churn_EDA_ML.ipynb

{"cells":[{"source":"In the business landscape, loyalty is the number one priority. However, unforseeable circumstances lead to some customers leaving the platform and may have jumped ship to a competitor. Customer churn is defined as the rate at which a business loses customers. From the business owner's POV, finding ways to keep its customer base can be a tall order. In this notebook, we will attempt to gain insights and perform modeling on a telco churning database.","metadata":{},"id":"9dda96b6-6ad1-4f19-8519-03f64348a71f","cell_type":"markdown"},{"source":"# PART 1: DATA CLEANING","metadata":{},"id":"9000d8e7-cdbf-46e3-be58-509065b4411d","cell_type":"markdown"},{"source":"## First things first, import the necessary libraries","metadata":{},"id":"863697e7-d651-43ee-bd2c-290dde2dde09","cell_type":"markdown"},{"source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.express as px\nimport plotly.figure_factory as ff\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.preprocessing import LabelEncoder, MinMaxScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nseed = 1001","metadata":{"executionCancelledAt":null,"executionTime":12,"lastExecutedAt":1713141643625,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.express as px\nimport plotly.figure_factory as ff\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.preprocessing import LabelEncoder, MinMaxScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nseed = 1001"},"id":"7a935f68-028b-4d5d-80bb-6ab929b4f9cd","cell_type":"code","execution_count":8,"outputs":[]},{"source":"## Load the dataset\nThe dataset is sourced from [1](https://www.kaggle.com/datasets/blastchar/telco-customer-churn/data). It has around 7000 entries and has 21 columns.","metadata":{},"id":"412ac58f-9c57-4f9b-9c83-d0fa0074b5f7","cell_type":"markdown"},{"source":"data = pd.read_csv('data/WA_Fn-UseC_-Telco-Customer-Churn.csv')\ndata.head(2)","metadata":{"executionCancelledAt":null,"executionTime":null,"lastExecutedAt":null,"lastExecutedByKernel":null,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":null,"outputsMetadata":{"0":{"height":121,"type":"dataFrame"}}},"id":"4486f47e-d5cb-45bf-bf6c-003ee9635548","cell_type":"code","execution_count":9,"outputs":[{"output_type":"execute_result","data":{"application/com.datacamp.data-table.v2+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"customerID","type":"string"},{"name":"gender","type":"string"},{"name":"SeniorCitizen","type":"integer"},{"name":"Partner","type":"string"},{"name":"Dependents","type":"string"},{"name":"tenure","type":"integer"},{"name":"PhoneService","type":"string"},{"name":"MultipleLines","type":"string"},{"name":"InternetService","type":"string"},{"name":"OnlineSecurity","type":"string"},{"name":"OnlineBackup","type":"string"},{"name":"DeviceProtection","type":"string"},{"name":"TechSupport","type":"string"},{"name":"StreamingTV","type":"string"},{"name":"StreamingMovies","type":"string"},{"name":"Contract","type":"string"},{"name":"PaperlessBilling","type":"string"},{"name":"PaymentMethod","type":"string"},{"name":"MonthlyCharges","type":"number"},{"name":"TotalCharges","type":"string"},{"name":"Churn","type":"string"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":{"index":[0,1],"customerID":["7590-VHVEG","5575-GNVDE"],"gender":["Female","Male"],"SeniorCitizen":[0,0],"Partner":["Yes","No"],"Dependents":["No","No"],"tenure":[1,34],"PhoneService":["No","Yes"],"MultipleLines":["No phone service","No"],"InternetService":["DSL","DSL"],"OnlineSecurity":["No","Yes"],"OnlineBackup":["Yes","No"],"DeviceProtection":["No","Yes"],"TechSupport":["No","No"],"StreamingTV":["No","No"],"StreamingMovies":["No","No"],"Contract":["Month-to-month","One year"],"PaperlessBilling":["Yes","No"],"PaymentMethod":["Electronic check","Mailed check"],"MonthlyCharges":[29.85,56.95],"TotalCharges":["29.85","1889.5"],"Churn":["No","No"]}},"total_rows":2,"truncation_type":null},"text/plain":"   customerID  gender  SeniorCitizen  ... MonthlyCharges TotalCharges  Churn\n0  7590-VHVEG  Female              0  ...          29.85        29.85     No\n1  5575-GNVDE    Male              0  ...          56.95       1889.5     No\n\n[2 rows x 21 columns]","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>OnlineBackup</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>Yes</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>No</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>1889.5</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":9}]},{"source":"print(data.shape)","metadata":{"executionCancelledAt":null,"executionTime":47,"lastExecutedAt":1713141643732,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"print(data.shape)","outputsMetadata":{"0":{"height":38,"type":"stream"}}},"id":"75289a84-c0b7-4266-aa6a-0b8a4f8dd7c6","cell_type":"code","execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":"(7043, 21)\n"}]},{"source":"","metadata":{},"id":"c207ed1b-2110-406a-ac13-adb4f8290dcd","cell_type":"markdown"},{"source":"Let us take a look at the unique values per column and if there are null values.","metadata":{},"id":"6a072346-21b2-4250-86a7-761793474b3e","cell_type":"markdown"},{"source":"data_info = pd.DataFrame(data.dtypes, columns=['Dtype'])\ndata_info['Unique'] = data.nunique().values\ndata_info['Null'] = data.isnull().sum().values\ndata_info","metadata":{"outputsMetadata":{"0":{"height":616,"type":"stream"}},"executionCancelledAt":null,"executionTime":60,"lastExecutedAt":1713141643792,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"data_info = pd.DataFrame(data.dtypes, columns=['Dtype'])\ndata_info['Unique'] = data.nunique().values\ndata_info['Null'] = data.isnull().sum().values\ndata_info"},"id":"48a775cf-79b9-4b61-8717-e06811713ff4","cell_type":"code","execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":"                    Dtype  Unique  Null\ncustomerID         object    7043     0\ngender             object       2     0\nSeniorCitizen       int64       2     0\nPartner            object       2     0\nDependents         object       2     0\ntenure              int64      73     0\nPhoneService       object       2     0\nMultipleLines      object       3     0\nInternetService    object       3     0\nOnlineSecurity     object       3     0\nOnlineBackup       object       3     0\nDeviceProtection   object       3     0\nTechSupport        object       3     0\nStreamingTV        object       3     0\nStreamingMovies    object       3     0\nContract           object       3     0\nPaperlessBilling   object       2     0\nPaymentMethod      object       4     0\nMonthlyCharges    float64    1585     0\nTotalCharges       object    6531     0\nChurn              object       2     0","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>Dtype</th>\n      <th>Unique</th>\n      <th>Null</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>customerID</th>\n      <td>object</td>\n      <td>7043</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>gender</th>\n      <td>object</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>SeniorCitizen</th>\n      <td>int64</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Partner</th>\n      <td>object</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Dependents</th>\n      <td>object</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>tenure</th>\n      <td>int64</td>\n      <td>73</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>PhoneService</th>\n      <td>object</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>MultipleLines</th>\n      <td>object</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>InternetService</th>\n      <td>object</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>OnlineSecurity</th>\n      <td>object</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>OnlineBackup</th>\n      <td>object</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>DeviceProtection</th>\n      <td>object</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>TechSupport</th>\n      <td>object</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>StreamingTV</th>\n      <td>object</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>StreamingMovies</th>\n      <td>object</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Contract</th>\n      <td>object</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>PaperlessBilling</th>\n      <td>object</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>PaymentMethod</th>\n      <td>object</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>MonthlyCharges</th>\n      <td>float64</td>\n      <td>1585</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>TotalCharges</th>\n      <td>object</td>\n      <td>6531</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Churn</th>\n      <td>object</td>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":11}]},{"source":"- The table reveals that there are apparently no null values. \n- Majority of the columns are categorical.\n- In this analysis, we can safely remove the `customerID` column because it primarily contains unique values.\n- Note that `TotalCharges` is supposed to be numerical, but it isn't. Let us investigate.","metadata":{},"id":"fbff885d-f7c8-42a8-88b6-68ffcce88695","cell_type":"markdown"},{"source":"# drop customerID\ndata.drop('customerID', axis=1, inplace=True)","metadata":{"executionCancelledAt":null,"executionTime":48,"lastExecutedAt":1713141643840,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# drop customerID\ndata.drop('customerID', axis=1, inplace=True)"},"id":"9bb53066-272f-462a-8c4e-ac6df3973af0","cell_type":"code","execution_count":12,"outputs":[]},{"source":"# check unique elements in TotalCharges\ndata['TotalCharges'].unique()","metadata":{"executionCancelledAt":null,"executionTime":48,"lastExecutedAt":1713141643888,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# check unique elements in TotalCharges\ndata['TotalCharges'].unique()"},"id":"2160a1e1-c3e2-4490-bea6-e457dbf8ab40","cell_type":"code","execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":"array(['29.85', '1889.5', '108.15', ..., '346.45', '306.6', '6844.5'],\n      dtype=object)"},"metadata":{},"execution_count":13}]},{"source":"Since all entries in `TotalCharges` are string, we can use pandas' `to_numeric` function.","metadata":{},"id":"ab8683dc-7f7e-4bca-93ec-50669bc9afb3","cell_type":"markdown"},{"source":"# convert TotalCharges to numeric, all non-numeric will be converted to NaN\ndata['TotalCharges'] = pd.to_numeric(data['TotalCharges'], errors='coerce')","metadata":{"executionCancelledAt":null,"executionTime":48,"lastExecutedAt":1713141643936,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# convert TotalCharges to numeric, all non-numeric will be converted to NaN\ndata['TotalCharges'] = pd.to_numeric(data['TotalCharges'], errors='coerce')","outputsMetadata":{"0":{"height":321,"type":"dataFrame"}}},"id":"ec82c8cc-9cbb-4ffe-9ff8-6b2bbb9372a3","cell_type":"code","execution_count":14,"outputs":[]},{"source":"Upon rechecking the dataframe `data` for null values...","metadata":{},"id":"60da630c-91f6-4d76-ad7c-70c8f514724b","cell_type":"markdown"},{"source":"data.isna().sum()","metadata":{"executionCancelledAt":null,"executionTime":52,"lastExecutedAt":1713141643988,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"data.isna().sum()"},"id":"85eff9bc-8c6f-4ee9-affd-46e70094cd81","cell_type":"code","execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":"gender               0\nSeniorCitizen        0\nPartner              0\nDependents           0\ntenure               0\nPhoneService         0\nMultipleLines        0\nInternetService      0\nOnlineSecurity       0\nOnlineBackup         0\nDeviceProtection     0\nTechSupport          0\nStreamingTV          0\nStreamingMovies      