Churn_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.compose import ColumnTransformer \n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import numpy as np\n",
    "import joblib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"gs://churn-model-sri/telecom_customer_churn.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</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>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No       1   \n",
       "1     5575-GNVDE    Male              0      No         No      34   \n",
       "2     3668-QPYBK    Male              0      No         No       2   \n",
       "3     7795-CFOCW    Male              0      No         No      45   \n",
       "4     9237-HQITU  Female              0      No         No       2   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
       "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
       "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
       "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
       "7042  3186-AJIEK    Male              0      No         No      66   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0              No  No phone service             DSL             No  ...   \n",
       "1             Yes                No             DSL            Yes  ...   \n",
       "2             Yes                No             DSL            Yes  ...   \n",
       "3              No  No phone service             DSL            Yes  ...   \n",
       "4             Yes                No     Fiber optic             No  ...   \n",
       "...           ...               ...             ...            ...  ...   \n",
       "7038          Yes               Yes             DSL            Yes  ...   \n",
       "7039          Yes               Yes     Fiber optic             No  ...   \n",
       "7040           No  No phone service             DSL            Yes  ...   \n",
       "7041          Yes               Yes     Fiber optic             No  ...   \n",
       "7042          Yes                No     Fiber optic            Yes  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0                  No          No          No              No  Month-to-month   \n",
       "1                 Yes          No          No              No        One year   \n",
       "2                  No          No          No              No  Month-to-month   \n",
       "3                 Yes         Yes          No              No        One year   \n",
       "4                  No          No          No              No  Month-to-month   \n",
       "...               ...         ...         ...             ...             ...   \n",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0                 Yes           Electronic check          29.85         29.85   \n",
       "1                  No               Mailed check          56.95       1889.50   \n",
       "2                 Yes               Mailed check          53.85        108.15   \n",
       "3                  No  Bank transfer (automatic)          42.30       1840.75   \n",
       "4                 Yes           Electronic check          70.70        151.65   \n",
       "...               ...                        ...            ...           ...   \n",
       "7038              Yes               Mailed check          84.80       1990.50   \n",
       "7039              Yes    Credit card (automatic)         103.20       7362.90   \n",
       "7040              Yes           Electronic check          29.60        346.45   \n",
       "7041              Yes               Mailed check          74.40        306.60   \n",
       "7042              Yes  Bank transfer (automatic)         105.65       6844.50   \n",
       "\n",
       "      Churn  \n",
       "0        No  \n",
       "1        No  \n",
       "2       Yes  \n",
       "3        No  \n",
       "4       Yes  \n",
       "...     ...  \n",
       "7038     No  \n",
       "7039     No  \n",
       "7040     No  \n",
       "7041    Yes  \n",
       "7042     No  \n",
       "\n",
       "[7043 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     5174\n",
       "Yes    1869\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Churn'])\n",
    "y = df['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=124)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_feat=  ['customerID','gender','PhoneService','MultipleLines', 'PaperlessBilling','PaymentMethod']\n",
    "numeric_features = ['tenure', 'TotalCharges']\n",
    "categorical_features = ['SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies','Contract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           0\n",
       "gender               0\n",
       "SeniorCitizen        0\n",
       "Partner              0\n",
       "Dependents           0\n",
       "tenure               0\n",
       "PhoneService         0\n",
       "MultipleLines        0\n",
       "InternetService      0\n",
       "OnlineSecurity       0\n",
       "OnlineBackup         0\n",
       "DeviceProtection     0\n",
       "TechSupport          0\n",
       "StreamingTV          0\n",
       "StreamingMovies      0\n",
       "Contract             0\n",
       "PaperlessBilling     0\n",
       "PaymentMethod        0\n",
       "MonthlyCharges       0\n",
       "TotalCharges        11\n",
       "Churn                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_transformer = Pipeline(steps=[\n",
    "                                     ('meanimputer', SimpleImputer(strategy='mean')),\n",
    "                                     ])\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "                                         ('onehotenc', OneHotEncoder(handle_unknown='ignore'))\n",
    "                                         ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_transformer = ColumnTransformer(transformers=[('drop_columns', 'drop', drop_feat),\n",
    "                                                   ('numeric_processing',numeric_transformer, numeric_features),\n",
