ai4EMR8SZgX+DRzT3nAKwH3itpmzJtOw74JHB4REwETgWulLR9UbVTImI/4GLgnFo02MzMzMysXkQNH5vp9RHxNED6/3alFVKH2reBzw0k8CYP14yIZ4FpwGkqaJZ0TuqBWizpk2UaOUXSdZKaJC2XNK5o38OSXi9pW0k/S3HulvTWtH+mpDmS5gGXS7q1uKdL0m2SJgGfB74REQ+kdnZGxMVl2vKJFH9ROt/IVP5+SUtT+S2pbJ/U67YwXdsevbw0ncAcoNwdUr8AfC4iVqS23UMha/90mbq3AzuVO0HqDWyX1P6rtY/00hQzMzMzM6uW4n+Xp8e0kv2/TblF6eP4fp7in4FfR8QTA2nXZq2uGRGPpuxyOwrdjS9GxMGShgG3pYRsoyQ3Irol/RI4AbhM0qHA8oh4RtKVwPkR8b+S/gq4AXhTOvRACr1gayV9FJgKnClpT2BYRCxOPXnfLj1nGT+PiEsAJH0d+BhwIfAV4G8j4qmiJPRU4LsR8UNJbUBzH7G/ByyW9K2S8n2ABSVl7cBHy8R4B/CLcsEjYg6FRJIbX/8B3+/RzMzMzCyp5eqaxf8ur7D/mEr7JD0jaYeIeFrSDsCzZaodBhwh6Z+B0UCbpJcjorf5e1luoaD0/78BJkk6MT3fCtgDeKjCcT+hkFBdBpyUngMcA+wt9YRlrKQxafuaiFibtq8Cvizpc8A/AXMH2O59U3I3jsILdkMqvw2YK+l/gJ+nstuBL0l6A4Xk8A+9BY6IlyRdDpwBrO2tLoXXrzhR+6GkURQSyQMGckFmZmZmZjZkXEOhs2dW+v8vSytExCk925KmAgf1leDBZq6uKWlXoItC1ing9DRnb3JE7BIRvU0OvB3YXdK2FCYZ9iRUTcBhRXF2iohVad/qnoMjYg1wI4UexH8Arky7llHo8evLXOC0iHgz8O/A8BT3VODfgJ2BhZK2jogrgXdTSNhukPT2fsT/DoXewVFFZfeVadsBqbzHKcAu6Xq+14/zmJmZmZlZMoRW15wFHCvpDxTWO5kFryxWeenmBN7kJC8lZ7OBiyIiKPSEfUpSa9q/Z+qRKisdczVwHnB/RDyXds0DTis6T28rTF4KXADcHRErU9k5wBfTEE7S/L/pZY4dAzyd2lucIe8WEXdGxFeAFcDOKZl9NCIuoJBxT+qlTT3XtxL4HwqJXo9vAWdL2rro2qZSWGSl+NgOConmX0t6E2ZmZmZmVlci4rmIODoi9kj/X5nK2yPi42Xqz42I0zaOtLGBDtccIWkh0EphgZErKCRpUEi4JgD3qDDW8i9UWAa0yE+AuykkOj3OAL4naXFq3y0U5sRtJCIWSHqJwpDPnrLFks4EfpQWUwngV2UO/zJwJ/A4sIRC0gdwTlpYRRTuV7GIwj0rPiSpA/gz8LU+rqvHtylKWCPiGkk7Af8nKYBVFFYBfbrMta1V4VYMM3htomhmZmZmZhV4wYoBJnkRUXHBkYjoBr6YHsVepHAbAyJiPjC/6Jh2Xp3T11O2AvhAmfgzS8sk7UihN3JeSd3rgOt6ixER3we+X6bOe0vLgG+mR58iYnTR9jPAyJL9Zc+b9k0ped6fBWTMzMzMzMxesVlz8rYkSR+h0BP3pZRgmpmZmZlZg+uu4WOwUmFqnA1EmlP3uzK7ji6aW1gTd+90QvY3sDvUd6UB2nrc6r4rDdAzK8f0XWkA2pq7ssaD6ryW67v7uoPHwI1o7swes7kp/1dfV/eQ/bvUZmttyf/5XN+RY4Hl6qvGb6mWKnw+c1vbmf/92VB5QM6gMrZlQ/aY1fhOevO952ePuXC/f8kesxq/i57uHp495g5N67LH7KzC743c3x/V+GxWy0FP/iL/h6kKPjPhpJolON9d/uNB+ZoMjd/wg0xK5HpbEMaqLHeCZ2ZmZmb1ITwrb+gO1zQzMzMzM7ONOckzMzMzMzOrIx6uaWZmZmZmdWPozHKsHvfkmZmZmZmZ1RH35JmZmZmZWd3o9sIrjdWTJ6lL0kJJyyQtkjRdUlPad5CkC3o5doKkk3vZv6Okn/Zx/jMljexl/0hJv5L0QGrjrP5cl5mZmZmZWY+GSvKAtRExOSL2AY4F3gV8FSAi2iPijF6OnQCUTfIktUTEnyLixD7OfyZQMclLzo2IicD+wFslvbOP+mZmZmZmlkQNH4NVoyV5r4iIZ4FpwGkqmCLpOgBJR6Yev4WS7pU0BpgFHJHKPitpqqSrJF0LzEs9fUvT8c2SzpW0RNJiSadLOgPYEbhJ0k0V2rQmIm5K2xuAe4A3lNaTNE1Su6T2q1cvz//imJmZmZnZkNXQc/Ii4tE0XHO7kl0zgE9HxG2SRgPrgLOAGRFxHICkqcBhwKSIWClpQtHx04BdgP0jolPS+FRnOnBURKzoq22SxgF/D3y3TLvnAHMA7t7phMH8RwQzMzMzs5rynLwG7skrojJltwHnpd63cRHRWeHYGyNiZZnyY4DZPcdVqFO5QVIL8CPggoh4dCDHmpmZmZlZY2voJE/SrkAX8GxxeUTMAj4OjADukDSxQojVlUKzecN05wB/iIjvbEYMMzMzM7OG013Dx2DVsEmepG2B2cBFEREl+3aLiCURcTbQDkwEVgFj+hl+HnBq6pFD0vhU3mcMSV8HtqKwSIuZmZmZmdmANNqcvBGSFgKtQCdwBXBemXpnSjqKQi/ffcD1FJL1TkmLgLnA872c51JgT2CxpA7gEuAiCj1010t6OiKOKj1I0huALwEPAPdIgkISeukmXKuZmZmZWcMJz8lrrCQvIpp72TcfmJ+2T69Q7eiS53OLjl8O7Ju2O4Hp6VF8jguBC3tpw5OUnyNoZmZmZmbWLw2V5JmZmZmZWX0bzHPlasVJ3hYi6U5gWEnxhyNiyZZoT7Hmpvw/Gs+9MCprvJambpo0+Lviq9HGkS0d2WNWQ0dXxY7zTVaNd7ytuSt7zK7u/NOd13fk/7oeCj9D1ZL7ParG92ZbFWJu1bY+e8zhI/J/J7340ojsMbsj/0CZhfv9S/aYkxd9O3vMarRz+6Z12WNW4zupGt/xuUUVPptq4O93K3CSt4VExKFbug1DWSP/49TMzMzMKvOcvAZeXdPMzMzMzKweOckzMzMzMzOrIx6uaWZmZmZmdcMLr7gnz8zMzMzMrK64J8/MzMzMzOpGd3jhlYbqyZPUJWmhpGWSFkmaLqkp7TtI0gW9HDtB0sm97N9R0k/7OP+Zkkb2Uec3qW3LJM2WlH8dejMzMzMzq1sNleQBayNickTsAxwLvAv4KkBEtEfEGb0cOwEom+RJaomIP0XEiX2c/0yg1yQP+IeI2A/YF9gWeH8f9c3MzMzMLIkaPgarRkvyXhERzwLTgNNUMEXSdQCSjkw9fgsl3StpDDALOCKVfVbSVElXSboWmJd6+pam45slnStpiaTFkk6XdAawI3CTpJt6addLabMFaKPM50fSNEntktqvXr0846tiZmZmZmZDXUPPyYuIR9Nwze1Kds0APh0Rt0kaDawDzgJmRMRxAJKmAocBkyJipaQJRcdPA3YB9o+ITknjU53pwFERsaK3dkm6ATgEuB7YaAhoRMwB5gDcvdMJg/mPCGZmZmZmNdU9qPvYaqNhe/KKqEzZbcB5qfdtXER0Vjj2xohYWab8GGB2z3EV6lQUEX8L7AAMA94+kGPNzMzMzKyxNXSSJ2lXoAt4trg8ImYBHwdGAHdImlghxOpKodnMYboRsQ64Bjh+c+KYmZmZmTWSqOF/g1XDJnmStgVmAxdFvHadVUm7RcSSiDgbaAcmAquAMf0MPw84VVJLijc+lfcaQ9JoSTuk7RYKC8M80P+rMjMzMzOzRtdoc/JGSFoItAKdwBXAeWXqnSnpKAq9fPdRmBvXDXRKWgTMBZ7v5TyXAnsCiyV1AJcAF1GYR3e9pKcj4qgyx40CrpE0DGgGfk8hETUzMzMzs37o3tINGAQaKsmLiIr3nIuI+cD8tH16hWpHlzyfW3T8cgq3PSDNxZueHsXnuBC4sJc2PAMcXGm/mZmZmZlZXxoqyTMzMzMzs/rm1TWd5G0xku6ksHpmsQ9HxJKBxOmOcouDbp6oQsxhLV3ZY3Z1521nNa67qwoxV3W2ZY85trkje8xtxldal2jTrXx+ZPaYXd1DY2pyWxV+hjq78l97NT7z1dCsvP8AaGnOPzhoQ1fFwSebTFV4e7aZuDZ7zOfvzP+zXo2f9Gr8Dl64379kjzl50bezx7xtny9kjzmqpdJi5puuGu9RU+bvj6q0MXtEG2qc5G0hEXHolm7DUJY7wTMzMzOz+jCYV72sFSf6ZmZmZmZmdcRJnpmZmZmZWR3xcE0zMzMzM6sbvoWCe/LMzMzMzMzqinvyzMzMzMysbkR44ZWG6smT1CVpoaRlkhZJmi6pKe07SNIFvRw7QdLJvezfUdJP+zj/mZL6tS60pGskLe1PXTMzMzMzsx6N1pO3NiImA0jaDrgS2Ar4akS0A+29HDsBODkd8xqSWiLiT8CJfZz/TOC/gTW9VZL0XuDlPmKZmZmZmVkJ3wy9wXryikXEs8A04DQVTJF0HYCkI1OP30JJ90oaA8wCjkhln5U0VdJVkq4F5qWevqXp+GZJ50paImmxpNMlnQHsCNwk6aZK7ZI0GpgOfL2XOtMktUtq/8Wax7K9JmZmZmZmNvQ1Wk/ea0TEo2m45nYlu2YAn46I21LStQ44C5gREccBSJoKHAZMioiVkiYUHT8N2AXYPyI6JY1PdaYDR0XEil6a9R/At+mlty8i5gBzAO7c8b3+U4WZmZmZWeLVNRu4J6+IypTdBpyXet/GRURnhWNvjIiVZcqPAWb3HFehzsYNkSYDu0fE1f2pb2ZmZmZmVqqhkzxJuwJdwLPF5RExC/g4MAK4Q9LECiFWVwoNmzQY+DDgQEnLgf8F9pQ0fxPimJmZmZk1pKjhf4NVwyZ5krYFZgMXRck6q5J2i4glEXE2hcVYJgKrgDH9DD8POFVSS4o3PpX3GiMivh8RO0bEBOBw4KGImNL/qzIzMzMzs0bXaHPyRkhaCLQCncAVwHll6p0p6SgKvXz3AddTGN7bKWkRMBd4vpfzXArsCSyW1AFcAlxEYR7d9ZKejoij8lySmZmZmZn18OqaDZbkRURzL/vmA/PT9ukVqh1d8nxu0fHLgX3TdieFFTKnl5zjQuDCfrb1lXhmZmZmZmb91VBJnpmZmZmZ1beSmVgNyUneFiLpTmBYSfGHI2LJQOI0Kf+HeMSwjuwxd5vb133iB27pB3+ZNd6w1kqLqA4uXevyT6WtxudoyYqts8fcTuuzxxzZlv/z3tFZcdDAJltfhZjDWrqyx+yqQjubq/D53H7Hl7LGe+qprbLGA3gu2rLH3Gvv3u7gs2mG7V16F6LNN3ZppXXNNl1Tc/7P0f0vvC57zO2b1mWPeds+X8ge863Lzs4e8659P5895h+bSv+ptfne0JX3d9GYYRuyxgMYPmxo/JvGqsdJ3hYSEYdu6TYMZbkTPDMzMzOrD75PXgOvrmlmZmZmZlaP3JNnZmZmZmZ1YzDfv65W3JNnZmZmZmZWR5zkmZmZmZmZ1REP1zQzMzMzs7rhm6E3WE+epC5JCyUtk7RI0nRJTWnfQZIu6OXYCZJO7mX/jpJ+2sf5z5Q0so868yU9mNq5UFL+tanNzMzMzKxuNVpP3tqImAyQkqcrga2Ar0ZEO9Dey7ETgJPTMa8hqSUi/gT0dTO4M4H/Btb0Ue+U1B4zMzMzMxsA3wy9wXryikXEs8A04DQVTJF0HYCkI4t60u6VNAaYBRyRyj4raaqkqyRdC8xLPX1L0/HNks6VtETSYkmnSzoD2BG4SdJNm9N2SdMktUtqv3r18s0JZWZmZmZmdabRevJeIyIeTcM1S4dEzgA+HRG3SRoNrAPOAmZExHEAkqYChwGTImKlpAlFx08DdgH2j4hOSeNTnenAURGxoo+mXSapC/gZ8PUo+XNERMwB5gDcvdMJ/lOFmZmZmVniOXkN3JNXRGXKbgPOS71v4yKis8KxN0bEyjLlxwCze46rUKeSUyLizcAR6fHhARxrZmZmZmYNrqGTPEm7Al3As8XlETEL+DgwArhD0sQKIVZXCg2b9ieEiHgq/X8Vhfl/h2xKHDMzMzOzRhQ1/G+watgkT9K2wGzgotLhkJJ2i4glEXE2hcVYJgKrgDH9DD8POFVSS4o3PpX3GkNSi6Rt0nYrcBywtP9XZWZmZmZmja7R5uSNkLQQaAU6gSuA88rUO1PSURR6+e4Drge6gU5Ji4C5wPO9nOdSYE9gsaQO4BLgIgrz6K6X9HREHFXmuGHADSnBawZ+m441MzMzM7N+6Pbqmo2V5EVEcy/75gPz0/bpFaodXfJ8btHxy4F903YnMD09is9xIXBhL21YDRxYab+ZmZmZmVlfGirJMzMzMzOz+uZ+PJBvFrhlSLqTwvDMYh+OiCUDiXPHju/N/gaWW250c7U2d2WP2R15W9rZnX+KajVey7aW/K9lV3f+lnZV4fVsburOHrOR29mk/N//uX8uq2VD5tdzVGtH1nhQnddyfWf+v+3m/7RDWxV+hqrxr51qfNqr8XNZDRu6Kg6O2mSHLP1W9pj3TJqRPWbu96gaP+vV+hT99Z9+PiS+5I/Y6eia/SDd+tTvBuVr4p68/5+9M4+To6ra8PNmEkiAhBBZFBHCjopsgiwiEAG3T0QUEZAdDSgE2fTDFcSNTVRAhbCDAgKKAoKEDwnIToCQsIOAoKIBQdkhy/v9cW8nnU5Pz3RVdTKZOU9+/ZvuW1Vv3b7dXalzz7nnLCBsb7yg+7Aws7DcSAZBEARBEATzl6iTN4CzawZBEARBEARBEPRHwpMXBEEQBEEQBEG/ITx54ckLgiAIgiAIgiDoV4SRFwRBEARBEARB0I+IcM0gCIIgCIIgCPoNUT1ggHnyJM2UNFnS/ZLulXSopEF524aSTmpx7GhJu7bYvrykS3s4/8GSFuthn0UkjZf0iKSHJH26p/cVBEEQBEEQBEFQY6B58l6zvR6ApGWBC4AlgSNtTwImtTh2NLBrPmYuJA22/Q9gxx7OfzDwS+DVFvt8A5hme41sgI7qQTMIgiAIgiAIgkwkXhlgnrx6bE8DxgIHKrGVpCsBJG2ZPX6TJd0jaThwDPCB3HaIpL0kXSLpCmBC9vTdl4/vknSCpKmSpkgaJ+kgYHngeknXt+jaPsAPcx9n2X6ucQdJYyVNkjTpd68+Uem4BEEQBEEQBEGwcDPQPHlzYfvx7C1btmHT4cABtm+WtATwOnAEcLjtjwNI2gvYFFjH9vOSRtcdPxZYGVjf9gxJo/I+hwJjmhluWXNkfvpdSVsBfwEOtP2vhn6PB8YD3Lb8p2KqIgiCIAiCIAgyDk/ewPXk1aEmbTcDJ2bv20jbM7o59lrbzzdp3wY4tXZcN/s0YzCwAnCz7Q2AW4ETenlsEARBEARBEATBwDbyJK0CzASm1bfbPgb4PDAMuE3SWt1IvNKdNBSaQvg3ab3eZfn1JcAGBXSCIAiCIAiCYEBie749+ioD1siTtAxwKnCKGz4hSavanmr7WFIylrWAl4DhvZSfAOwvaXDWqyVPaamR+3EFsFVu2hp4oJfnDIIgCIIgCIIgGHBr8oZJmgwMAWYA5wMnNtnvYEljSF6+B4CrgVnADEn3AucAL7Q4zxnAGsAUSdOB04FTSOvorpb0jO0x3Rz7v8D5kn4CPAvs3d5bDIIgCIIgCIKBS2TXHGBGnu2uFtsmAhPz83Hd7LZ1w+tz6o5/Elg7P58BHJof9ec4GTi5hz7+Fdii1T5BEARBEARBEATdMaCMvCAIgiAIgiAI+jd9ea3c/CKMvAWEpNuBRRuad7c9tR2dwYNmVdepzCBV/8P4z5uNb7U8I4a8WaleVwfed1cHPp9hQ6dXrvnSK9V/Pi/PHFK55ghV+5lDZ77vdrOkveWY2QHNTnw/O7HQWx34jIZV/N5fnr5IpXoAoxZ7rXLN91z1xco1p184vnLN+8+tXJJZHfgNdeK3vkjXzMo1O/HenxpU/f8bg9c5vHLNDaZUn6T87or7GeZI0AnCyFtA2N54QfdhYaZqAy8IgiAIgiDoH8SavAGcXTMIgiAIgiAIgqA/Ep68IAiCIAiCIAj6DQ5PXnjygiAIgiAIgiAI+hNh5AVBEARBEARBEPQjIlwzCIIgCIIgCIJ+w6wooTCwPHmSZkqaLOl+SfdKOlTSoLxtQ0kntTh2tKRdW2xfXtKlPZz/YEmLtdg+PPev9nhO0k96896CIAiCIAiCIAhg4HnyXrO9HoCkZYELgCWBI21PAia1OHY0sGs+Zi4kDbb9D2DHHs5/MPBL4NVmG22/BKxXp3sX8NseNIMgCIIgCIIgyETilQHmyavH9jRgLHCgEltJuhJA0pZ13rR7JA0HjgE+kNsOkbSXpEskXQFMyJ6++/LxXZJOkDRV0hRJ4yQdBCwPXC/p+p76J2l1YFngz022jZU0SdKk377yZFVDEgRBEARBEARBP2CgefLmwvbjOVxz2YZNhwMH2L5Z0hLA68ARwOG2Pw4gaS9gU2Ad289LGl13/FhgZWB92zMkjcr7HAqMsf1cL7q3C/Bre96gYtvjgfEAk1b4ZExVBEEQBEEQBEEm1uQNYE9eHWrSdjNwYva+jbQ9o5tjr7X9fJP2bYBTa8d1s09P7AxcWOC4IAiCIAiCIAgGMAPayJO0CjATmFbfbvsY4PPAMOA2SWt1I/FKd9JQPBhY0rrAYNt3FdUIgiAIgiAIgoGI5+O/MkgaJelaSY/mv0t1s99xOXHkg5JOktTMSTUXA9bIk7QMcCpwSmNIpKRVbU+1fSwpGctawEvA8F7KTwD2lzQ4643K7b3V2IXw4gVBEARBEARBf+YI4DrbqwPX5ddzIWkz4P3AOsDawEbAlj0JD7Q1ecMkTQaGADOA84ETm+x3sKQxJC/fA8DVwCxghqR7gXOAF1qc5wxgDWCKpOnA6cAppHV0V0t6xvaYFsfvBHysnTcWBEEQBEEQBMFCtSZve2Cr/PxcYCLwvw37GBgKLEKKFhwC/Ksn4QFl5NnuarFtImlgsT2um922bnh9Tt3xT5Ksa/JavEPzo/4cJwMn96Kfq/S0TxAEQRAEQRAECzXL2X4GwPYzucTbXNi+NWfmf4Zk5J1i+8GehAeUkRcEQRAEQRAEQf9mftbJkzSWlFm/xvicCb+2/f+AtzY59Bu91F8NeCewQm66VtIWtm9sdVwYeQsISbcDizY07257ajs6b87s1jnZpxg+eHqleraQqv0Bd2Isu9zjutj2ea16yarHEmDxrmo/c+hMP2fOqn5p8pCumZVrdgJ34Ps5qxPf+Q5ozqj4cx++yJuV6gG8/PoilWs+t9+xlWs+/8/FK9dM0UjVMnRwd4myizN9Ifk/eFAHrp0rzHyjcs1BXdX38+51Dq9cc4MpJ1Sq9+jG3QWQFec/rw6tXDNoTn1ps262b9PdNkn/kvS27MV7Gw3JIDM7ALfZfjkfczWwCRBGXl/E9sYLug8LM5242Q+CIAiCIAgWfhaiNXmXA3sCx+S/v2+yz1PAFyT9kBSuuSXwk56EB2x2zSAIgiAIgiAIggXIMcC2kh4Fts2vkbShpDPyPpcCfwGmAvcC99q+oifh8OQFQRAEQRAEQdBvmJ9r8spg+9/Mm9gR25NINbuxPRPYr13t8OQFQRAEQRAEQRD0I8LIC4IgCIIgCIIg6EdEuGYQBEEQBEEQBP0Ge9aC7sICZ0B58iTNlDRZ0v2S7pV0qKRBeduGkk5qcexoSbu22L68pEt7OP/BkhbrYZ9dJE2VNEXSHyUt3dP7CoIgCIIgCIIgqDGgjDzgNdvr2X43KYPNx4AjIS1wtH1Qi2NHA02NPEmDbf/D9o49nP9goFsjT9Jg4KfAGNvrAFOAA3vQDIIgCIIgCIIgMwvPt0dfZaAZebOxPY1Unf5AJbaSdCWApC2zx2+ypHskDSelNP1AbjtE0l6SLpF0BTAhe/ruy8d3STqhziM3TtJBwPLA9ZKu76Zbyo/FJQkYAfxjnp2ksZImSZr0+1efqHpogiAIgiAIgiBYiBnQa/JsP57DNZdt2HQ4cIDtmyUtAbwOHAEcbvvjAJL2AjYF1rH9vKTRdcePBVYG1rc9Q9KovM+hJC/dc930Z7qkL5LqYLwCPAoc0GS/8cB4gFve9um+O4UQBEEQBEEQBPMZLzzF0DvGgPXk1aEmbTcDJ2bv20jbM7o59lrbzzdp3wY4tXZcN/vM2xFpCPBFYH2S128K8LXeHBsEQRAEQRAEQQAD3MiTtAowE5hW3277GFIBwmHAbZLW6kbile6koVCQ7nr5/H9xmoK4GNisgE4QBEEQBEEQDEhiTd4ANvIkLQOcCpziBp+upFVtT7V9LDAJWAt4CRjeS/kJwP45kQqSRuX2njT+Drwr9w1ScpgHe3nOIAiCIAiCIAiCAbcmb5ikycAQYAZwPnBik/0OljSG5OV7ALgamAXMkHQvcA7wQovznAGsAUyRNB04HTiFtI7uaknP2B7TeJDtf0j6DnBjPu6vwF5F3mgQBEEQBEH/EWGsAAAgAElEQVQQDERiTd4AM/Jsd7XYNhGYmJ+P62a3rRten1N3/JPA2vn5DODQ/Kg/x8nAyT308VSShzEIgiAIgiAIgqBtBpSRFwRBEARBEARB/2ZWePLCyFtQSLodWLSheXfbU9vRGTJoVnWdyrw+s1uHZ2EWGzyzcs2qea27HKolqH4kYcTw1yvX/NfzvV1u2ntmNk1cW46hQ6r/kN6YUf1lcMas6pc7d3Xgt75IV/W/y+kdGM8uVf+f9eCKx1Md6GMnrsXPPD2ick27+t/6mx34DS215KuVa7740tDKNTsxnrM6oDl80Tcr13xjevXXj07c6j+6cXcBX8VY/faWQV6FOGzDziRnj2yACw9h5C0gbG+8oPsQBEEQBEEQBP0N9+Gsl/OLAZtdMwiCIAiCIAiCoD8SnrwgCIIgCIIgCPoNkV0zPHlBEARBEARBEAT9ijDygiAIgiAIgiAI+hERrhkEQRAEQRAEQb9hViRe6YwnT9JMSZMl3S/pXkmHSmp5LkmjJd2Xn28o6aRO9C3r7537N1nSm5Km5ufHdOqc+byrSXpN0j2SHpR0u6Td67Z/XtKzuS8PSTqok/0JgiAIgiAIgqD/0SlP3mu21wOQtCxwAbAkcGRvDrY9CZhUthOSBtuep7CW7bOBs/M+TwJjbD9X9ny95GHb6+dzrwZcJgnb5+ftv7J9sKRlgIclXWL7mfnUtyAIgiAIgiBYqInEK/NhTZ7tacBY4EAluiQdL+lOSVMk7dd4jKStJF0paZCkJyWNrNv2mKTlJC0j6TdZ505J78/bj5I0XtIE4DxJf5a0Xt3xN0tap1lfc98ekzSq7vXjkkZJ+qWkX2S9RyR9NO8zWNKJku7I7+fzbYzNY8BhwDweO9vPAo8Db2vSz7GSJkmadNkrT/b2dEEQBEEQBEEQDADmy5o824/ncM1lge2B/9reSNKiwM3ZIJvH5LY9S9LvgR2AsyVtDDxp+1+SLgB+bPsmSSsC1wDvzIe+F9jc9muS9gT2Ag6WtAawqO0p3fRzpqQLgV2BU4APA3fafl4SwDuALYHVgf/Lnrh9gWm235ffz22SJth+qpfDczewVmOjpNFAF3Bfk36OB8YD3Pn2HWKqIgiCIAiCIAgys8KTN1+zayr//RCwh6TJwO3AW0hGU3f8Gvhsfr5zfg2wDXBK1rkcGCFpeN52ue3X8vNLgI9LGgLsA5zTQz/PBPbMz/chh3VmLrY9y/bDwNO53x8C9q57PyN7eD+NqOH15yTdDzwGnGj7zTa0giAIgiAIgiAY4MwXT56kVYCZwDSSUTPO9jUN+4zu5vBbgdXyGrVPAt/L7YOATeuMuZoOwCu117ZflXQtyYO4E7Bhq77aflLSC5LGAOsDE+o3N+6e38+XbF/XSrcF6wMP1r2urcnbHLhc0jU55DUIgiAIgiAIgh6INXnzwZOXjbNTgVOcRvwa4IvZs4akNSQt3t3x+ZjLgBOBB23/O2+aABxYd571mhxe4wzgJHLoZS+6fSbwK+Ai27Pq2j+T1xWuQQrdfDS/ny9JGpz7saakYb04R834PR44uXGb7ZuAC4FxvdEKgiAIgiAIgiCAznnyhuXwxSHADOB8kpEGyeAaDdyt5HZ7luSha8WvgTtJa+tqHAT8TNIU0vu4Edi/2cG275L0InOHXrbiMuAs5g3tfCyfZ1lgrO03JZ0GrAhMzl7EaSSvYXesKekeYBjwIvCjusyajRwD3CHpGNuvdLNPEARBEARBEASZqJPXISPPdleLbbOAr+dHPf8F1s77TAQm1h0ziYa1a7nkwWdpwPZRjW2Slid5LSc02X90k25uANxh+9GG9httH9pw/EzgiPxoSc6m2a2Xz/YZDa+fpkl2zSAIgiAIgiAIgu6YL2vyFiSS9gC+DxzaEHrZ3f7fIJV82LnTfQuCIAiCIAiCoFpiTR4oBqF68vrAcxqaX7W9WdXn+vNbd6z8A1xikelVSzJsaLWa//xvt8s4C/OWxV6vXPPF1xatXPPRrqGVa643+MXKNZcc+VrPO7XJ0/8a2fNObTJqser7OX1Gt8EMhRnc1eMcVdu8/mb183xdg6rvZydYdvmXKtV76qmlKtUDGDp4RuWanaAT381Fh1b/3lf88Ucr17z/C3+qXLMT2I2JvMszfPE3Ktf8z8vV///WCWZVPJ4XLVr9tfhHk35YuSbAkKVXqf7L1AFGLL7KfDNwXnzl8T45Jv3ek7cgsD0ZaJUIJgiCIAiCIAiCDhB18uZvnbwgCIIgCIIgCIKgw4QnLwiCIAiCIAiCfoMju2Z48oIgCIIgCIIgCPoTYeQFQRAEQRAEQRD0IyJcMwiCIAiCIAiCfkMkXqnYkydppqTJku6XdK+kQyW1PIek0ZLuy883lHRSlX1qONfeuX+TJb0paWp+fkynzpnPu5okSzqyrm05STMk/SS//p6kv+f+PCrpN5LW6mS/giAIgiAIgiDof1TtyXvN9noAkpYFLgCWBI5seVTG9iRgUtlOSBpse54iO7bPBs7O+zwJjLH9XNnz9ZLHgE8A38mvdwLua9jneNs1o28X4HpJa9v+93zqYxAEQRAEQRAs1EQd8A6uybM9DRgLHKhEl6TjJd0paYqk/RqPkbSVpCslDZL0pKSRddsey96vZbKX6878eH/efpSk8ZImAOdJ+nMuSl47/mZJ6zTra+7bY5JG1b1+XNIoSb+U9Ius94ikj+Z9Bks6UdId+f18vocheQX4S12fdgIuaTF+FwLXAzs36e9YSZMkTbr81cd7OG0QBEEQBEEQBAOJjiZesf14PseywL7Af21vBGwEfEHSyt0cNwv4PbADgKSNgSdt/wv4KfDjrPNp4Iy6Q98LbG9719y+Vz5+DWBR21O6Od9M4EJg19z0YeBO28/n1+8AtgS2A8ZLWpRkwE6z/b78fg6QtGIPQ3IRsLOk0cCrwL962P9uYJ6QTdvjbW9oe8NPLLZKDxJBEARBEARBMHDwfPzXV5kf2TWV/34I2EPSZOB24C3A6i2O+zXw2fx85/waYBvglKxzOTBC0vC87XLbr+XnlwAflzQE2Ac4p4d+ngnsmZ/vQw7rzFxse5bth4Gnc78/BOxd935G9vB+AK4CPtLwflqhnncJgiAIgiAIgiCYQ0eza0paBZgJTCMZLONsX9Owz+huDr8VWE3SMsAnge/l9kHApnXGXE0HUkgkALZflXQtsD0pNHLDVn21/aSkFySNAdYHJtRvbtw9v58v2b6ulW7DOV6XNAX4MvBOYMceDlkfuKm3+kEQBEEQBEEw0Ik1eR305GXj7FTgFKeRvgb4YvasIWkNSYt3d3w+5jLgRODBuuQjE4AD686zXpPDa5wBnMTcoZetOBP4FXBRDhmt8Zm8rnANUujmo/n9fEnS4NyPNSUN68U5jge+avs/rXaStBMwht55/IIgCIIgCIIgCIDqPXnDcvjiEGAGcD7JSINkcI0G7lZyuz1L8tC14tfAneS1dZmDgJ9lj9hg4EZg/2YH275L0ovMHXrZisuAs5g3tPOxfJ5lgbG235R0GrAiMDl7EaeRvIYtsT0VmNrN5q9I2gtYPO8zJjJrBkEQBEEQBEHvCU9exUae7a4W22YBX8+Pev4LrJ33mQhMrDtmEg3r0nLJg8/SgO2jGtskLU/yVk5osv/oJt3cALjD9qMN7TfaPrTh+JnAEfnREtuPAfN4HG2fUff8m8A3e9IKgiAIgiAIgiBoxfxIvLJAkLQHKSHKNxpCL7vb/xskz2GjERoEQRAEQRAEwUKC5+Ojr9LRxCsLEtvnAee1sf/3ge83ad+ttxp5feA5Dc2v2t6stxpBEARBEARBEASlsB2PAfIgrScccJoLQx9DMzRDs29qLgx9DM3QDM2+qbkw9LFTmvFY8I9+G64ZNGXsANVcGPoYmqEZmn1Tc2HoY2iGZmj2Tc2FoY+d0gwWMGHkBUEQBEEQBEEQ9CPCyAuCIAiCIAiCIOhHhJE3sBg/QDUXhj6GZmiGZt/UXBj6GJqhGZp9U3Nh6GOnNIMFjPKCyyAIgiAIgiAIgqAfEJ68IAiCIAiCIAiCfkQYeUEQBEEQBEEQBP2IMPKCIAiChRJJP+hNW5uaE3rTFgRBEAR9mTDy+jmStmnStmdJzWWbtK1ZUnOkpI3yY8kyWp1C0gmS3t0B3Y9LqvS3KOldTdq2qvIcfR1JS0lapyKtlWq/JUnDJA2vQrdqJG0uae/8fBlJK5fUW7Q3bW1qHtubtl7ykSZt/1NESNIikkYAy0kaLmlEfqwArFiwfzXtLkn/V0ZjfiBpfUk7SnpnhZqDJW0n6Sv58XFJg6vS7ySS1pR0+oLuRxB0h6RhC7oPQd8ljLz+z7cl/ULS4pKWk3QFsF1JzT9L2qn2QtJhwGVFhPKN1TnAk6TsTqcDT0o6S9IiRTuYb3C/Lml81jpL0llF9TIPAeMl3S5p/wqN0Z2BRyUdV+HN1cWS/leJYZJOBn5YVEzSGpKuk3Rffr2OpG+W6WDu19cknZpfrybpoyU1J+Yb81HAvcDZkk4sqfkF4FLgtNy0AvC7EnpjJP1W0v35cWkVBrikI4H/Bb6Wm4YAvywpe2sv29ph2yZtbX3ukvaTdA+wpqS76x6PAg8W7NcBwP3AWvlv7XENcGpBTQBszwRerXICS9KWtUkMSTtJOkXSIUWNcEnfBn4NfBr4Q/7el+3j8qQxPAxYHng78BXg/rytqO4WrR4F9NaRNEHSfZK+l/+v/A1wHfBAiX5+WNK+kkY3tO9TUE/5s/5Mfr61pJMkfUkVThRK+lPJ45dueL1b7udYSSqouUO+rtf+fz9P0lRJv84TMUU0T5T0/iLHttAcJenbkj6fP6NvSLpS0vGSliqhu5yk9ZQnSCQtLelo4LESmktJ+qKkn+bH/mX6GPQ9IrtmPydfUA8D9stN37Z9YUnNt5EMsteB5Ug3VYfZfrmA1tHAqsD+tl/KbcOBnwF/tf2tgn28BfgzcBcws9Zu+zdF9Bq01wT2BnYBbgZOt319Sc0RWW9vwMDZwIW1MSmgtzhwLPBeYDjwK+BY27MK6t1Aujk7zfb6ue0+22sX0cvHXwhMBXa1vbakxYCba/oFNe+xvb6kzwPvsH2kpCm2C3v0JE0G3gfcXvfep9p+TwGt/wFOAY4G7gYEbAB8EzjQ9lUl+7k+cHddPwu9d0lvJd2Q/xLYNfcTYARwqu21Cmh+EfgSsArwl7pNw0mf+25taC0FvIU0cXFE3aaXbE9rt28N2gfb/kkZjW50LwY2Aa4FXqm12z6ogNbPgHWARYFHgCWAPwKbAV22P1dA835gI9uvSnoL8EfbG7Wr06B5DjC5cTwlHQS813ahqBKlycpGDKwLrGC7q02924FfkCYwPgJ8FbgA+Jbt1wv28QfA5qTf+XbAT2yfnLfdbXuDApo/B5YFFgFeJH3+VwAfA/5l+8sFNKc0NgFrAA8DFLx+zH5/SpOBHyCN58eBv9k+pIDmA7bflZ//GrgNuATYBvic7WaTRz1pPgv8FViGNMFxoe172tVp0LyK9P/aCOCd+fnFpMmtdW1vX0BzHHAU8DjJOXMicBJpTI+1/bcCmmsC1wN/Au4hfe7rA1sBY2w/0q5m0AexHY9+/ABGkS6EfwTuI90QqQLdA4C/AU8B7y+hcx+wWJP2JYD7SuhO7tB4dgHbkzw5d5E8J1cAF1WgvTRwMMmreTXwKDCuoNYiwPHAZNJM384l+3Zn/ntPVWMMTOqA5lTgbcAE0g0rwJSSmrfX9xMYXFQTmEj6j76xfR3ghpL9vCP/vTv/XbxEP/ck3QC8lP/WHpcDnyqouSQwGrgQWKnuMark+xbpxnf52qOgzpb57yeaPcr0sW5M53kU1Hog/x0K/Jtk2NXGYmpBzbtavS6o+VCLbQ+X1a/T2jxfM28Dtitw/OSG10/XxrREn6YCg/PzkcBVwI/z63uKaua/Q/Lnvkh+PbjE5345aTJnrfx7HJ3f/0rASgU166/pdwOL1/W7aD8frnve+F0t9P9G3TV9deBbJK/zQ8CRwBoFNSfnvwL+XlE/HwCWzs9HA29S4r4r61xCk/sCYCfg0jLa8eg7j4UiLj4oxW3AMbbPUordPpbkfdqsqKCka4FngLVJoWtnSbrR9uEF5GbZfrWx0fbLksq4ma+U9DGX8Iw0ohT2tx1p5usHtu/Im46V9HAJ3e2AfUgezfOB99melj1bDwInF5C9E/g9sBHJ43GapB1t71iwm89JWpU0W46kHUnfgTK8KWlonebKpP+8ynA0KbzuJtt3SlqFZCyX4QZJXweGSdqW5I1q5knoDW+1fW9jo+0pkpYr00lSiO5pwEilULt9SOHPbWP7XOBcSZ92Bd7vrPlf4L/ALpK6SFEAg4ElJC1h+6l2NbN38LukG96al9rAPGtSe8G2wA3AZ5p1n3QzXBjb5+Zr8Iq2C18vMq9nzdcl/dUpHBTbljS9oOaqkmrvUQ2vsf2JApqvtdg2z3W/XSRtTbo5N+mafG1BqaGS1meOx/plYJ1aaKHtuwtoDrY9Ix//n3ydHy/pEtIkXBFqetMl3Wn7zfx6hqSZrQ9tju1PSNqBFJ1zgu3LJU23/deCfYR0rVyf5HXqsv1KXb8L9ROYmCN/fpiff9L27ySNIV1XiuDcr0dJ15HvKoVA70IyylcroDkoRxoMJ13bRtt+MnvHi37ur9t+Lvf1SUmP2L65oFaNdWzPc62zfbGk75bUDvoIEa7Zz5G0YuPNk6QtbN+Yn7/b9v1tan7S9u/qXg8Gvma77QuDpHtJ4QHN4vSvt71uu5pZ9yWSJ+MNYHrWt+0RRfSy5j4kj908NydKa21WaHcs87HnAWfUPpOGbVvbvq6A5oa2JzW07W77/Px8KdsvtKG3CukmYDPgBeAJUohM4RsBSR8heZbfRZqF3xLYt8j77SRKa132BT5E+h5dY7uQ8STpLtvvbXdbG/rbMnc/i9701vQWJa3RGg1zJgVtH11C80BS6NG/qDPMXCws7DFgU9vPFu3P/CLf5J9A8r6sLGk94OgixpOkv5FCtgQckp+TXx9s+x0FNLdstd32DQU0HweaTf4JOM72qu1qZt3/Ab5Burn/XtkbXkmtwu1t+4MFNK8Ejm8cN0nfA75uu+01dJKuBj7jhqURObz6ctvva1ezTmNxkqGzGrCB7ULr3LJW43juavuZbOhcY3vDAppDSJ95bT3jCqSw5yuAIwpOEt3jEssDutHcBaiFJ38J+CJzJp6+Y3t8Ac1pzL2+erf617YPLaDZbchw0XDioO8RRt4ApxM/Zkm32t60l/s+SbrRa2bk2fYqBc4v0nqsti/6ZejUhbGd8WxDs62+SlrZ9hP5RmCQ7ZdqbSX7sQzJcBRwi8uvpzqbPDtbj+1CiQ6y5pdt/7Sntl5q/QeYx5gnvf/NbZdZmL8y8IzzGqLsNVrO9pMlNP9IupFuXNv6oxKajwEb2/53UY06rYnA1jVPVkmtlmvjbJ9UUv8u4IPARJdf23lkq+22v1Osl9WSf4/dYnvvgrqzSMsF7qX5772I17FS8u8P2/N4MyW93fbf8/O2J1qb6C1OComcVlZT0rqkiZNTG9qr6GcXsGhtorSoZp5UHdzsGtKOZo4g6DGXQLv9zO9T2cM6GFiPFLr5TN0+7fRz31bbbZ/Z277Vaf4NOK7ZJuDwIhNFQd8jwjWDQpmuemBob3e0Pbrqk+eQpctISUfmJ50YS2hjPNug3b7+hjS7+0pd26WUH+NNgfeTbtRmUjwMssaVdc+HAjsA/yipuSfQaNDt1aStN7RadH9CAb16LmHuMOyZua1M8owVbDcrU1CGpykeXtXIY8CfstfkjVpjQYNsmfx3dVKindp38eOkMM6yzLD9X82dXLDQLGsnjDhJU2nRnyKe1qJGXC8YU7WgUmmgA4B3k8bhAeBnRSeemhl3ddv+XvfyfFLypcLk63L9tbmwplM4+Twh5WU067RnMneYbiFNp9Dv7ui1Zm8MvHY1s279hNgMYFKT3drpZ9tGXC84mznXvEbO6cD5ggVAGHlBJ1y5vdaU1PIi52JrIQBuk7SR7TsLHl+ETrnFF9hnJGkt0k3PkpI+VbdpBCWNT6WyDu8CLspNB0n6kO1xRTXdsH5MKYNnofpkOexmV2Dl+rVJpLUWhbxQRULe2mCw8xqdfK43VaIMSeYWSe+xPbWkTj2Pk9bU/IG5DbMipS6eyY/CYdh15/8WgKRrgPVsv5hff4uUea8s90naFeiStDpwEHBLUTGlciNfI/2GakbJsS6+DvlnpARdza4Nny3Yx5ZhZAU/88p/R0pp9C8g3dyeB7Oz3t4h6XNlw0F7On1ohmaPO0ufA74M1GoSPwicZPuCIid3wczlwcJFGHnBgqZV2JdJ4U1FGAPsn8NBX2HOmrxKimMPINYkeTJGMnd9xZeAsnW0Pgis7RwzrlTHsDGdd1lWp3gh61tIBsTSzP09fYmC/eyEt6SOZyV9wvbl+VzbA8+V0IOUtXAvSU+QDLIqfkdP5cciFE9EAHTsRmUlcmKTzBtAqaLymXGkNUVvkDKMXkNaA9U2Sol19iOl+q95CTYEjpG0QpF1PyQj7wZg9wZPE5J2pnloV08ML3BMj2Qj+euk9cEnkhIMfYBUmmNfN6xH7gU/Aj7pudPn/z5HhJwGbFy+192yQCdaQ7Pva0rajfRbP4y5S+8cJ0m2f9XuydVD/dgi6/yCvkcYeUHZbIbN6PUMle0xAJKGuqEekVLmxaKUKqpdkE6MJSzAWUTbvyfd7Gxqu2wR7EYeIS2efzq/fhuppEZhlBLu1P/n+U9SmYu2cUoq81dSSGlVfLxCrUb2B34l6RTS5/s0sEdJzcp/R1WGGipl+m22JutDJWQvAG5XKoZt4FOkOpOlyOuQvpEfZTmEtIbz+bq2P2Xv3k2kJEntMoVkfN4m6VDbl9RtK3QN6uDawLNJHrcRwO2k0jM7kAy9n9G+UTbCTeqj2Z6sVLc1CBYkBwA72K6vLzohr2++gGLXp3GkMh+XkJJgdWq5SbAACSOvn5OTkHwOWMX20ZJWJKVxvwPA9iYFdVcCVrf9f3mB+WDPKdy9ewHJW5g3Pr1ZW6+w/VdJm+c+np0TfCxRRKtGp8Yya1c6nkrlDv5m+w1JW5HqsJ1n+z95l63blLxHUm29ymzju0xCE1LdtAcl3Ua6md6EFB7426z9qVYHN8N25TdkkjYhlbF4J8nz1AW84gKZWl0uLXlP2n8BNpG0BGnR/0s9HdMb2Qo05iJn3mtmmBXx2n+z7vlQUibQN7rZt1fk3/bVwBa5af8yYd9KhbtbeW+LJAlRg4FX0/p3w5q/drDt0yXdQJos+BhwQDZOS30PJK1BKja+nO21ldLUf8L29wpKLlHzVkrav84gvVbS8cW6OG/GYUmjSGUAOkknJgdDs39pLtlg4AFg+/GchKYIbyfVxNuJFO30a+C3tTD1oH/Q6YtXsOD5OckTsUt+/RJpprMwOVToUlIYCyRvzOySCrZ77Y2R9FZJ7yXX1ZG0QX5sBSxWoo9Hkjw4X8tNQ5g7BXERKh9LqHY86/gNMFPSasCZpHCz2bH7zW4Qe+B84K3Ah0khXSuQ3n8Zvk9KRPJD4Bjgk6SQsJ9RcFwlzVN+oVlbm5xC+swfBYYBn6dY7cL6Pm0i6U5JL0t6U9JMSYX+c82hPEg6NK+BGgt8oe51Gf5ASmbzB+A60nq6q0tqHg58JT++BUymeWKCHrF9e93jBtsHkZKmlKULeNYpi+jjeUKnKCeQwgGfINWNOz0/Xqa45/rFnAVxLnJbqd+l7UdI17l/kSZ3qghVPJ10LZ6ezzEF2LmE3qy6542/m1m0z49JnpEtJQ3Pj61I3/UfF+wj0PM1qcjkYGgOOM3K603anmb7FNtbkKJAliNNun6uiF7QNwlPXv9nY9sbSLoHwPYLFSRjOIB0I3V71nxUKTNZET5MylS4AulGqDYN/SJpzUVRdgDWJ8WvY/sfFYTddGIsodrxrDHLKX3zDsBPbJ9c63dBVrP9GUnbOxV1voC0pqgMqwMX9pAprVfk0N7FgKWVCtHWvkcjgOXL6tt+TFJXzpp2tqTCCTMyp5Buci8hraXag2KFdyHVg4QOrH9yQ3p/pURJ+5XUvKuh6ebsPWobSfXe1EGkbK9vK9q3rPlNUsbXVUkhgUNJEySbF9GrJQmR9N18Q1XjCknNymn0hsOAy5VKFNxF8rRtRMoEu1tBzdkuwJwR8AilEhoX0n0Wvt6ymO07GryMM0rorSVpCswu2l5bIyug7bI7tsdL+gdpjeS7c/P9pBp8hTL+duKaFJoDUxN4p6RmSegErFFQMwnMKf7+EVKSsmaZVYOFlDDy+j/TlWq21JJbLEOxmc563siZ+8iagykYzmP7XOBcSZ9uzIxYkjdtW1LtfS/e0wG9oBNjCRWOZx3TlbJD7smchClDyujlv/+RtDZprdvoEnrk4++WdDtwlu1CWTAz+5HW5SxPuumtnywo6219NRvzkyUdR0rGUvr7VJXhaPu0/L180XYpr0MvznW3pDIlGWohcDVqhtlbC8rdT/qtiGQ0PEH5hEA7MvcE0d8bjMmiLCNpFduPA7W6hoWMJ9s3ZQ/bl0iTZCKNxSa2/1mwf/Osn7M9MUdalDLsgeeUQshr184dSb+joryzZH/mwfaVzF2CpSyduCaF5sDUbLuWZk8oZQ3+BClZ0UXAka7Lzhz0D6IYej8nu94/S1rbdi7pBuZbti8uoXkc8B+S92Ec6UbjAduFEwpI+gFwXG3NWJ4BO8z2N1sf2a3e4SRP0bakcMB9SF6jwgWNOzGWWbcT4/kuUgjGrbYvzDeUn7V9TEG9z5NCQNchJT1YgvTeT2t5YM+6g0jJPfYG1iV5Dc5ywQLeksbZLhVK2URzJVLo2iKkhBdLAj+3/VgJzRuBbYAzSAbzM8BetucJwWtD83rnREZV0U3p9kIAACAASURBVBDuOYj03X+L7Q+X0HyCeQ2zo23fVKavVSHpdtsbS7o7e+4XA25zycy8kj5CSojyeG4aDexnu5RHPE82YfvZMjqdRNIqpPe+GSkj5hPAbkV/5y3O835gV9sHtHnct1tstu1CWVCzdieuSaE5ADV7cc6bbPcq4kDSLFKd0Vp9xbmMAdulaiIGfYMw8gYASrXOtibdVF1n+8GSeoOAfYEPZc1rbJ9eUvMe2+s3tN1d5kIjaduGPl5bpo9Zs9KxzJqVjmf26pxru2jY1nxF0rtJRt52wI2k0NWrbH+t5YHd661Nqh1WnyDmvJJ9rPRGOhuO00je1aoMx+9nnV9TVxjZxWtN1ta21pgBPAn8xg2ZcBcU2es9ljlJUiYCZ+Rww6Ka/0squ/ER4Huk3+altn9SrrcgaVFgrfzyIduFksQouf2PJIV6DyJdN2YCJ9s+umw/O0WOqBjkapIC1TTXI9Wz3IlkPP623ZtrSYc1aV6c9Nm/xXbhpF1KCat+1TCBuYvtn4dmaFZJs/uoFvuu2mq7myR6CRY+wsjr50g63/buPbW1qfll2z/tqa1NzSnARrWbHqUMk5Nsv7v1kd3qHWv7f3tqa1Oz8rHMGp0Yz2uA7aoIv5C0JulGunZz+iAw3ilBQxndWqjZi6TkML91ygY6CHjMdttra7JRshXJyLuK5CW8yfaOBbRqN9IHkm6iB5EMnT55I62UtbIRu1jWykbt4Vnr5Qq0hgBfZG7D7DTb07s9qHut00g34zUjfjfgddtjS/bxo8w96VI22UxNdzOSB2/2UokiExCSDgE+Boy1/URuW4WUwfKPnQ7bbRdJywE/AJa3/dEcabCp7TML6q