0\nContract             0\nPaperlessBilling     0\nPaymentMethod        0\nMonthlyCharges       0\nTotalCharges        11\nChurn                0\ndtype: int64"},"metadata":{},"execution_count":15}]},{"source":"Taking a close look at those containing null values...","metadata":{},"id":"890acd5b-1b9e-44f4-85b5-c662efac191f","cell_type":"markdown"},{"source":"data[data.isna().any(axis=1)]","metadata":{"executionCancelledAt":null,"executionTime":52,"lastExecutedAt":1713141644040,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"data[data.isna().any(axis=1)]","outputsMetadata":{"0":{"height":321,"type":"dataFrame"}}},"id":"5659a335-5326-4d9b-8c98-189befae6429","cell_type":"code","execution_count":16,"outputs":[{"output_type":"execute_result","data":{"application/com.datacamp.data-table.v2+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"gender","type":"string"},{"name":"SeniorCitizen","type":"integer"},{"name":"Partner","type":"string"},{"name":"Dependents","type":"string"},{"name":"tenure","type":"integer"},{"name":"PhoneService","type":"string"},{"name":"MultipleLines","type":"string"},{"name":"InternetService","type":"string"},{"name":"OnlineSecurity","type":"string"},{"name":"OnlineBackup","type":"string"},{"name":"DeviceProtection","type":"string"},{"name":"TechSupport","type":"string"},{"name":"StreamingTV","type":"string"},{"name":"StreamingMovies","type":"string"},{"name":"Contract","type":"string"},{"name":"PaperlessBilling","type":"string"},{"name":"PaymentMethod","type":"string"},{"name":"MonthlyCharges","type":"number"},{"name":"TotalCharges","type":"number"},{"name":"Churn","type":"string"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":{"index":[488,753,936,1082,1340,3331,3826,4380,5218,6670,6754],"gender":["Female","Male","Female","Male","Female","Male","Male","Female","Male","Female","Male"],"SeniorCitizen":[0,0,0,0,0,0,0,0,0,0,0],"Partner":["Yes","No","Yes","Yes","Yes","Yes","Yes","Yes","Yes","Yes","No"],"Dependents":["Yes","Yes","Yes","Yes","Yes","Yes","Yes","Yes","Yes","Yes","Yes"],"tenure":[0,0,0,0,0,0,0,0,0,0,0],"PhoneService":["No","Yes","Yes","Yes","No","Yes","Yes","Yes","Yes","Yes","Yes"],"MultipleLines":["No phone service","No","No","Yes","No phone service","No","Yes","No","No","Yes","Yes"],"InternetService":["DSL","No","DSL","No","DSL","No","No","No","No","DSL","DSL"],"OnlineSecurity":["Yes","No internet service","Yes","No internet service","Yes","No internet service","No internet service","No internet service","No internet service","No","Yes"],"OnlineBackup":["No","No internet service","Yes","No internet service","Yes","No internet service","No internet service","No internet service","No internet service","Yes","Yes"],"DeviceProtection":["Yes","No internet service","Yes","No internet service","Yes","No internet service","No internet service","No internet service","No internet service","Yes","No"],"TechSupport":["Yes","No internet service","No","No internet service","Yes","No internet service","No internet service","No internet service","No internet service","Yes","Yes"],"StreamingTV":["Yes","No internet service","Yes","No internet service","Yes","No internet service","No internet service","No internet service","No internet service","Yes","No"],"StreamingMovies":["No","No internet service","Yes","No internet service","No","No internet service","No internet service","No internet service","No internet service","No","No"],"Contract":["Two year","Two year","Two year","Two year","Two year","Two year","Two year","Two year","One year","Two year","Two year"],"PaperlessBilling":["Yes","No","No","No","No","No","No","No","Yes","No","Yes"],"PaymentMethod":["Bank transfer (automatic)","Mailed check","Mailed check","Mailed check","Credit card (automatic)","Mailed check","Mailed check","Mailed check","Mailed check","Mailed check","Bank transfer (automatic)"],"MonthlyCharges":[52.55,20.25,80.85,25.75,56.05,19.85,25.35,20,19.7,73.35,61.9],"TotalCharges":[null,null,null,null,null,null,null,null,null,null,null],"Churn":["No","No","No","No","No","No","No","No","No","No","No"]}},"total_rows":11,"truncation_type":null},"text/plain":"      gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n488   Female              0     Yes  ...          52.55           NaN    No\n753     Male              0      No  ...          20.25           NaN    No\n936   Female              0     Yes  ...          80.85           NaN    No\n1082    Male              0     Yes  ...          25.75           NaN    No\n1340  Female              0     Yes  ...          56.05           NaN    No\n3331    Male              0     Yes  ...          19.85           NaN    No\n3826    Male              0     Yes  ...          25.35           NaN    No\n4380  Female              0     Yes  ...          20.00           NaN    No\n5218    Male              0     Yes  ...          19.70           NaN    No\n6670  Female              0     Yes  ...          73.35           NaN    No\n6754    Male              0      No  ...          61.90           NaN    No\n\n[11 rows x 20 columns]","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>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>OnlineBackup</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>488</th>\n      <td>Female</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Two year</td>\n      <td>Yes</td>\n      <td>Bank transfer (automatic)</td>\n      <td>52.55</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>753</th>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>20.25</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>936</th>\n      <td>Female</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>80.85</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1082</th>\n      <td>Male</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>25.75</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>1340</th>\n      <td>Female</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Credit card (automatic)</td>\n      <td>56.05</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3331</th>\n      <td>Male</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>19.85</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>3826</th>\n      <td>Male</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>25.35</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>4380</th>\n      <td>Female</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>20.00</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>5218</th>\n      <td>Male</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>No internet service</td>\n      <td>One year</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>19.70</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>6670</th>\n      <td>Female</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>DSL</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Two year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>73.35</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>6754</th>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Two year</td>\n      <td>Yes</td>\n      <td>Bank transfer (automatic)</td>\n      <td>61.90</td>\n      <td>NaN</td>\n      <td>No</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":16}]},{"source":"...we see that we can safely drop these rows.","metadata":{},"id":"ef17597e-a080-488e-ac88-d346e15fb832","cell_type":"markdown"},{"source":"data.dropna(inplace=True)","metadata":{"executionCancelledAt":null,"executionTime":44,"lastExecutedAt":1713141644084,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"data.dropna(inplace=True)"},"id":"3b5205c8-b765-4ec7-9ac9-a111137782d8","cell_type":"code","execution_count":17,"outputs":[]},{"source":"Let's take a look at the unique elements per column.","metadata":{},"id":"70a00e6b-7ff6-4c94-9f51-48e6d136ea56","cell_type":"markdown"},{"source":"for i in data.columns:\n    print(f'Unique elements in {i}: {data[i].unique()}')","metadata":{"executionCancelledAt":null,"executionTime":52,"lastExecutedAt":1713141644136,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"for i in data.columns:\n    print(f'Unique elements in {i}: {data[i].unique()}')","outputsMetadata":{"0":{"height":500,"type":"stream"}}},"id":"d58cd2cc-a3e9-4f2a-9a14-63220a6a0adc","cell_type":"code","execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":"Unique elements in gender: ['Female' 'Male']\nUnique elements in SeniorCitizen: [0 1]\nUnique elements in Partner: ['Yes' 'No']\nUnique elements in Dependents: ['No' 'Yes']\nUnique elements in tenure: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\nUnique elements in PhoneService: ['No' 'Yes']\nUnique elements in MultipleLines: ['No phone service' 'No' 'Yes']\nUnique elements in InternetService: ['DSL' 'Fiber optic' 'No']\nUnique elements in OnlineSecurity: ['No' 'Yes' 'No internet service']\nUnique elements in OnlineBackup: ['Yes' 'No' 'No internet service']\nUnique elements in DeviceProtection: ['No' 'Yes' 'No internet service']\nUnique elements in TechSupport: ['No' 'Yes' 'No internet service']\nUnique elements in StreamingTV: ['No' 'Yes' 'No internet service']\nUnique elements in StreamingMovies: ['No' 'Yes' 'No internet service']\nUnique elements in Contract: ['Month-to-month' 'One year' 'Two year']\nUnique elements in PaperlessBilling: ['Yes' 'No']\nUnique elements in PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n 'Credit card (automatic)']\nUnique elements in MonthlyCharges: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\nUnique elements in TotalCharges: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\nUnique elements in Churn: ['No' 'Yes']\n"}]},{"source":"This time, we convert `SeniorCitizen` column into a Yes-No one using pandas' `map`.","metadata":{},"id":"d0e48f69-1c32-40b9-b682-77b37e5a0a9b","cell_type":"markdown"},{"source":"data['SeniorCitizen'] = data['SeniorCitizen'].map({0: 'No', 1: 'Yes'})\n\n\n# upon checking...\ndata['SeniorCitizen'].unique()","metadata":{"executionCancelledAt":null,"executionTime":49,"lastExecutedAt":1713141644185,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"data['SeniorCitizen'] = data['SeniorCitizen'].map({0: 'No', 1: 'Yes'})\n\n\n# upon checking...