    "                                                    ('categorical_processing', categorical_transformer, categorical_features)\n",
    "                                                  ], remainder='drop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "                     ('transform_column', col_transformer),\n",
    "                    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('transform_column',\n",
       "                 ColumnTransformer(transformers=[('drop_columns', 'drop',\n",
       "                                                  ['customerID', 'gender',\n",
       "                                                   'PhoneService',\n",
       "                                                   'MultipleLines',\n",
       "                                                   'PaperlessBilling',\n",
       "                                                   'PaymentMethod']),\n",
       "                                                 ('numeric_processing',\n",
       "                                                  Pipeline(steps=[('meanimputer',\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  ['tenure', 'TotalCharges']),\n",
       "                                                 ('categorical_processing',\n",
       "                                                  Pipeline(steps=[('onehotenc',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  ['SeniorCitizen', 'Partner',\n",
       "                                                   'Dependents', 'PhoneService',\n",
       "                                                   'InternetService',\n",
       "                                                   'OnlineSecurity',\n",
       "                                                   'OnlineBackup',\n",
       "                                                   'DeviceProtection',\n",
       "                                                   'TechSupport', 'StreamingTV',\n",
       "                                                   'StreamingMovies',\n",
       "                                                   'Contract'])]))])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_train=pipeline.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5634, 34)"
      ]
     },
     "execution_count": 14,
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       "[5634 rows x 34 columns]"
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     "data": {
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       "array([[7.10000e+01, 7.69090e+03, 1.00000e+00, ..., 0.00000e+00,\n",
       "        0.00000e+00, 1.00000e+00],\n",
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     "metadata": {},
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   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('onehotenc', OneHotEncoder(handle_unknown='ignore'))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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    "pipeline.named_steps['transform_column'].transformers_[2][1]i"
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  {
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   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols=pipeline.named_steps['transform_column'].transformers_[2][1].named_steps['onehotenc'].get_feature_names(categorical_features)"
   ]
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  {
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   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names=numeric_features+cat_cols.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tenure',\n",
       " 'TotalCharges',\n",
       " 'SeniorCitizen_0',\n",
       " 'SeniorCitizen_1',\n",
       " 'Partner_No',\n",
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       " 'Dependents_No',\n",
       " 'Dependents_Yes',\n",
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       " 'PhoneService_Yes',\n",
       " 'InternetService_DSL',\n",
       " 'InternetService_Fiber optic',\n",
       " 'InternetService_No',\n",
       " 'OnlineSecurity_No',\n",
       " 'OnlineSecurity_No internet service',\n",
       " 'OnlineSecurity_Yes',\n",
       " 'OnlineBackup_No',\n",
       " 'OnlineBackup_No internet service',\n",
       " 'OnlineBackup_Yes',\n",
       " 'DeviceProtection_No',\n",
       " 'DeviceProtection_No internet service',\n",
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       " 'TechSupport_No',\n",
       " 'TechSupport_No internet service',\n",
       " 'TechSupport_Yes',\n",
       " 'StreamingTV_No',\n",
       " 'StreamingTV_No internet service',\n",
       " 'StreamingTV_Yes',\n",
       " 'StreamingMovies_No',\n",
       " 'StreamingMovies_No internet service',\n",
       " 'StreamingMovies_Yes',\n",
       " 'Contract_Month-to-month',\n",
       " 'Contract_One year',\n",
       " 'Contract_Two year']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "feature_names"
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.0</td>\n",
       "      <td>442.45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.0</td>\n",
       "      <td>7968.85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>74.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>1607.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>5629</th>\n",
       "      <td>8.0</td>\n",
       "      <td>551.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5630</th>\n",
       "      <td>43.0</td>\n",
       "      <td>1188.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5631</th>\n",
       "      <td>7.0</td>\n",
       "      <td>533.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5632</th>\n",
       "      <td>2.0</td>\n",
       "      <td>98.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5633</th>\n",
       "      <td>27.0</td>\n",
       "      <td>2287.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5634 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tenure  TotalCharges  SeniorCitizen_0  SeniorCitizen_1  Partner_No  \\\n",
       "0        5.0        302.60              0.0              1.0         0.0   \n",
       "1       12.0        442.45              1.0              0.0         1.0   \n",