1BWtO6C/Bv0sTG4bZXqqCvw0lFp/cFLgZ+ZHtaCb3JttdraOv1zXhohmYb5yw1Md6NZq+9g0HfI9bk9X/mMpKyl+e9JTX3BBoNkL2atLXDL4HrlBIJmBReeW4JvW2Ztz7aR5u0tUMnxhI6M55PkhJaXM7cXp2WBVAbkbQp8FtSqNV40k3v+sBESZ+yfVu7HZM0OHtaVgB2dl6jVNfHWZKKpJWHFEK7LnCP7b3zzeUZBbUOJiXg2KjxRlrSIUVupNXBYuhVh2rCbK/o+cCo/Po5YE8Xy/ha4xckD2ZtRnv33Pb5AlqbNIS4TpBUOHFA/k1flcNRKzHs6rTPJyVzmUzyukH6LhTxMu8BbGt7drF7p3TquwETKJkRsgOcQwrzroWgP0IyzAoZecBDwJ9JE1mPwWzDtzBKa0UPJZXJORfYwA0lFQoySJKcZ9Tzd6xswq7QHJiaC4Iq8hkEC4gw8vopkr5Gyk45TCk1e23x75sUK5SLUiKPXYGVs/FQYzhpNrUwto/LN8C1UMjvusBaFUlfJK1pq8+4VutjocQWnRjLrNux8QT+kR+DKJd18dukEJOJdW2/k/QnkperSLHsO0g3UN1mTy1hRLyWjcQZSskyplEg216mEzfSlRdDV0rAMZ5kQEwF9qkijDgzHjjU9vX5XFsxZ21VUTZqMMz+VMIwmyVptPPaLkmjKZEMyfZMpZIWI1x9vagNgXfVbvpKMqT+e1nD9rPZU9rXWNr2xflailPm35k9HdSCT5M8edcrZQC9iDnX5bZRqq33KdJ3+z1VeKzruAa4WNKpJKN+f+CPoRmaHaATBc0j3G8hJsI1+zmSfuiCa5uaaK0ErExKZHJE3aaXgCll1sFUhVJh0KVo0ke3XxuuUbuyscx6HR9PSYvbfqXnPbs9/hHbTVM0S3rY9poFNDsWsiLp5ySDfGdSmvmXgcm29y6gdZ/ttdvdNr+RNIlUg+xGUra0z7tEYpQG7XsbDLKmbW1q3g18prbmI3tHLy0SZqS07vZMkmdIpDIU+7pEplZJFwKbkAz5ei94qZqDki4BDrJdJqtkTavbsKxOhGyVRdJEkmF2rVMym02AY21vWVJ3cVJ9zV2AD5I8cJfZntCmzizgDVI4dv1NkUhhyoWzqyqFn+/HnAnMCaR1o4WN3NAcmJpZdwVgddvXK63xHVz7P17SurYrLYHQF68nQe8JI28AoLTgd3XmTkRRtD5TR5D0EnP+c12EFM71StH/XNW59XN9fixhdpjlmcAStldUKpK8n+0vtalzl+2mIalFL/6S/gZ0Gzbabkhpi/OMBkY4FV4ucnzHbqSr/L439qXK/5QlXUYqJXB+btoN2ND2J0tobk0K3XucdPOzErB3zVtYQG8YKaW+SFlpWxUO7o3evs3aXXD9WJ3u9cB6JE/27IQrttsOTc5esGaTNwKG2u5T3jyl+oonA2uTCsAvA+xY9LfZzTlGAZ8hZRH+YG5bqqKQy9o5KtULgnaQtA9pjfiStldVWpv6c9vbdPCcHV1HGHSWCNfs5yilvv8yaf3TZNIM9a2kWc+impuQ/sN+J+kGtYsSBhmA7blCCiV9kpRlsSiN6+cGU3L9XCfGMutWPp7AT0iF5i8HsH2vpC1aH9KUd0hqVnZCwNsL9q2LVIKh8tASSdfZ3hqgLoRvdlubrFsXnts4uz+0+SG9o+Lv+0hJn+rute3fFtSFtDb2O6R1mZC8hW17ReuxfZ2k1YE1SWNZJsvk/sBFzhlEJS0laXfbbYdRS1qalEnxzIb2d5LCfstyVAUaANjuqkqr02SPxlBgS+Z85g+7QKKdVuRIjdPyo8Z1pLIfVdFrPUkX295J3azDdYH1t6E5MDXrOIj0/8TtWesRScuW0ANaewdJ+QGChZTw5PVz8oVmI1Kdp/WUSgB8x/ZnS2hOIoXDXUJaZ7IHsJpL1HXr5jy32d6kzWNmr58DXq01k9fPlQm37MRYZt3Kx1Nzan3NnoUrEmYnac9W252K2bfbt05kABsKLAZcT8quWTMgRwBX2668eHLVFPm+5+PObrHZtvcp0a3KUYWpxVVhBjtJFwCnN3oUlTJtfs4VlCTJIdqr2/4/pfp7Xa6wnEBfRdKttjddAOet1AvRjp6kt9l+Jn/m82D7rwXOH5oDULNO+zbbm9S+h0rJXCbbLlwsfUF4B4P5R3jy+j+v235dEpIWtf2QUkr8Uth+TFJXji8/W1KhpCY1GjwRg0jGTtszELZ/CPxQFa+fy3RkLKH68QSeVkrXbkmLkGYA207GUcSI6wWdWBy+Hykb5vLAXXXneBH4WRnhToT+VvV9B3DKIjqIFP52cdE+NUPStaT1c/UG2UUut+bvC7Znfya2X5D0BeZk22yHuTxaeRyKhiqu2yxk1PbVSok5SpHf41hSptJVSZ7wU0lrdvo7EyR9mlQmZX7OLFd9rl7r5Rv9LuDMqm6YQ3NgatZxs6SvAkMljSHVybyypGZHvINB3yCMvP7P3ySNBH4HXCvpBVLWxTK8mg2HyZKOA56hfJrd7eqe14oub19UzPbXVP36uU6MJXRmPPcnlWB4O/B3UqavA9oVkXQFrVP+Fyl1sHXWrsx4cqop+FNJ49xmIeReUHnoL9V/32dJOpBU16tKlq4ZePk8L1RwA1BlavFrlRKl1DLYfREomnSl1f+HVaxxO4C5b6YeHUA3U4eSrmkzJL1OBQlNFgacsrW+KmlJ2/8NzdAsyVdJE0UPkZaOXMPc4clFeN32m1KaF83X405MxAYLgDDy+jm2d8hPj1Ja+L8k5dP47k7yPhwIHAK8g5Q5rTAukP2wFerA+rkOjSV0ZjyfI9V7KssJFWjMhedkOa2s7qCkjYCnawaepD1IY/hX4CgXyKyqeUtnQF3ob5F+1qj6+565VtLhpPpj9Vkhy2SVnSVpRdtPwexww7LekSpTi3+FZNgdwpwMdkVvev4i6cNuKN0i6UPAEwU163mj4WZqMAMkPbkb1qDOR6q+WS2i9zowNXvF63+XB5XoR2gOQM1sPJ4B3EC6djxqu3DJmEwnvINBHyHW5PVzJL0HWCu/fNDlihjX6y4DqS5TSZ2Tae0pKnRBVGfWInZkLLN2JeNZp7cKyZO3CWl8bwUOcUPh8QWBOrBuUikt/za2n1dKMHMRMI6UzfCdtncs0d/KQ38lrUzq32jqJtsKekZrms0MEdsuWicQSR8hGbQ35KYtgLGNhlCbmoNIs9HbQKWpxUcCy9t+oODxawFXkN7rXbl5Q9J73s72QyX7dxzwH9Ka23Gkep4PuOK1zH0RNUl+1KytgO6qwN9sv6FUw3Ed4Ly68OJR7UxyVK2Xj2m2rtm2z2tHJzRDs+56/BTp2rkCKfy9rZIhDZpdpOvxh7LmNcBpFRiPQR8gPHn9FKV6cb8HVgTuJf143yPpKWB7Fyj0qzQFfSTJ4yRS2NUM4GTbRxfs6qT89/3Au0heCEipsO9qekTvqGz9XCfGMut2YjxrXEBai1bzPu4MXAhsXLCvq5Pq+b2LucNf2zYg3Jl1k111N1+fJRmLvwF+I2lyGeEc+vt2Uqr/eoOsTOjv70glLq6gRPHuemyvXIVOg+YfldLfb0L6jh7iJkW429ScRQqvPFUp7f0KRQ08SdeRvuNdpN/m85Kutf2VAv16KE/k7E5K9Q8ptPJAlyzLkDkC2JdUsH4s8AfbZ1Sg22fRnIRIS+fw+fqESMtXcIrfABtKWo30e7qcdO37GBTyYletBzAyh5PPRtKXC+iEZmj+hDSZ+UjWW4N0b1I4sViHvINBX8F2PPrhAziJFGo3qK5tEHAcyYgoonkIcC2wcl3bKqSZn0NK9vd6YEjd6yHA9SX0LgNGktKW30i6EF7VV8ZyPozn7U3abiuhdxNpLd0UkrFzFMkzWvZ7+nZgM5K3ZAtgi4I695HSPkNar7BF/baSfTyGtGbuKpJRdgVwedWfTwVjuRjwTZKBC2k96sdLar4fWDw/341U33ClkpoTSTf5o0gz0ncBJxbUuif/3Rf4LsmImFL12JZ8v9sDB9S9voMU/vk4KVnOAu9jB9/7l/N7fSO/39rjXpLxXFb/7vz3K8C4+u9EX9Cr12xoC83QLKJ5Y2/a2tT8SL4O3wTcTFri8KEymvHoO4/w5PVftgHWcd2MjFNyhq+TZpKLsAewretm8m0/Lmk3UsjVj0v0d3lgOFCbKV2CEjO9rnb9XCfGEjo7ntdLOoIUtmiSd+sP2XOC25+RHuZU30xOKaCPkvRnkieyEJKOIXkYHwBqnhyTjPJ2uRC4QdJzwGvAn/M5VgPKLnzfAVjTBWu5dcNPJR1J+pzrC2PfXULzbJLBtFl+/TdSWY4y6yt+QaoXuC7pxvcs4DxSzbOiLGn7xbxu9mzbR0oqWhR7cA51/gzwbduurXlrF6VESs1Cx2tJQkYV7ONXSd/zGouQ1p4uQfrMLi2ouzBwCykZ0I62T84hbJ8mTZpcUIH+dEm7AHsyJ5lRmSQ5lellnV2BlSVdXrdpBPDv0AzNAtyXNS8mXas+A9wh6RMAti9vdXA3VO4dDPoOYeT1X960PaOx0fYMSUVvVoe4SaiW7Wcllc0+dwxwTzbIIN1EHtWuSA4P2h9YjWSAnWn7htZH9UgnxhI6O561tYf7NbTvQ/rPod0wy9fzWqpHlbI4/h0omxmwMuPJ9vdz6N7bgAm2azfrtYQ2ZXicdKNXpZFXCwv8IHPCNU2JxEDAqrY/m28ysP2ailo8c5iRDaftgZNsn9nNWpN2GCzpbcBOQNn1aN8nhRndZPsOpbWoRZOkLF2yL92xiO2n617flCdZnpdUNotuX+c00g3kyUprZX/InLWy44HCa2Uze5Ou99+3/YTSWtdf9hG9W0iZkpcGflTX/hIpIiI0Q7NdhpMmLWslbF4CliMZeyaFF7fLtJqBB7NLKFSSGyBY8ETilX6KpIeAXZg3G5iAX7pAcWi1KGLdalsb+m8lrRkzcIftfxbQ+DUwneTJ+SjwV9ul4uA7MZZZt6PjWSVK2SsfJIXAfpc0K3m87dtKaF5NqsH2cjW97FhNu98A6wLXMbfXrXCWtPydWsf2m0U1mmjeQgqpvdn2BkpJJC60/b4SmjeQPOB7k8Jpn6V88d3PAN8iGTtfyobZ8bZLZZStmuz1rl9/WqhciqTHbK/Wzba/2F61YBf7PJLutb1ufv4z4FnbR+XX8xSyL3mupYB32C57I12pXjbkX8vRH2uQknddbXt6aIZmm5ojXVfSpgok/ZyUwKXeO/gYOaKmoHcw6COEkddPqfOINcX2mAKaM6lLBVy/CRhqu5T3KYccbJFf3mD7igIaU2s3oEopyu+owPisfCyzbqfHc23mTZRSKLOXpFVccWbODhlPcxnHSpnDptp+VwnNpp4rlygUnycjxtmeVlSjiea2pDV57yKFgb4f2Mv2xBKabyWFHt1p+8+SVgS2Kvo9qgpJh9n+kaQf0yTE0vahJbT/hxQqvQIpvOrtwCO212p5YPd6vwIm2j69oX0/0ljuUrSvfR1J9wHr5aiHh0iZWW+sbbO9dmuFHvUnAp8gRSVNJk1C3FD0869aL2veBXwAWAq4jZRs7FXbhUvchOaA1fwLaU3v2S6RUbNB8/wWm217jyrOEywYIlyzn1IzPCQNtf16/TZJixbU7Kqib81QWp+1EfCr3HSQpM3cfvbF2bNk+caidN86MZZZt5PjeSSwFemG/yqSV/Mm0nqqIpyjlGHyTtIM359tl1mPCCm0pJJZQnW2pt25koYBK9p+uFxPZ7Mc8JCkO5nbwC1cQsH2tUqlJGqZML/cLBy4Tc1/kpKt1F4/RcHvkKSv2j5O3ZRNadO4/0v+W1kZkzq+TzKQJ9hePxvPZbyMhwC/k7QrUFtz+V5gUeCTpXra9+nkWlmodn1nJ/QgTaa/KmlfUqKu4yTdE5qhWYDVSaGaX8ie8QuBc23/pfVhLRlXtXcw6DuEkdf/uQVo9GTd2qStR3L4Ure4XNHlj5FmfGflc50L3AO0a+St23CTX7vpryVPGFGij5WNJXR8PHckecnusb23pOWAwunabW8haRGSIb4VKYnLEi6ejKJS48mdKcsAgKTtSNlVFyEtpl8POLqMQUaJhDU9sCWwOcmIGkLKMts2km6yvbmkl5jbICvzO3ow/53Ucq9eYPt3+e+ZZbWaMMNpXewgScrG8/eLimVv7WaSPgi8Ozf/wfafKultH8at18qOq+AUVa7v7IQegCRtCnyOlAUWyt97heYA1Mz3R1cDVyvVcfwVcIikO4Cv2b6jgOxd+fjKvINB3yGMvH5KDrN6O8nIWR/mqk+0WEHZu0g3fCLVjHshPx9JSsFbtk7XSOZk11yyiEAnvGMdGkvo7HjW1gLMkDQCmEb7yVZmI2lzUujJB3L/riTPypfQrNx4cmdq2h0FvI+U+h/bk5USMhQih5B+y/Y2JfrUTPfnpIRDF+am/SRtY/uAdrVsb57/Dq+qf7Xw6zJhrvVI+hwpRX+t/uWDpOQwZbM2/ldpPc1NwHmSplFBLcNs1PV7w64RN1m367pEDyU5mlRy5ibbdyqt73y0D+kBHEyarLzM9v1Zs+USgNAMzWZIGkkyGvcg3S8cQprIey+pxnCR/5c64R0M+gixJq+forSOaC9gQ1KIXc0weZH0A/5tCe1TSXXCrsqvP0rKoHZYCc1dSBk2r8993YI0M3VRUc2q6ORYZv1OjOfPSeGLOwOHAS+TEmbsXVBvJskD80NSvcHSCUPymoUPktYrrZ/bZq+pLKjZtCxDGcNR0u22N5Z0T10/p9hep4Tm5cDutqsIWatp3g+sXfOWKGVDnWr73a2PbKn5HlLCAIAHbN9fso970twwaysEVKnMyFdJ3+27Sb/JDUi1K4+3/asWh/ekPRx4leRt2oM04XRe2dDXIAiCMkh6lFR65CynUkb1275u+wcl9bcieQdHkNb+FfUOBn2EMPL6Mfkmb5cyNzzd6N5l+70NbZNsb1hQT6QkBzNI4YAiFYtuO7tmp+jUWGbtSsezif5oYIRLZInLM4jvJxnfG5E8G7fa/lYJzU4YTw+TslZWVu5A0pmk5DBHkNZmHUQqf7F/Cc2LSWvnrqUu+U6b69IaNX8LHFL7z1/SSsAxLpDYQ9KSpFpJK5IKV4tU9uEpYHvbL7Y4vDvNPUgzz4cyt2F2PPDTdgw9SbcCuzXONufZ8gtsb9Ju/+o0fmD76z21BQsepZI5+5LCYOsTTO2zoPUk/cT2wZKuoPka1LYnnkJzwGr+wPbXJQ1yXb3eKmjiHTyLOd7BC22XjdAKFiARrtmPyeF6+zEnmUlVPCfpm6T6QQZ2o0SRT9uW9Lts6PTJdL0dHEuoeDxrSPoUc9Zn3USJGj22/yPpceAdJIN8M8oVHYZU2HVXoEvS6iTj6ZaSmp2oaTeOtD7nDVIoyzWkMhJl+EN+VMlbgAfz+gpIxvit2WvY7s3Fd0me2w96zjrZQSRv+/cptp7qS8AOtp+sa/uTpE8DF9FeQpclm4UT2X48G6hl+AjJC17P/zRpCxY85wMPkcLNjibdrD7Y8oj5p1fLWnhCif6EZmhCviZVbeBl7iR5B3dq8A7eJun/2zv3aMmq6tz/vm4gzaNbgxK9oCjyEBlo4wMjDzWIkItRFIFhDAo25t7mgrxEojFGUBQjEQwigoICkoiSMG4PuIJAuBk0ggItb5BWeQWCXiGitPKQ7nz3j7WqT3V1nUPX3mv32WfX/I1Ro6v2PjVrjV3VVXvuOef3nTXJc4IZQlTyOo6kvyWpmn2HVSsGlUU9lARDjmPC7mAx8KmaMU8HzrV9Y9UYTdPEscxxmzieg/NZ7wHuqTKflePdAywlJYvXkCqttVo2JW1ASp72JFV1LgdO8ICC6Ygxi9syNIWSkM02+eFS1/BPyvHePNV+21ePEOsuUkV0+cD2dUgtoFV8Nu/yJFYWU+2b5O9Xq36vyb5nibmQZIS9Demz3mMusKRKRTRoll4XQK8DQNK6wOW239KGeH1xNwGwXcxkOmKOT0xJt5IEz4bKhVc5V2iyOhi0h0jyOo6k+4Zstu3KIhxNkE8qXw7cT0qgeip+lVv3SjNTjiWUn8+aKT8EasbT7nWkKs5LWVXMpU5b6Z8A55E+7yJVSA9yPYGYYmgKo+qp9j1LzGKJmaQnSBWX1XYB29jesML6/pBUDf0cqTW3xzIX9DMMyiHpBtuvl7SYVCn+BckbtdJ3csl4eQzhOOBDpM/lLNJIwmm2P11xfRFzPGM+DfwHw5O8SucgGvCUDbpJtGt2nCb6qSVtA3yE1U9661zt3Kvmshqnqd70ho7nUtI8Va/94sXUaNcEtpJ0BvAC29tLehWwt+3PVA3YRPLkZjzt/gk4FridAiqLmZOBPXtrzJ+BC0hzEJWQ9AbgNOAVJMXS2cDvXM3uYI5WVZJd+TIkf7cqvELDPcfE6MqvlcV5JsP2Y6SZlP0lbU9qdYZUuY4kr518LSfnf0tq9d8I+GRL4h1FmmPe0fZ9sHJm9AxJR9v+YsSMmGvIXc5z6wWZnT/rxaqDQfuISt4YkE9YtmPVQfKqpti91oEzSRYAPQVDbP+oQqw5pBaprUgn0V8fbBFrE6WPZY5Z7Hj2xbyaNJO1ynwWSTVw5OHvHO9Y4KueEEm5w/b2Nda4lCHJ08BcwKgxV9oy2N5CBWwZlD3jqj5/kpirCcwM2zZizCUkZdF/JinBHghs7QqCIZKmlPq2vVuFmC95lpiV3/eSSDoMOAxYlDe9Ezjd9lemb1XBTEPJ+HoPD6iy5ha+K6qctEfM8Y1ZOslrojoYtI+o5HUcSceRerm3Ay4lVcy+z2giB4Mst31G/dUBqWXtGdLV8r1I6zyyUOyiNHQsoezx7FHnavYwNrB9Q+pEWUndZPwR26WFdo6noKdd5jhJZ7P6nF8d64wlSqqdvSH9A0hJfi1s/0zSbNsrgHMkVRKy6SVxkuZ4YEZSUqVKXskkTtJjDFGuY6LNe+Ma4RcCr7f92/xaJ5IEgSLJawmSPjzVftunTGe8zLqDJ/o51iN51q8KEXM8Y54K6SKz7Tsqxhikiepg0DIiyes++5GEKG62vUDSC4Cza8a8RNKhJJnd/pPeKuX97Zx90fJJb5s9WZo4llD2ePZYwoQh+jYkr7PLXF3c41FJW5JPrCXtB/y8xvqgmeRpue3fDCSjddsVFpCO37pMVBwN1Fnn/yJVi44gJSaLqZ9EPKEk5nKLpJNI78/Is2kDXEeyOejnB0O2rTGF2kqfX/X11wCRLjz1eIZJWpqCaWNuy+MBTCVMVVW0KmKOYUzb5+a7Z+bv+HNJVjG/rhIvGB8iyes+vRP95ZLmkWZL6pbhe+IWx/Ztc8W4K0+mbC8fODlvG00cSyh7PHssBt6Ye+6vIiV97yFVjKpwGPA1YFtJ/wHcR7J6qEMTyVMTtgzzXcOgvR9Jm9v+dycfv1PyrRTvJw35f4jkR/dikq/fyEh6IbAZsP7AbN48YIOa6/wyq7eVbjVKgFyp7F/vxvS1UAMPj7ooSevkVvHzSfLhF+Vd+5A6DoKWYPtTbY6XmS9pmJ+kWPWzGjEj5hphe9f8u3YwqRvkBuAc21dWCNdEdTBoGTGT13GUpPQ/TjqpOgb4LXCL7QXTurCMpBVM2BEIWJ80N9Zru6oiGtEIbT+W/SgrZ0k6HFjf9kmqqIo4EHdDYJbtZQXWeHup5KkvZhO2DGcBX7R9V4H1rVQ0k3SR7UpJ2BTx1yMlzibZMlS6cqykUvoBUhJ2IxNJ3uPAeXWqrZKW2H5d/wyipOts71wh1p8BXyR5N/4nKTH9ie1tK8Tqf292BN5IrrK6xdYu40iuVN9r+8yB7UcDL7T90emMFwRNImk28C7gS6TvZJF89Eb+Xpb0fVJHxblEdbBzRJI3Rkh6KTDPdiWVRUl/ZfukfH9/2//ct+/EKgIPM5W6xzLHaOx45uHvQ0knwB+0fWedpCrPYe3L6kqYlSShc8xiyVOTSPoxsCWpevk0ExcgRhZJ6R+gLz1MnxOeM4F78hq3ABbavqxGzH1tX/TsfzlSzMXAW0mtzr8gtZV+wPb8CrFuAfYgCxpI2gPY1/YhFWIVZ4/zkgAAE3VJREFUFzcImkHJcmd7D9i6KFnF3OYRBaFKxwuCJlBStV4A/BlwJUmo7iZJmwI/sD2luNUUcXvVwf1JIzNVq4NBy4gkr+NIusr27s+2bQ1j9V/pXsVjZfBxFyl5LPNzGzuekt5EsmW41vbnlSScj3JFU3BJ3wN+w+oKoCfXWGOx5KkvZhOedkN/OKsIiUz1ntdF0t3A223/LD/eEvhulapWX8wTgZN6V3dz++8xtj9RI+ZLSK3O65LaSp8DfKW37hFj9aqCtwI72Lay11mFWA8xRfusq4lvBA0g6U5P4vk51b61FS8ImiBfIDsL+BfbTw7se7/t84c/c41iF6sOBu0hZvI6ipI1wQbA87WqF8o8YNOqYSe5P+xxZ2joWEKzx/Mh99kG2L6XNJ9WlRfZ/u811zRI6XjQjKfd4cA3ClUce/MaIs279WY3SrQn/3IgUbqX+v5ue/VXlG0/JultQOUkry85fhKoOwv1m9xC/H3gm5J+SfX3fTbJF62z32Ud4glJW9v+af/GXJF4cpLnrM14QVCUnIQ9OFkiVzXBG1IdfEd/dZB6M/LBNBNJXndZSDLl3JRVpdmXAadXjOlJ7g973CWaOJbQ7PE8V9JmpHmqxcA1tm+vEe86Sa+sGWOQkslTjyZsGe4GzpK0DnAOcIHt31QJZHt20ZWtyp2SLgUuJH1+9gdulPTu/NpVfqxnS/qDLBSDktF8JQsFSbczxee6YrX1XcBTpP+fB5Kqgm+vsj7g53Xaj4O1yieByyR9honv5NcBf036LEx3vCAoiu0Vkp4nab2qs9aT8GVSdfDj/dVB2w9LqnwxL2gH0a7ZUbJwwEPAfrZPy0IK+wL3A8e7gjx/n0hKv0AK+fEc21U9YFpNE8cyx230eGYRjh1J3n4LgY1c0T8sz6xsRdnWyr8kXUGsnTz1xdwdeC9lbRl6sV9OWu97gWuBs2xPaRq+NpF0zhS7bfvgCjH/Ctib9P6YNLdxcW+WdMRYxc3Qh82uVp1njZm8mYWk7UlV+9683B3AF6peiCodLwhKI+mrJPuai5kQrKvcSp6rg9+0XVV1O2g5keR1FEk3AW+1/as8n/VtUuVkB+AVtveb1gXOIGbisZS0K0kd8I3Ac4FbSNW8CyrGKzaXNiR2seRJ0j+S1CXvpM+WoUqCMxB3NqlCtIBkTXAhsCvJ3+3P68RuO5L2AnYnJfZX2L58mpe0kmFzjZJurSjisnHVCzbB9KDC8u+l4wVBSSQdN2y7a1iA5Hn7vQtXB4OWEEleR+k/0ZF0OqmN7fj8uLaU/jgxE49lrhIuAT4HXFriCzwnjlvbPkfSJqTK4H01YxZNntSMLcMppGrWVSQ1sxv69i21/fKSr1cVJRn4z5BmiL4HzCeJ7fzjtC5sAEnLmGjbXI8kwDKSGbqkhcAhwDbA0r5dc4Eltt9baLlBi9GE/HuvE6CW/HvpeEHQBJI2tP27Z//LNYpVtDoYtItZ072AoDFm5xkiSFfh/2/fvpjFHI2ZeCyfB3wa2An4nqR/lXRC1WD5CuJHSTMqkE7MayUPOXlaCrwNONH2a21/3vY7gKptcz+UtF2ddQ3hDuBVthf2J3iZkVUcG2RP24+TkuaHSAnQsXUCSlom6fF8e0rSCg03+l1jbM+1PS/f5pBan788YpgLSTOHl+Z/e7ddIsEbH2zvCrwP2JxkDv0tJRuNVsQLgpJI2imPTvw4P56v5N9bh4eB/0PKB+b23YIO0NYT1KA+FwBXS3qUdGX/GgBJW5Gk8IM1Z8YdS9u/lnQvqTr2ImBnUmJWlX1IiddNOf7Dkur+ENwBfML2E0P2VU2edgUOklRsdtD2NyT9YZ7ZmdO3fXHdGcLC9N7ft5GqEL+S6glF2l7lPZb0LgontrYXSfrYiM95DHgM2D+/L7vmXddQX1E0mEHY/kkWiFhCkn9/tdIHv5L8e+l4QVCQfwD+lFR1w/ateYSkMr1Wz5LVwaA9RJLXUWx/VtJVwH8jzdH02qNmkebJgjVkJh5LSfeQqmTfJxlkL6jZsvl725bkHH/DumtsKHkqbsuQBWKOJCXLtwBvIElLv6X0a9XkEiWvvCeBQ3NL7VMlX6BKQjZIT+0zM4ukYlhpbkDSYcBhwKK86UJJp9uue3U7mAGosPx76XhBUBrbDw5cvFsx2d+uCZJ2Ar5Oso/ZXNJ8YKHtQ+vEDdpBzOQFQQeRNMt2KZ84JH0E2BrYgzTndzCpWvSlGjGHJk+2KydPkr5AYVsGJen/HYEf2t5B0rbAp2y/p9RrlELJx/HxLLe9ITDX9i9qxBuWkL3Z9k41YvargC4nqdSeZXvkCpyk24Cdbf82P94IuK5O5TaYOaiwOXTpeEFQEkn/ApxCam9/A8n79nV1xL8kXQ/sR1JNfnXedoft7ad+ZjATiEpeEHSTrSSdAbzA9vb5CvXetj9TJZjtL+TZlMeBlwOftH1lzTUeyUTytFsveaoZs5inXR9P2X5KEkqecXdnRdBWIekasieipGttL6NvkL4i7+i730vI3lknoO0FdZ4/gIBn+h4/Q5iZjwUqbA5dOl4QNMAhwKnAZqS56ytInQy1KF0dDNpDJHlB0E3OIolufBXA9m2SvkVSX6xETuquhHRCJOkA2/9UY43FkyfbZwNna8KW4TZJdT3tHpL0XFJL4JWSHiMNq7eNg0izafsCfy/paZJtxtFVAxZOyACQtAWpzfml9P0G2d57hBjr2F4OnE8S27ko79oHOK/caoO24sLm0KXjBUEDyOU97R6UtDNgJW/dI8jCLsHMJ5K8IOgmG9i+YeDq3PJRg0iaR7pSuBlp2PvK/PhYUotlnSSvkeQpX5HfNt8eBW4FPixpYZW2Ftv75LvHS/o34Dkki4JWYfteSU8Cv8+33YBXVIkl6TSmmJOzfUSlRSYWkWZALmHCy3BUbgBeY/uk/J68kVTBO8T2jTXWFswsHgCulVRK/r10vCAoyXVZVOw7wEWFLD4aqQ4G7SCSvCDoJo9K2pJ8oi5pP+DnFeKcT1Ix/AHwl6Tkbj3gnbZvqbPAJpInreppd2Kf5cHnJS2d/JlDY80h/QBuBdxO8si7us76miSL7TwKfIuURB1eYy5zSf53F2A70kkFJJuCH9VZJ6mCW3mWM7Py6kVO6iKxG08ezree/Hvb4gVBMWxvLen1wJ8Df5PtFL5d0wu1iepg0BJCeCUIOoiklwFfI1knPAbcB7zP9v0jxllpLp4rZI8Cm+d5r6prG5Y8jVxlnCT2waQfvdVsGSQ9Z5T5PEnfIc14XQPsBTxg+8gS62wCSUeS2jVfTJpNvBpYbPueGjH/jeS/90x+vC5JYXa3GjH/giTicwXJ5gIA2zeNEOMhkgDBUKLyMl6Uln8POfmg7Uh6Puk78ADbs2vE+Snp/KBkdTBoCVHJC4IOYvte4K1ZYXFWjaRspahFnlm5r06ClzmPVZOn7UgiLLUpbMuwXV+C+3VSi2BrsX0qcGpWmFwAHE9SLq18AgBsSqpo/Co/3ihvq8MrgfeTLCh6lUYzmiXF7LyWEFkZY0rLv4ecfNBm8vjEPqRK3pbA/6amb2lD1cGgJUQlLwg6hKQDp9pv+5sjxlvBxGyKgPWBJ5gwGZ9XYY391cF1gBtsv2bUOJPELmbLIOmm/nUNPm4bkk4mVfI2IrXXXkMSXrm3RsxestgTrXkzcLztyuIm2cvvVXXELdr+XgRrh9Ly7yEnH7SZPI+3CLjQ9g8aiF+kOhi0h6jkBUG32HHINpGk8DcDRkryGvqi768OLh8Qh6lLSVuG+ZIez/cFrJ8fV05wG+aHwEm2/1+pgLbPkXQZ8MekatvH6vjuZW4FnguM7IvXR1TwAqC8/HvIyQct5mUuXJlpojoYtIdI8oKgQ9g+vHdf6UzlAOCjpATgs9O1rgGaTJ6K2TLMwCuZFwF/IWkL2ydI2hx4YZ/4TFVeT1KvhJToXVIz3guAuyXdyKozeWtsoQDsXnMNQTcoLf8ecvJB65D0D7aPAi6WtFqSN+J35yC3kqqDn26iOhhML5HkBUHHyC2QHwCOAa4H9rM9krJkkzScPM0UT7smOJ004/YW4ARgGSnxG1bdXSMk/V1+fs8q4whJO9v+6xrrPK7GcwGw/atn/6tgDCgt/x5y8kEbOT//+4UGYhevDgbtIWbygqBDSDqM1LJ4FfB3th+Y5iVNG5LeTLZlGAdz496cmqSb++aJbrU9v0bM24AdelYMWWH1ZtuvqhhvNnC57bdWXVMQ9JC0ie1H2hovCNpKrzoo6RKGeKLWrA4GLSEqeUHQLU4jzTrtClzSN1vSa4WsdHLedmaap11DPJOTqJ434iZUNxvv57lMqGs+p06grND6xKh2FkEwCaXNoZswmw6CWki6nSGJGPV+15usDgYtIZK8IOgWW0z3AqaJxmwZZhBfIg3N/5Gkz5JUAj9RM+bngJuzX56ANwF1WjUBngJul3QlE8qt2D6iZtxgzCgt/x5y8kFLeXvpgLZ/lP8dt4uhY0W0awZBxxjHlrgmbRlmEllNdHdSQnaV7cqiEVm450XActJcnoDr66prSjpo2PY6tgxBUFr+PeTkgzYi6SXA1rb/VdL6wDpVvGsbqg4GLSMqeUHQMca0Ja5JW4YZg+27gbsLxbKkRbZfC1xcImaOe15WLtwmb1pq+5mpnhMEwygt/x5y8kGbkfQ/gP8JbEz6fL4IOJNqasPFq4NB+4gkLwi6ybi1xM00T7tiSFrGxBVZ9d1fB1jPdp3v+R9K2tH2jXXW2I+kPyG1195PWu+LJR1ke3Gp1wjGhtLy7yEnH7SZw0gXHa4HsP1TSX9UJVC/KNuw6mCJxQbTT7yRQdBNvptvY8E4t1PZntv/WNJc4FBgIakSUYfdgEMk3U+6WFCiledkYM+erYekbYALgNfWXGswfpSWfw85+aDNPG37971OlTyaUOvzWrg6GLSMSPKCoIP0tcRtS/oRWDoONgLjTPYHPAo4EPgWsKPt/6wZdq/aC1uddft9G23/RNK6DbxO0FFKm0M3bDYdBKW4WtLHSZ0qe5Au5l1SM2ax6mDQPiLJC4IOIultwFeBe0jVly0kLbR92fSuLChNFog4BngP8A3g1XVnMSexpFhed62ZJZK+zoSE9wHAjwrFDsaD0vLvIScfzAQ+BnyQ9J28ELgUOLtmzOLVwaA9hLpmEHQQSXcDb7f9s/x4S+C7tred3pUFpZH0O+AR4BxgNZU126dUiPkdVrWkeMB2EUsKSX9Aunq8K+kCxGLgK7afLhE/CIKgq2T/U2w/UijeScCvSR0gh5Oqg3fZ/psS8YPpJZK8IOggkhbbflPfYwFX928LuoGk45n8yqttf7pCzOKWFJI2t/3vdWIEAZSXfw85+aDN5N/v44APkT6TAlYAp1X5fh+IPYtUHdwzx70cODtmU7tBtGsGQYeQ9O58905JlwIXkk5e9geKKSQG7cH28QCSdrF9bf8+SbtUDNuEJcUi4DV5XRfZ3rdE0GAsKS3/HnLyQZs5CtiFNGd9H4CklwFnSDra9herBrb9X5IWAYtKVQeD9hCVvCDoEJLOmWK3bR+81hYTrFUk3TRYbRu2bQ1jrWDCekPA+sAT1LCkkHSz7VcP3g+COpQyh24qXhDURdLNwB62Hx3YvglwRZXv0iarg0F7iEpeEHQI2wumew3B2kXSTsDOwCaSPty3ax5QyVqiIUsKT3I/CCpRWv495OSDlrLuYIIHaS6vhjJxY9XBoD1EkhcEHULSJ6fYbdsnrLXFBGuL9YCNSN/n/Z55jwP7TcuKhtMzrO83q4cxMKwPGqO0/HvIyQdtZCr7o6rWSAcyUB20fa+k9wFXAJHkdYBI8oKgW/xuyLYNSYPVzwMiyesYtq8m+Seda/sBSRvaHvY5mFbG2bA+aIzS8u8hJx+0kfl9F8X6ETCnYswmqoNBy4gkLwg6hO2Te/clzQWOBBYA3wZOnux5QSfYVNJlpKre5pLmAwttHzrN6wqCpihtDt2E2XQQ1KKhC2RNVAeDlhHCK0HQMSRtDHyYZDJ9HnCq7cemd1VB00i6ntSeeXGfwMkdtref3pUFQTOUln8POflgXBgQ11plFzDHdlTzOkBU8oKgQ0j6e+DdwNeAV9r+7TQvKViL2H5wwO5gxXStJQiaprT8e8jJB+NCtM+PB7OmewFBEBTlGGBT4BPAw5Iez7dlk/T0B93hQUk7A5a0nqSPAD+e7kUFQWmUOF7So8DdwFJJjzyL8NRaixcEQdAGIskLgg5he5bt9W3PtT2v7zY31As7zyEkdcDNgIeAHfLjIOga/fLvz7O9MfDHwC6Sjm5BvCAIgmknZvKCIAiCIJgxlDaHbsJsOgiCYLqJmbwgCIIZTHgjBmNIafn3kJMPgqBzRJIXBEEwswlvxGDcKC3/HnLyQRB0jmjXDIIg6Ah93ogfBC4ETrb9y+ldVRCUpbT8e8jJB0HQRaKSFwRBMMMZ4o34mvBGDLpKafn3kJMPgqCLRJIXBEEwgwlvxCAIgiAIBol2zSAIghmMpP8CngaWA/1f6CIJr4R1RhAEQRCMGZHkBUEQBEEQBEEQdIgwQw+CIAiCIAiCIOgQkeQFQRAEQRAEQRB0iEjygiAIgiAIgiAIOkQkeUEQBEEQBEEQBB0ikrwgCIIgCIIgCIIO8f8Blpp/41di0ZgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(data.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "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>StartDate_x</th>\n",
       "      <th>EndDate_x</th>\n",
       "      <th>ProductID</th>\n",
       "      <th>Pattern</th>\n",
       "      <th>StartDate_y</th>\n",
       "      <th>EndDate_y</th>\n",
       "      <th>NbrNewspapers_x</th>\n",
       "      <th>RenewalDate</th>\n",
       "      <th>PaymentType</th>\n",
       "      <th>PaymentStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>DeliveryClass_NOR</th>\n",
       "      <th>District_2</th>\n",
       "      <th>District_3</th>\n",
       "      <th>District_4</th>\n",
       "      <th>District_5</th>\n",
       "      <th>District_6</th>\n",
       "      <th>District_7</th>\n",
       "      <th>District_8</th>\n",
       "      <th>DeliveryType_DR</th>\n",
       "      <th>DeliveryType_MD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1150046</th>\n",
       "      <td>733815</td>\n",
       "      <td>733816</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733786</td>\n",
       "      <td>733843</td>\n",
       "      <td>50.0</td>\n",
       "      <td>733834</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1150046</th>\n",
       "      <td>733787</td>\n",
       "      <td>733814</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733786</td>\n",
       "      <td>733843</td>\n",
       "      <td>50.0</td>\n",
       "      <td>733834</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150046</th>\n",
       "      <td>733817</td>\n",
       "      <td>733843</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733786</td>\n",
       "      <td>733843</td>\n",
       "      <td>50.0</td>\n",
       "      <td>733834</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150046</th>\n",
       "      <td>733786</td>\n",
       "      <td>733786</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733786</td>\n",
       "      <td>733843</td>\n",
       "      <td>50.0</td>\n",
       "      <td>733834</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89085</th>\n",
       "      <td>733042</td>\n",
       "      <td>733406</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733042</td>\n",
       "      <td>733406</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733367</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89085</th>\n",
       "      <td>732678</td>\n",
       "      <td>733041</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>732678</td>\n",
       "      <td>733041</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733011</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89087</th>\n",
       "      <td>733042</td>\n",
       "      <td>733242</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733042</td>\n",
       "      <td>733406</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733367</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89087</th>\n",
       "      <td>733251</td>\n",
       "      <td>733406</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733042</td>\n",
       "      <td>733406</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733367</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89087</th>\n",
       "      <td>733243</td>\n",
       "      <td>733250</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733042</td>\n",
       "      <td>733406</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733367</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89087</th>\n",
       "      <td>732678</td>\n",
       "      <td>732772</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>732678</td>\n",
       "      <td>733041</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733011</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89087</th>\n",
       "      <td>732781</td>\n",
       "      <td>732886</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>732678</td>\n",
       "      <td>733041</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733011</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89087</th>\n",
       "      <td>732893</td>\n",
       "      <td>733041</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>732678</td>\n",
       "      <td>733041</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733011</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89087</th>\n",
       "      <td>732773</td>\n",
       "      <td>732780</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>732678</td>\n",
       "      <td>733041</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733011</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89087</th>\n",
       "      <td>732887</td>\n",
       "      <td>732892</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>732678</td>\n",
       "      <td>733041</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733011</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325943</th>\n",
       "      <td>733816</td>\n",
       "      <td>734180</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734180</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734150</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191542</th>\n",
       "      <td>733829</td>\n",
       "      <td>733829</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734196</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191542</th>\n",
       "      <td>733816</td>\n",
       "      <td>733827</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734196</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191542</th>\n",