\ndata['SeniorCitizen'].unique()"},"id":"2e59d4bd-3bfa-4c8c-9d53-51d54fba553f","cell_type":"code","execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":"array(['No', 'Yes'], dtype=object)"},"metadata":{},"execution_count":19}]},{"source":"## Services availed\nWe can define a variable `SvcAvailed` that sums the number of services the customer has either availed or has subscribed to. In the same manner, we also define the variable `SvcDuration` (in months) as the quotient of the total charges `TotalCharges` over `MonthlyCharges`.","metadata":{},"id":"b65e244d-afbb-4f54-ad79-cd5b6587781c","cell_type":"markdown"},{"source":"svcList = ['PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity' , 'OnlineBackup', 'StreamingMovies', 'PaperlessBilling' ,'TechSupport', 'StreamingTV', 'DeviceProtection']\n\nsvcDat = data.copy()\n\nsvcDat[svcList] = svcDat[svcList].replace({'No':0, 'Yes':1,'No phone service': 0,'Fiber optic': 1, 'DSL': 1,'No internet service': 0})\n\n# define 'SvcAvailed' and 'SvcDuration'\ndata['SvcAvailed'] = svcDat[svcList].sum(axis=1)\ndata['SvcDuration'] = data['TotalCharges'] / data['MonthlyCharges'] \ndata.head()","metadata":{"executionCancelledAt":null,"executionTime":null,"lastExecutedAt":null,"lastExecutedByKernel":null,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":null,"outputsMetadata":{"0":{"height":196,"type":"dataFrame"}}},"id":"12cc0e0d-b3c2-46c1-8c08-50f6622379ba","cell_type":"code","execution_count":20,"outputs":[{"output_type":"execute_result","data":{"application/com.datacamp.data-table.v2+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"gender","type":"string"},{"name":"SeniorCitizen","type":"string"},{"name":"Partner","type":"string"},{"name":"Dependents","type":"string"},{"name":"tenure","type":"integer"},{"name":"PhoneService","type":"string"},{"name":"MultipleLines","type":"string"},{"name":"InternetService","type":"string"},{"name":"OnlineSecurity","type":"string"},{"name":"OnlineBackup","type":"string"},{"name":"DeviceProtection","type":"string"},{"name":"TechSupport","type":"string"},{"name":"StreamingTV","type":"string"},{"name":"StreamingMovies","type":"string"},{"name":"Contract","type":"string"},{"name":"PaperlessBilling","type":"string"},{"name":"PaymentMethod","type":"string"},{"name":"MonthlyCharges","type":"number"},{"name":"TotalCharges","type":"number"},{"name":"Churn","type":"string"},{"name":"SvcAvailed","type":"integer"},{"name":"SvcDuration","type":"number"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":{"index":[0,1,2,3,4],"gender":["Female","Male","Male","Male","Female"],"SeniorCitizen":["No","No","No","No","No"],"Partner":["Yes","No","No","No","No"],"Dependents":["No","No","No","No","No"],"tenure":[1,34,2,45,2],"PhoneService":["No","Yes","Yes","No","Yes"],"MultipleLines":["No phone service","No","No","No phone service","No"],"InternetService":["DSL","DSL","DSL","DSL","Fiber optic"],"OnlineSecurity":["No","Yes","Yes","Yes","No"],"OnlineBackup":["Yes","No","Yes","No","No"],"DeviceProtection":["No","Yes","No","Yes","No"],"TechSupport":["No","No","No","Yes","No"],"StreamingTV":["No","No","No","No","No"],"StreamingMovies":["No","No","No","No","No"],"Contract":["Month-to-month","One year","Month-to-month","One year","Month-to-month"],"PaperlessBilling":["Yes","No","Yes","No","Yes"],"PaymentMethod":["Electronic check","Mailed check","Mailed check","Bank transfer (automatic)","Electronic check"],"MonthlyCharges":[29.85,56.95,53.85,42.3,70.7],"TotalCharges":[29.85,1889.5,108.15,1840.75,151.65],"Churn":["No","No","Yes","No","Yes"],"SvcAvailed":[3,4,5,4,3],"SvcDuration":[1,33.1782265145,2.008356546,43.5165484634,2.1449787836]}},"total_rows":5,"truncation_type":null},"text/plain":"   gender SeniorCitizen Partner  ... Churn  SvcAvailed SvcDuration\n0  Female            No     Yes  ...    No           3    1.000000\n1    Male            No      No  ...    No           4   33.178227\n2    Male            No      No  ...   Yes           5    2.008357\n3    Male            No      No  ...    No           4   43.516548\n4  Female            No      No  ...   Yes           3    2.144979\n\n[5 rows x 22 columns]","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>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>OnlineBackup</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      <th>SvcAvailed</th>\n      <th>SvcDuration</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>No</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>Yes</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      <td>3</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>No</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>No</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      <td>4</td>\n      <td>33.178227</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Male</td>\n      <td>No</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>Yes</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      <td>5</td>\n      <td>2.008357</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Male</td>\n      <td>No</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>No</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      <td>4</td>\n      <td>43.516548</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Female</td>\n      <td>No</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>No</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      <td>3</td>\n      <td>2.144979</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":20}]},{"source":"# PART 2: EXPLORATORY DATA ANALYSIS","metadata":{},"id":"c6ba397c-05dc-4098-b3ef-a7b84dd19631","cell_type":"markdown"},{"source":"Now, it's time to probe the dataset and see if insights can be extracted. \n\nFirst, we plot a histogram of `SvcAvailed` against `Churn`.","metadata":{},"id":"17407d3e-423b-49b0-b5be-1cef624c5763","cell_type":"markdown"},{"source":"fig = px.histogram(data_frame=data, x=\"SvcAvailed\", color=\"Churn\", barmode=\"group\", width=800)\nfig.show()","metadata":{"executionCancelledAt":null,"executionTime":null,"lastExecutedAt":null,"lastExecutedByKernel":null,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":null,"outputsMetadata":{"0":{"height":467,"type":"plotly"}}},"id":"ed409795-17a4-4115-a246-ddd068da066f","cell_type":"code","execution_count":21,"outputs":[{"output_type":"display_data","data":{"application/vnd.plotly.v1+json":{"data":[{"alignmentgroup":"True","bingroup":"x","hovertemplate":"Churn=No<br>SvcAvailed=%{x}<br>count=%{y}<extra></extra>","legendgroup":"No","marker":{"color":"#636efa","pattern":{"shape":""}},"name":"No","offsetgroup":"No","orientation":"v","showlegend":true,"x":[3,4,4,6,2,4,4,1,6,8,9,1,7,6,1,6,5,5,10,8,6,6,1,2,7,4,3,7,1,9,8,5,3,8,7,6,7,8,7,9,2,8,7,6,4,5,7,5,7,1,5,3,2,9,3,6,8,3,4,7,3,4,8,2,7,5,1,2,2,4,3,9,9,8,7,1,1,1,6,4,1,8,2,5,1,5,6,6,5,2,1,6,7,1,3,1,6,3,1,6,5,3,3,4,1,2,10,7,6,2,8,1,5,7,6,9,8,1,4,5,1,5,2,5,3,8,1,1,10,1,6,7,1,2,8,2,2,6,4,2,1,4,7,1,8,1,7,3,9,9,1,7,5,7,4,2,5,7,8,1,1,1,4,7,5,7,9,4,2,2,2,5,8,6,9,9,7,1,2,3,4,1,8,8,7,6,5,3,5,8,7,8,2,7,9,9,5,6,6,9,9,2,8,3,6,7,6,6,3,4,3,2,4,4,3,5,5,5,1,1,3,7,7,5,6,6,5,2,6,6,8,2,6,9,3,8,2,6,7,3,3,7,8,5,3,5,2,2,7,10,6,4,9,7,1,2,4,1,4,6,7,2,5,5,8,6,1,7,3,2,4,5,1,7,10,1,3,8,6,8,1,9,5,6,8,8,7,6,2,6,9,3,1,6,10,9,6,1,1,1,4,2,2,7,6,3,4,7,3,3,8,3,3,3,7,2,4,3,8,4,4,5,8,4,2,5,1,10,6,4,9,6,10,5,3,3,6,7,2,7,7,2,8,6,7,1,4,1,6,10,4,7,6,1,1,6,4,7,8,1,4,9,5,7,9,8,5,5,1,5,1,9,5,5,6,9,5,1,6,8,8,8,4,2,1,8,4,4,5,10,7,1,8,2,1,4,8,6,5,6,6,8,10,5,1,4,8,8,1,9,10,1,3,7,1,6,2,4,8,3,5,7,5,7,6,7,5,2,5,5,9,8,6,3,7,2,2,2,6,1,8,9,9,2,8,6,2,6,5,6,2,4,1,4,8,9,8,7,3,9,1,9,6,3,2,8,7,5,3,7,5,1,6,5,7,8,4,9,1,6,6,7,2,6,3,7,2,1,2,6,10,7,1,2,1,7,1,4,3,6,2,2,3,9,2,7,4,7,6,8,2,4,3,2,4,2,8,1,2,2,9,5,7,6,4,5,2,3,6,8,4,7,5,3,6,2,6,6,5,5,5,1,1,2,2,4,8,1,4,4,5,4,8,7,5,10,7,5,3,3,9,10,3,1,6,5,8,7,5,1,1,7,1,6,1,3,5,1,4,5,2,9,3,9,7,2,5,5,4,6,7,7,6,8,2,6,3,6,4,3,1,4,3,6,7,4,9,2,2,1,5,8,7,8,8,8,6,3,2,5,2,7,6,7,6,8,1,8,3,1,9,3,10,9,10,9,2,6,1,8,6,8,1,6,4,2,3,8,7,1,6,3,6,6,5,1,9,4,2,5,9,4,6,5,5,7,10,7,7,1,8,7,4,6,1,9,5,8,1,3,4,8,5,8,2,1,4,8,4,7,8,4,3,4,1,4,5,9,1,1,2,4,7,8,2,6,1,3,4,5,6,2,4,8,5,2,2,4,7,1,7,7,6,7,3,5,2,1,4,2,2,1,1,1,4,2,2,2,1,1,5,3,1,1,6,2,4,6,6,3,9,3,4,2,3,4,5,5,3,9,6,3,6,2,7,2,10,4,4,2,2,4,7,7,4,2,3,8,1,2,9,3,5,4,1,6,4,5,1,7,3,5,3,2,7,1,5,4,8,7,6,7,6,4,7,6,5,3,1,8,2,4,6,2,2,4,4,9,7,5,3,6,8,6,2,9,6,4,7,2,5,2,1,4,10,1,1,6,1,9,2,4,4,4,3,5,4,1,3,6,3,7,1,7,7,6,7,9,1,5,4,1,5,10,7,5,5,7,7,4,6,6,6,6,6,7,4,8,8,6,3,5,8,2,2,4,8,4,2,1,3,6,7,3,1,1,3,6,5,2,6,2,6,7,7,5,5,2,8,8,1,5,2,6,2,5,8,5,7,1,5,9,2,1,4,5,4,6,5,5,8,9,2,7,1,7,7,5,8,2,6,2,10,2,5,8,1,6,9,1,7,8,8,9,8,3,3,6,9,6,8,1,5,8,5,7,1,2,2,6,6,7,2,3,6,4,1,5,2,2,4,3,1,1,7,1,7,2,5,6,2,6,2,2,4,9,2,9,8,5,5,6,2,5,1,9,2,8,6,6,7,2,2,8,8,2,5,6,6,1,1,9,5,6,1,1,5,2,3,5,8,6,7,7,6,7,6,4,6,2,7,10,1,7,8,3,2,2,6,1,7,9,3,4,1,7,6,5,9,7,4,1,1,2,6,5,6,2,9,8,7,6,1,5,3,4,7,7,1,5,7,7,1,5,3,8,6,7,7,8,3,6,7,1,5,7,2,5,7,4,1,5,7,3,6,1,6,8,2,5,1,7,5,7,1,2,7,2,9,1,5,1,4,1,3,1,1,6,8,8,3,5,1,2,4,8,1,4,5,2,7,5,1,7,1,4,3,2,5,7,6,9,2,1,1,7,1,6,6,4,5,4,6,7,9,2,8,6,1,1,1,2,3,8,1,8,7,6,2,8,2,9,5,10,1,6,2,2,5,9,2,6,7,5,4,6,6,8,2,6,6,2,2,6,1,10,1,6,6,5,5,3,1,1,6,3,6,2,8,4,1,3,10,4,8,7,2,5,2,9,3,3,2,9,1,3,3,2,1,6,7,1,2,9,7,7,6,2,1,4,6,8,3,7,6,3,5,5,7,1,4,1,1,8,5,6,1,5,6,3,7,7,6,6,1,9,3,7,2,7,2,8,2,10,1,9,6,3,8,7,5,6,5,1,1,4,3,6,3,7,9,2,10,4,7,2,4,9,3,5,2,6,8,6,10,8,2,2,6,7,2,9,7,1,2,3,3,4,7,1,6,2,8,5,6,7,1,1,1,6,3,5,2,1,5,6,5,2,4,1,7,3,3,6,3,2,7,1,6,4,1,1,1,7,2,5,1,6,2,4,7,8,3,2,3,7,1,7,8,10,6,9,2,5,5,4,7,6,4,3,5,7,4,9,7,6,1,1,10,5,3,3,8,1,1,3,8,2,7,5,4,9,4,2,9,4,1,4,7,4,2,9,1,2,2,6,1,1,2,7,3,1,4,1,6,7,7,3,7,6,8,4,6,3,6,6,7,2,5,4,7,5,2,4,8,1,6,6,2,7,1,7,7,5,7,5,1,6,8,3,1,4,9,8,1,5,3,1,7,8,5,5,10,7,1,1,5,1,6,6,3,3,9,1,6,2,1,9,1,7,6,6,1,8,9,4,9,7,9,3,7,1,4,3,8,7,6,1,1,7,4,5,8,9,4,2,4,4,3,1,8,2,1,6,5,5,6,5,3,4,7,2,2,1,1,5,7,6,10,7,4,2,1,1,7,5,7,2,8,5,1,1,8,6,3,6,5,1,5,6,4,3,10,1,1,7,5,3,4,4,1,8,9,2,9,6,8,1,8,7,6,4,2,4,1,2,8,1,1,7,9,8,4,1,10,3,2,8,9,2,2,1,5,6,2,4,8,9,7,2,7,8,1,2,6,9,9,8,4,8,6,7,4,3,2,8,2,1,1,7,5,8,3,5,6,3,10,3,2,7,7,1,5,5,1,6,1,7,8,3,6,7,4,1,4,10,8,10,2,5,4,1,7,4,3,7,4,6,5,8,9,8,2,5,4,6,10,7,7,3,6,8,6,8,8,1,8,10,7,1,2,7,4,10,7,1,2,8,5,6,6,5,3,6,6,5,5,1,4,9,7,3,4,2,7,2,3,6,5,6,2,4,1,5,3,7,1,8,1,4,3,6,6,10,7,10,3,9,4,1,9,5,5,8,1,4,2,6,2,3,7,4,8,1,5,6,5,6,6,3,5,6,5,1,9,7,1,7,4,9,5,2,7,3,6,4,3,4,3,1,2,4,6,5,2,5,1,5,4,5,1,2,3,8,3,4,5,9,4,2,2,6,8,2,1,1,7,5,1,7,1,3,7,1,8,7,9,5,2,8,2,3,1,6,5,9,6,1,6,2,1,1,5,6,6,1,2,1,2,2,3,9,3,5,7,1,10,7,6,8,8,1,1,8,7,3,7,4,9,10,9,4,2,10,6,6,7,7,7,4,3,1,1,1,2,1,4,5,6,2,1,10,8,4,3,9,1,2,7,2,1,8,1,7,1,8,6,6,1,1,1,3,5,7,1,1,9,8,5,9,4,8,2,8,9,10,6,2,7,7,10,1,7,8,4,4,7,6,5,2,6,6,6,8,2,10,4,7,5,1,8,2,6,2,6,3,6,4,6,1,8,4,6,9,7,7,8,9,7,7,1,1,1,1,1,8,4,8,7,5,10,5,4,1,1,9,4,9,1,7,1,1,2,4,2,6,1,2,2,7,3,2,9,6,7,1,1,1,1,1,6,8,8,1,4,1,4,5,8,6,6,7,9,7,1,2,6,2,2,4,1,5,1,1,2,6,4,7,3,10,6,1,6,7,2,3,1,4,5,9,7,8,4,2,6,6,7,3,5,4,6,1,1,8,2,5,9,1,5,3,8,1,2,5,9,1,4,2,2,1,3,2,4,1,5,6,6,1,7,6,1,1,5,1,5,1,7,5,4,5,1,7,10,9,5,7,1,4,5,2,10,7,2,6,8,5,1,5,7,2,1,2,5,4,6,5,2,10,10,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same holds true for `tenure`.","metadata":{},"id":"50925bf6-6d0b-4837-8204-b4cbf7a4f59f","cell_type":"markdown"},{"source":"Let's take a good look on churn proportion against select variables.","metadata":{},"id":"b25d15c2-ca50-4748-ae82-4d5c7ccb22ba","cell_type":"markdown"},{"source":"fig, (ax0, ax1, ax2, ax3, ax4) = plt.subplots(1, 5, figsize=(16, 4))\nsns.histplot(data, x=\"gender\", hue=\"Churn\", multiple=\"fill\", bins=6, ax=ax0, legend=False)\nsns.histplot(data, x=\"Partner\", hue=\"Churn\", multiple=\"fill\", ax=ax1, legend=False)\nsns.histplot(data, x=\"Dependents\", hue=\"Churn\", multiple=\"fill\", ax=ax2, legend=False)\nsns.histplot(data, x=\"SeniorCitizen\", hue=\"Churn\", multiple=\"fill\", ax=ax3, legend=False)\nsns.histplot(data, x=\"PaymentMethod\", hue=\"Churn\", multiple=\"fill\", ax=ax4)\nax0.set_xticklabels(ax0.get_xticklabels(), rotation= 90)\nax2.set_xticklabels(ax2.get_xticklabels(), rotation= 90)\nax3.set_xticklabels(ax3.get_xticklabels(), rotation= 90)\nax4.set_xticklabels(ax4.get_xticklabels(), rotation= 90)\nax0.set_ylabel('Proportion')\nax1.set_ylabel('')\nax2.set_ylabel('')\nax3.set_ylabel('')\nax4.set_ylabel('')\nfig.suptitle(\"Churn proportion by select variables\")\nplt.show()","metadata":{"executionCancelledAt":null,"executionTime":596,"lastExecutedAt":1713141645349,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"fig, (ax0, ax1, ax2, ax3, ax4) = plt.subplots(1, 5, figsize=(16, 4))\nsns.histplot(data, x=\"gender\", hue=\"Churn\", multiple=\"fill\", bins=6, ax=ax0, legend=False)\nsns.histplot(data, x=\"Partner\", hue=\"Churn\", multiple=\"fill\", ax=ax1, legend=False)\nsns.histplot(data, x=\"Dependents\", hue=\"Churn\", multiple=\"fill\", ax=ax2, legend=False)\nsns.histplot(data, x=\"SeniorCitizen\", hue=\"Churn\", multiple=\"fill\", ax=ax3, legend=False)\nsns.histplot(data, x=\"PaymentMethod\", hue=\"Churn\", multiple=\"fill\", ax=ax4)\nax0.set_xticklabels(ax0.get_xticklabels(), rotation= 90)\nax2.set_xticklabels(ax2.get_xticklabels(), rotation= 90)\nax3.set_xticklabels(ax3.get_xticklabels(), rotation= 90)\nax4.set_xticklabels(ax4.get_xticklabels(), rotation= 90)\nax0.set_ylabel('Proportion')\nax1.set_ylabel('')\nax2.set_ylabel('')\nax3.set_ylabel('')\nax4.set_ylabel('')\nfig.suptitle(\"Churn proportion by select variables\")\nplt.show()"},"id":"f9819599-267b-43f9-8660-db4ed6aa0950","cell_type":"code","execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x400 with 5 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the plots above, we see that:\n- based on the churn proportion, gender doesn't affect the client's decision.