       "2       70.0       7968.85              1.0              0.0         0.0   \n",
       "3        1.0         74.60              1.0              0.0         0.0   \n",
       "4       17.0       1607.20              1.0              0.0         0.0   \n",
       "...      ...           ...              ...              ...         ...   \n",
       "5629     8.0        551.95              0.0              1.0         0.0   \n",
       "5630    43.0       1188.20              1.0              0.0         1.0   \n",
       "5631     7.0        533.60              1.0              0.0         1.0   \n",
       "5632     2.0         98.50              1.0              0.0         1.0   \n",
       "5633    27.0       2287.25              0.0              1.0         1.0   \n",
       "\n",
       "      Partner_Yes  Dependents_No  Dependents_Yes  PhoneService_No  \\\n",
       "0             1.0            1.0             0.0              0.0   \n",
       "1             0.0            1.0             0.0              1.0   \n",
       "2             1.0            1.0             0.0              0.0   \n",
       "3             1.0            1.0             0.0              0.0   \n",
       "4             1.0            0.0             1.0              0.0   \n",
       "...           ...            ...             ...              ...   \n",
       "5629          1.0            1.0             0.0              0.0   \n",
       "5630          0.0            1.0             0.0              0.0   \n",
       "5631          0.0            1.0             0.0              0.0   \n",
       "5632          0.0            1.0             0.0              0.0   \n",
       "5633          0.0            1.0             0.0              0.0   \n",
       "\n",
       "      PhoneService_Yes  ...  TechSupport_Yes  StreamingTV_No  \\\n",
       "0                  1.0  ...              0.0             1.0   \n",
       "1                  0.0  ...              1.0             1.0   \n",
       "2                  1.0  ...              1.0             0.0   \n",
       "3                  1.0  ...              0.0             1.0   \n",
       "4                  1.0  ...              0.0             0.0   \n",
       "...                ...  ...              ...             ...   \n",
       "5629               1.0  ...              0.0             0.0   \n",
       "5630               1.0  ...              0.0             0.0   \n",
       "5631               1.0  ...              0.0             1.0   \n",
       "5632               1.0  ...              0.0             1.0   \n",
       "5633               1.0  ...              0.0             1.0   \n",
       "\n",
       "      StreamingTV_No internet service  StreamingTV_Yes  StreamingMovies_No  \\\n",
       "0                                 0.0              0.0                 1.0   \n",
       "1                                 0.0              0.0                 1.0   \n",
       "2                                 0.0              1.0                 0.0   \n",
       "3                                 0.0              0.0                 1.0   \n",
       "4                                 0.0              1.0                 1.0   \n",
       "...                               ...              ...                 ...   \n",
       "5629                              0.0              1.0                 1.0   \n",
       "5630                              1.0              0.0                 0.0   \n",
       "5631                              0.0              0.0                 1.0   \n",
       "5632                              0.0              0.0                 1.0   \n",
       "5633                              0.0              0.0                 0.0   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                     0.0                  0.0   \n",
       "1                                     0.0                  0.0   \n",
       "2                                     0.0                  1.0   \n",
       "3                                     0.0                  0.0   \n",
       "4                                     0.0                  0.0   \n",
       "...                                   ...                  ...   \n",
       "5629                                  0.0                  0.0   \n",
       "5630                                  1.0                  0.0   \n",
       "5631                                  0.0                  0.0   \n",
       "5632                                  0.0                  0.0   \n",
       "5633                                  0.0                  1.0   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  Contract_Two year  \n",
       "0                         1.0                0.0                0.0  \n",
       "1                         1.0                0.0                0.0  \n",
       "2                         0.0                1.0                0.0  \n",
       "3                         1.0                0.0                0.0  \n",
       "4                         1.0                0.0                0.0  \n",
       "...                       ...                ...                ...  \n",
       "5629                      1.0                0.0                0.0  \n",
       "5630                      0.0                0.0                1.0  \n",
       "5631                      1.0                0.0                0.0  \n",
       "5632                      1.0                0.0                0.0  \n",
       "5633                      1.0                0.0                0.0  \n",
       "\n",
       "[5634 rows x 34 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(transformed_train, columns=feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_pos_weight=y_train.value_counts()[0]/y_train.value_counts()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.768561872909699"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale_pos_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_xgb = Pipeline([\n",