       "      <td>733828</td>\n",
       "      <td>733828</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734196</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191542</th>\n",
       "      <td>733833</td>\n",
       "      <td>734011</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734196</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191542</th>\n",
       "      <td>734026</td>\n",
       "      <td>734196</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734196</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191542</th>\n",
       "      <td>733830</td>\n",
       "      <td>733832</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734196</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191542</th>\n",
       "      <td>734012</td>\n",
       "      <td>734025</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734196</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83885</th>\n",
       "      <td>733816</td>\n",
       "      <td>734006</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734180</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734150</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83885</th>\n",
       "      <td>734007</td>\n",
       "      <td>734019</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734180</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734150</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83885</th>\n",
       "      <td>734020</td>\n",
       "      <td>734180</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734180</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734150</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809659</th>\n",
       "      <td>733816</td>\n",
       "      <td>734180</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733816</td>\n",
       "      <td>734180</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734150</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89154</th>\n",
       "      <td>733170</td>\n",
       "      <td>733534</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733170</td>\n",
       "      <td>733534</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733504</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89154</th>\n",
       "      <td>732804</td>\n",
       "      <td>733169</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>732804</td>\n",
       "      <td>733169</td>\n",
       "      <td>305.0</td>\n",
       "      <td>733139</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89154</th>\n",
       "      <td>732439</td>\n",
       "      <td>732803</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>732439</td>\n",
       "      <td>732803</td>\n",
       "      <td>303.0</td>\n",
       "      <td>766280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150113</th>\n",
       "      <td>733788</td>\n",
       "      <td>733815</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733787</td>\n",
       "      <td>733815</td>\n",
       "      <td>25.0</td>\n",
       "      <td>733835</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1149106</th>\n",
       "      <td>733785</td>\n",
       "      <td>733785</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733785</td>\n",
       "      <td>733981</td>\n",
       "      <td>152.0</td>\n",
       "      <td>766280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149106</th>\n",
       "      <td>733922</td>\n",
       "      <td>733935</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733785</td>\n",
       "      <td>733981</td>\n",
       "      <td>152.0</td>\n",
       "      <td>766280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143748</th>\n",
       "      <td>733798</td>\n",
       "      <td>733826</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733798</td>\n",
       "      <td>733826</td>\n",
       "      <td>25.0</td>\n",
       "      <td>733811</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149771</th>\n",
       "      <td>733785</td>\n",
       "      <td>733813</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733785</td>\n",
       "      <td>733813</td>\n",
       "      <td>25.0</td>\n",
       "      <td>733821</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89035</th>\n",
       "      <td>733042</td>\n",
       "      <td>733406</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733042</td>\n",
       "      <td>733406</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733367</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89035</th>\n",
       "      <td>732678</td>\n",
       "      <td>733041</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>732678</td>\n",
       "      <td>733041</td>\n",
       "      <td>304.0</td>\n",
       "      <td>733011</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661185</th>\n",
       "      <td>733815</td>\n",
       "      <td>733927</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734207</td>\n",
       "      <td>304.0</td>\n",
       "      <td>766280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661185</th>\n",
       "      <td>733941</td>\n",
       "      <td>733976</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734207</td>\n",
       "      <td>304.0</td>\n",
       "      <td>766280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661185</th>\n",
       "      <td>733992</td>\n",
       "      <td>734207</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734207</td>\n",
       "      <td>304.0</td>\n",
       "      <td>766280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661185</th>\n",
       "      <td>733977</td>\n",
       "      <td>733991</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734207</td>\n",
       "      <td>304.0</td>\n",
       "      <td>766280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661185</th>\n",
       "      <td>733928</td>\n",
       "      <td>733940</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734207</td>\n",
       "      <td>304.0</td>\n",
       "      <td>766280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705154</th>\n",
       "      <td>733815</td>\n",
       "      <td>734014</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734189</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734161</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705154</th>\n",
       "      <td>734024</td>\n",
       "      <td>734189</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734189</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734161</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705154</th>\n",
       "      <td>734015</td>\n",
       "      <td>734023</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734189</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734161</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477393</th>\n",
       "      <td>734016</td>\n",
       "      <td>734139</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734185</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477393</th>\n",
       "      <td>734146</td>\n",
       "      <td>734185</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734185</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>477393</th>\n",
       "      <td>733815</td>\n",
       "      <td>733866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734185</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477393</th>\n",
       "      <td>733873</td>\n",
       "      <td>733971</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734185</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>477393</th>\n",
       "      <td>733984</td>\n",
       "      <td>733997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734185</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>477393</th>\n",
       "      <td>733998</td>\n",
       "      <td>734015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734185</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477393</th>\n",
       "      <td>734140</td>\n",
       "      <td>734145</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734185</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477393</th>\n",
       "      <td>733972</td>\n",
       "      <td>733983</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734185</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477393</th>\n",
       "      <td>733867</td>\n",
       "      <td>733872</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734185</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734155</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249391</th>\n",
       "      <td>733815</td>\n",
       "      <td>734179</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734179</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734150</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652668</th>\n",
       "      <td>733815</td>\n",
       "      <td>734179</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734179</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734149</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729149</th>\n",
       "      <td>733815</td>\n",
       "      <td>734179</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733815</td>\n",
       "      <td>734179</td>\n",
       "      <td>304.0</td>\n",
       "      <td>734149</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799724</th>\n",
       "      <td>733786</td>\n",
       "      <td>733786</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733786</td>\n",
       "      <td>733844</td>\n",
       "      <td>50.0</td>\n",
       "      <td>733836</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799724</th>\n",
       "      <td>733787</td>\n",
       "      <td>733844</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733786</td>\n",
       "      <td>733844</td>\n",
       "      <td>50.0</td>\n",
       "      <td>733836</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788976</th>\n",
       "      <td>733787</td>\n",
       "      <td>733843</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733786</td>\n",
       "      <td>733843</td>\n",
       "      <td>50.0</td>\n",
       "      <td>733834</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788976</th>\n",
       "      <td>733786</td>\n",
       "      <td>733786</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1111110.0</td>\n",
       "      <td>733786</td>\n",
       "      <td>733843</td>\n",
       "      <td>50.0</td>\n",
       "      <td>733834</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21649 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            StartDate_x  EndDate_x  ProductID    Pattern  StartDate_y  \\\n",
       "CustomerID                                                              \n",
       "1150046          733815     733816        8.0  1111110.0       733786   \n",
       "1150046          733787     733814        8.0  1111110.0       733786   \n",
       "1150046          733817     733843        8.0  1111110.0       733786   \n",