\n- clients who have partners and dependents are less likely to churn.\n- age affects the client's decision, as elderlies tend to churn more than those who are younger.\n- clients doing transactions via mailed check as well as automatic bank transfer and credit card transactions tend to churn less.","metadata":{},"id":"3ec64ef8-53ce-4c5a-92e5-71f4bc4dbe49","cell_type":"markdown"},{"source":"fig, (ax0, ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 6, figsize=(16, 4))\nsns.histplot(data, x=\"InternetService\", hue=\"Churn\", multiple=\"fill\", bins=6, ax=ax0, legend=False)\nsns.histplot(data, x=\"OnlineBackup\", hue=\"Churn\", multiple=\"fill\", ax=ax1, legend=False)\nsns.histplot(data, x=\"OnlineSecurity\", hue=\"Churn\", multiple=\"fill\", ax=ax2, legend=False)\nsns.histplot(data, x=\"DeviceProtection\", hue=\"Churn\", multiple=\"fill\", ax=ax3, legend=False)\nsns.histplot(data, x=\"TechSupport\", hue=\"Churn\", multiple=\"fill\", ax=ax4, legend=False)\nsns.histplot(data, x=\"Contract\", hue=\"Churn\", multiple=\"fill\", ax=ax5)\nax0.set_xticklabels(ax0.get_xticklabels(), rotation= 45)\nax1.set_xticklabels(ax1.get_xticklabels(), rotation= 45)\nax2.set_xticklabels(ax2.get_xticklabels(), rotation= 45)\nax3.set_xticklabels(ax3.get_xticklabels(), rotation= 45)\nax4.set_xticklabels(ax4.get_xticklabels(), rotation= 45)\nax5.set_xticklabels(ax5.get_xticklabels(), rotation= 45)\nax0.set_ylabel('Proportion')\nax1.set_ylabel('')\nax2.set_ylabel('')\nax3.set_ylabel('')\nax4.set_ylabel('')\nax5.set_ylabel('')\nfig.suptitle(\"Churn proportion by select variables\")\nplt.show()","metadata":{"executionCancelledAt":null,"executionTime":694,"lastExecutedAt":1713141646043,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"fig, (ax0, ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 6, figsize=(16, 4))\nsns.histplot(data, x=\"InternetService\", hue=\"Churn\", multiple=\"fill\", bins=6, ax=ax0, legend=False)\nsns.histplot(data, x=\"OnlineBackup\", hue=\"Churn\", multiple=\"fill\", ax=ax1, legend=False)\nsns.histplot(data, x=\"OnlineSecurity\", hue=\"Churn\", multiple=\"fill\", ax=ax2, legend=False)\nsns.histplot(data, x=\"DeviceProtection\", hue=\"Churn\", multiple=\"fill\", ax=ax3, legend=False)\nsns.histplot(data, x=\"TechSupport\", hue=\"Churn\", multiple=\"fill\", ax=ax4, legend=False)\nsns.histplot(data, x=\"Contract\", hue=\"Churn\", multiple=\"fill\", ax=ax5)\nax0.set_xticklabels(ax0.get_xticklabels(), rotation= 45)\nax1.set_xticklabels(ax1.get_xticklabels(), rotation= 45)\nax2.set_xticklabels(ax2.get_xticklabels(), rotation= 45)\nax3.set_xticklabels(ax3.get_xticklabels(), rotation= 45)\nax4.set_xticklabels(ax4.get_xticklabels(), rotation= 45)\nax5.set_xticklabels(ax5.get_xticklabels(), rotation= 45)\nax0.set_ylabel('Proportion')\nax1.set_ylabel('')\nax2.set_ylabel('')\nax3.set_ylabel('')\nax4.set_ylabel('')\nax5.set_ylabel('')\nfig.suptitle(\"Churn proportion by select variables\")\nplt.show()"},"id":"8bad4207-1de7-4246-8e25-5b0ee09148af","cell_type":"code","execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x400 with 6 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"},"metadata":{}}]},{"source":"Furthermore:\n- clients having fiber optic connections tend to churn more than those having DSL connections and those having no internet service providers. \n- customers that have availed of protection systems tend to stay more than those who don't.\n- clients who don't have technical support are more likely to refuse than those who do.\n- customers on short-term contracts tend to leave more than those who are on annual, long-term contracts.","metadata":{},"id":"9af08133-412d-40fa-94d5-f35878625dfa","cell_type":"markdown"},{"source":"# PART 3: MODELING","metadata":{},"id":"c8a4c037-7b6b-4585-8986-f99bbbae2a21","cell_type":"markdown"},{"source":"## Label Encoder\nSince majority of the columns are categorical, we need to transform them through label encoding.","metadata":{},"id":"f6a24cae-1cf6-48c0-aad4-0cd71b1dbdfe","cell_type":"markdown"},{"source":"data_model = data.copy()\ndata_model.drop(['tenure', 'MonthlyCharges', 'TotalCharges', 'SvcAvailed', 'SvcDuration'], axis=1,inplace=True)\n\nscaler = LabelEncoder()\ndata_model = data_model.apply(lambda x: scaler.fit_transform(x))\n\ndata_model[['tenure', 'MonthlyCharges', 'TotalCharges', 'SvcAvailed', 'SvcDuration']] = data[['tenure', 'MonthlyCharges', 'TotalCharges', 'SvcAvailed', 'SvcDuration']]\ndata_model.head()","metadata":{"executionCancelledAt":null,"executionTime":null,"lastExecutedAt":null,"lastExecutedByKernel":null,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":null,"outputsMetadata":{"0":{"height":196,"type":"dataFrame"}}},"id":"1235e8e0-52bd-4d4a-806f-4dcedf8dc4bf","cell_type":"code","execution_count":26,"outputs":[{"output_type":"execute_result","data":{"application/com.datacamp.data-table.v2+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"gender","type":"integer"},{"name":"SeniorCitizen","type":"integer"},{"name":"Partner","type":"integer"},{"name":"Dependents","type":"integer"},{"name":"PhoneService","type":"integer"},{"name":"MultipleLines","type":"integer"},{"name":"InternetService","type":"integer"},{"name":"OnlineSecurity","type":"integer"},{"name":"OnlineBackup","type":"integer"},{"name":"DeviceProtection","type":"integer"},{"name":"TechSupport","type":"integer"},{"name":"StreamingTV","type":"integer"},{"name":"StreamingMovies","type":"integer"},{"name":"Contract","type":"integer"},{"name":"PaperlessBilling","type":"integer"},{"name":"PaymentMethod","type":"integer"},{"name":"Churn","type":"integer"},{"name":"tenure","type":"integer"},{"name":"MonthlyCharges","type":"number"},{"name":"TotalCharges","type":"number"},{"name":"SvcAvailed","type":"integer"},{"name":"SvcDuration","type":"number"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":{"index":[0,1,2,3,4],"gender":[0,1,1,1,0],"SeniorCitizen":[0,0,0,0,0],"Partner":[1,0,0,0,0],"Dependents":[0,0,0,0,0],"PhoneService":[0,1,1,0,1],"MultipleLines":[1,0,0,1,0],"InternetService":[0,0,0,0,1],"OnlineSecurity":[0,2,2,2,0],"OnlineBackup":[2,0,2,0,0],"DeviceProtection":[0,2,0,2,0],"TechSupport":[0,0,0,2,0],"StreamingTV":[0,0,0,0,0],"StreamingMovies":[0,0,0,0,0],"Contract":[0,1,0,1,0],"PaperlessBilling":[1,0,1,0,1],"PaymentMethod":[2,3,3,0,2],"Churn":[0,0,1,0,1],"tenure":[1,34,2,45,2],"MonthlyCharges":[29.85,56.95,53.85,42.3,70.7],"TotalCharges":[29.85,1889.5,108.15,1840.75,151.65],"SvcAvailed":[3,4,5,4,3],"SvcDuration":[1,33.1782265145,2.008356546,43.5165484634,2.1449787836]}},"total_rows":5,"truncation_type":null},"text/plain":"   gender  SeniorCitizen  Partner  ...  TotalCharges  SvcAvailed  SvcDuration\n0       0              0        1  ...         29.85           3     1.000000\n1       1              0        0  ...       1889.50           4    33.178227\n2       1              0        0  ...        108.15           5     2.008357\n3       1              0        0  ...       1840.75           4    43.516548\n4       0              0        0  ...        151.65           3     2.144979\n\n[5 rows x 22 columns]","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>gender</th>\n      <th>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>PhoneService</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>OnlineBackup</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>Churn</th>\n      <th>tenure</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n      <th>SvcAvailed</th>\n      <th>SvcDuration</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</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>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>29.85</td>\n      <td>29.85</td>\n      <td>3</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\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>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>34</td>\n      <td>56.95</td>\n      <td>1889.50</td>\n      <td>4</td>\n      <td>33.178227</td>\n    </tr>\n    <tr>\n      <th>2</th>\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>2</td>\n      <td>2</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>3</td>\n      <td>1</td>\n      <td>2</td>\n      <td>53.85</td>\n      <td>108.15</td>\n      <td>5</td>\n      <td>2.008357</td>\n    </tr>\n    <tr>\n      <th>3</th>\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      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</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>45</td>\n      <td>42.30</td>\n      <td>1840.75</td>\n      <td>4</td>\n      <td>43.516548</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <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>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>70.70</td>\n      <td>151.65</td>\n      <td>3</td>\n      <td>2.144979</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":26}]},{"source":"Now, the dataset is ready for modeling 💃! But first, let's see if any of these variables are correlated.","metadata":{},"id":"b5ab0aea-2285-4684-ad69-6ccc7733ea83","cell_type":"markdown"},{"source":"## Correlation","metadata":{},"id":"d5e83943-47c3-4c62-9ec5-e41063248872","cell_type":"markdown"},{"source":"# Calculate the correlation matrix  \ncorr_matrix = data_model.corr()\n\n# This is to generate text for annotations BUT hide it by setting it to an array of empty strings\ntext = [['' for _ in range(len(corr_matrix.columns))] for _ in range(len(corr_matrix.index))]\n\n\n# Create the heatmap\nfig = ff.create_annotated_heatmap(\n    z=corr_matrix.values,\n    x=list(corr_matrix.columns),\n    y=list(corr_matrix.index),\n    annotation_text=text,\n    colorscale='RdBu',\n    showscale=True\n)\n\n# Update the layout to add titles and adjust the size\nfig.update_layout(\n    xaxis=dict(tickangle=-45),\n    yaxis=dict(tickmode='array', tickvals=list(range(len(corr_matrix.index))), ticktext=list(corr_matrix.index)),\n    width=800, 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ML models\nFor this notebook, we will perform a classification task (of whether a customer's gonna churn or not) using logistic regression, random forest classifier, and k-nearest neighbors (KNN) classifier. Furthermore, we will assess the performances of the models using a 5-fold cross-validation with accuracy as the scoring metric.","metadata":{},"id":"42c08c30-b2fe-429d-9553-0ddf96dcbae6","cell_type":"markdown"},{"source":"X = data_model.drop(\"Churn\", axis=1)\ny = data_model[\"Churn\"]\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)\nprint(f\"X_train: {X_train.shape}\\nX_test: {X_test.shape}\")","metadata":{"executionCancelledAt":null,"executionTime":null,"lastExecutedAt":null,"lastExecutedByKernel":null,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":null,"outputsMetadata":{"0":{"height":59,"type":"stream"}}},"id":"98d631b0-19a4-466d-bec7-92a8a6e7f8e3","cell_type":"code","execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":"X_train: (5625, 21)\nX_test: (1407, 21)\n"}]},{"source":"models = {\n    \"LR\": LogisticRegression(random_state=seed, max_iter=10000),\n    \"KNN\": KNeighborsClassifier(),\n    \"RF\": RandomForestClassifier(random_state=seed)}\n\nscoring = \"accuracy\"\n\nscores = []\nfor name, model in models.items():\n    cvScores = cross_val_score(model, X_train, y_train, scoring=scoring, cv=5, n_jobs=-1)\n    print(f\"{name} model -> average score = {np.mean(cvScores):.3f} ± {np.std(cvScores):.3f}\")\n    scores.append(cvScores)","metadata":{"executionCancelledAt":null,"executionTime":null,"lastExecutedAt":null,"lastExecutedByKernel":null,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":null,"outputsMetadata":{"0":{"height":80,"type":"stream"}}},"id":"87ae300b-19be-4889-a6a5-31eb4b3abf67","cell_type":"code","execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":"LR model -> average score = 0.803 ± 0.008\nKNN model -> average score = 0.764 ± 0.008\nRF model -> average score = 0.797 ± 0.008\n"}]},{"source":"Based on the average CV scores of roughly 0.8, the models were shown to be fairly accurate despite not having undergone parameter optimization.","metadata":{},"id":"b8695c4f-e5a9-4957-8034-98d0967224b5","cell_type":"markdown"},{"source":"import matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve, auc\n\n# Calculate the ROC curve and AUC for each model\nplt.figure(figsize=(10, 8))\n\nfor name, model in models.items():\n    model.fit(X_train, y_train)\n    y_pred_prob = model.predict_proba(X_test)[:, 1]\n    fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n    roc_auc = auc(fpr, tpr)\n    \n    plt.plot(fpr, tpr, label=f'{name} (area = {roc_auc:.2f})')\n\nplt.plot([0, 1], [0, 1], 'k--')  # Random predictions curve\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver Operating Characteristic (ROC) Curve')\nplt.legend(loc=\"lower right\")\nplt.show()","metadata":{"executionCancelledAt":null,"executionTime":9055,"lastExecutedAt":1713141659396,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"import matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve, auc\n\n# Calculate the ROC curve and AUC for each model\nplt.figure(figsize=(10, 8))\n\nfor name, model in models.items():\n    model.fit(X_train, y_train)\n    y_pred_prob = model.predict_proba(X_test)[:, 1]\n    fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n    roc_auc = auc(fpr, tpr)\n    \n    plt.plot(fpr, tpr, label=f'{name} (area = {roc_auc:.2f})')\n\nplt.plot([0, 1], [0, 1], 'k--')  # Random predictions curve\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver Operating Characteristic (ROC) Curve')\nplt.legend(loc=\"lower right\")\nplt.show()"},"id":"06e57497-e926-4409-8b6d-69b7c8d0f518","cell_type":"code","execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA1kAAAK9CAYAAADWo6YTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hTZRvH8W+aNt2LDkYppewNQgHZUxmC66UUEEVUFAQcCAooCCoCoogbXCCITFERUQQElL1kyZ4tZXVA987z/hGaNh3QQtrTcX+uqxc9J6c5d0Pa5pfnOfejU0ophBBCCCGEEEJYhY3WBQghhBBCCCFEWSIhSwghhBBCCCGsSEKWEEIIIYQQQliRhCwhhBBCCCGEsCIJWUIIIYQQQghhRRKyhBBCCCGEEMKKJGQJIYQQQgghhBVJyBJCCCGEEEIIK5KQJYQQQgghhBBWJCFLCFFiVa9enSeffFLrMsqdzp0707lzZ63LuK0pU6ag0+mIjIzUupQSR6fTMWXKFKvc1/nz59HpdCxYsMAq9wewe/duDAYDFy5csNp9WtuAAQPo37+/1mUIIUopCVlClFMLFixAp9OZP2xtbfHz8+PJJ58kPDxc6/JKtISEBN5++22aNGmCk5MT7u7udOjQgYULF6KU0rq8Ajl69ChTpkzh/PnzWpeSS0ZGBvPnz6dz585UqFABe3t7qlevztChQ9m7d6/W5VnFDz/8wJw5c7Quw0Jx1vT6668zcOBAAgICzPs6d+5s8TvJ0dGRJk2aMGfOHIxGY573ExUVxbhx46hbty4ODg5UqFCBHj16sGbNmnzPHRsby9SpU2natCkuLi44OjrSqFEjXnvtNS5dumQ+7rXXXuPHH3/k4MGDBf6+ysNzVwhRMDpVWl4RCCGsasGCBQwdOpS33nqLwMBAkpOT2blzJwsWLKB69eocOXIEBwcHTWtMSUnBxsYGOzs7TevI7urVq3Tr1o1jx44xYMAAOnXqRHJyMj/++CN///03ISEhLF68GL1er3Wpt7Ry5UqCg4PZtGlTrlGr1NRUAAwGQ7HXlZSUxKOPPsoff/xBx44d6du3LxUqVOD8+fMsX76ckydPEhoaStWqVZkyZQpTp04lIiICb2/vYq/1bvTp04cjR44UWchNTk7G1tYWW1vbu65JKUVKSgp2dnZWeV4fOHCAe+65h+3bt9OmTRvz/s6dO3PmzBmmT58OQGRkJD/88AN79uxh4sSJTJs2zeJ+Tpw4Qbdu3YiIiGDo0KEEBQVx48YNFi9ezIEDBxg7diyzZs2y+JqzZ8/SvXt3QkNDCQ4Opn379hgMBg4dOsSSJUuoUKECJ0+eNB/funVr6taty8KFC2/7fRXmuSuEKAeUEKJcmj9/vgLUnj17LPa/9tprClDLli3TqDJtJSUlqYyMjHxv79Gjh7KxsVG//PJLrtvGjh2rADVjxoyiLDFP8fHxhTp+xYoVClCbNm0qmoLu0MiRIxWgPvzww1y3paenq1mzZqmwsDCllFJvvvmmAlRERESR1WM0GlViYqLV7/eBBx5QAQEBVr3PjIwMlZSUdMdfXxQ15eWFF15Q1apVU0aj0WJ/p06dVMOGDS32JSUlqYCAAOXq6qrS09PN+1NTU1WjRo2Uk5OT2rlzp8XXpKenq5CQEAWopUuXmvenpaWppk2bKicnJ/XPP//kqismJkZNnDjRYt/777+vnJ2dVVxc3G2/r8I8d+/G3f4/CyGKh4QsIcqp/ELWmjVrFKDeffddi/3Hjh1T//vf/5Snp6eyt7dXLVq0yDNoXL9+Xb300ksqICBAGQwG5efnpx5//HGLF8LJyclq8uTJqmbNmspgMKiqVauqcePGqeTkZIv7CggIUEOGDFFKKbVnzx4FqAULFuQ65x9//KEA9euvv5r3Xbx4UQ0dOlT5+voqg8GgGjRooL755huLr9u0aZMC1JIlS9Trr7+uqlSponQ6nbp+/Xqej9mOHTsUoJ566qk8b09LS1O1a9dWnp6e5hfm586dU4CaNWuWmj17tqpWrZpycHBQHTt2VIcPH851HwV5nDP/7zZv3qxGjBihfHx8lIeHh1JKqfPnz6sRI0aoOnXqKAcHB1WhQgXVr18/de7cuVxfn/MjM3B16tRJderUKdfjtGzZMvXOO+8oPz8/ZW9vr7p27apOnTqV63v49NNPVWBgoHJwcFAtW7ZUf//9d677zEtYWJiytbVV99133y2Py5QZsk6dOqWGDBmi3N3dlZubm3ryySdVQkKCxbHffvut6tKli/Lx8VEGg0HVr19fff7557nuMyAgQD3wwAPqjz/+UC1atFD29vbmF80FvQ+llFq7dq3q2LGjcnFxUa6uriooKEgtXrxYKWV6fHM+9tnDTUF/PgA1cuRI9f3336sGDRooW1tb9dNPP5lve/PNN83HxsbGqhdffNH8c+nj46O6d++u9u3bd9uaMp/D8+fPtzj/sWPHVHBwsPL29lYODg6qTp06uUJKXqpVq6aefPLJXPvzCllKKdWvXz8FqEuXLpn3LVmyRAHqrbfeyvMcN27cUB4eHqpevXrmfUuXLlWAmjZt2m1rzHTw4EEFqFWrVt3yuMI+d4cMGZJnoM18TmeX1//z8uXLlaenZ56PY0xMjLK3t1evvPKKeV9Bn1NCCOsp+DwCIUS5kDlVyNPT07zvv//+o127dvj5+TF+/HicnZ1Zvnw5Dz/8MD/++COPPPIIAPHx8XTo0IFjx47x1FNP0bx5cyIjI1m9ejUXL17E29sbo9HIgw8+yNatW3n22WepX78+hw8f5sMPP+TkyZP8/PPPedYVFBREjRo1WL58OUOGDLG4bdmyZXh6etKjRw/ANKXv3nvvRafTMWrUKHx8fPj99995+umniY2N5aWXXrL4+rfffhuDwcDYsWNJSUnJd5rcr7/+CsATTzyR5+22trYMGjSIqVOnsm3bNrp3726+beHChcTFxTFy5EiSk5P56KOP6Nq1K4cPH6ZixYqFepwzPf/88/j4+DB58mQSEhIA2LNnD9u3b2fAgAFUrVqV8+fP88UXX9C5c2eOHj2Kk5MTHTt25IUXXuDjjz9m4sSJ1K9fH8D8b35mzJiBjY0NY8eOJSYmhvfee4/HHnuMXbt2mY/54osvGDVqFB06dODll1/m/PnzPPzww3h6et52mtTvv/9Oeno6jz/++C2Py6l///4EBgYyffp09u/fz9dff42vry8zZ860qKthw4Y8+OCD2Nra8uuvv/L8889jNBoZOXKkxf2dOHGCgQMH8txzzzFs2DDq1q1bqPtYsGABTz31FA0bNmTChAl4eHjw77//8scffzBo0CBef/11YmJiuHjxIh9++CEALi4uAIX++fjrr79Yvnw5o0aNwtvbm+rVq+f5GA0fPpyVK1cyatQoGjRoQFRUFFu3buXYsWM0b978ljXl5dChQ3To0AE7OzueffZZqlevzpkzZ/j1119zTevLLjw8nNDQUJo3b57vMTllNt7w8PAw77vdz6K7uzsPPfQQ3333HadPn6ZWrVqsXr0aoFDPrwYNGuDo6Mi2bdty/fxld6fP3YLK+f9cu3ZtHnnkEVatWsW8efMsfmf9/PPPpKSkMGDAAKDwzykhhJVonfKEENrIHM3YsGGDioiIUGFhYWrlypXKx8dH2dvbW0xr6datm2rcuLHFu55Go1G1bdtW1a5d27xv8uTJ+b7rmzk1aNGiRcrGxibXdJ25c+cqQG3bts28L/tIllJKTZgwQdnZ2ano6GjzvpSUFOXh4WExuvT000+rypUrq8jISItzDBgwQLm7u5tHmTJHaGrUqFGgKWEPP/ywAvId6VJKqVWrVilAffzxx0qprFEAR0dHdfHiRfNxu3btUoB6+eWXzfsK+jhn/t+1b9/eYgqVUirP7yNzBG7hwoXmfbeaLpjfSFb9+vVVSkqKef9HH32kAPOIXEpKivLy8lItW7ZUaWlp5uMWLFiggNuOZL388ssKUP/+++8tj8uU+a5/zpHFRx55RHl5eVnsy+tx6dGjh6pRo4bFvoCAAAWoP/74I9fxBbmPGzduKFdXV9W6detcU7qyT4/Lb2peYX4+AGVjY6P++++/XPdDjpEsd3d3NXLkyFzHZZdfTXmNZHXs2FG5urqqCxcu5Ps95mXDhg25Rp0zderUSdWrV09FRESoiIgIdfz4cTVu3DgFqAceeMDi2GbNmil3d/dbnmv27NkKUKtXr1ZKKXXPPffc9mvyUqdOHdWrV69bHlPY525hR7Ly+n9et25dno9l7969LZ6ThXlOCSGsR7oLClHOde/eHR8fH/z9/enXrx/Ozs6sXr3aPOoQHR3NX3/9Rf/+/YmLiyMyMpLIyEiioqLo0aMHp06dMncj/PHHH2natGme7/jqdDoAVqxYQf369alXr575viIjI+natSsAmzZtyrfWkJAQ0tLSWLVqlXnfn3/+yY0bNwgJCQFMF+n/+OOP9O3bF6WUxTl69OhBTEwM+/fvt7jfIUOG4OjoeNvHKi4uDgBXV9d8j8m8LTY21mL/ww8/jJ+fn3m7VatWtG7dmrVr1wKFe5wzDRs2LFcjguzfR1paGlFRUdSqVQsPD49c33dhDR061OId8w4dOgCmZgIAe/fuJSoqimHDhlk0XHjssccsRkbzk/mY3erxzcvw4cMttjt06EBUVJTF/0H2xyUmJobIyEg6derE2bNniYmJsfj6wMBA86hodgW5j/Xr1xMXF8f48eNzNY7J/Bm4lcL+fHTq1IkGDRrc9n49PDzYtWuXRfe8OxUREcHff//NU089RbVq1Sxuu933GBUVBZDv8+H48eP4+Pjg4+NDvXr1mDVrFg8++GCu9vFxcXG3fZ7k/FmMjY0t9HMrs9bbLRNwp8/dgsrr/7lr1654e3uzbNky877r16+zfv168+9DuLvfuUKIOyfTBYUo5z777DPq1KlDTEwM3377LX///Tf29vbm20+fPo1SikmTJjFp0qQ87+PatWv4+flx5swZ/ve//93yfKdOneLYsWP4+Pjke1/5adq0KfXq1WPZsmU8/fTTgGmqoLe3t/kFQ0REBDdu3ODLL7/kyy+/LNA5AgMDb1lzpswXUHFxcRZTl7LLL4jVrl0717F16tRh+fLlQOEe51vVnZSUxPTp05k/fz7h4eEWLeVzhonCyvmCOvOF8vXr1wHMax7VqlXL4jhbW9t8p7Fl5+bmBmQ9htaoK/M+t23bxptvvsmOHTtITEy0OD4mJgZ3d3fzdn7Ph4Lcx5kzZwBo1KhRob6HTIX9+Sjoc/e9995jyJAh+Pv706JFC3r37s0TTzxBjRo1Cl1jZqi+0+8RyHepg+rVq/PVV19hNBo5c+YM06ZNIyIiIldgdXV1vW3wyfmz6ObmZq69sLXeLjze6XO3oPL6f7a1teV///sfP/zwAykpKdjb27Nq1SrS0tIsQtbd/M4VQtw5CVlClHOtWrUiKCgIMI22tG/fnkGDBnHixAlcXFzM69OMHTs2z3f3IfeL6lsxGo00btyY2bNn53m7v7//Lb8+JCSEadOmERkZiaurK6tXr2bgwIHmkZPMegcPHpzr2q1MTZo0sdguyCgWmK5Z+vnnnzl06BAdO3bM85hDhw4BFGh0Ibs7eZzzqnv06NHMnz+fl156iTZt2uDu7o5Op2PAgAH5rjVUUPm1787vBXNh1atXD4DDhw/TrFmzAn/d7eo6c+YM3bp1o169esyePRt/f38MBgNr167lww8/zPW45PW4FvY+7lRhfz4K+tzt378/HTp04KeffuLPP/9k1qxZzJw5k1WrVtGrV6+7rrugvLy8gKxgnpOzs7PFtYzt2rWjefPmTJw4kY8//ti8v379+hw4cIDQ0NBcITtTzp/FevXq8e+//xIWFnbb3zPZXb9+Pc83SbIr7HM3v9CWkZGR5/78/p8HDBjAvHnz+P3333n44YdZvnw59erVo2nTpuZj7vZ3rhDizkjIEkKY6fV6pk+fTpcuXfj0008ZP368+Z1uOzs7ixc/ealZsyZHjhy57TEHDx6kW7duBZo+lVNISAhTp07lxx9/pGLFisTGxpov8Abw8fHB1dWVjIyM29ZbWH369GH69OksXLgwz5CVkZHBDz/8gKenJ+3atbO47dSpU7mOP3nypHmEpzCP862sXLmSIUOG8MEHH5j3JScnc+PGDYvj7uSxv53MhWVPnz5Nly5dzPvT09M5f/58rnCbU69evdDr9Xz//fdWbSDw66+/kpKSwurVqy1ekBdmmlRB76NmzZoAHDly5JZvPuT3+N/tz8etVK5cmeeff57nn3+ea9eu0bx5c6ZNm2YOWQU9X+Zz9XY/63nJDCPnzp0r0PFNmjRh8ODBzJs3j7Fjx5of+z59+rBkyRIWLlzIG2+8kevrYmNj+eWXX6hXr575/6Fv374sWbKE77//ngkTJhTo/Onp6YSFhfHggw/e8rjCPnc9PT1z/UxC1mhwQXXs2JHKlSuzbNky2rdvz19//cXrr79ucUxRPqeEEPmTa7KEEBY6d+5Mq1atmDNnDsnJyfj6+tK5c2fmzZvH5cuXcx0fERFh/vx///sfBw8e5Keffsp1XOaoQv/+/QkPD+err77KdUxSUpK5S15+6tevT+PGjVm2bBnLli2jcuXKFoFHr9fzv//9jx9//DHPF4HZ6y2stm3b0r17d+bPn8+aNWty3f76669z8uRJXn311VzvPP/8888W11Tt3r2bXbt2mV/gFuZxvhW9Xp9rZOmTTz7J9Q65s7MzQJ4v9O5UUFAQXl5efPXVV6Snp5v3L168ON+Ri+z8/f0ZNmwYf/75J5988kmu241GIx988AEXL14sVF2ZI105p07Onz/f6vdx//334+rqyvTp00lOTra4LfvXOjs75zl9825/PvKSkZGR61y+vr5UqVKFlJSU29aUk4+PDx07duTbb78lNDTU4rbbjWr6+fnh7+/P3r17C1z/q6++SlpamsVITL9+/WjQoAEzZszIdV9Go5ERI0Zw/fp13nzzTYuvady4MdOmTWPHjh25zhMXF5croBw9epTk5GTatm17yxoL+9ytWbMmMTEx5tE2gMuXL+f5u/NWbGxs6NevH7/++iuLFi0iPT3dYqogFM1zSghxezKSJYTIZdy4cQQHB7NgwQKGDx/OZ599Rvv27WncuDHDhg2jRo0aXL16lR07dnDx4kUOHjxo/rqVK1cSHBzMU089RYsWLYiOjmb16tXMnTuXpk2b8vjjj7N8+XKGDx/Opk2baNeuHRkZGRw/fpzly5ezbt068/TF/ISEhDB58mQcHBx4+umnsbGxfL9oxowZbNq0idatWzNs2DAaNGhAdHQ0+/fvZ8OGDURHR9/xY7Nw4UK6devGQw89xKBBg+jQoQMpKSmsWrWKzZs3ExISwrhx43J9Xa1atWjfvj0jRowgJSWFOXPm4OXlxauvvmo+pqCP86306dOHRYsW4e7uToMGDdixYwcbNmwwT9PK1KxZM/R6PTNnziQmJgZ7e3u6du2Kr6/vHT82BoOBKVOmMHr0aLp27Ur//v05f/48CxYsoGbNmgV6F/2DDz7gzJkzvPDCC6xatYo+ffrg6elJaGgoK1as4Pjx4xYjlwVx//33YzAY6Nu3L8899xzx8fF89dVX+Pr65hlo7+Y+3Nzc+PDDD3nmmWdo2bIlgwYNwtPTk4MHD5KYmMh3330HQIsWLVi2bBljxoyhZcuWuLi40LdvX6v8fOQUFxdH1apV6devH02bNsXFxYUNGzawZ88eixHP/GrKy8cff0z79u1p3rw5zz77LIGBgZw/f57ffvuNAwcO3LKehx56iJ9++qlA1zqBabpf7969+frrr5k0aRJeXl4YDAZWrlxJt27daN++PUOHDiUoKIgbN27www8/sH//fl555RWL54qdnR2rVq2ie/fudOzYkf79+9OuXTvs7Oz477//zKPQ2VvQr1+/HicnJ+67777b1lmY5+6AAQN47bXXeOSRR3jhhRdITEzkiy++oE6dOoVuUBMSEsInn3zCm2++SePGjXMtxVAUzykhRAEUf0NDIURJkN9ixEoplZGRoWrWrKlq1qxpbhF+5swZ9cQTT6hKlSopOzs75efnp/r06aNWrlxp8bVRUVFq1KhRys/Pz7zo5ZAhQyzaqaempqqZM2eqhg0bKnt7e+Xp6alatGihpk6dqmJiYszH5WzhnunUqVPmBVO3bt2a5/d39epVNXLkSOXv76/s7OxUpUqVVLdu3dSXX35pPiazNfmKFSsK9djFxcWpKVOmqIYNGypHR0fl6uqq2rVrpxYsWJCrhXX2xYg/+OAD5e/vr+zt7VWHDh3UwYMHc913QR7nW/3fXb9+XQ0dOlR5e3srFxcX1aNHD3X8+PE8H8uvvvpK1ahRQ+n1+gItRpzzccpvkdqPP/5YBQQEKHt7e9WqVSu1bds21aJFC9WzZ88CPLpKpaenq6+//lp16NBBubu7Kzs7OxUQEKCGDh1q0SI7s9119oWusz8+2RdgXr16tWrSpIlycHBQ1atXVzNnzlTffvttruMyFyPOS0HvI/PYtm3bKkdHR+Xm5qZatWqllixZYr49Pj5eDRo0SHl4eORajLigPx/cXKQ2L2Rr4Z6SkqLGjRunmjZtqlxdXZWzs7Nq2rRproWU86spv//nI0eOqEceeUR5eHgoBwcHVbduXTVp0qQ868lu//79CsjVUjy/xYiVUmrz5s252tIrpdS1a9fUmDFjVK1atZS9vb3y8PBQ3bt3N7dtz8v169fV5MmTVePGjZWTk5NycHBQjRo1UhMmTFCXL1+2OLZ169Zq8ODBt/2eMhX0uauUUn/++adq1KiRMhgMqm7duur777+/5WLE+TEajcrf318B6p133snzmII+p4QQ1qNTykpXLAshhMjl/PnzBAYGMmvWLMaOHat1OZowGo34+Pjw6KOP5jllSZQ/3bp1o0qVKixatEjrUvJ14MABmjdvzv79+wvViEUIIUCuyRJCCGFFycnJua7LWbhwIdHR0XTu3FmbokSJ8+6777Js2bJCN3ooTjNmzKBfv34SsIQQd0SuyRJCCGE1O3fu5OWXXyY4OBgvLy/279/PN998Q6NGjQgODta6PFFCtG7dmtTUVK3LuKWlS5dqXYIQohSTkCWEEMJqqlevjr+/Px9//DHR0dFUqFCBJ554ghkzZmAwGLQuTwghhCgWck2WEEIIIYQQQliRXJMlhBBCCCGEEFYkIUsIIYQQQgghrKjcXZNlNBq5dOkSrq6uBVoEUQghhBBCCFE2KaWIi4ujSpUq2NhYb/yp3IWsS5cu4e/vr3UZQgghhBBCiBIiLCyMqlWrWu3+yl3IcnV1BUwPpJubm8bVCCGEEEIIIbQSGxuLv7+/OSNYS7kLWZlTBN3c3CRkCSGEEEIIIax+GZE0vhBCCCGEEEIIK5KQJYQQQgghhBBWJCFLCCGEEEIIIaxIQpYQQgghhBBCWJGELCGEEEIIIYSwIglZQgghhBBCCGFFErKEEEIIIYQQwookZAkhhBBCCCGEFUnIEkIIIYQQQggrkpAlhBBCCCGEEFYkIUsIIYQQQgghrEhClhBCCCGEEEJYkYQsIYQQQgghhLAiCVlCCCGEEEIIYUUSsoQQQgghhBDCiiRkCSGEEEIIIYQVScgSQgghhBBCCCuSkCWEEEIIIYQQViQhSwghhBBCCCGsSEKWEEIIIYQQQliRhCwhhBBCCCGEsCIJWUIIIYQQQghhRZqGrL///pu+fftSpUoVdDodP//8822/ZvPmzTRv3hx7e3tq1arFggULirxOIYQQQgghhCgoTUNWQkICTZs25bPPPivQ8efOneOBBx6gS5cuHDhwgJdeeolnnnmGdevWFXGlQgghhBBCCFEwtlqevFevXvTq1avAx8+dO5fAwEA++OADAOrXr8/WrVv58MMP6dGjR1GVKYQQQgghyiClFElpGVqXITSUmJpeJPeracgqrB