    "                     ('transform_column', col_transformer),\n",
    "                     ('xgboost', xgb.XGBClassifier(scale_pos_weight=scale_pos_weight))\n",
    "                    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:42:32] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('transform_column',\n",
       "                 ColumnTransformer(transformers=[('drop_columns', 'drop',\n",
       "                                                  ['customerID', 'gender',\n",
       "                                                   'PhoneService',\n",
       "                                                   'MultipleLines',\n",
       "                                                   'PaperlessBilling',\n",
       "                                                   'PaymentMethod']),\n",
       "                                                 ('numeric_processing',\n",
       "                                                  Pipeline(steps=[('meanimputer',\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  ['tenure', 'TotalCharges']),\n",
       "                                                 ('categorical_processing',\n",
       "                                                  Pipeline(steps=[('onehotenc',\n",
       "                                                                   OneHotEncoder(...\n",
       "                               importance_type='gain',\n",
       "                               interaction_constraints='',\n",
       "                               learning_rate=0.300000012, max_delta_step=0,\n",
       "                               max_depth=6, min_child_weight=1, missing=nan,\n",
       "                               monotone_constraints='()', n_estimators=100,\n",
       "                               n_jobs=4, num_parallel_tree=1, random_state=0,\n",
       "                               reg_alpha=0, reg_lambda=1,\n",
       "                               scale_pos_weight=2.768561872909699, subsample=1,\n",
       "                               tree_method='exact', validate_parameters=1,\n",
       "                               verbosity=None))])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_xgb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7757274662881476"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_xgb.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipeline_xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[832, 203],\n",
       "       [113, 261]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.88      0.80      0.84      1035\n",
      "         Yes       0.56      0.70      0.62       374\n",
      "\n",
      "    accuracy                           0.78      1409\n",
      "   macro avg       0.72      0.75      0.73      1409\n",
      "weighted avg       0.80      0.78      0.78      1409\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred, labels=['No','Yes']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=2.768561872909699,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=pipeline_xgb.named_steps['xgboost']\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotImp(model, X , num = 20):\n",
    "    feature_imp = pd.DataFrame({'Value':model.feature_importances_,'Feature':X})\n",
    "    plt.figure(figsize=(40, 20))\n",
    "    sns.set(font_scale = 3)\n",
    "    sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", \n",
    "                                                        ascending=False)[0:num])\n",
    "    plt.title('XGBoost Feature Importance')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSZIkSVIPmdrtAiRJ0vCt9PZndbuEIZk3d163S5AkSZIkSZIkSZIkSZJ6ngFgSZJ62H33PcL8+Qu6XYYkSZIkSZIkSZIkSZKkMdTX7QIkSZIkSZIkSZIkSZIkSZIk1WcAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHmIAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHmIAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHmIAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHmIAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHjK12wVIkqThmzlzerdLaGve3Hnc/9Bj3S5DkiRJkiRJkiRJkiRJmjAMAEuS1MPuOfGvzH94brfLGNTK73tRt0uQJEmSJEmSJEmSJEmSJpS+bhcgSZIkSZIkSZIkSZIkSZIkqT4DwJIkSZIkSZIkSZIkSZIkSVIPMQAsSZIkSZIkSZIkSZIkSZIk9RADwJIkSZIkSZIkSZIkSZIkSVIPMQAsSZIkSZIkSZIkSZIkSZIk9RADwJIkSZIkSZIkSZIkSZIkSVIPMQAsSZIkSZIkSZIkSZIkSZIk9RADwJIkSZIkSZIkSZIkSZIkSVIPMQAsSZIkSZIkSZIkSZIkSZIk9RADwJIkSZIkSZIkSZIkSZIkSVIPmdrtAqReExGHA4eVh0dk5uHdq0aTXURsB1xUHl6cmdt1rxpJkiRJkiRJkiRJkiRJ0lgwAKwhi4hZwLYtTj8BPFS+/gNcA1wN/CYz7xuTAjWoiDgZ2H+QIXOA+4BrgV8D38/MR0a/MmnoIuLFwOXAImXXrpn5syFcvzpwI7B02fX+zDymo0VKkiRJkiRJkiRJkiRJUof1dbsATTiLAysDzwW2Az4I/AC4IyK+HxGbdK801bQ0sDawG3AsMDsidutiPYOKiJMjYkH5OqDb9YxERGxXuZdZ3a6nF2Tm1cBXK13HRcSyQ5jiWAbCv5cDX+9UbZIkSZIkSZIkSZIkSZI0WtwBWCN1FXBl5bgPmAEsB2wIrF72TwPeDOwZEYcDX8zM+WNXplr4O3BhQ98M4EXA+uXxTODHEbFHZv54DGuT6vo0sCuwHrAqcBTwtnYXRcRewGvLw7nAW/19SZIkSZIkSZIkSZIkSVIvMACskTovMw9vdTIi1gAOBN4FrAQsChwJrEONgN54VN7v4V0uo1OuyMxDm52IiF2AUynC3FOA70TEbzJzzlgWqMFl5iyKz2fSyszHI+JtwEUU78VBEXFGZl7U6pqIWB74WqXrc5l54yiXKkmSJEmSJEmSJEmSJEkd0dftAjSxZebtmfkZ4PnAbyqnDoqI93SpLNWQmb8A3lTpmkmxi7M07mTmxcDxla7vRsSSg1zyVYofSgC4DvjiaNUmSZIkSZIkSZIkSZIkSZ1mAFhjIjPvA3YGrqx0fzYiZnSnItWRmb8Crq10vaJLpUh1fAS4vWw/B/hss0ER8Upgv/JwHvCWzHxq9MuTJEmSJEmSJEmSJEmSpM6Y2u0CNHlk5lMRsQ+QFOHzZYBDgc8Ndl1E7ATsAWwFPAtYArgX+DPwU+DUZuG9iJgG/BeYUXZtnJl/bVdnREwBbgNWL7u2ycw/VM4fDhxWHh6RmYfXmHMzYC9gu3Le5YHHgdnANcB5wLmZObfNPBtS7MK7A7BmeW8PADcB5wPfLsPWnXQpsEnZXruhnpOB/cvDAzPz5IiYCRwI7A6sQ7HL6pzMnNE4cUSsB7wV2JHifpYG7qP4jvwS+G5mPtSsqIiYBWzb0H1SRJzUZHjLzykiFgP2AXYBNi3rXQDcBVwCnJ6Zv252bStD/bwj4gCgse5tI2JBk+lvzcy1K2ttB1xUHl6cmdvVqG9X4A3AS4GVy+67gMuAH2bmz2rM8f9ry8wpZd/zgXdSfD/XAJ4G/knx6/SYzJzTbt6RyMw5EfF2iu8OwHsj4qzM/P8/eBARS7HwTsFHZ+Y1jXNFRB/Fd3g3YHOK92kqcDfFDzKcDZyTmc0+o8a5Xkrx6+SlwFrAUsCDwD0Uv0f9AfgVcGVmzhvCLUuSJEmSJEmSJEmSJEmapAwAa0xl5j8j4ufArmXXG2kRAI6ItYFTga2bnF6tfO0CfCwidm8M92bmkxFxDnBQ2fUm4OM1ytyWgfDvbRQh0GEpw7AnMHC/VUsDG5WvA4ALgFe1mGcJ4BsUwdrGnbtXKl9bAh+OiLdn5pnDrbmJByrtZQcbGBHbAGcCq7QZN4Vid9aPAIs2nF6lfG0HfDQiDs7Mnwyx5loiYgfguzQEm0vPLl/7RcSvgL0z88E283Xk8x4tEbEacBbFd6VR//3uExGXAHtm5n+GMPe7gK8AizWc2rR8HRQRr8jMfw6r+Joy87yIOJ0i1L0IcEJEvKgSrv8cRTAdiuD8YY1zRMQLgVMoPqtGa5WvNwBXl7/33N5kHBGxKPAdis+70Qrl63nA9sCngdcCv6hxm5IkSZIkSZIkSZIkSZImOQPA6oazGQhIbhgRy2VmNWRKRGwAXMjA7qTzKXZO/RvwJMVusVsDSwLrApdExJaZeX3DWqczEADeOyI+UWPHzn0q7TPq7PDZTESsQbEz63Mq3fcCf6TYRXTxsvYXlu3FW8wzHfgNsEWl+58U78dDwEyK92IlioDuGRExLTNPGU7dTSxXaTfdjbe0HvB1iqDrQ8DFFLvKrkgRAK36LsXOv/0epHiv7qP4bLejCJKuAJwTEftm5hkNc/wEuB54BbB+2Xch8PcmtV3Z2BERe1OEPPsDyI8BlwO3UuwA/DyKnV/7KIK6v4+Il2bmo81ufoSf99+Ab1GE2ncr+/5T3mOjYe3wXIZ//0gRXu13A/AnivvdFNiw7N+KgV9Td9aY+y3AN8vDG8s5nwQ2BjYr+9cEfhoRL2y2Y3eHvRd4JcWviQ2BTwCHR8RLgPeUYxYAB2XmE9ULyx2Vfw5ML7ueAq4Abi7b61IEqBcFXgxcFhGbtXifvsLC4d/bgKsovheLlvVtTPHeSJIkSZIkSZIkSZIkSVJtBoDVDVdV2lMoAoK/7u+IiKWAHzEQ/j0fODQz/1WdJCJWoNgVdy9gGeDMiNgkM5+uDLsY+DfFjr5rUQT3Wu7oGxGLAa+vdJ0+pDsbmGdRiqBzfxj0YYpQ4qmZOb9h7HRgd+AlLaY7joHw7z+AgzPz4iZ1fwA4kiKwemxEXJKZNw+n/gYvq7RvGWTcRyl2XD0G+GRmPlapb1ql/WYWDv9+HfhoNYgZEatTvPfbUNzPdyLiyurusZn5tXLsyQwEgE/LzJPb3VBEbESxU++iFEHQo4DPN+7wGxHPB35AEdLciCLQeUiT+Ub0eWfmFcAVZfh0t7L7psw8tN29DMHJDIR/HwD2zcxfNtT2WuD7FEHydYATgVfXmPtYirD3Ppl5YcOcu1HsOrwYsAFFwP7kYd5DLZl5X0S8h2I3aoCPR8S5wPcY2EH725n5+4ZaVwF+yED49xTgY5n534Zxa1Dcw8spQtsnAzs1jFkBeEd5+DSwf5MQe//Y9YB9KYLwkiRJkiRJkiRJkiRJktRWX/shUsfdRLGjb7+VG85/EIiy/TNgl8bwL0Bm3ksRJuwPD28AvKFhzAKKAGe/6u6+zbyGgR1vr2uyo3Bdb6XYPRaKnVBfkZknN4ZByxofycxTm4U9I2Ib4M3l4Wxgq8bwbznH3Mz8InBY2bUk8JFh1l5d/1XAJpWuiwYZvghwfGa+vxr+Let7spxvEYqQcr/vZeZ7G3dhzcx/AztT7CYLsBTwqWHdRHNfA5Yo2x/JzI80hn/LOm4EdgD6d3d9Sxn+bNSRz3u0RMQrKe4DisDzro3h37K2n1OEk/t3vX5VRGxfY4kFwA6N4d9yzp8CX6107TWE0octM88Czi0PF6P4YYCNyuPbKQLrjY6k2LEa4FuZeUBj+Lec+3ZgF+CvZdeOEfHShmFbUPyaADizVfi3nO+mzPx0Zrb84QRJkiRJkiRJkiRJkiRJqjIArDFXhnLnVLr6A7f9O6n2ByOfBt7RLERZmWs+CwdD39RkWHUX3zeUa7RSvX5Yu/+W3l9pfz4zr+7APB/JzHvajP8yxe6uAHtFxLB/jUfEzkA1tPgAcNoglzwOfKzNtK8C1izbjwIfbjUwMx8BPlTp2jMiZrSZv62IeAHQH2q9Hjh6sPHle35MeTgVeGOTYZ36vEfLwZX2DzLzD60GZubvKHYz7vf2GvN/p01Y/uRKe7Ma83XKOxjYVXfpSv8hmVn9PYiIWImBHxC4lzYB+sx8HPhcpavx955lKu12v24lSZIkSZIkSZIkSZIkaUgMAKtbHqm0q8G8zRjYgfP3mfmfGnNdRREmBdiy8WRm/oUi6AkwE9ip2SQRsTTFrp5Q7Gj6g2bj2omIdYDnlofzgeOGOc9U4JXl4RPAT9tdU+60e3l5uAwDO562snlEfLPhdWpE3Aj8goFw9gKK0OTDg8z162a76DbYrtI+t8b4CxjYfXca0LjL6nDsXGn/sAyktzOr0t6qeqJTn/co27bSPrnG+JMq7e1qjD+7zfmkCIgDLB8R02vMOWKZeScLh8gBvp+Z5zcZvgPFTsEAP2vcxbqFWZX2Vg3nbq+0d4+IFWrMJ0mSJEmSJEmSJEmSJEm1TO12AZq0qqHfaqh0i0p75Yj45hDnXS4ippe7x1adDnyhbL+JItzaaHdgibJ9cWbe3mRMHZtX2jfU2LW3lY2Bpcr2E8BXI6LOdc+ptNcA/jLI2PXL12AeAA7OzHPajPtTjdo2qbQvazc4M+dHxOXA/5RdLwSahTeHovod26rmd2zZSnuNhnOd+rxHRUSsxkCofgFwRY3LLivHTgFWiohV24TxB9v9l8xcEBEPMPDra1kW/iGAUZOZJ0TE9ypdJ7YYWv1ePL/m92JKpd34vbgc+DewOrAWcENEnAicC1ydmU/XmF+SJEmSJEmSJEmSJEmSmjIArDEXEX0sHAB+oNJetdLeoHwN1QyeGS48A/g8RWBv14hYKjMfbRizT6V9+jDW7bdSpf2vEcxTfS9mAO8axhwzhnHNo8C9FMHhCyh2TJ1T47o6wdcVK+3batZza6XdiV1Uq+/rjuVrKGY0HHfq8x4t1ff8wTa7OAOQmQ9FxEMM3OsKwGAB4Idq1PFUpb1ojfFjrfq92IKFA8F1zKgeZOZTEbEf8HOKIP9KwMfK16NlsH0WxU7Yfx1mzZIkSZIkSZIkSZIkSZImKQPA6obnsvDOmf+ttJdl5J7xvc7M2yLiEmBrYElgNyoh34hYGXh5eTgXaLfb7WCq4eaR7HI6Ku9Fg1My84AOrAPweI0x0yvtxgB2K9VxS7ccVd9I39fG97RTn/doGc573j92Rtke9H3PzAVDrGk8Gun3YpHGjsy8KCJeCBwOvB6YVp5aCnhF+fpsRPwReE9m1tlFW5IkSZIkSZIkSZIkSZIMAKsrNqu05wNXVY6rAcX/y8yPdXDd0ykCwFDs9lvd5XcvBgJ8v8zMB0ewTnW33OktR7VXfS+uyMyh7kg6HlUDskvVvKY6rs5OxO1U39dXZ+avRjhfpz7v0TKc97xxbCfe9/Gu+r14R2Z+uxOTZuZNwD4R8XZgG2Cr8vUSYLFy2JbApRGxU2Ze3Il1JUmSJEmSJEmSJEmSJE1sfd0uQJPSHpX2dZn5UOX4rko7Orzu2RS7+wK8MiJWrJx7U6VdDQYPR/Ue1unQPOtFxJSWI3vHPZX2mjWvWavSvrcDNXT6O9apz3u0VN/zGRGxTLsLImJZFt4RtxPv+3g3mr/3kJlzMvOXmfnxzNwaWAHYH5hdDpkGdCR0LEmSJEmSJEmSJEmSJGniMwCsMRUR6wG7VLp+2DDkykp7u4hYtFNrZ+b9QP9ur1OBN5Y1rUuxGyfAQ8AvR7jUFZX2hg1B46G4loHA8vLAi0dS1DhxbaX90naDy9Dz5pWuPzcZtmCINVS/YzsO8dpmOvV5w9Dvpa3MvIOBEHDj+9nKFuVYgLsy8z+drmsc6vT3YlBlIPhU4OXAk2X3+hHx7NFeW5IkSZIkSZIkSZIkSVLvMwCsMVOGeU9j4Hv3EPCthmF/LPsBZgAHdriM0yrtNzX8E+CczHxiJAtk5mwgy8M+4B3DnOdxYFal6/0jqWucuKjSfl1EzGgzfkdg1bL9JHBZkzHVz6tOYPy8SvtVEfG8Gte01KnPuzTUe6lrVqW9f43xB1TaF3ewjvHsAmBe2X5+RIx6CBggM28Bbqh0PWss1pUkSZIkSZIkSZIkSZLU2wwAa0xExEzgFwzstAvwicx8uDquDN9+o9L1pYhYv+YafTV2X/050L/myyJiHRYOAJ9eZ60ajqm0Px4Rw92990uV9t4RsXfdCyNi5WGuOZouAG4r29OBL7caGBFLAkdVus7KzAebDL2v0l6tXQGZeRnwh/KwDzgtIpZqd11Z0/QWY4+ptEfyeQ/pXobgO5X23hGxdauBEbEtsGel69sdrGPcysx/Az+odH237q+hiFgsIpZr6JtZ89pFWDj0e3ed6yRJkiRJkiRJkiRJkiRNbgaANaoiYo2I+BRwI8Vurv2Oz8xjW1z2JQZ2VF0WuDQi9o+IqS3WWCUi3lOusWezMf3KgPGPK11HAVG276Bzu52eAFxVthcHLoyIAyLiGb/mylDpfhHxzSb1XgicUek6LSKObLVzbkQsFRF7RsSFwDPm67bMnAd8otJ1UER8NSIWr46LiFUpAuMbll2PAp9pMe31lfauEbFYjVLeDTxetjcFLi+Dr01FxIYR8XngVmCdJkM68nkD/wIeK9trRcRmNe6lrcz8LfDb8rAPODciXt2kttcAPwWmlF2/ysyLGsdNYB9nIIC7JnBVRLwuIqY0GxwRz46IT1B8bls2nH53RPw5Ig6JiJVaXL8ccDwDu1z/MzP/OeK7kCRJkiRJkiRJkiRJkjThNQ1USkPwmohYoXLcBywDLAdsBKzRMP4p4NMsvLPtQjJzTkTsShFYXL2c62TgKxFxGXBnuc7yFAHR5zAQWKzjdOCAsr17pf8HmTl/CPO0lJlPRcQbgFnA2hTvyUkUOxr/kSJkuDiwLkUAdXFah4/fRrEb7LYU9/0J4AMRcQUwG3gSmAGsB2wA9Adgf9SJe+m0zDw9IrYH3lp2vQ/YLyIuAu6nCF5uz8B9zAcOycybW0x5PkWYdwlgE+BvETELeBBYUI75dWb+ulLDXyLizRTfhcUpvkezImI2cDXFTrxLACuVc1Z3aG12Tx35vDNzfkT8lIFdqWdFxK8odk2eV/bdn5mfH6yeFg4ALqV4f5cDzouI64FryvObUvya7TcbeMsw1ulZmfnviNidInw+g+L3r3OBO8tfb3dTfC9XADameC8HswnFDsrHRcQ/gBsovluLU4R+twKmlWPnU/xakCRJkiRJkiRJkiRJkqS2DABrpDYrX+08AZwDfDkzr2s3ODMzIl4EfAfYtexeHth5kMvuAm6qUcvvKELEqzT0n17j2toy89aI2IIiCNq/2+qKwG4tLnm0xTyPRcQOwJHAeyjCg4tTBIJb7Vr7OAPBzvHobRSf14eBRSk+29c3GXcvRfj3x03OAZCZD0XEB4BjKYLgzy5fVY8Av2647scRsTXF7r0bl91rl69WbqAIKTeroyOfN0XA++UUoeMlWTikDsUuxEMOAGfmHRGxJXAW8LKye0MGdlmu+iPwxsy8c6jr9LrM/GO58/KJwNZl9yq0/hyhCEv/u6FvTqU9hWKn8aC5+4C3Z+Yvh1qvJEmSJEmSJEmSJEmSpMnJALA67UngIeBh4A6KEOrVwAWZ2TQ42Upm3g3sFhEvBPaiCLuuTREWfZoiiPmPco1fAxdl5tM15p0fEWcC769035iZ1w6lvjoy8y6KXZK3ZuAeVqXYIfYRijDnVRQ7jv5ikHmeBj4aEccA+1MERJ8HzAQWoXjPbwH+AlwInJ+ZD3f6fjolMxcAn4yIk4GDgB0pdlNdmuJz/TvF+/HdzHyoxnzfjoi/AocAm1PsmLwkbXaGzsyrI2ITimD56yiCsasAy1KEqO8qa7mU4j39c5v5Rvx5l0HiFwCHUrwvz6V4X0b8+3Vm/hvYstxhe09gC2Dl8vRdwGXADzPz3JGu1csy85/ANhGxDUUwfRuK79RyFL/H3QskcAVwAXBp+Z2uznF0RPwY2Inie7UhsBbFd+Hpco6/UuxgfVpmPjAGtyZJkiRJkiRJkiRJkiRpgpiyYMGC9qMkSdJ4szZwyz0n/pX5D8/tdi2DWvl9L+Kee+a0HyhJmjRWXHFpnw2SpJ7lc0yS1Kt8hkmSepnPMUlSr/IZppHo65vCzJnTAdah+FvKFz4/1gVJkiRJkiRJkiRJkiRJkiRJGj4DwJIkSZIkSZIkSZIkSZIkSVIPMQAsSZIkSZIkSZIkSZIkSZIk9RADwJIkSZIkSZIkSZIkSZIkSVIPmdrtAiRJk1NEbA7sO8JpbsrMr3WiHkmSJEmSJEmSJEmSJEnqFQaAJUnd8jzgXSOc42LAALAkSZIkSZIkSZIkSZKkSaWv2wVIkiRJkiRJkiRJkiRJkiRJqs8dgCVJXZGZJwMnd7kMSZIkSZIkSZIkSZIkSeo57gAsSZIkSZIkSZIkSZIkSZIk9RADwJIkSZIkSZIkSZIkSZIkSVIPMQAsSZIkSZIkSZIkSZIkSZIk9ZCp3S5AkiQN34pv2ajbJbQ1b+68bpcgSZIkSZIkSZIkSZIkTSgGgCVJ6mH33fcI8+cv6HYZkiRJkiRJkiRJkiRJksZQX7cLkCRJkiRJkiRJkiRJkiRJklSfAWBJkiRJkiRJkiRJkiRJkiSphxgAliRJkiRJkiRJkiRJkiRJknqIAWBJkiRJkiRJkiRJkiRJkiSphxgAliRJkiRJkiRJkiRJkiRJknqIAWBJkiRJkiRJkiRJkiRJkiSphxgAliRJkiRJkiRJkiRJkiRJknqIAWBJkiRJkiRJkiRJkiRJkiSphxgAliRJkiRJkiRJkiRJkiRJknqIAWBJkiRJkiRJkiRJkiRJkiSph0ztdgGSJGn4Zs6c3u0Smpo392nuf+jxbpchSZIkSZIkSZIkSZIkTUgGgCVJ6mH3nvJ75s95ottlPMNKh+7Y7RIkSZIkSZIkSZIkSZKkCauv2wVIkiRJkiRJkiRJkiRJkiRJqs8AsCRJkiRJkiRJkiRJkiRJktRDDABLkiRJkiRJkiRJkiRJkiRJPcQAsCRJkiRJkiRJkiRJkiRJktRDDABLkiRJkiRJkiRJkiRJkiRJPcQAsCRJkiRJkiRJkiRJkiRJktRDDABLkiRJkiRJkiRJkiRJkiRJPcQAsCRJkiRJkiRJkiRJkiRJktRDDABLkiRJkiRJkiRJkiRJkiRJPcQAsCRJkiRJkiRJkiRJkiRJktRDDABLkiRJkiRJkiRJkiRJkiRJPcQAsCRNEhFxeEQsKF+Hd7seSZIkSZIkSZIkSZIkSdLwTO12AdJIRMQsYNvy8IjMPLx71ajbImJR4HXArsCLgVWApYEngAeA2cANwBXAbzPz9u5Uqm6JiAUNXSdm5ltrXjsbWKs83D4zZ3WwNEmSJEmSJEmSJEmSJEmqzR2ApSGKiAMqu6ie3O16JpqImF15f9cewnXbAH8DzgH2BZ4HzAAWAZYCVge2Ag4BTgRucxdcAftFxHrdLkKSJEmSJEmSJEmSJEmShsIdgCX1vIjYGfgJsGil+xbgWuAeYDFgJeCFFLsC95sxNhVqHJsKHA7s0+U6JEmSJEmSJEmSJEmSJKk2A8CSelpEzAROZSD8eyNwSGZe0mJ8AHsAB41NheNHZh5OEXbVwvaKiCMz88ZuFyJJkiRJkiRJkiRJkiRJdfR1uwBJGqG3AsuX7f8C27QK/wJk4UjgOcAxo1+exrEryn/2AZ/pZiGSJEmSJEmSJEmSJEmSNBQGgCX1uh0r7ZMz8746F2Xm/MycPTolqUccDswr27tHxAu7WIskSZIkSZIkSZIkSZIk1Ta12wVIoykitgMuKg8vzsztyv4dgbcBmwHPAuYA1wGnAadk5vwmcx0OHNbQvX9E7N9k6f+/Vou6dgL2ALYq118CuBf4M/BT4NTMfGqQ69cGbikPb83Mtcv+lwMHAFsAqwJLAe/PzGMGuWZz4B1lLasBjwN/A84Gjs3Mua3qaFLXhsCbgR2ANYEZwAPATcD5wLdbBXQjYkGT7lsiotnw7TNzVtletdI/u26tQ1G+d/tThI2fTbHj8ByK9/PXFPd1e5s5ZgHblofbZ+asiFiDYgfj1wJrACtQfA83p9jNeEY5fuPM/GuNOqcAtwGrl13bZOYfKucPZ+A7fERmHl5jzs2AvYDtynmXp/iOzAauAc4Dzm33PRnJd2MU/R04HdgPmAJ8FtilU5NHxAzgIGBnIICZFN+bWym+Nydk5j87tZ4kSZIkSZIkSZIkSZKkycMAsCaViJgGHAcc2HBqGvDy8vWmiNg1Mx8bhfXXBk4Ftm5yerXytQvwsYjYvU7os5x3GnAs8JYh1DIF+BzwcYrwY7/FgZeVrwMi4pWZeU+buZYAvkHxvjbuLL5S+doS+HBEvD0zz6xbZw3VsPazOzgvEdFH8R59EFis4fTM8vVi4AMR8YnM/OoQ5n49cCKwTOO5zHwyIs6hCI8CvInic2pnWwbCv7cBl9Stp0l9M4ETgF2bnF4a2Kh8HQBcALyqxTzd/G7UcQTF+zsV2DkiNs/MK0Y6aUTsAXyb4jtS1f+92RT4YER8ETgsM5sF4CVJkiRJkiRJkiRJkiSpKQPAmmy+Q7Hb5zzgMuAfwKIUu9+uU47ZATgGOLjh2iuBbwHrA68o+/4OXNhknZsaOyJig3LsymXXfIodVP8GPEmxK+rWwJLAusAlEbFlZl5f476+xkD498/A9eX8GwKtgoVHAJ8oz18N3FBesznw/HLMCyh2Rd6p1cIRMR34DcWuw/3+Wd7bQxRhx60pgp7LAmdExLTMPKVhqm+V/9yPImAKRVh6TpNl76i0bwY2KNv7R8TRmXl3q3rriohFgB+xcAD238AVwH0Uwd2XUXxuiwNfiYjpmfnZGtNvCRxO8Xvw3cAfgPspdjPuD4yezkAAeO8yYNwuJLpPpX3GcEOl5c7EFwHPqXTfC/yxrHdxiu/oC8v24i3m6dR3Y9Rk5r8i4iSKHcGhCHy/ciRzRsR+wEkMBJ6fAmZR7Py7PLA9sBzF7z2forj/t49kTUmSJEmSJEmSJEmSJEmTiwFgTSZbUOyQejmwX2b+/5BuGfb8LAO7rB4UEV/IzFv6x2TmecB5EXEAAwHgKzLz0HYLR8RSFGHS/vDv+cChmfmvhnErUOyWuhdFwPTMiNgkM58eZPrVgUOA2cDemXl5w5zTmlyzCvC/FAHovTPzTw3XvJOBQO6OEbFdZs5qsf5xDAQ8/wEcnJkXN8y3GPAB4EiKUOSxEXFJZt7cP6b/fYyIXRgIAB+WmbNbrNvvXOB1ZXtl4PKIOBL4UWY+2ObawRzBQPj3TuCdwLnVUG25Q/ABwDeBJYDDI+J3mfnHGnP3AR8FvlL9fCuf18UUgePVgbUoQsMtd/Qt3+PXV7pOb1NDq3kWBc5mIPz7MPBe4NTMnN8wdjqwO/CSFtN15LsxBj5LETyfBuwQEds21llXRDyX4r77w78XAftm5h2VMUsAXwbeVXYdEhGzurD7sSRJkiRJkiRJkiRJkqQe1fjXsUsT2TQggR2q4V+AzJyXmZ+gCAcDTAH27ODaHwSibP8M2KUx/FvWcS/FLq6/Lrs2AN7QZu5FgEcp7uvyxpOZ+WSTaxaj2NF128bwb3nNscBZla69mi0cEdsAby4PZwNbNQtOZubczPwicFjZtSTwkVY3NETfp9j1uN86wPeAeyLimog4NiIOLIOZtUTEOsDHysMHgW0y86eNO+pm5vzMPJGB3aL7gE/XWGIR4BOZ+aXGcHf/51Wu9YPKqeruvs28hmJXWYDrau4c3cxbKXaBhmJn6ldk5smN4d+yxkcy89RmIfhx8t2oJTNvp9gdvF+dXZxbOYziHgCuA3auhn/L9R4v37NTK91fKAPlkiRJkiRJkiRJkiRJktSWYSNNNh/PzEcHOX9Kpb1ZJxYsd1TtD0g+DbyjWZiyX3nuU5WuN9VY5hvD2DH1C5n530HOn1xpt3ov3l9pfyQz72mz5peBB8r2Xp0IPGbmU8DOwGUNp6YCmwLvAE4EMiLuiIijImLtNtO+hyKkC/D5zPxnmxpOA24sD3csd3IezL+Bo9qMgYV38X1D+V1qpfo9Gdbuv6XqZ/r5zLy6A/N05bsxRJ8HHi/bW0fEjkOdICKWA/aodH0oMx9vNZ5i5+P+82sDOw11TUmSJEmSJEmSJEmSJEmTkwFgTSZPAL9oM6a6k+xaHVp3M2DFsv37zPxPjWuuotjVF2DLGuPPaj/kGc5uc37Q9yIipgKvLA+fAH7absFyd9v+XYqXATZqW2UNmXknsBWwH8V718qqFLsx/z0iPjrIuJ0r7TNrllHd3bbdZ/ajxp1/m8nMvwD9O/nOpEVANCKWBnYpDxt3Dq6t3Pm4f6fk+cBxw5xn3Hw36irD8N+qdA1nF+CXUeyuDXAH8Ns2a95HsSN4v+2GsaYkSZIkSZIkSZIkSZKkSWhqtwuQxlCWu8UO5v5Ke9kOrbtFpb1yRHxziNcvFxHTM/ORFuefYiAkWtdDmfnvNmPavRcbA0uV7SeAr0ZEnbWfU2mvAfylzkXtlDsnfx/4fkSsBbyc4r3fFHgBUN09dxrwxYhYLjM/Vp0nImYC65WH84GP1ryv6i7Ja7QZ+6c6E5ZOB75Qtt9E8xD77sASZfvizLx9CPNXbV5p31Bj195WxtV3Ywj+D3g7MB14SUS8LjN/1uaaqk0q7cszc0GNay4F9izbLxzCWpIkSZIkSZIkSZIkSZImMQPAmkweqjGmGhBetOWooVm10t6gfA3VDKBVAPiBOrvJNmj7XmTmU5XQZrPfK6r3NQN41xBr6L+u4zLzVuCk8kVELAnsABzKwM60UIR7f5KZV1T6Vqm0+xid+xpKsPYM4PPAFGDXiFgqMx9tGLNPpX36EOZutFKl/a8RzDNuvxuDycx7I+JrwCfLrs9ExM9rBnlhYKdvgNtqXnNrpb1CzWskSZIkSZIkSZIkSZIkTXJ93S5AGkN1Q3yd1omdhAcL6z8+jPk68V6M9n11TGY+lpk/y8wdgfc0nG4Mp47FfdX+zDLzNuCS8nBJYLfq+YhYmWK3Y4C5wDl1525i6Uq7VeC8jp75bjRxFPBg2X4BsMcQrp1eaTeGtFupjlu65ShJkiRJkiRJkiRJkiRJqnAHYGn0VQN+/5eZH+taJZ1Vva8rMnOLrlUyBJn5jYjYBdix7Nq6YUj1vu7KzGeNTWWDOp2BOvdh4V1+9wIWKdu/zMwHR7DOnEp7estR7fXkdwMgMx+MiKOBz5ZdR0TEjzJzfo3Lq6HppWouWR03p+UoSZIkSZIkSZIkSZIkSapwB2Bp9N1VaUfXqui86n2tFxFTulbJ0P2q0m4M+Fbva+WI6MRutiN1NsXuvgCvjIgVK+feVGlXg8HDUb33dTo0T699NwCOAe4t28+jCF3XcU+lvWbNa9aqtO9tOUqSJEmSJEmSJEmSJEmSKgwAS0O3YIjjr6y0t4uIRTtZTBddy0AodXngxR2ad6jv73A8UWk/WT2RmXcCt1e6dqTLMvN+BkLLU4E3AkTEusBLyv6HgF+OcKkrKu0NG4LGQ3Eto/PdGBOZ+QjwpUrXYRFRZ8f8ayvtLWoGn19aaf+5xnhJkiRJkiRJkiRJkiRJMgAsDUM1PFonzPtHinAmwAzgwE4X1A2Z+Tgwq9L1/g5NPdT3dzheUGnf3uT8+ZX2+0aphqE6rdJ+U8M/Ac7JzOp7N2SZORvI8rAPeMcw5xmt78ZY+ibw37L9HOCAGtdcykDweTXgFYMNjojlgddVumYNqUJJkiRJkiRJkiRJkiRJk5YBYGno7qu0V2s3uAxlfqPS9aWIWL/OQhHRN4JdWMdCdZfUvSNi77oXRsTKLU4N6f2NiCMi4kVDWPe5wL6VrguaDDsamFe2XxYRHx/C/K3ua6R+Djxctl8WEeuwcAD49A6tc0yl/fGIGO7uvaPx3RgzZYj585WuTwGLtbnmAeDsStfREbHEIJccBSxZtmfT/LsoSZIkSZIkSZIkSZIkSc9gAFgauusr7c0jYs0a13yJgZ1VlwUujYj9I2Jqs8ERsUpEvAe4EdhzRNWOosy8EDij0nVaRBwZETOajY+IpSJiz4i4kGKH1Waq7+8bapSxE3B1RPw2It4cEcu2WHuRiHg9cDEDocvHgK83js3MfwD/V+n6fEQcHxHPajH3YhGxS0T8mCKo23FlkPzHla6jgCjbd1DcVyecAFxVthcHLoyIAyLiGc+LiJgeEftFxDM+y1H6boy17zCwQ/SawCo1rjmC4nsFsDHwi4hYtTogIhaPiK+x8G7gH8/M+SOsV5IkSZIkSZIkSZIkSdIk0TR8KKm1zPxvRFwKvIwiIPmXiPgVcCfQH+C7OTOPq1wzJyJ2BX4LrA4sB5wMfCUiLiuv7QOWBzYEngNMGZs7GrG3UezUuy3FPXwC+EBEXEGxq+mTwAxgPWADBnZR/VGL+X4EHFK231nu7vsnBkKVAMdl5s0N172ifM2PiBuAmyh2E+6jCG5uBlR3U54HvC0zb2tRx6eAZwN7lccHA2+JiKvLuR+jCHOvQxH