       "1150046          733786     733786        8.0  1111110.0       733786   \n",
       "89085            733042     733406        8.0  1111110.0       733042   \n",
       "89085            732678     733041        8.0  1111110.0       732678   \n",
       "89087            733042     733242        8.0  1111110.0       733042   \n",
       "89087            733251     733406        8.0  1111110.0       733042   \n",
       "89087            733243     733250        8.0  1111110.0       733042   \n",
       "89087            732678     732772        8.0  1111110.0       732678   \n",
       "89087            732781     732886        8.0  1111110.0       732678   \n",
       "89087            732893     733041        8.0  1111110.0       732678   \n",
       "89087            732773     732780        8.0  1111110.0       732678   \n",
       "89087            732887     732892        8.0  1111110.0       732678   \n",
       "325943           733816     734180        2.0  1111110.0       733816   \n",
       "191542           733829     733829        8.0  1111110.0       733816   \n",
       "191542           733816     733827        8.0  1111110.0       733816   \n",
       "191542           733828     733828        8.0  1111110.0       733816   \n",
       "191542           733833     734011        8.0  1111110.0       733816   \n",
       "191542           734026     734196        8.0  1111110.0       733816   \n",
       "191542           733830     733832        8.0  1111110.0       733816   \n",
       "191542           734012     734025        8.0  1111110.0       733816   \n",
       "83885            733816     734006        8.0  1111110.0       733816   \n",
       "83885            734007     734019        8.0  1111110.0       733816   \n",
       "83885            734020     734180        8.0  1111110.0       733816   \n",
       "809659           733816     734180        3.0  1111110.0       733816   \n",
       "89154            733170     733534        8.0  1111110.0       733170   \n",
       "89154            732804     733169        8.0  1111110.0       732804   \n",
       "89154            732439     732803        8.0  1111110.0       732439   \n",
       "1150113          733788     733815        8.0  1111110.0       733787   \n",
       "...                 ...        ...        ...        ...          ...   \n",
       "1149106          733785     733785        8.0  1111110.0       733785   \n",
       "1149106          733922     733935        8.0  1111110.0       733785   \n",
       "143748           733798     733826        8.0  1111110.0       733798   \n",
       "1149771          733785     733813        3.0  1111110.0       733785   \n",
       "89035            733042     733406        8.0  1111110.0       733042   \n",
       "89035            732678     733041        8.0  1111110.0       732678   \n",
       "661185           733815     733927        2.0  1111110.0       733815   \n",
       "661185           733941     733976        2.0  1111110.0       733815   \n",
       "661185           733992     734207        2.0  1111110.0       733815   \n",
       "661185           733977     733991        2.0  1111110.0       733815   \n",
       "661185           733928     733940        2.0  1111110.0       733815   \n",
       "705154           733815     734014        2.0  1111110.0       733815   \n",
       "705154           734024     734189        2.0  1111110.0       733815   \n",
       "705154           734015     734023        2.0  1111110.0       733815   \n",
       "477393           734016     734139        1.0  1111110.0       733815   \n",
       "477393           734146     734185        1.0  1111110.0       733815   \n",
       "477393           733815     733866        1.0  1111110.0       733815   \n",
       "477393           733873     733971        1.0  1111110.0       733815   \n",
       "477393           733984     733997        1.0  1111110.0       733815   \n",
       "477393           733998     734015        1.0  1111110.0       733815   \n",
       "477393           734140     734145        1.0  1111110.0       733815   \n",
       "477393           733972     733983        1.0  1111110.0       733815   \n",
       "477393           733867     733872        1.0  1111110.0       733815   \n",
       "249391           733815     734179        8.0  1111110.0       733815   \n",
       "652668           733815     734179        2.0  1111110.0       733815   \n",
       "729149           733815     734179        2.0  1111110.0       733815   \n",
       "799724           733786     733786        8.0  1111110.0       733786   \n",
       "799724           733787     733844        8.0  1111110.0       733786   \n",
       "788976           733787     733843        8.0  1111110.0       733786   \n",
       "788976           733786     733786        8.0  1111110.0       733786   \n",
       "\n",
       "            EndDate_y  NbrNewspapers_x  RenewalDate  PaymentType  \\\n",
       "CustomerID                                                         \n",
       "1150046        733843             50.0       733834            0   \n",
       "1150046        733843             50.0       733834            0   \n",
       "1150046        733843             50.0       733834            0   \n",
       "1150046        733843             50.0       733834            0   \n",
       "89085          733406            304.0       733367            0   \n",
       "89085          733041            304.0       733011            0   \n",
       "89087          733406            304.0       733367            0   \n",
       "89087          733406            304.0       733367            0   \n",
       "89087          733406            304.0       733367            0   \n",
       "89087          733041            304.0       733011            0   \n",
       "89087          733041            304.0       733011            0   \n",
       "89087          733041            304.0       733011            0   \n",
       "89087          733041            304.0       733011            0   \n",
       "89087          733041            304.0       733011            0   \n",
       "325943         734180            304.0       734150            0   \n",
       "191542         734196            304.0       734168            0   \n",
       "191542         734196            304.0       734168            0   \n",
       "191542         734196            304.0       734168            0   \n",
       "191542         734196            304.0       734168            0   \n",
       "191542         734196            304.0       734168            0   \n",
       "191542         734196            304.0       734168            0   \n",
       "191542         734196            304.0       734168            0   \n",
       "83885          734180            304.0       734150            0   \n",
       "83885          734180            304.0       734150            0   \n",
       "83885          734180            304.0       734150            0   \n",
       "809659         734180            304.0       734150            0   \n",
       "89154          733534            304.0       733504            0   \n",
       "89154          733169            305.0       733139            0   \n",
       "89154          732803            303.0       766280            0   \n",
       "1150113        733815             25.0       733835            0   \n",
       "...               ...              ...          ...          ...   \n",
       "1149106        733981            152.0       766280            0   \n",
       "1149106        733981            152.0       766280            0   \n",
       "143748         733826             25.0       733811            1   \n",
       "1149771        733813             25.0       733821            0   \n",
       "89035          733406            304.0       733367            0   \n",
       "89035          733041            304.0       733011            0   \n",
       "661185         734207            304.0       766280            0   \n",
       "661185         734207            304.0       766280            0   \n",
       "661185         734207            304.0       766280            0   \n",
       "661185         734207            304.0       766280            0   \n",
       "661185         734207            304.0       766280            0   \n",
       "705154         734189            304.0       734161            0   \n",
       "705154         734189            304.0       734161            0   \n",
       "705154         734189            304.0       734161            0   \n",
       "477393         734185            304.0       734155            0   \n",
       "477393         734185            304.0       734155            0   \n",
       "477393         734185            304.0       734155            0   \n",
       "477393         734185            304.0       734155            0   \n",
       "477393         734185            304.0       734155            0   \n",
       "477393         734185            304.0       734155            0   \n",
       "477393         734185            304.0       734155            0   \n",
       "477393         734185            304.0       734155            0   \n",
       "477393         734185            304.0       734155            0   \n",
       "249391         734179            304.0       734150            0   \n",
       "652668         734179            304.0       734149            0   \n",
       "729149         734179            304.0       734149            0   \n",
       "799724         733844             50.0       733836            0   \n",
       "799724         733844             50.0       733836            0   \n",
       "788976         733843             50.0       733834            0   \n",
       "788976         733843             50.0       733834            0   \n",
       "\n",
       "            PaymentStatus       ...         DeliveryClass_NOR  District_2  \\\n",
       "CustomerID                      ...                                         \n",
       "1150046                 1       ...                         0           0   \n",
       "1150046                 1       ...                         1           0   \n",
       "1150046                 1       ...                         1           0   \n",
       "1150046                 1       ...                         0           0   \n",
       "89085                   1       ...                         1           0   \n",
       "89085                   1       ...                         1           0   \n",
       "89087                   1       ...                         1           0   \n",
       "89087                   1       ...                         1           0   \n",
       "89087                   1       ...                         1           0   \n",
       "89087                   1       ...                         1           0   \n",
       "89087                   1       ...                         1           0   \n",