07dtC9e3eLfT169OCll17K92tSUlJISUkxb8fGxhZVeUIIIYQQopRQStFv7g72XbiudSkllBF0RvO/Ol1G1r6bn+t0mcdk3NxvRJft81zbmL5Wl+1z0zGW92s6n+X9osvIdlzWbXdyXzako9elY6PL4MbBmCJ59EpVyLpy5QoVK1a02FexYkViY2NJSkrC0dEx19dMnz6dqVOnFleJQgghhBBlRlke6UlMzSgBASsDnV0MNnbX0dldR2eTkjso3AwRuhzBIa/QocsRZgoaOnTZjs86n9LwcSl6GTc/DDVz5wdrKFUh605MmDCBMWPGmLdjY2Px9/fXsCIhhBBCiJLPaFT0+WQrRy+X/VlAe9/ojpNBb/X7zTBmEJkUwaWES1xOCOdSwiUuxV/i8s3ta4nXyFClJ8Tq0GFrY4tep0d/819b87969DrbrNt1evQ2+mzbN2+z0Ztvt7WxxVaXtS+/+9bb6G8eZ4utxbbpfs3H2ujRo8c24RqGyJPYRRzF7upR7BKuYatAj8LGqNh0LI3ubRti5x9EfPWGNGKE1R+rUhWyKlWqxNWrVy32Xb16FTc3tzxHsQDs7e2xt7cvjvKEEEIIIcoEpcpPwAoK8MTL2YBOpyv01yqliEqO4mLcRS7FXyI8Ptz8cSn+EpcSLpFuvPU1PwYbA1VcqlDFpQpuBjdz4LCzsTMHDPO2TVZ4Md+eGVayB5I8wkuu7XyOz/VvtttsdCVw9SejESKOw4VtNz+2Q7xlXsDGFqrcQ7xvECO+2cP3q//i9cBuvDPynZuXEpXzkNWmTRvWrl1rsW/9+vW0adNGo4qEEEIIIUqXgkwBTEzNMAesQG9n1oxuzx1kkFLB0U6fb8BSSnEj5QaX4i9xMT7vIJWSkZLn12ay1dlSybkSfi5++Ln6UcW5Cn6uflR1qUoVlyp4O3qXzPBSUhkz4MohU5g6vw1Ct0NSjmmfenuoGgQBbSGgHVRtyX+nLxAcHMyxY8fQ6/W4ubkVaZmahqz4+HhOnz5t3j537hwHDhygQoUKVKtWjQkTJhAeHs7ChQsBGD58OJ9++imvvvoqTz31FH/99RfLly/nt99+0+pbEEIIIUQ5URauT1IKgufuKNQI1ZrR7XG2L1XvyxdKXGpcVnCKM03pC48LJzzBtJ2YnnjLr9eho6JzRVOIyvZRxaUKVV2q4uPkg61N2X38ilx6Klw+YBqlOr8NwnZBSo7nr50T+LeCgPamYOXXAuwczDcvXLiQESNGkJiYSJUqVVi6dCkdOnQo0rI1/R/fu3cvXbp0MW9nXjs1ZMgQFixYwOXLlwkNDTXfHhgYyG+//cbLL7/MRx99RNWqVfn666+lfbsQQgghilR57UQXFOBZJNcqFafEtMQ8R6DC48O5GH+RuNS4296Hj6MPVVyqWAYpVz/8nP2o5FwJO71dMXwn5URaEoTvMwWqC9vg4h5IyxF07d2gWpuskaoqzSCP/4OkpCRGjx7NN998A8B9993H999/j6+vb5F/GzqlVNluHZJDbGws7u7uxMTEFPkwoRBCCCFKr+wjV4mpGQS9s0HjiqynQWU3Vgxvc9spgLeaSldSJKYlcjXxaq4glTkqFZ0cfdv7qOBQwTyNL3MEKjNUVXaujIOtw23vQ9yhlHjT6NSF7aZQFb4PMlItj3GsYApU1W+OVFVsBDa3D//Hjh2jRYsWJCcnM3XqVCZOnIheb/l1RZUNZOxSCCGEEOXO7ab+3WpaXVF1oitOpSU8RSRFcC3xGpFJkRb/RiRFEJEYQURSBAlpCbe9L1eDq0VwyhmknOyciuE7EgAk3YDQnVmNKi4dgJwdFl0qQfV2WSNV3nXBpvDXrdWvX5/58+fj7e1Nt27drFJ+QUnIEkIIIUS5cjdT/+6mE50wySs8ZQamwoanTE62TrmCk7nRxM2ufUIjCZFZo1QXtsGVI0COiXTu1SxDVYUa3EmnlZSUFMaOHcuAAQNo164dACEhIVb4JgpPQpYQQgghyoSCNqYozCK0OafVlYYRIK0URXhytHXE18kXH0cf04eTD75Ovng7elv862znXITfmSiU2MtZger8Nog8kfsYr1o3A1V7CGgDHtXu+rRnz56lf//+7Nu3j19++YWTJ0/i4KDdNE8JWUIIIYQo9e50dOp2U/8kVBVDeHIyBajsoSlzv4SnEk4puHEha6Tq/Da4fi73cb4NTCNUAW1NH66VrFrGzz//zJNPPklMTAxeXl7MmzdP04AFErKEEEIIUQIVtl16YUanMpXnqX+pGalEJ0dzPfk615OvE50STVRSlIQncWtKQdTprEB1YTvEXrQ8RmcDlRpntVMPaAtOFYqknLS0NMaPH8/s2bMB05q6y5Ytw9/fv0jOVxgSsoQQQgihqZyB6k7WcsquoI0pytIoVVJ6UlZgSo7meorp86jkKPP+7LcVJjiBKTxVdKqIt6O3RXjKHqYkPJVBRiNEHMtqp35hOyRcszzGxhaqNM/q/uffChzci7y02NhYevbsyY4dOwB45ZVXmD59OnZ2JaOdvoQsIYQQQmjG2utPlYXRKaUUiemJliNNydFZ2ynXLW67nnKdpPSkQp/HVmeLh4MHng6eVLCvQAWHCuZrniQ8lVMZ6XDlULZGFdsh+YblMXp7qNoyq1FF1ZZgKP7nh6urK1WqVMHDw4MFCxbw0EMPFXsNtyIhSwghhBCaudU0v4Ku5ZRdSR6dikmJISwujGuJ18whKfuok3k7+TqpxtTb32EOdjZ2psDkUAFPe08qON7816ECng6eFrd5OnjiZnArsY+VKCbpqXDpX7iw1RSoQndBzsWZ7ZyhWuuszn9+LcDWXpty09NJSUnB2dkZnU7HN998Q3R0NIGBgZrUcysSsoQQQghRZG51bZVS0OeTrebtnNP8SnJgyotSiqjkKMLiwgiNDSU0LpSwuDDCYsMIjQslNrVw0x8d9A75BiSL/famz53tnEvV4yU0kJYEF/dkjVSF7YGco6D27qaOf5nd/yo3Ab32U/AuXbrEwIEDqVy5MkuWLEGn0+Hu7o67e9FPTbwTErKEEEIIUSQKMxWwQWW3UjHNz6iMXEu8RmisKUBlBqnM7cT0xFt+va+jL5WcK5kDUvaQZBGe7D1lgVxx91LiIGyXKVSd3wbh+8CYZnmMk1e2duptoWJDsClZi21v2LCBQYMGERERgaurK2fOnKFWrVpal3VLErKEEEIIYVWZo1cF7fjXoLIba0a3LxEBy6iMRCdHk5CWQHhcOKFxliNSYXFht5zKp0NnWhTXtSrVXKtRzbUa/m7+VHOtRlXXqjjaOhbjdyPKnaTrELoTzt+c/nf5IKgcI8mulbO1U28HPnXvaOHf4pCRkcE777zD1KlTUUrRtGlTVqxYUeIDFkjIEkIIIYQV5Td6dauOf8U5LTDDmEFEUgTh8eFcir/EpfhLXE64bN6+nHCZtJzv9Odgq7PFz9UPf1dTePJ39aeam+lfPxc/DHpDsXwvQhAfkdWg4sJ2uHoEUJbHeFTLGqWq3g48A0tsqMru6tWrDB48mA0bNgAwbNgwPvroIxwdS8cbFRKyhBBCCJFLYdepypTX6FVxdvxLN6ZzNfGqOUBdir9EeHy4OUhdTbhKukq/5X3o0OFg60AV5yrmUajMMOXv5k9l58rY2shLKKGBmPBsnf+2QeTJ3Md41c5qp16tDXhov2ZUYSml6NOnD3v37sXJyYm5c+fy+OOPa11WochvCCGEEEJYsFZb9czRK2uOVKVlpHEl4QrhCeFcjs8agbqUYApUVxOvYlTGW96Hrc6Wis4V8XPxo7JzZfxc/KjiUsX84evki52N9hf6i3JOKbh+3jJUXT+f+zjfhlnt1Ku1BdeKxV2p1el0Oj744ANGjRrF0qVLadCggdYlFZqELCGEEEJYSEor2LVUt3K3o1exqbGcvXGWszFns/6NOcul+EuonNOhcrCzsaOKSxVzgKrsXJkqLlXMYcrH0Qd9CbuwXwiUgshTWe3UL2yH2HDLY3Q2UKmJaZQqoK1ppMqpgjb1WllUVBQHDx6ka9euAHTs2JEDBw5gY2OjcWV3RkKWEEIIIfJ1q2upbqUgo1eZLc/P3DiTK0xFJkXm+3UOegcqu5iCUxXnKhb/+rn44eXohY2udL4wE+WI0QjX/rNc+DchwvIYGzvwa57V/c+/FTi4aVNvEdq5cyf9+/cnOjqaPXv2UL9+fYBSG7BAQpYQQghR5tzp9VSZElOzvtbJoMfJcHcvF4zKyOWEy5y5cYZzMec4G3PWHKzici58mk1Fp4rUcK9BDY8apn/da1DdvTpeDl4lohOhEIWSkQ5XDma1Uw/dDskxlsfYOkDVllnd/6q2BEPZbeWvlGLOnDm8+uqrpKenU7t2bTIy7vx3V0kiIUsIIYQoQ6x1PdXdCosNY92FdWwK3cSpG6dIyrng6U02OhuqulSlhnsNAj0Cqele0/S5eyAuBpdirloIK0pPgUv/ZrVTD9sFqfGWx9g5Q7XWN0NVO9Oola29NvUWsxs3bvDUU0/x008/ARASEsKXX36Jm1vZGKmTkCWEEEKUYjlHrQq6NlVBBAV44mhX8KmCF+Mu8ueFP1l3fh1Ho45a3GZnY0eAWwA13GtQ0yMrSFV3r469vny8qBRlXGoiXNyTNf3v4h5IT7Y8xsHd1Jwis516paagL38vx/ft20dwcDDnzp3DYDDw4YcfMmLEiDI1Ql3+/leFEEKIMuJ2o1Z3ej1VpoJcVxUeH86f503B6r+o/8z79To9rSq14v7q9xNUMYiqrlWl7bkoW1LiIHRXVue/8P2Qc401J++sduoBbcG3AUjTFX788UfOnTtHYGAgK1asoEWLFlqXZHXy204IIYQopW7VBbAo16a6HH/ZPGJ1OPKweb+NzoaWlVrSo3oPulXrRgWHstH1TAgAEqMhdGdWqLp8EHIuF+BaJaudekA78K5TKhb+LW5vvfUWtra2jBkzBg8PD63LKRISsoQQQogS7FZNLLI3qMg5amXNtakAriRcMY1YXVjHoYhD5v02OhtaVmzJ/dXvp1u1bng5elntnEJoKv5aVte/C9vh6n+Qc/kAj4CsUaqAduBZXUJVHg4dOsTMmTOZP38+BoMBW1tb3nrrLa3LKlISsoQQQogSqjBNLKzRBTCnqwlXWX9hPevOr+NAxAHzfh06gioF0SOgB90CuuHt6G3V8wqhiZiLWddTnd8GUadyH+NdJ6udekAbcK9a/HWWIkopvv32W0aNGkVycjK1atVi6tSpWpdVLCRkCSGEECVAXiNWBW1iUdgGFfm5nnyd49HHORZ9jC1hW9h/bb/5Nh06mldsTo/qPeherTs+Tj53fT4hNKMUXD+X1U79wja4cSHHQTqo2DCrnXpAW3Dx1aTc0ighIYHnn3+ehQsXAtCrVy9Gjx6tcVXFR0KWEEIIkcPdrjNV+PNB8NwdHL0cm+8xt2piUdipgUopLiVc4nj0cdNHlClYXU28muvY5r7Nub/6/dwXcB++TvICU5RSSkHkyax26he2Q9wly2N0NlC5aVY79Wr3gpNcV3gnjh07Rr9+/Th69Cg2Nja88847vPbaa6V6ceHCkpAlhBBCZFNS1pnK7m6aWKQb0zkXcy4rUN38iE3NO9BVc61GvQr1uMf3HroHdKeSc6W7LV+I4mc0wtUjWdP/LmyHxEjLY2zswK9FVjt1/9Zg76pNvWXImjVrCAkJITExkcqVK7NkyRI6deqkdVnFTkKWEEIIkc2tOvYVtQaV3VgxvE2u6+YLOlIVkxLDmRtnOH3jNMeij3E86jinbpwiJSMl17G2NrbU8qhFvQr1qFehHvUr1KeOZx1ZAFiUThnppm5/mZ3/QndAcozlMbYOULVlVqMKvyAwOGlTbxlWr1499Ho93bp1Y/HixVSsWFHrkjQhIUsIIUS5crupgLfq2FfUChqmYlNjOXvjLKdvnDaHqjM3zhCRFJHn8U62TuYwlflRy6MWdno7a38LQhSP9BTTulQXbk7/C90FaQmWxxhcTKNT1W9O/6tyD9jKwtdFISYmBnd3dwBq1arF9u3bqV+/Pnp9+V0TTEKWEEKIcqOwUwGLomNfYcSnxnMm5oxFkDp94zTXEq/l+zWVnStTw6MG9TzrUc/LNELl7+qPja78XAshyqDURLi4O+t6qot7ID3Z8hgHj6wGFQHtoFIT0MtL3aK2YsUKhg0bxsqVK+nevTsAjRo10rgq7ckzTwghRJmT32hVQbv1gfU69hWEUooT109wLOqYKUjFmALVlYQr+X5NRaeK1PKoRU2PmuZ/a7jXkOl+omxIjoWwXVnt1C/9C8Y0y2OcfbK1U28Lvg2gHDVW0FpKSgpjx47l008/BWDu3LnmkCUkZAkhhNBIUXXwK0inPrj9VEBrL+ablysJV1hzdg2/nP6F87Hn8zzG19GXmh41LcJUTY+auBrkAn1RhiRGm66jymynfuUQKKPlMa5Vsqb+BbQD79qy8K9Gzp07R//+/dm7dy8AEyZMKPOLCxeWhCwhhBDFTusOfnfTre9uJaYlsjF0I7+c+YXdl3ejUAA46B1o6tOUWp5Zo1M13Gvgbu9e7DUKUeTirmZ1/buwHa79l/sYz+pZo1TV24FHgISqEmD16tUMGTKEGzduUKFCBRYtWkTv3r21LqvEkZAlhBCiWCmliEpILfKAlV+nPiieUarsjMrI3it7+eXML6y/sJ6k9CTzbUEVg3iw5oPcX/1+nO2ci60mIYpVzMWsUaoL2yDqdO5jvOveDFTtoVobcPcr/jrFLe3evZuHHnoIgHvvvZdly5ZRrVo1jasqmSRkCSGEKDZ5jWAVVQe/4g5SeTkfc57VZ1az5uwaLidcNu/3d/Wnb82+9K3Rl6quVTWsUIgioBREn822RtU2uBGa4yAdVGyUNUpVrS24+GhSrii4li1b8sQTT+Dl5cWMGTMwGAxal1RiScgSQghRaHd6PVXOxhNaTtuzlnRjOteTrxORFEFkUiRRSVFcTbzK1vCtHIw4aD7Oxc6FHtV78FCth2jm06xUf89CWFAKIk5ktVO/sB3iLlseo9ND5aZZ11RVuxccPbWpVxTK+vXradGiBRUqVECn0zF//nxspMHIbUnIEkIIUShGo6LPJ1tv21jidva+0b1EB6zEtEQikyLN4SnzIyIxgsjkSCITTdvXU65jzHmB/k02OhvaVmnLgzUfpIt/FxxsHYr5uxCiCBgz4Op/WaNUF7ZDYpTlMXoD+LXIaqfu3wrspVlLaZKens6UKVOYNm0affr04ZdffsHGxkYCVgFJyBJCCFFgSlknYJWUEazk9GQuxF7gXOw5zsWc43zMec7FnCM0LpSEnAub3oKNzoYKDhXwdvQ2f9TyqEXvwN74OMkUKFHKZaTB5YNZ7dRDd0JKjOUxto7g3zKrUUXVILBz1KZecdcuX77MwIED2bJlCwDVqlUjIyNDAlYhSMgSQgiRr5zTAhNTM8wBK9DbmTWj299Rs6/ivF5KKUVUchTnYs5lfcSaAtWl+Evm7n551mnriI+jj0V48nHywcvBy/y5t6M3nvae6G2KZ00tIYpcWjJc2p/VqCJsN+R808HgCtVaZ7VTr3IP2Mr1OWXBX3/9xcCBA7l27RouLi589dVXDBgwQOuySh0JWUIIIcyyh6rbrTe1ZnR7nO1Lzp+RtIw0wuLCzCEq+8hUXFpcvl/nZnAj0D0w68MtkAD3ACo5VcLJzqkYvwMhNJKaYApSmddTXdwDGSmWxzh43AxUNxtVVGwM+pLz8y/uXkZGBtOmTWPKlCkopWjcuDErVqygbt26WpdWKslPhxBCCKBwa1cFBXgWSUfAwopMimTh0YVsCt1EWFwYGSrvZhw2Ohv8XPyo7lbdMlC5B+Jp76n5tEUhilVyDITuyrqm6tK/YEy3PMbZN6udekBb8KkPMlWsTIuPj2f+/PkopXj66af5+OOPcXKSN5rulIQsIYQQACSlZeQZsPJab0rr9uhXEq4w/8h8fjz1IynZ3nF3snUyh6fsgaqaWzXs9faa1SuEphKjLdupXzkMOZu1uPmZRqoyu/951ZKFf8sZd3d3VqxYwX///ceQIUO0LqfUk5AlhBAil+xrV2kdqLILiwvjm8Pf8MuZX0i/+c57E+8mPNnoSZp4N8HXybfE1CqEZuKuZHX9u7Adrh3NfYxnYFagCmgHHtUkVJUzRqOR999/H09PT4YNGwZAUFAQQUFBGldWNkjIEkIIkYuTQY+ToeT8iTh74yxfH/6atefWmqcEtqzUkmebPEvrSq0lWIny7UZY1ijV+W0QfSb3MT71stqpB7QFtyrFX6coMaKjoxkyZAhr1qzB3t6e7t27ExgYqHVZZUrJ+QsqhBBCM0opElMLv7hwUTsRfYIvD33J+gvrzV0A2/m149nGz9K8YnONqxNCA0pB9NmsQHVhO8SE5jhIB5UaZY1SBbQFZ29NyhUlz65du+jfvz+hoaHY29vz6aefUr16da3LKnMkZAkhRDlXmIYXxSE2NZZ159ex+vRqDkQcMO/v6t+VZ5s8S0PvhtoVJ0RxMxoh8gSc35o1/S/+iuUxOj1UaZYVqqrdC44eWlQrSjClFB9//DHjxo0jLS2NWrVqsWLFCpo1a6Z1aWWShCwhhCiHsrdqT0y1bHgRFOCJo13xdg5MN6az/dJ2Vp9ZzabQTaQaUwFTV8AeAT14pskz1PGsU6w1CaEJY4apMYW5UcV2SIq2PEZvAL+grHbqVVuBvYs29YpSQSlFSEgIK1asACA4OJivv/4aNzc3jSsruyRkCSFEOZEZrG61/tXeN7rj5WwotmucTkSfYPWZ1fx29jeikqPM+2t51OLBmg/yQI0H8HXyLZZahNBERhpcOpB1TVXoTkjJ8bNp6wj+rbK6//m1ADtHTcoVpZNOp6NJkyb8/PPPzJ49m5EjR8q1rEVMQpYQQpQDBZkSGBTgWSwBKyopirXn1rL6zGqORx837/e096R3jd70rdmXBhUayAsAUTalJUP4vqxQFbYb0hItjzG4mqb8ZXb/q9wMbA2alCtKL6UUMTExeHh4ADBx4kQeffRRGjRooG1h5YSELCGEKAdyTgmE3OtfFWWrdqUUm8M28+OpH9kavtXcIdDWxpZOVTvxYM0H6eDXATu9XZGcXwjNpCZA2C7TtL/z2yB8L2SkWh7j6JnVoCKgHVRqDDbaL/YtSq+4uDiee+45/vvvP3bu3ImjoyM2NjYSsIqRhCwhhCjjlFIEz91h3s5cA6u41r9KTEtkyo4p/H7ud/O+Rl6NeLDWg/Sq3gsPB48ir0GIYpN042aoutn97/IBuLmmm5mzr+UaVT71wMZGi2pFGXT48GH69evHyZMn0ev1/P333/To0UPrssodCVlCCFHGJaVlmK+/alDZrVivuTpz4wxjNo/hbMxZ9Do9j9V/jP/V/h81PGoUy/mFKHIJURB6c5TqwjZT04qbyw2YuVW1DFVeNWXhX1Ek5s+fz8iRI0lKSsLPz49ly5bRrl07rcsqlyRkCSFEGZZz/SvT9MDieXH365lfeXvn2ySlJ+Hr6Mt7nd6jRcUWxXJuIYpM7OWsrn8XtkHE8dzHVKiRFaiqtwOPasVfpyhXEhMTGTlyJAsWLACgZ8+eLFq0CG9vWR9NKxKyhBCijLlVF8HiyFcpGSnM3D2TFSdNrYJbV27NzA4z8XL0KvqTC2Ft1y/cDFQ316mKPpv7GJ/6We3Uq7UFt8rFX6co1zIDlo2NDW+//Tbjx4/HRqagakpClhBClCG36iJYHOtfhcWF8crmVzgWfQwdOp5t8iwjmo5ALxfxi9JAKYg6k9X578J2iAnLcZDO1Jgic5SqWhtwltECoa233nqLvXv38sknn9C5c2etyxFIyBJCiDIlKS3/LoJOhqJpdGFURmJTYtl7dS+Tt00mLi0OD3sPZnSYQTs/uRZAlGBGo2m6X/ZQFX/V8hidHqrck3VNlX9rcPTQpFwhMiUnJ7N27VoeffRRAPz9/Tl48KCMXpUgErKEEKKMKo4ugqevn+aVLa9wNiZrClVTn6a83+l9KjlXKpJzCnHHjBlw5VBWO/XQ7ZCUY9RXbw9Vg7LaqVdtCfYu2tQrRB5Onz5NcHAwBw4c4Oeff+ahhx4CkIBVwkjIEkKIUiTzeqv8ZG9y4WTQ42Qoul/zG0M3MvGfiSSmmxZStbOxY2C9gbzU/CVZ70qUDOmpphbqme3Uw3ZBSqzlMXZO4N8qq1GFXwuwc9CkXCFu58cff+Spp54iNjYWb29vnJ2dtS5J5ENClhBClBK3ut6qOBmVkbkH5/LFwS8AaFWpFe91fA9PB09sdPJOqtBQWhKE78tqp35xD6QlWh5j7wbV7s0KVVWagbwpIEq41NRUXn31VT766CMA2rdvz9KlS/Hz89O4MpEfCVlCCFFK5HW9VX6KqslFfGo8E7dOZFPYJgAG1x/MmKAx2NnIi1ShgZT4mwv/3mynHr4PMlItj3GskDX1r3o7qNgIpBGLKEUuXLhA//792b17NwCvvvoq77zzDnZ28nu3JJOQJYQQJVzmFMHsUwEzr7fKT1Fch3Uh9gIv/PUCZ2POYrAxMKnNJB6u9bBVzyHELSXdgNCdWY0qLh0AlWP6rEvFm6NUbaF6e/CuC3KtiijFdu3axe7du/H09GThwoX06dNH65JEAUjIEkKIEiC/a63yWusKiv56q5z+ufgPr/39GnFpcfg6+TKn8xwa+zQutvOLciohMmuU6sI2uHIEUJbHuFfLWqMqoJ1pIeBiWnBbiOLQv39/wsPDefTRRwkICNC6HFFAErKEEEJjhb3WqjjWu8qklOKbI9/w8f6PUSia+TTjwy4f4u0o6wKJIhB7OStQnd8GkSdyH1OhZlagCmgLHtWKv04hitDFixd58cUX+fzzz6lYsSIAL7/8ssZVicKSkCWEEBpLTL39tVaZa13pdEUzFTAv8anxTN0xlT/O/wHA/2r/j4mtJ2LQG4r83KIcUApuXMgaqTq/Da6fy32cb4Ob11TdvK7KVZYGEGXXunXrGDx4MJGRkRiNRn766SetSxJ3SEKWEEJoRClFYmoGfT7Zat6X37VWxRWsLsdfZsvFLWy+uJk9l/eQakzFVmfLhNYT6F+3f5GfX5RhSkHU6axAdWE7xF60PEZnA5UaZ3X+q9YGnL20qVeIYpSRkcGUKVOYNm0aSimaNWvGrFmztC5L3AUJWUIIoYG8pgg2qOyGl7OhWMJUJqMyciTyCJvDNrPl4hZOXj9pcXuAWwBT206lRcUWxVaTKCOMRog4ltVO/cJ2SLhmeYyNLVS5J1uoag0O7trUK4RGrly5wqBBg9i0ydS19bnnnmPOnDk4OMh6baWZhCwhhChmSimiElJzBaw1o9sXS8BKTEtkx6UdbL64mb8v/k10crT5NhudDU19mtKpaic6+3emhnuNYg19ohTLSIcrh25O/9sOodshKcc0WL09VG2Z1aiiakswyGKqovw6dOgQ999/P1evXsXZ2Zl58+bx2GOPaV2WsAIJWUIIUYzyGsHa+0b3Ih/Buhx/mc0XTaNVuy/vJs2YZr7Nxc6FtlXa0tm/M+392uPp4FlkdYgyJD0VLv2b1agidBekxlkeY+cM/q2y1qiq0hzs5N15ITIFBgbi4eGBt7c3K1eupF69elqXJKxEQpYQQhSjnAsKBwV4FlnAik2NZeF/C9kUtinXNMCqLlXp7N+ZTv6daOHbAju9LGopbiMtCS7uzQpVYXsgPcnyGHt3qHZvVve/yk1BnltCWLhx4wbu7u7odDpcXV35/fff8fX1xdlZRnXLEglZQghxl/Jb4yovORcULqqAdTHuIs9vfJ5zMaZubTY6G5r5NKOTfyc6V+1MoHugTAMUt5