07N/p9ZoW83XC6cABZXv3Sv8POhUezcynIuINwCxgbWAZ4CSKnav/CNxN8b1fF9i0bLcKH3f6uzGmMvPJiPgccPwQrrkpIt5B8Z71AS8HbomIWcCtFL/2X07xa73f8Zl5ZscKlyRJkiRJkiRJkiRJkjThGQCWhue9wO+ApSkCjHs1nL8YOK7akZlZhlm/A+xadi8P7DzIOndRhE3Hrcx8LCJ2AI4E3kMRCF2cIvS5bYvLHqdFUDYzfxMRPwD2Lrs2L19VvwD6A8C/pAiqrlwe9wEbla9WbgbekZm/aTWgDNTuHRHXUISBl6H4PXOL8tXMU8Dlg6w7Ur+jCIs37kR7eicXycxbI2ILihDrq8vuFYHdWlzyaIt5Ovrd6JITgY9QhPJrycxTI+Jxit8DZlIEm3dsMvQpip2mP92BOiVJkiRJkiRJkiRJkiRNIs/4K90ltZeZV1Ps+voV4M/AQxQ7yra77u7M3I1i59QvAVdQhHyfogg+3gFcBBxFERhcPTMvGIVb6KjMfDozP0qxY+7Hgd8A/6a4p7nAPcCVwHcpwtLPyswvDDLlPuXrF+U8Twyy9meBVSlCuR8FzgGuBx4Ani6vvYvivT6eInC9/mDh34b5jwLWogh9/4JiF9dHy7nvp/j8TwUOBFbLzEPrzDscZSi5cafYGzPz2lFY667MfA2wDXAscAPFezqP4vt+HXAC8D8MBNqbzdPp78aYysyngc8M47qzKXZJ/gjFDwT0/zrv/858CdggMz+VmQs6V7EkSZIkSZIkSZIkSZKkyWDKggXmjiRJ6kFrA7fce8rvmT+nZUa+a1Y6dEfuuWdOt8uQJI1TK664tM8JSVLP8jkmSepVPsMkSb3M55gkqVf5DNNI9PVNYebM6QDrALOfcX6sC5IkSZIkSZIkSZIkSZIkSZI0fAaAJUmSJEmSJEmSJEmSJEmSpB5iAFiSJEmSJEmSJEmSJEmSJEnqIQaAJUmSJEmSJEmSJEmSJEmSpB4ytdsFSJLUTEQsD3xmpPNk5qEdKEeSJEmSJEmSJEmSJEmSxg0DwJKk8WoZ4F0dmMcAsCRJkiRJkiRJkiRJkqQJpa/bBUiSJEmSJEmSJEmSJEmSJEmqzx2AJUnjUmbOBqZ0uw5JkiRJkiRJkiRJkiRJGm/cAViSJEmSJEmSJEmSJEmSJEnqIQaAJUmSJEmSJEmSJEmSJEmSpB4ytdsFSJKk4Vth/226XUJT8+Y+3e0SJEmSJEmSJEmSJEmSpAnLALAkST3svvseYf78Bd0uQ5IkSZIkSZIkSZIkSdIY6ut2AZIkSZIkSZIkSZIkSZIkSZLqMwAsSZIkSZIkSZIkSZIkSZIk9RADwJIkSZIkSZIkSZIkSZIkSVIPmdrtAiRJ0rAsAtDXN6XbdUiSNCw+wyRJvcznmCSpV/kMkyT1Mp9jkqRe5TNMw1X57izS7PyUBQsWjF01kiSpU7YC/tDtIiRJkiRJkiRJkiRJkiSNqq2BSxo7DQBLktSbpgGbAXcC87pciyRJkiRJkiRJkiRJkqTOWgRYBbgKeLLxpAFgSZIkSZIkSZIkSZIkSZIkqYf0dbsASZIkSZIkSZIkSZIkSZIkSfUZAJYkSZIkSZIkSZIkSZIkSZJ6iAFgSZIkSZIkSZIkSZIkSZIkqYcYAJYkSZIkSZIkSZIkSZIkSZJ6iAFgSZIkSZIkSZIkSZIkSZIkqYcYAJYkSZIkSZIkSZIkSZIkSZJ6iAFgSZIkSZIkSZIkSZIkSZIkqYcYAJYkSZIkSZIkSZIkSZIkSZJ6yNRuFyBJ0mQREcsA+wN7AOsBywN3A38DzgTOyMwnR2ntncq1NwNWAx4DbgN+CZyQmbNHY11J0sTQjWdYRKwNbA68pPznpsAS5elbM3PtTq4nSZq4xvo5FhHLAa8EXg5sAjwHWBZ4HLgLuBL4MXBuZs7r1LqSpIlnLJ9hEbEosDHFfz/cDHgesGL5WhJ4CPgXcBnw/cy8phPrSpImrm7+f7EmtbwW+FlD9zr+/zFJUjNj/GextYFbhnjZCZl5UCfWV++bsmDBgm7XIEnShBcRWwGnA2sOMuw6YK/M/FsH150BnADsPsiwx4D3ZeZ3O7WuJGniGOtnWERMp/ifyisOMswAsCSplrF8jpXPsDOBHYFFa1xyI/DmzPzzSNaVJE1MXfiz2B7A2UO45EfAIZl530jXliRNPN36/2ItalkauAFYo+GUAWBJ0jN04c9ia2MAWCNgAFiSpFEWEZsCvweWKrvmAhcC/wGeDWwL9JXn7gRekpn/7sC6iwG/AbapdP8FuLasZXtgZuXcQZl5wkjXlSRNHN14hpU/vPJAm2EGgCVJbY31cywinlXOU/Uf4BqKnX8Xp9hRMSrnHwV2yMzLh7uuJGni6dKfxaoB4CcpflDlZoo/n/VR/K1iL6XY1b7f9cBWmfnQSNaWJE0s3fr/YoPU8w3g0CanDABLkhbSpT+Lrc1AAHgOcGqNyy7NzDNGsq4mjqndLkCSpImsDOH+iIF/QbwG2K36L4ER8Xzg5xT/wrgKxU+TbduB5Q9nIPz7GLBvZv64su6SwDeBA8uu4yLikszMDqwtSepxXX6GATxSrnlF+VoP+GKH5pYkTXBdfo7dD5wMnJSZ1zep7TXl+RXL+n4YEetn5mMdWFuS1OO6+Ay7E/gscD5wTWbObVLbNOA9wBeARYANgc8D7xrh2pKkCWIc/DfFxno2B95ZHv4A2Hs01pEk9b5x8gy7PzOb/dCK1FJf+yGSJGkEDgHWLtv3Aa9p/AmwzLwReC3FzhoA20TEq0eyaESsDLy30vXuavi3XPcx4K0UP8EGxV9R+5mRrCtJmlC68gyjCP5uDCybmdtl5kfLZ9hdI5xXkjS5dOM5Npfiz1TrZOYHm4V/y3XPA14NPF12rQHsP4J1JUkTS1f+LJaZf8zMT2fmZc3Cv+WYJzPzyxQbD/TbLyIWH8nakqQJpVv/TfEZImJR4LsUuZhbKH7QRZKkVsbNM0waCgPAkiSNrndW2l/OzLubDSr/RfHkFtcNxwHAkmX7b5l5Yot1FwAfrXTtERErjXBtSdLE0JVnWGY+nZl/zcz5I5lHkjTpjflzLDPvz8zDMvPhGmOvodhRpJ//o0CS1K9b/z1xKE6qtKcD647h2pKk8W08Pcc+AmxUtt8FPD4Ka0iSJo7x9AyTajMALEnSKImI5wLrV7pOaXNJ9fwOETF9BMvvWnfdzLwcyPKwD3jdCNaVJE0AXX6GSZI0Ij30HLu00l57jNaUJI1jPfQMu7fheOkxWleSNI6Np+dYRKwH/G95eHZmnt+puSVJE894eoZJQ2UAWJKk0bN9pZ2Z+d82468CHi3biwMvHc6iEbEEsHml6+Ial1XHbN9ylCRpsujKM0ySpA7plefYgkp7kTFaU5I0vvXKM+x5Dce3jtG6kqTxbTw9x44v53wYeF8H55UkTUzj6RkmDYkBYEmSRk/1P4T/qd3gzHwa+GuL64ciGHjGLwCurXFNtb7hritJmji69QyTJKkTeuU5tlGlffsYrSlJGt/G/TMsIhYFvljpujwz/zPa60qSesK4eI5FxFsZCHJ90ueUJKmGcfEMA6ZGxI4R8YmI+EpEfC4i3h0RL4kINxBQU1O7XYAkSRPYcyvt22peczuwRdmODqx7d2Y+UXPdfsNdV5I0cXTrGSZJUieM++dYRCwF7FHp+u1orylJ6gnj8hlWhn6fBWwFfAjYtDz1KPDu0VhTktSTuv4ci4iVgC+Xh1cDx450TknSpND1Z1hpNeCCFufuiIgvA9/MzHkdWk8TgAFgSZJGz8xK+66a11T/Konlu7TukhExLTOfHOb6kqTe161nmCRJndALz7HDgOXK9qPAqWOwpiRp/Bs3z7CI+DfF/3xu5V/A6zPz2k6tKUnqeePhOfZ1ij9rzQMOycz5HZhTkjTxjYdnWDurAccAr4uI3TPzoTFYUz2gr/0QSZI0TNMr7cdrXlMdN73lqNFddyRrS5Imhm49wyRJ6oRx/RyLiG2A91e6vpiZd4/mmpKknjGun2GlBcBXgecZ/pUkNejqcywiXgPsWR5+MzPb/hXukiSVuv1nsYeBE4A3AOsCSwGLA88G3gJcVxn7cuCsiFhkhGtqgnAHYEmSRs/ilfbcmtdUd91dokvrjmRtSdLE0K1nmCRJnTBun2MRsRrwQwb+u+xlwBdHaz1JUs8ZT8+wkxjYrX5JYC2Kv952SYofZNk1It6Zma3+elpJ0uTTtedYRCwFHFse3gF8arhzSZImpW7+WexOYLXMfKTJuVuAWyLi+8A3gLeX/TsB+wInj2BdTRAGgCVJGj1PVNqL1bxmWqVd9yfLOr3uSNaWJE0M3XqGSZLUCePyORYRywLnASuXXXcCe2Xm06OxniSpJ42bZ1hmPiM4FRHLAB8GPkGxE9V5EbFXZp7dqXUlST2tm8+xIyl+WAXgvZk5ZwRzSZImn649wzLzSZ65YVvjmKcj4l1AANuX3R/GALCAvm4XIEnSBFb9Ca26P/FVHdfsJ7zGYt2RrC1Jmhi69QyTJKkTxt1zLCKWAH4ObFx2PQS8OjNv6/RakqSeNu6eYVWZ+XAZDH5v2dUHfDciVhjNdSVJPaMrz7GI2Aw4tDz8ZWb+aDjzSJImtXH9ZzGAzJwPfLbS9fyIWHO019X4ZwBYkqTRc3+lvXLLUQurjru/5ajRXfex8qfMJEmTV7eeYZIkdcK4eo5FxKLA2cDWZdfjwC6Z+ZdOriNJmhDG1TNsEN8C/lG2lwXePEbrSpLGt249x74LLAI8xkAQWJKkoeiVP4tdAsytHK8/RutqHJva7QIkSZrAEnhV2a77k1fVcTmCdfutFBHTagR6O7GuJGni6NYzTJKkThg3z7GI6AO+D+xcdj0FvD4zL+nUGpKkCWXcPMMGk5kLIuJC4Lll10uBY8ZibUnSuNat59gLyn/OBc6MiFbjpjUc/yQi+v//2ZmZecww15ck9b5e+bPYUxFxH7BK2TVzLNbV+GYAWJKk0fO3SvuF7QZHxCLARi2uH4oE5lPs9D8F2AS4os011fqGu64kaeLo1jNMkqROGBfPsYiYAnwH2LPsmg/sk5nnd2J+SdKENC6eYTU9WGkvN4brSpLGr24/x2YAmw9h/CaV9uUjXFuS1Nu6/QwbiiUr7UfHcF2NU33dLkCSpAnsoko7IqLdXxXxYmCpsv0kcNlwFs3Mx1k48LttjcuqYy5qOUqSNFl05RkmSVKHjJfn2FeBt5btBcDbMvPsDs0tSZqYxsszrI5nVdpj9dfdSpLGt156jkmSVNUTz7CIWBtYttJ151isq/HNALAkSaMkM/8B/L08nALs1+aS/Svt32bmIyNY/twW8z5DRLwEWL88nA/8bATrSpImgC4/wyRJGpHx8ByLiM8C7610vT8zTxzpvJKkiW08PMPqiIhFgR0rXf4tMJKkrj3HMnNKnRewTsOl61TOv284a0uSJoZe+bMYcGClPQf48xitq3HMALAkSaPr2Er7wxGxQrNBEbE+C//L2rdGuO4pwGNl+/kR0TQEXP6VtF+sdJ2TmXePcG1J0sTQrWeYJEmd0LXnWER8EPjfStenM/NrI51XkjRpjPkzLCKmRsQyQ7jkMGC1yvFPhru2JGnC8b8pSpJ6VTf+LLZYRCxWc+xmwIcrXWdl5tPDXVsThwFgSZJG1/HA7LK9InBeRFT/4zgR8TzgF8DiZdcfMvP8ZpNFxOERsaB8zW42BiAz/wtU/wfztyJit4a5lgC+B2xfdj0FfLrGPUmSJoeuPMMkSeqQrjzHIuIg4KhK11GZ+dnh3YIkaZLqxjNsOvCviPh0RDynVWERsUZEnAB8stL9g8y8rt1NSZImDf+boiSpV3XjGbYq8M+I+EDjWpV5Fo2Ig4HfAUuU3XOAz9S7LU10U7tdgCRJE1lmzo2I1wN/AJYENgNujogLgTuBtSkCuP0/lPNfYJ8OLX84sCWwDbAU8JOIuBa4tjzeHqj+1No7MzM7tLYkqcd18xkWEZ8BXtfQvXylvWr5TGt0UGZe3YkaJEm9rRvPsYjYiOJ/FPR7GFgiIr5Zs+ZDR7K+JGli6OKfxWYCRwBHRMRtwF+Be4AngWWB5wEbsfDmQlcAb+/A2pKkCaLL/19MkqRh6