       "89087                   1       ...                         1           0   \n",
       "89087                   1       ...                         1           0   \n",
       "89087                   1       ...                         1           0   \n",
       "325943                  1       ...                         1           0   \n",
       "191542                  1       ...                         0           0   \n",
       "191542                  1       ...                         1           0   \n",
       "191542                  1       ...                         0           0   \n",
       "191542                  1       ...                         1           0   \n",
       "191542                  1       ...                         1           0   \n",
       "191542                  1       ...                         0           0   \n",
       "191542                  1       ...                         0           0   \n",
       "83885                   1       ...                         1           0   \n",
       "83885                   1       ...                         1           0   \n",
       "83885                   1       ...                         1           0   \n",
       "809659                  1       ...                         1           0   \n",
       "89154                   1       ...                         1           0   \n",
       "89154                   1       ...                         1           0   \n",
       "89154                   1       ...                         1           0   \n",
       "1150113                 1       ...                         1           0   \n",
       "...                   ...       ...                       ...         ...   \n",
       "1149106                 1       ...                         0           0   \n",
       "1149106                 1       ...                         0           0   \n",
       "143748                  1       ...                         1           0   \n",
       "1149771                 1       ...                         1           0   \n",
       "89035                   1       ...                         1           0   \n",
       "89035                   1       ...                         1           0   \n",
       "661185                  1       ...                         1           0   \n",
       "661185                  1       ...                         1           0   \n",
       "661185                  1       ...                         1           0   \n",
       "661185                  1       ...                         0           0   \n",
       "661185                  1       ...                         0           0   \n",
       "705154                  1       ...                         1           0   \n",
       "705154                  1       ...                         1           0   \n",
       "705154                  1       ...                         0           0   \n",
       "477393                  1       ...                         1           0   \n",
       "477393                  1       ...                         1           0   \n",
       "477393                  1       ...                         1           0   \n",
       "477393                  1       ...                         1           0   \n",
       "477393                  1       ...                         1           0   \n",
       "477393                  1       ...                         1           0   \n",
       "477393                  1       ...                         1           0   \n",
       "477393                  1       ...                         1           0   \n",
       "477393                  1       ...                         0           0   \n",
       "249391                  1       ...                         1           0   \n",
       "652668                  1       ...                         1           0   \n",
       "729149                  1       ...                         1           0   \n",
       "799724                  1       ...                         0           0   \n",
       "799724                  1       ...                         1           0   \n",
       "788976                  1       ...                         1           0   \n",
       "788976                  1       ...                         0           0   \n",
       "\n",
       "            District_3  District_4  District_5  District_6  District_7  \\\n",
       "CustomerID                                                               \n",
       "1150046              0           0           1           0           0   \n",
       "1150046              0           0           1           0           0   \n",
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       "83885                0           0           1           0           0   \n",
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       "89154                0           0           1           0           0   \n",
       "89154                0           0           1           0           0   \n",
       "89154                0           0           1           0           0   \n",
       "1150113              0           0           1           0           0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "1149106              0           0           1           0           0   \n",
       "1149106              0           0           1           0           0   \n",
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       "661185               0           0           0           0           0   \n",
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       "477393               0           0           0           0           0   \n",
       "477393               0           0           0           0           0   \n",
       "477393               0           0           0           0           0   \n",
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       "477393               0           0           0           0           0   \n",
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       "788976               0           0           1           0           0   \n",
       "788976               0           0           1           0           0   \n",
       "\n",
       "            District_8  DeliveryType_DR  DeliveryType_MD  \n",
       "CustomerID                                                \n",
       "1150046              0                0                1  \n",
       "1150046              0                0                1  \n",
       "1150046              0                0                1  \n",
       "1150046              0                0                1  \n",
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       "191542               0                0                1  \n",
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       "83885                0                0                1  \n",
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       "89154                0                0                1  \n",
       "89154                0                0                1  \n",
       "1150113              0                0                1  \n",
       "...                ...              ...              ...  \n",
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       "143748               0                0                1  \n",
       "1149771              0                0                1  \n",
       "89035                0                0                1  \n",
       "89035                0                0                1  \n",
       "661185               0                0                1  \n",
       "661185               0                0                1  \n",
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       "661185               0                0                0  \n",
       "705154               0                0                1  \n",
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       "477393               0                0                1  \n",
       "477393               0                0                1  \n",
       "477393               0                0                1  \n",
       "477393               0                0                1  \n",
       "477393               0                0                1  \n",
       "477393               0                1                0  \n",
       "477393               0                1                0  \n",
       "477393               0                1                0  \n",
       "477393               0                0                0  \n",
       "249391               0                0                1  \n",
       "652668               0                0                1  \n",
       "729149               0                0                1  \n",
       "799724               0                0                1  \n",
       "799724               0                0                1  \n",
       "788976               0                0                1  \n",
       "788976               0                0                1  \n",
       "\n",
       "[21649 rows x 32 columns]"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(data,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [],
   "source": [
    "names=[\n",
    "    \"Nearest Neighbors\", \n",
    "    \"Decision Tree\",\n",
    "    \"Random Forest\", \n",
    "    \"AdaBoost\",\n",
    "    \"Neural Net\",\n",
    "    \"Naive Bayes\", \n",
    "    \"Linear Discriminant Analysis\",\n",
    "    \"Quadratic Discriminant Analysis\",\n",
    "    \"Logistic Regression\",\n",
    "    \"XGBoost\"\n",
    "    ]\n",
    "\n",
    "classifiers=[\n",
    "    KNeighborsClassifier(),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=500),\n",
    "    AdaBoostClassifier(),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    GaussianNB(),\n",
    "    LinearDiscriminantAnalysis(),\n",
    "    QuadraticDiscriminantAnalysis(),\n",
    "    LogisticRegression(),\n",
    "    xgboost.XGBClassifier()\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "Nearest Neighbors\n",
      "\n",
      " Score \n",
      "97.62124711316396%\n",
      "\n",
      " Confusion Matrix \n",
      " [[3001   21]\n",
      " [  82 1226]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.9755183967231286\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9762124711316397\n",
      "\n",
      " F1 Score \n",
      " 0.9596868884540117\n",
      "\n",
      " AUC \n",
      " 0.9651799140654569\n",
      "\n",
      " Average Precision Score \n",
      " 0.9404618406392148\n",
      "\n",
      " <bound method BaseEstimator.get_params of KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
      "           weights='uniform')> \n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "Decision Tree\n",
      "\n",
      " Score \n",
      "97.50577367205543%\n",
      "\n",
      " Confusion Matrix \n",
      " [[3004   18]\n",