YSB2G7TFP/zm+D8H2QbQQUACevrK5/Ae2gYkOwKZ7lBYQojbZt20ZISAhjxoxhzJgxgGk0S5Q9ErKEEOIuFHaNq+ycDEXTMfBQxCFG/zWa6ORoKjpV5MXmL9LBrwMeDh5WP5coQ5KuQ+jOrO5/lw+CyvHmgUulm6NUbSGgPfjUlYV/hSgApRQffPAB48ePJyMjg/nz5zN69Gjs7GSkt6ySkCWEEHehIGtc5aWoFhTecGED4/8ZT0pGCvUq1OPTrp9S0bmi1c8jyoD4CFNzisx26lePAMryGI9qpjCVuU5VhRoSqoQopOjoaJ588kl+/fVXAAYOHMi8efMkYJVxErKEEKKAck4LVIoCrXGVF2uve6WUYuHRhXyw9wMUig5+HZjVaRbOdjLHX9wUeylbO/VtEHky9zFetbNN/2sLHv7FX6cQZcju3bvp378/Fy5cwGAw8PHHH/Pss8/KdO1yQEKWEEIUwO2mBWqxxlWmdGM6M3bPYNmJZQCE1A1hfKvx2NrIr/hySym4fj6rnfqFrabtnHwbZrVTr9YWXGXUUwhriYyMpGvXriQkJFCjRg1WrFhB8+bNtS5LFBP5CyyEEAWQsytgdsW5xlVOiWmJjN0yln/C/0GHjleCXuGJBk/Iu6TljVIQeSprlOrCdogNtzxGZwOVmkD1m9P/qrUBpwra1CtEOeDt7c3bb7/N1q1b+fbbb3F3l4W2yxOdUkrd/rCyIzY2Fnd3d2JiYnBzc9O6HCFEKZGYmk6DyeuA3NMCrT31r6CuJlxl1F+jOB59HHu9PTM6zKB7QPdir0NowGiEa0ctQ1VChOUxNnbg1zxr+p9/a3CQv3tCFKUDBw5ga2tLo0aNANMsCEDe+CrBiiobyEiWEEIUkpNBj5NB21+fJ6JPMHLjSK4mXqWCQwU+6foJTXyaaFqTKEIZ6XDlYLbpf9sh+YblMbYOULVlVqiq2hIMTpqUK0R5o5Tiq6++4oUXXiAgIIC9e/fi6uoq4aock5AlhBClzNbwrbyy+RUS0xMJdA/k826fU9W1qtZlCWtKT4FL/2a1Uw/bBanxlsfYOUO11llrVPk1B1t7beoVohyLj49n+PDhLF68GIDatWuTnp6ucVVCaxKyhBCiFFlxcgXTdk4jQ2XQslJLPuz8Ie72Ms+/1EtNhPC9Wd3/Lu6B9GTLYxzcTc0pMkeqKjcFvfwZF0JLR44cITg4mOPHj6PX63n33XcZO3YsNjY2WpcmNCa/nYUQIoecrdrBtB6WljKMGXz070fMPzIfgL41+jK17VTs9LLOSqmUEgehu7KupwrfB8Y0y2OcvLMCVfV24NsAbKy/tpoQ4s589913jBgxgqSkJKpUqcKyZcto37691mWJEkJClhBCZGM0Kvp8spWjl2O1LsXsevJ1xv8znu2XtgPwfNPnGd50uMz1L00SoyF0Z1ajissHQRktj3GtnBWoAtqBdx1Z+FeIEspoNLJw4UKSkpK47777+P777/H19dW6LFGCSMgSQoiblLp9wAoK8MTRrvhGEw5HHGbMljFcSbiCg96BKW2n8ECNB4rt/OIuXToAf74B57cCOZr5egRktVMPaAuegRKqhCglbGxsWLx4MYsWLWLMmDHo9TLKLCxJyBJClHuZ0wMTUzPMASvQ2/nm2leWxxZXu3alFMtPLGfGnhmkG9MJcAtgdufZ1PGsU+TnFlaQGA1/vQ1752MOV951sqb/BbQFd2lWIkRpsnTpUvbt28esWbMAqFSpEuPGjdO4KlFSScgSQpRbSikSUzMInrsj1+jVmtHtcbbX5ldkYloib+98mzVn1wDQrVo33m73Nq4GV03qEYVgzID938HGtyDp5uLVjfpB9zfBo5q2tQkh7khycjJjxozhiy++AKB79+706NFD46pESSchSwhRLiml6Dd3B/suXM91W1CAp8Viw8XpfMx5Xt78MqdvnEav0/NS85cY0nCIXH9VGoTthrVjTddbgalRRe9ZpimBQohS6ezZswQHB7N//34AXn/9dbp166ZxVaI0kJAlhCiXktIyLAJWg8purBjeBp2u+KYE5rT+wnombZtEQloC3o7ezOo4i6BKQcVehyik+GuwYQocMK2Rg70bdHkdWj4jLdaFKMV++uknhg4dSkxMDF5eXixatIhevXppXZYoJeS3vxCiXFLZehDsfaM7Xs4GzUaL0oxpfLTvI747+h0AzX2b836n9/Fx8tGkHlFAGemw52vY9C6kxJj2NRtsmhroIl3GhCjNpkyZwtSpUwFo06YNy5Ytw9/fX+OqRGkiIUsIUe4opQieu8O87WTQZuQKICIxgrFbxrL/mmkqypMNn+SF5i9gZyPrX5Vo57fC2lfh2n+m7cpNofcH4N9S27qEEFbRvHlzdDodY8aMYfr06djZye9kUTgSsoQQ5U5SWlYXwQaV3Yq1JXt2e6/sZeyWsUQlR+Fs58w77d6he0B3TWoRBRR7Cf6cBEdWmrYdPaHbm9D8CVkoWIhSLjo6mgoVKgDw4IMPcuTIERo0aKBxVaK0stG6ACGE0JLpOqziHcVSSrHgyAKe+fMZopKjqOVRi6UPLJWAVZKlp8LWOfBJ0M2ApYOgp2H0fggaKgFLiFIsPT2dCRMmULduXcLCwsz7JWCJuyEjWUKIcif79VjFPUswIS2BN7a+wYbQDQD0qdGHSfdOwsnOqXgLEQV3eiP8/hpEnTJtV21l6hpYpZmmZQkh7t6lS5cYOHAgf//9N2BqdvHCCy9oXJUoCyRkCSHKrMxFhi33QZ9PtmpST1J6EiM3jmTf1X3Y2tgyvuV4+tftL+3ZS6obobBuIhz71bTt7AP3vQVNBoCNTAQRorRbv349jz32GBEREbi6uvLNN98QHBysdVmijJCQJYQok261Dlam4rweKzUjlZc3v8y+q/twsXPhi+5f0My3WbGcWxRSWjJs/xj++QDSk0Gnh9bPQefx4OCudXVCiLuUkZHB22+/zVtvvYVSiqZNm7JixQpq166tdWmiDJGQJYQoc5RSRCWk3jZgrRndvlhGkdKN6bz696tsC9+Go60jn3X7TAJWSaQUnPwD/hgP18+b9lXvAL3eg4pybYYQZcXHH39sbs8+bNgwPvroIxwdHTWuSpQ1ErKEEGVC5tRApSB47g5z90AwrYPlZLAcsSquBYeNysikbZPYGLoROxs7PuryEc0rNi/y84pCijpjClen/jRtu1aBHu9Aw0eL/8I9IUSRGj58OCtXrmTEiBEMHjxY63JEGSUhSwhR6t1qamBQgKdmCw0rpXhn5zusObsGW50tszvPpk2VNsVeh7iF1ATTtMDtn0BGKtjYQdtR0GEs2LtoXZ0QwgqMRiNLly4lJCQEvV6Po6MjW7dulethRZGSkCWEKLUyR68SUzNyBawGld1YMbyNZgsNK6V4f+/7rDi5Ah06pneYTmf/zsVeh8iHUnD0Z1j3OsSGm/bV7GaaGuhdS9PShBDWExUVxRNPPMHatWs5c+YMkyZNApCAJYqchCwhRKmRvVtgXtMCIWtqYHFNB8zPFwe/YOHRhQBMbTuVnoE9NatF5HDtOPw+Ds6ZWjbjUQ16zoC6vWVqoBBlyI4dOwgJCSEsLAwHBwf8/Py0LkmUIxKyhBClQkG6BWo5NTC7+Ufm88XBLwAY32o8j9R+RNN6xE3JsbBlJuyaC8Z0sHWA9i9DuxfBTi56F6KsUEoxZ84cXn31VdLT06lduzYrV66kSZMmWpcmyhEJWUKIUiEpLfeUQMiaFqjTFV8zi1tZenwps/fNBuDF5i/yWP3HNK1HYBr2PLQM1k+G+KumffX6QI9p4Fld09KEENZ148YNhg4dys8//wxASEgIX375JW5ubtoWJsodCVlCiFIne7fAkhCsMv177V+m7ZoGwLDGw3im8TMaVyS4fAjWjoOwnabtCjVN113V7q5tXUKIInHhwgV+//13DAYDH374ISNGjCgxfyNE+SIhSwhRYmW/BisxNcO838mgx8lQ8n597bmyB4C2Vdoy+p7RGldTziVGw6Z3Ye83oIxg5wQdx0GbkWBrr3V1Qogi0rRpUxYsWECtWrUICgrSuhxRjpW8VylCCEHBrsEqCa4lXuOPc3+w9txa/ov6D4BKzpXknVOtGI3w7yLYOBUSo0z7Gj4K978N7lW1rU0IYXWxsbGMHDmS0aNH06pVKwAGDBigcVVCSMgSQpRQ+V2DFRTgiaOdPo+vKD4xKTFsuLCBtefWsufKHhQKAL1Oz72V7+WJBk9oWl+5dXEfrB0Ll/abtn3qQe9ZENhR27qEEEXi4MGDBAcHc+rUKXbt2sXRo0extZWXtqJkkGeiEKLEUUpZTA8sCddgJaYlsuXiFtaeXcvWS1tJN6abb7vH9x56B/bm/ur3U8GhQrHXVu4lRMKGKaYRLAB7N+g8AVoNA72dpqUJIaxPKcW3337LqFGjSE5Oxt/fn++++04ClihR5NkohChRjEZFn0+2Wqx/pdU1WGkZaWy/tJ2159ayKWwTSelJ5tvqeNahd2BvegX2oopLlWKvTQAZ6bBvPvz1NiTHmPY1HQTdp4BrRU1LE0IUjYSEBEaMGMGiRaY3VXr37s3ChQvx8vLSuDIhLEnIEkKUGErlDlhaTA+MSYnhq0Nf8fOZn4lJiTHvr+pSlV6Bvegd2JtanrWKtSaRw4Udpq6BVw+btis1ht7vQ7V7ta1LCFFkrl27RpcuXTh69Cg2NjZMmzaNV199FRsbG61LEyIXzUPWZ599xqxZs7hy5QpNmzblk08+MV+4mJc5c+bwxRdfEBoaire3N/369WP69Ok4ODgUY9VCiKKQmJphDliB3s6sGd0eJ0PxTQ9My0hjyfElzDs0j9hUUx1eDl70DOxJ78DeNPZuLA0ttBZ3xbTe1aFlpm0HD+g2CVoMBRttr9UTQhQtb29vAgMDuX79OkuXLqVjR7neUpRcmoasZcuWMWbMGObOnUvr1q2ZM2cOPXr04MSJE/j6+uY6/ocffmD8+PF8++23tG3blpMnT/Lkk0+i0+mYPXu2Bt+BEMIaMq/B6vPJVvO+NaPb42xfPL+ilFJsDN3Ih/s+JDQuFIDanrV58Z4Xae/XHr28eNdeRhrsmgubZ0BqPKCDFkOg62RwlmlCQpRVSUlJKKVwcnLCxsaGhQsXkp6enufrRCFKEk1D1uzZsxk2bBhDhw4FYO7cufz22298++23jB8/Ptfx27dvp127dgwaNAiA6tWrM3DgQHbt2lWsdQshrCevVu0NKruZG10UtSORR5i1Zxb7r5k60nk5eDH6ntE8XOthCVclxZlN8PtrEHnCtO0XZOoa6Ndc27qEEEXq1KlTBAcH06RJE7777jt0Oh0VKkhzIVE6aBayUlNT2bdvHxMmTDDvs7GxoXv37uzYsSPPr2nbti3ff/89u3fvplWrVpw9e5a1a9fy+OOP53uelJQUUlJSzNuxsbH5HiuEKD6ZCw0npmbkClhrRrcv8ml5l+Mv89G/H/Hb2d8AcNA78ETDJ3iq0VM42zkX6blFAd0Igz9fh6O/mLadvOG+qabmFnINhhBl2ooVK3j66aeJi4vj0qVLXL58mSpVpMmQKD00C1mRkZFkZGRQsaJlB6iKFSty/PjxPL9m0KBBREZG0r59e5RSpKenM3z4cCZOnJjveaZPn87UqVOtWrsQ4u7kt9Dw3je64+VsKNKAFZ8azzdHvmHR0UWkZJjegHmw5oOMvmc0lZwrFdl5RSGkJcOOT+DvDyA9CXQ20OpZU1t2Rw+tqxNCFKGUlBTGjh3Lp59+CkDHjh1ZsmSJBCxR6mje+KIwNm/ezLvvvsvnn39O69atOX36NC+++CJvv/02kyZNyvNrJkyYwJgxY8zbsbGx+Pv7F1fJQog85LXQcFCAZ5EFrOT0ZLZf2s6RyCP8eOpHopOjAWhZqSVjg8bSwKuB1c8p7tDJdaapgdfPmbYD2kGv96BSI23rEkIUuXPnztG/f3/27t0LmF7DvfXWW7L+lSiVNHvWent7o9fruXr1qsX+q1evUqlS3u8mT5o0iccff5xnnnkGgMaNG5OQkMCzzz7L66+/nmcLT3t7e+zt7a3/DQgh7kh+Cw1be5HhhLQE/rn4D+svrOef8H8s1riq7ladl1u8TBf/LtItsKSIPgt/TICTf5i2XSvD/e9Ao/+B/B8JUeZlZGTQq1cvTpw4QYUKFVi0aBG9e/fWuiwh7phmIctgMNCiRQs2btzIww8/DIDRaGTjxo2MGjUqz69JTEzMFaT0etOF6UqpIq1XCHH38pomaM2FhmNSYtgctpkNFzaw/dJ2Uo2p5tsqO1emmW8zgioG8UjtR7CzsbPKOcVdSk2ErbNh28eQkQI2ttBmJHQcB/auWlcnhCgmer2ezz77jMmTJ7NkyRKqVaumdUlC3BVNx1/HjBnDkCFDCAoKolWrVsyZM4eEhARzt8EnnngCPz8/pk+fDkDfvn2ZPXs299xzj3m64KRJk+jbt685bAkhSq6cTS6ssdBwcnoyq8+sZsOFDey5sod0lW6+LcAtgO7VunNfwH008Gogo1YliVJw7FdYNxFiwkz7anQxTQ30qaNtbUKIYhEWFsbJkyfp1q0bAN26daNr167yu1qUCZqGrJCQECIiIpg8eTJXrlyhWbNm/PHHH+ZmGKGhoRYjV2+88QY6nY433niD8PBwfHx86Nu3L9OmTdPqWxBC5CGzc6DlPizWwbJWk4sP9n7A0hNLzdu1PWtzX7X76B7QnVoeteSPdUkUcRJ+fxXObjJtu/tDz+lQr49MDRSinPj99995/PHHSUtLY9++fdSqVQtAfmeLMkOnytk8u9jYWNzd3YmJicHNzU3rcoQoc/LrHJhdg8pu/PbC3bdpT0pPYvj64ey/tp9egb0Y2WwkAW4Bd3WfogilxMGW92Dn52BMB709tHsR2r8MBietqxNCFIP09HTefPNN3n33XQCaN2/OypUrCQwM1LgyUV4VVTaQdi1CiELJa5Qqu5xTAnOyxjpYCWkJLDuxjO/++87cKbBrta4SsEoqpeDwSvjzDYi/YtpXpxf0fBcq1NC2NiFEsbl8+TIDBw5ky5YtADz//PN88MEHODg4aFyZENYnIUsIUWBGo6LPJ1s5erlgi3pndg7M7m66CMakxPDD8R/4/uj3xKaaavBz8ePpxk9zf8D9d3SfoohdOQJrx0HodtO2ZyD0mgl1emhblxCiWP31118MHDiQa9eu4eLiwtdff01ISIjWZQlRZCRkCSEKRKnCBSxrrnsVnRzNoqOLWHJ8CQlpCYCpDfuwJsPoFdhLOgWWREk3YNO7sOcrUEawdYSOY6HNKLCTd62FKG9+/fVXrl27RuPGjVm5ciV16kiDG1G2ScgSQhRIYmqGOWAFejvfnPKX//HWWPfqWuI1Fvy3gBUnVpCckQyYGls82+RZ7qt2H3ob6Spa4hiNcPAHWP8mJEaa9jV4CO6fBh6yELwQ5dXMmTPx8fHh5ZdfxtHRUetyhChyErKEELellCJ47g7z9prR7XG2L9pfH5vDNvPK5lfMa1019GrIc02eo5N/J2x0uRceFyVA+H7T1MDwvaZt7zqmluw1u2hblxCi2P3zzz98+umnLF68GFtbWwwGAxMnTtS6LCGKjYQsIUQuOZtbZB/FalDZLdd1Vtb2X9R/vPr3q6QaU2nq05Tnmz5PmyptpLVvSZUQBX+9Bfu+AxQYXKDzeGj1HNgatK5OCFGMjEYjs2bN4vXXXycjI4OgoCDGjRundVlCFDsJWUIIC7drwb5ieNGGnSsJVxi9cTRJ6Um0q9KOT7t9iq2N/KoqkYwZsG8+bHwbkm+Y9jUJgfveAtdKmpYmhCh+UVFRDBkyhN9++w2AwYMHM2LECI2rEkIb8spFCGEhKS3/FuxBAZ5FOooVnxrP8xufJyIpgtqetXm/0/sSsEqq0F2w9hW4cti0XbER9J4FAW21rUsIoYldu3bRv39/QkNDsbe359NPP+Xpp5+WGQii3JJXL0KIfOVswW6NZhb5STemM/bvsZy6fgpvR28+6/oZLgaXIjmXuAtxV2HDm3BwiWnbwR26ToIWQ0Evf1KEKI8WL17M0KFDSUtLo1atWqxYsYJmzZppXZYQmpK/iEIIM6UUialZ12I5GfQ4GYr+14RSihm7Z7AtfBsOegc+7foplV0qF/l5RSFkpMHuL2HTdEiNA3TQ/HHo9iY4e2tdnRBCQ82bN8dgMPDwww/z9ddf4+bmpnVJQmhOQpYQArj9tVhFadHRRSw7sQwdOmZ0nEFD74bFXoO4hXN/m7oGRhw3bVdpDr3fh6ottK1LCKGZyMhIvL1Nb7DUr1+f/fv3U7t2bZkeKMRN0gdZiHLONHqVTlRCqkXACgrwxNGu6Neh2hi6kff3vg/AK0Gv0K1atyI/pyigmIuw4kn4rq8pYDl5wYOfwDMbJWAJUU4ppZg7dy4BAQFs2bLFvL9OnToSsITIRkayhCiHMlu0KwXBc3eY27Nn2vtGd7ycDUX+BzM+NZ4J/0xAoQipG8ITDZ4o0vOJAkpPgR2fwt/vQ1oi6Gyg5TPQZSI4empdnRBCI3FxcTz33HMsWWK6JnPx4sV06tRJ46qEKJkkZAlRztxuWmBQgGexBCyAa4nXSEpPwsXOhfGtxsu7oCXBqQ3w+6sQfca0Xa2NqWtgpcba1iWE0NThw4fp168fJ0+eRK/XM3PmTMaMGaN1WUKUWBKyhChnElNzt2hvUNnt5vpXRdtBMLt0YzrzDs0DwMPeQ1q1a+36efhjIpwwrW+DS0W4721o0h8k/ApRrs2fP5/nn3+e5ORkqlatyrJly2jbVpZrEOJW5FWNEOWIUorguTvM25kt2osrWGVKy0jjtX9eY/2F9djqbBkbNLbYzi1ySEuCrXNg64eQkQI2ttB6OHR6DRykQ5gQ5d2GDRt46qmnAOjZsyeLFi0yN7wQQuRPQpYQ5UhSWob5+qsGld2KbVpgdikZKYzdPJbNFzdjZ2PHB50+oEu1LsVagwCUguO/wboJcCPUtC+wE/R6D3zraVubEKLE6NatGwMHDqRx48a89tpr2NhIzzQhCkJClhDllGl6YPEGrKT0JF7a9BLbL23HXm/PnC5zaO/XvlhrEEDkKfj9NTiz0bTtVhV6TIMGD8nUQCEEP/74I927d8fd3R2dTsfixYvlmlkhCknejhCinCruv5eJaYmM3DiS7Ze242jryGfdPpOAVdxS4mH9m/B5G1PA0hugw1gYtRsaPiwBS4hyLjk5meHDh9OvXz+eeuoplFIAErCEuAMykiVEOWFaDytDk3PHpcbx/IbnORBxAGc7Zz7v9jnNKzbXpJZySSk48iP8OQniLpn21b4fes4Ar5ra1iaEKBFOnz5NcHAwBw4cQKfT0ahRI5RSErCEuEMSsoQoB27Xtr0oxaTEMHz9cI5EHcHV4Mq87vNo7CPtwIvN1aOmluzn/zFte1aHnjOhbk9NyxJClBw//vgjTz31FLGxsXh7e7N48WLuv/9+rcsSolSTkCVEOZCUZtm2PSjAE0c7fZGfNzo5mufWP8fx6ON42Hvw5X1fUt+rfpGfVwDJMbB5BuyaByoDbB2gwyvQ9gWwc9C6OiFECZCamsq4ceP4+OOPAWjfvj1Lly7Fz89P48qEKP0kZAlRhimlSErLsJgmuPeN7sXSVTAyKZJhfw7j9I3TeDl48dX9X1Hbs3aRnlMARiMcWgrrJ0NChGlf/b5w/zTwDNC2NiFEiRIXF8eqVasAePXVV3nnnXews7PTuCohygYJWUKUMZnBSikInrvD3LI9k5OhaNfECosNY9mJZfx0+idiU2PxdfTl6x5fE+geWGTnFDddOgBrx8HF3aZtr9rQaybU6qZpWUKIksnLy4tly5YRHR1Nnz59tC5HiDJFQpYQZcjtrr0qqmmCRmVkW/g2lhxfwtbwrShMHamqu1Xn826f4+/mb/VzimwSo+Gvt2HvfECBnTN0fg1ajwBbg9bVCSFKiLS0NF5//XUaNmzIkCFDAGjbtq3GVQlRNknIEqIMyXntFZgWHTatiQWOdtYdxYpJieHn0z+z7MQywuLCzPvbVWnHwHoDae/XHr1N0V/7VW4ZM2D/d7DxLUi6+f/eOBjuewvcqmhbmxCiRLl48SIhISFs374dJycnevbsScWKFbUuS4gyS0KWEGXU3je642TQWz1YARyPPs7S40v57exvJGckA+Bq58rDtR8mpG4IAW5y7U+RC9sNa8fC5YOmbd+G0Ps9qC5rjwkhLK1bt47BgwcTGRmJm5sb8+fPl4AlRBGTkCVEGZFzHSwngx4ng/V+xM/eOMs/4f+wMXQj/17717y/jmcdBtYbSO/A3jjZOVntfCIf8ddgwxQ4sNi0be8OXV+HoKdBL7/ShRBZMjIymDJlCtOmTUMpxT333MOKFSuoWVPWxxOiqMlfZCHKAKNR0eeTrbmaXFhLmjGNYX8O41rSNQBsdbZ0D+jOwHoDucf3HlmssjhkpMOer2HTu5ASY9rXbDB0nwIuPpqWJoQoedLT0+nZsycbN24EYMSIEcyePRsHB1nCQYjiICFLiFJOqdwBy9oNLraHbzcHrOFNh9O/Tn98nOSFfbE5v9XUNfDaUdN25WbQ+33wb6lpWUKIksvW1pY2bdqwc+dOvvrqKwYOHKh1SUKUKxKyhCilsq+BlRmwAr2dWTO6vdXbtP9y5hcABtcfzMhmI612v+I2Yi/Bn5PgyErTtqMndHsTmj8B0lBECJGD0Wjk+vXreHl5ATBlyhSGDh1KjRo1NK5MiPJHQpYQpcjt1sBaM7o9zvbW/bGOSYlhc9hmAB6q9ZBV71vkIz0Vdn4OW96DtARAB0FPQdc3wKmC1tUJIUqgiIgIHn/8cSIjI9m2bRv29vbo9XoJWEJoREKWEKVEQdbAcjJYf3Tjj3N/kGZMo45nHepVqGf1+xc5nN4Iv78KUadN21VbQe9ZUKWZpmUJIUqubdu2ERISQnh4OI6Ojuzfv582bdpoXZYQ5ZqELCFKAaUUUQmpxboGVqbVZ1cD8GDNB61+3yKb6xdg3UQ4vsa07exrWu+qSQjY2GhbmxCiRFJK8cEHHzB+/HgyMjKoW7cuK1eupFGjRlqXJkS5JyFLiBIur86BRbkGVnbnYs5xKOIQep2eB2o8UGTnKdfSkmDbx7B1NqQng04PrYdD59fAwV3r6oQQJVR0dDRPPvkkv/76KwCDBg1i3rx5uLi4aFyZEAIkZAlRouXXOdDL2VAsbdN/PWP6493Orx3ejt5Ffr5yRSk48Tv8MR5uXDDtq97BNDXQt762tQkhSrxhw4bx66+/Ym9vz0cffcSzzz4ry2kIUYJIyBKiBEtKK/rOgfkxKiO/njWFrL41+xb5+cqVqDPw+2twer1p27UK9JgGDR8BeZEkhCiA999/n9DQUL788kvuuecercsRQuQgIUuIUqIoOgfeyu4ru7mScAVXgytd/LsU23nLtNQE+OcD2P4JZKSCjR20HQ0dXgF7meIjhMhfTEwM69ato3///gAEBgaye/duGb0SooSSkCVECaZU1ufF/Xd09WlTw4ue1Xtir7cv3pOXNUrB0Z9h3esQG27aV6s79JwJ3rU0LU0IUfL9+++/BAcHc+bMGdzc3OjZsyeABCwhSjAJWUKUUEopgufu0OTcCWkJbAjdAEhXwbt27Tj8Pg7O/W3a9qgGPWdA3d4yNVAIcUtKKb788ktefPFFUlJSCAgIoEIFWStPiNJAQpYQJVT267EaVHbD0c76a2DlZ/2F9SSlJxHgFkBTn6bFdt4yJTkWtsyEXXPBmA62DtD+ZWj3Itg5al2dEKKEi4+P57nnnuOHH34AoE+fPnz33XcSsoQoJSRkCVEKmNbCKp5RD6