+IzbA3gaOCoiLgZuAG4D5gPrAK8DFiuMn4usEdm3t6BtTUBGACWJGmUZeafIuJVwGnAmsA04DVNhv4V2KtT/6JW/gvqbsAJwP+U3ZuUr6rHgA9k5vc6sa4kaeLo1jOsXOsFg5xftMX56R1aX5I0AXThOTaThUNRywDvGsL1BoAlSUBXnmHzgacZ+P+Ga5avVp4GvgF8KjMfHeHakqQJpov/TVGSpBHp8jNsCrBu+WrlOmD/zLy2g+uqx/W1HyJJkkYqM/9AsUPGe4FLKH4abC5wB/Ab4K3AZpl5Y4fXfSAzdwdeDZwJ/At4AngA+AvweWDDzDy+9SySpMmsW88wSZI6weeYJKlXjeUzLDMfpvibwt4AHANcDPwbeByYBzwE3Az8BPgAsHpmfsDwrySpFf8sJknqVWP8DLsV2Jjib1Y5FbgamA08AjwF3Av8GTgO2DEzX2D4V42mLFiwoNs1SJIkSZIkSZIkSZIkSZIkSarJHYAlSZIkSZIkSZIkSZIkSZKkHmIAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHmIAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHmIAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHmIAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHmIAWJIkSZIkSZIkSZIkSZIkSeohBoAlSZIkSZIkSZIkSZIkSZKkHjK12wVIkiRJkiRJkiSpuYiYBWxbHh6RmYd3rxpJkiRJkiSNFwaAJUmSJEmSJEnShBcRXwA+VunaKDOvH8F8RwMfKA8XAOtl5s0jKFGSJEmSJEmqra/bBUiSJEmSJEmSJI2BExuODxzuRBExFXhzpev3hn8lSZIkSZI0lgwAS5IkSZIkSZKkCS8zbwL+UOl6c0QsOszpdgFWqhyfMOzCJEmSJEmSpGEwACxJkiRJkiRJkiaLalB3JWDnYc5T3T34YeBHw65IkiRJkiRJGgYDwJIkSZIkSZIkabI4myKw2+/AVgNbiYiVgddUun6QmY+NtDBJkiRJkiRpKAwAS5IkSZIkSZKkSaEM6p5V6XpNGegdin2BqZXjE0dcmCRJkiRJkjREU9sPkSRJkiRJkiRJmjBOAN5WtqdSBHqPGsL11V2Dr8/MK/sPImJJ4JXAtsCmwHrA8sAU4AHgX8AfgJMyM4d7A40iYm3glkrXOpk5u8Z1s4G1ysMDM/PkGtdMBV4P7Ay8BFgJWAq4F/g78CvghMy8v/YNSJIkSZIkacjcAViSJEmSJEmSJE0amXkFcEOl68BWYxtFxObA8ytdJ1bOHQzcA/wUeD9FCHhVYHFgGvAs4GXAR4EbI+L4iJg2vLvojojYCbgROJMiOB3AcsBiFPf6cuBLwM0R8ZZu1SlJkiRJkjQZGACWJEmSJEmSJEmTzYmV9vPLYG8d1bDwXOC0yvFzgSUrx/cBVwO/o9j199bKuT7gYOCnETGlbtHdFBHvAH5JsatxvweAy4GLgOqOxjOAEyLik2NWoCRJkiRJ0iQztdsFSJIkSZIkSZIkjbHvA18EFi2PDwSuGOyCiFgC2KvS9fPMvKdyvAC4GDgDOD8zb28yx7MpdgA+uOx6FfBu4OvDuIcxExGvBr4F9IeVrwI+DvwuMxdUxj0HOBrYtez6bERclZm/Hst6JUmSJEmSJgN3AJYkSZIkSZIkSZNKGdz9eaVrrzLgO5jdgWUrxyc0nD8iM7fLzO80C/+W6/4rMw+hCM/2+2BELFK39rEWEdOBUxgI/54LbJmZF1bDvwCZeTPwP8CpZdcU4Cu9ssuxJEmSJElSLzEALEmSJEmSJEmSJqNqgHdZioDvYA6stO8AFtrVNjMfGcLaXwL+XbbXBF48hGvH2kHAimX7v8C+mflUq8FlKPhQ4IGyawNg+1GtUJIkSZIkaRIyACxJkiRJkiRJkiajCyiCvP3e0mpgRKwFvLzSdXJmzhvuwpk5H7ii0vWS4c41Bg6otL+TmXPaXVCO+Umla4dOFyVJkiRJkjTZTe12AZIkSZIkSZIkSWMtM+dFxCnAJ8qu7SNi7cyc3WT4AcCUsr0AOGmwuSNiTYrA8MbAysDSwGINwzaqtFcfUvFjJCJmsHCdvx3C5ddV2uN5h2NJkiRJkqSeZABYkiRJkiRJkiRNVicCH6cI904B9geOqA6IiP7+fr/PzJubTRYRGwJHA69kIDBcx4whjB1LG7Hw3yb5uYh4vOa1q1XaK3auJEmSJEmSJIEBYEmSJEmSJEmSNEll5s0R8Xtg27LrgIj4TGYuqAzbHlincnxCs7kiYmfgR8C0YZQynGvGwsyG422GOc+yIy1EkiRJkiRJC+trP0SSJEmSJEmSJGnCqgZ616YI/FYdWGk/TBHyXUhErAacxUCQ9zHg28D/AM+j2OF3WmZO6X8Bp3Si+FG2VIfm8f9HSZIkSZIkdZg7AEuSJEmSJEmSpMnsHOAbDOxS+xbgdwARsQywe2XsDzLzsSZzvJ+BsOxDwMsy88Y26y497Io7Z5E25x9sOF4+Mx8YpVokSZIkSZI0BP7EtSRJkiRJkiRJmrQy83HgzErX7hHRHwbeC1iycu7EFtO8qtL+Wo3wL8Aa9atsa27D8aI1r1uuzfn/NhyvV3NeSZIkSZIkjTIDwJIkSZIkSZIkabI7odJeAtizbB9Y6b8+M69scf1alXarMf9fREwHXjCkCgf3cMPx8jVqWIeBXYtbuQ54pHK84xDrkiRJkiRJ0igxACxJkiRJkiRJkia1zLwK+Gul6y0R8Txgi0pfq91/of6Ou/32BRYb4jUtZeYjwP2Vro1rXLZ7jXmfAi6sdB0cEdOGWJ4kSZIkSZJGgQFgSZIkSZIkSZKkhQO+mwNfqhzPBU4b5Nr/VNrbDLZIRKwMfGbI1bV3daW9Z8tRRQ0rAB+qOe+XK+01gP8bYl2SJEmSJEkaBQaAJUmSJEmSJEmSioDv3MrxLpX2zzPznkGu/V2l/a6IeHGzQRGxJvAbYIVhV9naOZX2KyJi3xY1rAT8AnhWnUkz84/AmZWu90bENyJiycGui4ipEbFLRPwuItaqs5YkSZIkSZLqm9rtAiRJkiRJkiRJkrotM++NiJ8BezQ5fUKby48BDgAWAZYC/hAR36MI+94PrAS8ohyzJHA78FfgNR0ovd8ZwP8Ca5bHp0TEjsBPgHuAmcDWwFuBZYHfAusDq9eY+63Ac4FNy+NDgb0i4gfApcBdwHxgBrAu8CLgVcBy5fgpI7gvSZIkSZIkNWEAWJIkSZIkSZIkqXACzwwA3wH8erCLMvP6iPgA8LWya3GKkOyhTYbfA/wP8O6RlfqMGh4td/09nyJkPAV4c/lq9CfgjcCfa879WERsC5wMvL7sXoHiHjp6H5IkSZIkSaqnr9sFSJIkSZIkSZIkjRO/ptidt+qUzJzX7sLM/DpFeHh2iyFzgbOBjTPzmpEUOUgNvwe2Ba5qMWQOcDTwssx8YIhzP5KZewCvBmYB7d6T2cBxwFaZOXsoa0mSJEmSJKm9KQsWLOh2DZIkSZIkSZIkSRNCRCwCbAFsAswAHqDYRfjizHxwDOvYsKxjRYrg763A7zLz0Q7NvyywJbA6MBNYADxEEfy9MTNv7cQ6kiRJkiRJas4AsCRJkiRJkiRJkiRJkiRJktRD+rpdgCRJkiRJkiRJkiRJkiRJkqT6DABLkiRJkiRJkiRJkiRJkiRJPcQAsCRJkiRJkiRJkiRJkiRJktRDDABLkiRJkiRJkiRJkiRJkiRJPcQAsCRJkiRJkiRJkiRJkiRJktRDDABLkiRJkiRJkiRJkiRJkiRJPcQAsCRJkiRJkiRJkiRJkiRJktRDDABLkiRJkiRJkiRJkiRJkiRJPcQAsCRJkiRJkiRJkiRJkiRJktRDDABLkiRJkiRJkiRJkiRJkiRJPcQAsCRJkiRJkiRJkiRJkiRJktRD/h/lVEvaVIPTUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotImp(model, feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.joblib']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(pipeline_xgb, 'model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "BUCKET_NAME=\"churn-model-sri\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "REGION=\"us-west1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "us-west1\n"
     ]
    }
   ],
   "source": [
    "!echo $REGION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated property [core/project].\n"
     ]
    }
   ],
   "source": [
    "!gcloud config set project srivatsan-project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating gs://churn-model-sri/...\n",
      "ServiceException: 409 Bucket churn-model-sri already exists.\n"
     ]
    }
   ],
   "source": [
    "!gsutil mb -l $REGION gs://$BUCKET_NAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying file://./model.joblib [Content-Type=application/octet-stream]...\n",
      "/ [1 files][391.3 KiB/391.3 KiB]                                                \n",
      "Operation completed over 1 objects/391.3 KiB.                                    \n"
     ]
    }
   ],
   "source": [
    "!gsutil cp ./model.joblib gs://$BUCKET_NAME/model.joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(\"./model.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "instance = [\"7317-GGVPB\", \"Male\", 0, \"Yes\", \"No\", 71, \"Yes\", \"Yes\", \"Fiber optic\", \"No\", \"Yes\", \"Yes\", \"Yes\", \"Yes\", \"Yes\", \"Two year\", \"Yes\", \"Credit card (automatic)\", 108.6, \"7690.9\"]\n",
    "_COLUMN_NAMES=['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling','PaymentMethod', 'MonthlyCharges', 'TotalCharges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(pd.DataFrame(data=[instance], columns=_COLUMN_NAMES))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "name": "common-cpu.m59",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m59"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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