      " [  90 1218]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.9757492435706002\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9750577367205543\n",
      "\n",
      " F1 Score \n",
      " 0.9575471698113207\n",
      "\n",
      " AUC \n",
      " 0.9626181701163941\n",
      "\n",
      " Average Precision Score \n",
      " 0.9384168217866407\n",
      "\n",
      " <bound method BaseEstimator.get_params of DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best')> \n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "Random Forest\n",
      "\n",
      " Score \n",
      "97.11316397228637%\n",
      "\n",
      " Confusion Matrix \n",
      " [[3022    0]\n",
      " [ 125 1183]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.9715921163284047\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9711316397228638\n",
      "\n",
      " F1 Score \n",
      " 0.9498193496587716\n",
      "\n",
      " AUC \n",
      " 0.952217125382263\n",
      "\n",
      " Average Precision Score \n",
      " 0.9333026110416622\n",
      "\n",
      " <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)> \n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "AdaBoost\n",
      "\n",
      " Score \n",
      "97.71362586605082%\n",
      "\n",
      " Confusion Matrix \n",
      " [[2998   24]\n",
      " [  75 1233]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.9765576910380205\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9771362586605081\n",
      "\n",
      " F1 Score \n",
      " 0.9614035087719298\n",
      "\n",
      " AUC \n",
      " 0.9673593950175775\n",
      "\n",
      " Average Precision Score \n",
      " 0.941983274492134\n",
      "\n",
      " <bound method BaseEstimator.get_params of AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
      "          learning_rate=1.0, n_estimators=50, random_state=None)> \n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "Neural Net\n",
      "\n",
      " Score \n",
      "95.81986143187066%\n",
      "\n",
      " Confusion Matrix \n",
      " [[3013    9]\n",
      " [ 172 1136]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.9503419187597826\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9581986143187067\n",
      "\n",
      " F1 Score \n",
      " 0.9262128006522625\n",
      "\n",
      " AUC \n",
      " 0.9327616844465763\n",
      "\n",
      " Average Precision Score \n",
      " 0.9013977432199929\n",
      "\n",
      " <bound method BaseEstimator.get_params of MLPClassifier(activation='relu', alpha=1, batch_size='auto', beta_1=0.9,\n",
      "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
      "       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n",
      "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
      "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
      "       verbose=False, warm_start=False)> \n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "Naive Bayes\n",
      "\n",
      " Score \n",
      "95.61200923787528%\n",
      "\n",
      " Confusion Matrix \n",
      " [[2952   70]\n",
      " [ 120 1188]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.9588315508253199\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9561200923787528\n",
      "\n",
      " F1 Score \n",
      " 0.9259547934528449\n",
      "\n",
      " AUC \n",
      " 0.9425467064159467\n",
      "\n",
      " Average Precision Score \n",
      " 0.8854315704701261\n",
      "\n",
      " <bound method BaseEstimator.get_params of GaussianNB(priors=None)> \n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "Linear Discriminant Analysis\n",
      "\n",
      " Score \n",
      "97.09006928406467%\n",
      "\n",
      " Confusion Matrix \n",
      " [[3021    1]\n",
      " [ 125 1183]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.9713611527737299\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9709006928406466\n",
      "\n",
      " F1 Score \n",
      " 0.949438202247191\n",
      "\n",
      " AUC \n",
      " 0.9520516720401054\n",
      "\n",
      " Average Precision Score \n",
      " 0.9325387307623003\n",
      "\n",
      " <bound method BaseEstimator.get_params of LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,\n",
      "              solver='svd', store_covariance=False, tol=0.0001)> \n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "Quadratic Discriminant Analysis\n",
      "\n",
      " Score \n",
      "96.28175519630486%\n",
      "\n",
      " Confusion Matrix \n",
      " [[2945   77]\n",
      " [  84 1224]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.962411494259006\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9628175519630485\n",
      "\n",
      " F1 Score \n",
      " 0.9382905327711767\n",
      "\n",
      " AUC \n",
      " 0.9551500009107524\n",
      "\n",
      " Average Precision Score \n",
      " 0.8997949996072686\n",
      "\n",
      " <bound method BaseEstimator.get_params of QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
      "               store_covariance=False, store_covariances=None, tol=0.0001)> \n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "Logistic Regression\n",
      "\n",
      " Score \n",
      "96.85912240184757%\n",
      "\n",
      " Confusion Matrix \n",
      " [[3010   12]\n",
      " [ 124 1184]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.9696866544979956\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9685912240184757\n",
      "\n",
      " F1 Score \n",
      " 0.9456869009584664\n",
      "\n",
      " AUC \n",
      " 0.9506139482733148\n",
      "\n",
      " Average Precision Score \n",
      " 0.9247539281791644\n",
      "\n",
      " <bound method BaseEstimator.get_params of LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)> \n",
      "\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      " Classifier is \n",
      "XGBoost\n",
      "\n",
      " Score \n",
      "97.82909930715935%\n",
      "\n",
      " Confusion Matrix \n",
      " [[3001   21]\n",
      " [  73 1235]]\n",
      "\n",
      " Cross Validation Score \n",
      " 0.9782900427415122\n",
      "\n",
      " Testing Accuracy \n",
      " 0.9782909930715935\n",
      "\n",
      " F1 Score \n",
      " 0.9633385335413416\n",
      "\n",
      " AUC \n",
      " 0.9686202810379339\n",
      "\n",
      " Average Precision Score \n",
      " 0.9452621152532861\n",
      "\n",
      " <bound method XGBModel.get_params of XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
      "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
      "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
      "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
      "       silent=True, subsample=1)> \n",
      "\n",
      "                   importance\n",
      "RenewalDate          0.215264\n",
      "FormulaID            0.127202\n",
      "EndDate_y            0.105675\n",
      "NbrNewspapers_x      0.093933\n",
      "NetNewspaperPrice    0.088063\n",
      "DOB                  0.054795\n",
      "PaymentStatus        0.052838\n",
      "StreeID              0.046967\n",
      "PaymentDate          0.043053\n",
      "ZIP                  0.029354\n",
      "StartDate_y          0.029354\n",
      "EndDate_x            0.027397\n",
      "FormulaDiscount      0.025440\n",
      "PaymentType          0.023483\n",
      "TotalDiscount        0.021526\n",
      "StartDate_x          0.005871\n",
      "TotalCredit          0.003914\n",
      "ProductID            0.003914\n",
      "Gender               0.001957\n",
      "ProductDiscount      0.000000\n",
      "Pattern              0.000000\n",
      "DeliveryClass_ABN    0.000000\n",
      "DeliveryClass_NOR    0.000000\n",
      "District_2           0.000000\n",
      "District_3           0.000000\n",
      "District_4           0.000000\n",
      "District_5           0.000000\n",
      "District_6           0.000000\n",
      "District_7           0.000000\n",
      "District_8           0.000000\n",
      "DeliveryType_DR      0.000000\n",
      "DeliveryType_MD      0.000000\n"
     ]
    }
   ],
   "source": [
    "for i,j in zip(names,classifiers):\n",
    "    j.fit(x_train,y_train)\n",
    "    \n",
    "    score = j.score(x_test,y_test)\n",
    "    ypred=j.predict(x_test)    \n",
    "    cv_acc=cross_val_score(estimator=j,X=x_train,y=y_train,cv=5)\n",
    "    cm=confusion_matrix(y_test,ypred)\n",
    "    cv_acc=cv_acc.mean()\n",
    "    acc=accuracy_score(y_test,ypred)\n",
    "    f1=f1_score(y_test,ypred,average='binary')\n",
    "    auc = roc_auc_score(y_test,ypred)\n",
    "    aps=average_precision_score(y_test,ypred)   \n",
    "    \n",
    "    print(\"+\"*103)\n",
    "    print(\"\\n Classifier is \\n\" + str(i))\n",
    "    print(\"\\n Score \\n\" + str(score*100) + \"%\")\n",
    "    print('\\n Confusion Matrix \\n',cm)\n",
    "    print('\\n Cross Validation Score \\n',cv_acc)\n",
    "    print('\\n Testing Accuracy \\n',acc)\n",
    "    print('\\n F1 Score \\n',f1)\n",
    "    print('\\n AUC \\n',auc)\n",
    "    print('\\n Average Precision Score \\n',aps)\n",
    "    print(\"\\n\",j.get_params,\"\\n\")\n",
    "    \n",
    "    if i==\"XGBoost\":\n",
    "        feature_importances=pd.DataFrame(j.feature_importances_,index = x_train.columns,columns=['importance']).sort_values('importance',ascending=False)\n",
    "        print(feature_importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [],
   "source": [
    "#As observed, here XGBoost yields best accuracy. Accordingly, we have the corresponding feature importances above.\n",
    "#According to the mentioned importance, we can reduce the number of features to make the model better. Here, we remove features that have no or very little impact with regard to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data[[\"RenewalDate\",\"FormulaID\",\"EndDate_y\",\"NbrNewspapers_x\",\"NetNewspaperPrice\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Re-Modeling\n",
    "x_train,x_test,y_train,y_test=train_test_split(data,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Score \n",
      "98.10623556581987%\n",
      "                   importance\n",
      "RenewalDate          0.338710\n",
      "FormulaID            0.215726\n",
      "EndDate_y            0.183468\n",
      "NbrNewspapers_x      0.131048\n",
      "NetNewspaperPrice    0.131048\n"
     ]
    }
   ],
   "source": [
    "model=xgboost.XGBClassifier()\n",
    "model.fit(x_train,y_train)\n",
    "score = model.score(x_test,y_test)\n",
    "feature_importances=pd.DataFrame(model.feature_importances_,index = x_train.columns,columns=['importance']).sort_values('importance',ascending=False)\n",
    "print(\"\\n Score \\n\" + str(score*100) + \"%\")\n",
    "print(feature_importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apart from features highlighted above, we also observe that:\n",
    "#1. Complaints for Product with ID 8 are maximum.\n",
    "#2. Complaint Type 1 are maximum.\n",
    "#3. Maximum customers are from District 5, followed by District 1.\n",
    "#4. There are more Male customers."
   ]
  }
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