UUv5z+BTCNYsl0lEJSCg4tg/WTIf6qaV+9PqbGFp7VNS1NCFE6HDlyhODgYI4fP45er2f69Om88sor2MiaeUKUGhKyhCiBlFIkpmaYt4s65ySlJ7H78m42X9zM32F/cy3pGjp09K0hXQUL5fIhWDsOwnaativUhF7vQe3u2tYlhChV9u3bx/Hjx/Hz82PZsmW0a9dO65KEEIUkIUuIEkYpRb+5O9h34XqRnuda4jW2XNzClrAt7Lq8i+SMZPNtjraOPNvkWSq7VC7SGsqMxGjY9C7s/QaUEeycoOM4aDMSbKVpiBCicIYMGUJMTAwDBw7Ex8dH63KEEHdAQpYQJUxSWoZFwAoK8LTK9VhKKY5GH2VL2BY2h23mWPQxi9srOVeiU9VOdPbvTMtKLaWjYEEYjfDvItg4FRKjTPsaPgr3vwPuftrWJoQoNU6cOMHLL7/Md999Zw5VL7zwgsZVCSHuhoQsIUoIpRRJaRkW0wT3vtEdL2fDXV8XlZiWyBvb3mD9hfXmfTp0NPZuTCf/TnSq2ok6nnXk+qvCuLgP1o6FS/tN2z71ofd7ENhR27qEEKXKkiVLePbZZ4mPj2fMmDEsWrRI65KEEFYgIUuIEiC/KYJOBv1dB5+rCVcZ/ddojkUfw9bGlo5+Hens35kOVTvg7eh9V/ddLiVEwoYpphEsAHs36DwBWg0DvZ2mpQkhSo/k5GRefvll5s6dC0Dnzp2ZNWuWxlUJIaxFQpYQJUDOKYJgnWmC/0X+x+i/RhORFIGnvSdzusyhecXmd3Wf5VZGOuz9Fja9A8kxpn1NB0H3KeBaUdPShBCly5kzZwgODubff/8F4I033uDNN9/E1lZelglRVshPsxAlzN43uuNk0ONod3ejWH+e/5PXt75OckYytTxq8UnXT6jqWtWKlZYjF7abugZePWLartQEer8P1VprW5cQotTZvn07vXr1IjY2Fi8vL77//nt69uypdVlCCCuTkCVECeNk0ONkuPMfTaUUXx76kk8PfApAB78OvNfxPVwMLtYqsfyIvWxa7+rwctO2gwd0mwwtngSb4lscWghRdjRs2BBvb28aNWrE0qVL8ff317okIUQRkJAlRBmSkpHC5G2TWXtuLQCD6w9mbNBY9BIICic9FXbNhS0zITUe0JmCVddJ4OyldXVCiFLm2rVr+Pj4oNPpcHd3Z+PGjfj5+WFnJ9dxClFWydLhQpQRJ6+fZOgfQ1l7bi22Olsmt5nMa61ek4BVWGc2wdx2sH6SKWD5BcGzm6DvHAlYQohCW7NmDfXq1eOLL74w76tevboELCHKOBnJEkJjSimLtu2FFZcax+cHPmfJ8SVkqAzcDG7M7jyb1pXleqFCuREG6ybCsdWmbSdvuO8taDoQbOT9KCFE4aSnp/PGG28wc+ZMAH744QeGDx+Ojfw+EaJckJAlRDHJXAfLch8Ez93B0cuxd3R/a86u4YO9HxCVbFoI976A+xgXNI7KLpWtUnO5kJYMOz6Bvz+A9CTQ6aHVs9B5PDh6aF2dEKIUCg8PZ+DAgfzzzz8AjB49mlmzZknAEqIckZAlRDHIbx2snArStj0mJYZvDn/D1ktbOXX9FADV3aozofUE2lZpa7Way4WT6+D31+D6OdN2QDvo9R5UaqRtXUKIUmv9+vU89thjRERE4OrqyjfffENwcLDWZQkhipmELCGKQWJq7nWwsmtQ2Y0Vw9vccvFhpRTrL6zn3V3vmkeuHG0debbJszzR4AkMekOR1F4mRZ+FPybAyT9M266V4f53oNH/4C4XfxZClF9hYWE88MADpKWl0bRpU1asWEHt2rW1LksIoQEJWUIUMaUUwXN3mLcz18HK7nZrYl1LvMa0ndP4K+wvAALdAwmpG0K3at2o5FypaAovi1ITYets2PYxZKSAjR20eR46jgN7V62rE0KUcv7+/kydOpXz588zZ84cHB0dtS5JCKERCVlCFLGktAzzNVcNKrvh5Wwo8CLDSilWnVrFB3s/IC4tDludLU83fppnmzwrI1eFoRQc+9XU2CImzLSvRhfT1ECfOtrWJoQo1bZs2YKvry/169cHYPz48Xe1kLwQomyQkCVEMVoxvE2B//iGxoYydcdUdl/ZDUAjr0ZMaTuFuhXqFmWJZU/ESfj9VTi7ybTtXg16vgv1+sjUQCHEHTMajcyYMYNJkybRoEEDdu3ahZOTkwQsIQQgIUuIYlWQv73pxnS+P/o9nx34jOSMZBz0Doy6ZxSD6w+WNa8KIyUOtrwHOz8HYzro7aH9S9DuJTA4aV2dEKIUi4yM5PHHH+ePP0zXdTZv3lzjioQQJY2ELCFKkFPXT/HGtjc4GnUUgNaVWvNm2zfxd/XXuLJSRCk4vBL+fAPir5j21e0NPd6FCoHa1iaEKPW2b99OSEgIFy9exMHBgc8++4yhQ4fKCJYQwoKELCFKiNDYUIauG0pMSgyuBlfGBY3j4VoPyx/uwrhyBNaOg9Dtpm3PQNN1V3Xu17YuIUSpp5Ri9uzZjB8/nvT0dGrXrs3KlStp0qSJ1qUJIUogCVlClACxqbGM+msUMSkxNPJqxMddP8bHyUfrskqPpBuw6V3Y8xUoI9g6Qsex0GYU2DloXZ0QogzIyMjg559/Jj09nZCQEL788kvc3Ny0LksIUUJJyBLCipRSJKVlWOxLTM3I52iTdGM6r255lXMx56joVJFPun2Ct6N3UZZZdhiNcGAxbJgCiZGmfQ0eNq155SFTLIUQ1mNra8vSpUv57bffGDZsmMwyEELckoQsIaxEKUW/uTtuuehwXt7f+z7bLm3D0daRT7pKwCqw8P2wdiyE7zNte9eF3u9Bjc6aliWEKBuUUnz++eeEhoYyc+ZMAPz8/Hj22Wc1rkwIURpIyBLCSpLSMm4ZsIICPHG0s+wOuPzEchYfWwzAu+3fpb5X/SKtsUxIiIKNU2H/QkCBwRU6j4fWz4HeTuvqhBBlQGxsLM888wwrVqwA4MEHH6Rdu3YaVyWEKE0kZAlRBPa+0R0ng2WgcrTTm6eXGJWRPy/8yfRd0wEYfc9ougd0L/Y6SxVjBuz9Fv56B5JvmPY1GQD3TQXXSpqWJoQoOw4ePEi/fv04ffo0tra2zJo1i7Zt22pdlhCilJGQJUQRcDLocTLk/eO1LXwbM3bP4HzseQB6B/ZmWONhxVhdKRS60zQ18Mph03bFxtB7FgS00bYuIUSZoZTi66+/ZvTo0aSkpODv78/y5cu59957tS5NCFEKScgSwgqUUrdtcHE5/jLv7XmPDaEbzPu6+HdhatupcgF1fuKuwoY34eAS07aDO3SdBC2Ggl5+fQkhrGfUqFF8/vnnAPTu3ZuFCxfi5eWlcVVCiNJKXqUIcZdu1/AiNSOVhUcXMu/gPJIzktHr9AyqP4jnmz6Pi8GlmKstJTLSYPeXsGk6pMYBOmj+OHR7E5ylMYgQwvo6d+7MvHnzmDZtGuPGjcPGxkbrkoQQpZiELCHuUs6GF9kbXITFhTFq4yjOxpwFoEXFFkxsPZE6nnU0qbVUOPe3aUHhiOOm7SrNoff7ULWFtnUJIcqcq1evUrFiRQCCg4Np3rw5NWvW1LgqIURZICFLCCva+0Z3vJwN6HQ6jkUdY8SGEUQlR+Hl4MXYlmN5IPABmRqYn5iL8Ocb8N9Ppm0nL+g+BZoNBnlHWQhhRUlJSbz44ov88ssv/Pvvv1SpUgVAApYQwmokZAlxF3Jei+VkMHUQ3Hl5Jy9teomEtATqetbli+5f4OPko2GlJVh6Cuz4FP5+H9ISQWcDLZ+BLhPB0VPr6oQQZcypU6cIDg7m4MGD6HQ6NmzYwBNPPKF1WUKIMkZClhB3KL9rsX4/9zsTt04k3ZhOq0qtmNNlDq4GV42qLOFObYDfX4XoM6btam1MXQMrNda2LiFEmbR8+XKeeeYZ4uLi8PHx4YcffqB7d1k+QwhhfRKyhLgDSimiElJzXYu18tQPzNo7C4Ae1Xvwbvt3MegNWpVZcl0/D39MhBO/mbZdKsL970DjYJDplEIIK0tJSWHs2LF8+umnAHTs2JElS5aYpwkKIYS1ScgS4ialFElpt27DbjoOgufu4OjlWPO+3a935bvjnzJr70IABtUbxGutXsNGJ9cSWUhLgq1zYOuHkJECNrZw7wjo+Co4uGldnRCijJoxY4Y5YE2YMIG33noLW1t5CSSEKDo6pZTSuojiFBsbi7u7OzExMbi5yYs6YXK7Nuy30jzAmer1V7P+wnoAXmz+Ik83eloaXGSnFBz/DdZNgBuhpn2BnUxTA33qalubEKLMS0hIoFevXowfP57evXtrXY4QogQpqmwgb+MIASSmZhQ6YDWo7MZXQ+sxfusY1l84gK2NLe+0e4cHajxQRFWWUpGn4PfX4MxG07ZbVej5LtR/UKYGCiGKRFpaGgsXLmTo0KHY2Njg7OzMli1b5M0vIUSxkZAlyj2lFMFzd5i3977RHSeD/rZfF5l8iec2DuVC7AVcDa581OUjWlZqWZSlli4p8fD3LNjxGRjTQG+Adi9C+5fB4Kx1dUKIMiosLIyQkBB27NhBdHQ048aNA5CAJYQoVhKyRLmXlJZhvr6qQWU38zpXt3Io4hCjNo7iesp1qjhX4fPun1PTQ9ZXAUxTA4/8CH9OgrhLpn21e0DP6eAlj5EQouj8/vvvPP7440RFReHu7k7t2rW1LkkIUU5JyBLlSl7NLbKvc7VieJvbBqyNoRsZ//d4kjOSqV+hPp93/xxvR+8iqbfUuXoU1o6DC1tN257VoedMqNtT07KEEGVbeno6b775Ju+++y4ALVq0YPny5dSoUUPjyoQQ5ZWELFFuFKS5xe1mkyw+tpiZu2eiUHSs2pFZHWfhZOdk5UpLoaQbsHkG7P4SVAbYOkKHV6DtaLBz0Lo6IUQZdvnyZQYOHMiWLVsAGDlyJB988AH29vYaVyaEKM8kZIlyIa91rXIKCvDE0S7va7GMysj7e99n0dFFAATXCWZi64nY2pTzHyGjEQ4ugQ1vQkKEaV/9B6HHNPCopm1tQohy4eLFi2zfvh0XFxe+/vprQkJCtC5JCCEkZImyz2hU9Plkq8W6Vnk1t3C00+c7VfC7/74zB6yXmr/EU42ekouoLx0wTQ28uNu07VUber8HNbtqWpYQonxp2bIlCxcupHnz5tSpU0frcoQQAgBZKVWUaUrlDlhBAZ54ORtwMthafOQXms7eOMun/5oWsZzYeiJPNy7na2ApBevfhC87mwKWwQXuewtGbJeAJYQocteuXePBBx/kwIED5n0DBgyQgCWEKFFkJEuUaYmpWZ0DA72dWTO6PU6G/EescsowZjBp2yRSjam082vHgLoDirLc0uGvd2DbHNPnjYNNAcutiqYlCSHKh3/++YcBAwZw6dIlQkND+ffff8v3m15CiBJLRrJEmZVz/as1o9vjbJ//iFVeFh5dyKHIQ7jYuTClzRT5Y77jM/jnfdPnfT6E/30tAUsIUeSMRiMzZ86kS5cuXLp0ifr167N48WL5nSyEKLFkJEuUWTnXvyrIAsPZZZ8m+GrLV6nkXMnqNZYqB5bAuommz7tNhqCntK1HCFEuREVFMWTIEH777TcABg8ezBdffIGLi4vGlQkhRP4kZIkySSlV6PWvsss5TfDhWg8XQZWlyInf4ZeRps/bjIL2Y7StRwhRLoSGhtKhQwdCQ0Oxt7fn008/5emny/l1sUKIUkFClihz8loPq7B/j2WaYDbnt8GKJ03rXzUdBPe9XfgHVAgh7oCfnx9169bF3t6eFStW0LRpU61LEkKIApGQJcqcpLQMi4B1q/Wv8nI2RqYJml0+CEsGQHoy1O0ND34CNnIppxCi6MTExGAwGHB0dESv1/PDDz9gMBhwc3PTujQhhCgwebUkygzTFMF0i2mCe9/oXuipgrP3zibVmEp7v/ble5pg1Bn4/n+QEgsB7aDft6CX92WEEEVn//79NG/enBdffNG8z9vbWwKWEKLUkVdMokzIa4ogUKh27QAnok+w5eIWbHQ2vNbytfI7TTD2Eix8GBIioFJjGLgE7By1rkoIUUYppZg7dy4vvfQSqampGI1GoqOjqVChgtalCSHEHZGRLFHqKaWISkjNFbAKO00Q4JvD3wBwf8D9VHevbq0SS5fEaFj0KMSEQoUaMHgVOLhrXZUQooyKi4tj0KBBPP/886SmpvLggw+yf/9+CVhCiFLtrkaykpOTcXBwsFYtQhRaXiNYe9/ojpNBj6Nd4UaxLsReYN2FdQA80/gZq9daKqQmwA/9IeIYuFaGx38GF1+tqxJClFGHDx+mX79+nDx5EltbW2bMmMGYMWPK7ywCIUSZUeiRLKPRyNtvv42fnx8uLi6cPXsWgEmTJvHNN99YvUAhbiWvJhdezgacDIVbdBjg2yPfYlRGOlbtSN0Kda1dasmXngrLHoeLe8DBwzSC5RmgdVVCiDIqNTWV3r17c/LkSapWrcqWLVt45ZVXJGAJIcqEQoesd955hwULFvDee+9hMBjM+xs1asTXX39t1eKEgOwNLfL6uLsmF5muJFxh9ZnVAAxrPMxqtZcaxgz46Tk4sxHsnOCxFVCxgdZVCSHKMIPBwLx58+jduzf//vsvbdu21bokIYSwmkJPF1y4cCFffvkl3bp1Y/jw4eb9TZs25fjx41YtTgijUdHnk60cvRx722ML2+Qiu0VHF5FuTCeoYhDNfJvd0X2UWkrB76/Cf6vAxg5CFoF/K62rEkKUQceOHePSpUt069YNgN69e9OrVy8ZvRJClDmFHskKDw+nVq1aufYbjUbS0tKsUpQQYBrBKmjAupMmF5kS0xL56dRPAAxtNPSO7qNU2zwd9nwN6ODReVCru9YVCSHKoMWLF9OyZUuCg4M5f/68eb8ELCFEWVTokawGDRrwzz//EBBgea3GypUrueeee6xWmBCJqRnmgBXo7cya0e3J729xYZtcZPf7ud+JS4vDz8WP9n7t77Tc0mnnXNgy0/T5A+9Do/9pW48QosxJTk7mxRdf5MsvvwSgdevWODrKkhBCiLKt0CFr8uTJDBkyhPDwcIxGI6tWreLEiRMsXLiQNWvWFEWNohxSShE8d4d5e83o9jjbW39ZN6UUS08sBSCkbgg2unK0qsGh5fDHa6bPu7wBLctpR0UhRJE5ffo0wcHBHDhwAJ1Ox6RJk5g8eTJ6/Z3NPBBCiNKi0K8oH3roIX799Vc2bNiAs7MzkydP5tixY/z666/cd999RVGjKEcym1xEJaSaR7EaVHbDyVA0f5APRhzkePRx7PX2PFLrkSI5R4l0ch38PML0eesR0HGstvUIIcqclStX0rx5cw4cOICPjw9//PEHU6dOlYAlhCgX7mhooEOHDqxfv97atYhyLq81r4A77hhYEJmjWD2r98TDwaNIzlHiXNgBy58AYzo0CYEe75LvPEwhhLhDf/31F3FxcbRv356lS5fi5+endUlCCFFsCj2SVaNGDaKionLtv3HjBjVq1LBKUaJ8yrnmFZgaWhTVKFZUUhR/nv8TgIH1BhbJOUqcK0fghxBIT4baPeChz8CmHE2RFEIUm9mzZ/Phhx+yadMmCVhCiHKn0CNZ58+fJyMjI9f+lJQUwsPDrVKUKJ+Uyvp87xvdcTLo76qhxe38dPon0oxpNPJqREPvhkVyjhIl+ix8/yikxEC1NhC8APR2WlclhCgjVq9ezaJFi1i6dCl6vR4HBwdeeuklrcsSQghNFDhkrV692vz5unXrcHd3N29nZGSwceNGqlevbtXiRPmRs9GFk0GPk8H6jS4yZRgzWH5iOQAD6g0osvOUGHFXYNEjEH8VKjaCgUvB4KR1VUKIMiAtLY3XX3+dWbNmAfDVV19ZrKMphBDlUYFfxT788MOAaT2LIUOGWNxmZ2dH9erV+eCDD6xanCg/ktIyLBpd3OmaVwW15eIWLidcxsPeg56BPYv0XJpLug6LHoXr58EzEAavAkcPrasSQpQBFy9eJCQkhO3btwPw0ksv8dRTT2lclRBCaK/AF2MYjUaMRiPVqlXj2rVr5m2j0UhKSgonTpygT58+hS7gs88+o3r16jg4ONC6dWt27959y+Nv3LjByJEjqVy5Mvb29tSpU4e1a9cW+ryi5CrKRheZlp1YBsAjtR/BXm9fpOfSVGqi6Rqsa/+BS0V4/Cdwrah1VUKIMmDdunXcc889bN++HTc3N3788Uc+/PBDDAaD1qUJIYTmCj0f69y5c1Y7+bJlyxgzZgxz586ldevWzJkzhx49enDixAl8fX1zHZ+amsp9992Hr68vK1euxM/PjwsXLuDh4WG1moQ2sl+PVdSN7s7HnGf7pe3o0NG/Tv+iPZmWMtJMXQTDdoGDu2kEq0Kg1lUJIcqAzz77jNGjR6OU4p577mHFihXUrFlT67KEEKLEuKOLXhISEtiyZQuhoaGkpqZa3PbCCy8U+H5mz57NsGHDGDp0KABz587lt99+49tvv2X8+PG5jv/222+Jjo5m+/bt2NmZLtiX68BKv5zXYxWVyKRIDkccZsXJFQB0qNqBqq5Vi/y8mjAaTetgnV4Pto4waDlUaqR1VUKIMqJjx444ODjw5JNPMnv2bBwcHLQuSQghSpRCh6x///2X3r17k5iYSEJCAhUqVCAyMhInJyd8fX0LHLJSU1PZt28fEyZMMO+zsbGhe/fu7NiR9wvu1atX06ZNG0aOHMkvv/yCj48PgwYN4rXXXst3ccOUlBRSUlLM27GxsYX4bkVxSEy1/vVYCWkJ/Bf5H4cjD3Mk8ghHoo5wJeGKxTFltm27UvDHa3B4BdjYQv+FUO1erasSQpRyly9fpnLlygA0btyYo0ePyhudQgiRj0KHrJdffpm+ffsyd+5c3N3d2blzJ3Z2dgwePJgXX3yxwPcTGRlJRkYGFStaXh9SsWJFjh8/nufXnD17lr/++ovHHnuMtWvXcvr0aZ5//nnS0tJ488038/ya6dOnM3Xq1IJ/g6JY5RzFssb1WMeijvHMn88Qm2oZqHXoqOlRk0bejbi38r20q9Lurs5TYm15D3Z/afr84blQ535t6xFClGpGo5F3332Xd955h82bN3PvvaY3bSRgCSFE/godsg4cOMC8efOwsbFBr9eTkpJCjRo1eO+99xgyZAiPPvpoUdQJmH7R+/r68uWXX6LX62nRogXh4eHMmjUr35A1YcIExowZY96OjY3F39+/yGoUBaeUIioh1WIUyxoLD3/y7yfEpsbi7ehNc9/mNPJuRCPvRjTwaoCznfNd33+Jtvsr2Pyu6fNe70GTYG3rEUKUahERETz++OOsW7cOMM0oyQxZQggh8lfokGVnZ4eNjakpoa+vL6GhodSvXx93d3fCwsIKfD/e3t7o9XquXr1qsf/q1atUqlQpz6+pXLkydnZ2FlMD69evz5UrV0hNTc2zo5G9vT329mW4e1wpZTQq+nyy1RywwDqjWMejj/NP+D/Y6GxY0HMBAW4Bd1tq6XF4JawdZ/q803ho/Zy29QghSrWtW7cyYMAAwsPDcXR05Isvvsi1hIsQQoi8FbiFe6Z77rmHPXv2ANCpUycmT57M4sWLeemll2jUqOAX1hsMBlq0aMHGjRvN+4xGIxs3bqRNmzZ5fk27du04ffo0RqPRvO/kyZNUrlxZWsaWIkrlDlhBAZ5WGcX65vA3APQI6FG+AtapDfDTc4CCVs9C59yNY4QQoiCMRiOzZs2ic+fOhIeHU69ePXbv3i0BSwghCqHQIevdd981X/g6bdo0PD09GTFiBBEREcybN69Q9zVmzBi++uorvvvuO44dO8aIESNISEgwdxt84oknLBpjjBgxgujoaF588UVOnjzJb7/9xrvvvsvIkSML+20IDWVfeDjQ25n/pvawyijW+ZjzrDtvmtLydOOn77rOUiN0FywbDMZ0aNQPes4s+j74Qogy6+eff+bVV18lIyODQYMGsWfPnkK9iSqEEOIOpgsGBQWZP/f19eWPP/6445OHhIQQERHB5MmTuXLlCs2aNeOPP/4wN8MIDQ01T00E8Pf3Z926dbz88ss0adIEPz8/XnzxRV577bU7rkEUv+xrYq0Z3R5n+ztaSSCX+f/NR6HoXLUzdSvUtcp9lnhX/4MfgiE9CWrdBw9/ATaFfu9ECCHMHnnkEQYMGECXLl0YNmxYkS8OL4QQZZFOqewvee/c/v37mTx5MmvWrLHG3RWZ2NhY3N3diYmJwc3NTetyyh2lFA98nDVV8OhbPXAy3H3Iuhx/md4/9SbdmM73vb+nqU/Tu77PEu/6efimB8RfAf/W8PjPYHDSuiohRCmjlGLBggX069cPV1dX8z4JV0KI8qCoskGh3vJet24dY8eOZeLEiZw9exaA48eP8/DDD9OyZUuLa6WEyEv2qYLWWhNLKcXbO98m3ZhOq0qtykfAir8GCx82BSzfBjBomQQsIUShxcTEEBwczFNPPcWzzz5L5vuuErCEEOLuFHgI4ZtvvmHYsGFUqFCB69ev8/XXXzN79mxGjx5NSEgIR44coX79+kVZqyhjrHEdFsDqM6v5J/wf7GzsmNh6ohUqK+GSbsCiR+H6OfAIgMGrwNFT66qEEKXMv//+S3BwMGfOnMHOzi7fplNCCCEKr8AjWR999BEzZ84kMjKS5cuXExkZyeeff87hw4eZO3euBCxRaNZ4o/RqwlVm7p4JwMhmI6npUfPu77QkS0uCJQPh6mFw9oXHfwK3ylpXJYQoRZRSzJs3jzZt2nDmzBkCAgLYunUrL7zwgoxgCSGElRQ4ZJ05c4bgYNPCpo8++ii2trbMmjWLqlWrFllxouyxzhWAmfelmLpjKnFpcTT2bsyQhmW8vXBGGqwYCqHbwd4dHl8FXmU8VAohrCo+Pp7BgwczfPhwUlJS6Nu3L/v376dVq1ZalyaEEGVKgUNWUlISTk6maz50Oh329vbmVu5CFIRSiuC5O6x2f7+c+cU8TfDtdm9ja2OdLoUlktEIv4yCk7+DrQMMWgqVGmtdlRCilImPj2fjxo3o9XpmzZrFL7/8QoUKFbQuSwghypxCvSr9+uuvcXFxASA9PZ0FCxbg7e1tccwLL7xgvepEmWLNphdXE67y3u73gHIwTVAp+PN1OLQUdHoI/g4C2mpdlRCiFKpUqRLLly9Hr9fTrl07rcsRQogyq8At3KtXr37budo6nc7cdbCkkhbu2klISafhm6bFgv+b2uOO18dSSjFy40j+Cf+Hxt6NWdhrYdkexfp7Fvz1junzR+ZB0wHa1iOEKDUSExMZNWoU999/PwMGyO8OIYTIqaiyQYFfmZ4/f95qJxXlT86pgndzbfWas2vKzzTBPd9kBayeMyRgCSEK7MSJE/Tr148jR46watUqevbsiYeHh9ZlCSFEuVCodbKEuFPWmiqYbkzn8wOfAzCi6YiyPU3wyCr47RXT5x3Hwb0jtK1HCFFqLFmyhKCgII4cOULFihX56aefJGAJIUQxkpAlit3drI+17vw6LsZfxNPek8fqP2blykqQ0xth1bOAgqCnoMvrWlckhCgFkpOTGTFiBIMGDSI+Pp7OnTtz4MABunTponVpQghRrpTheVaipLrTqYJGZeTrw18DMLjBYJzsnKxYVQkStgeWDQZjGjR8FHq/b51FxYQQZVpycjLt27dn37596HQ6Xn/9daZMmYJef+dNhoQQQtwZCVmiWFhjfawtYVs4feM0znbODKhXRq9NunYMfgiGtESo2dXU6MJGXiAJIW7PwcGBLl26cOHCBb7//nt69OihdUlCCFFuyXRBUeSMRkWfT7be1X0opcyjWAPqDsDNUAY7Q16/AIsegaTrULUlhHwPtgatqxJClGCpqalERESYt999910OHjwoAUsIITR2RyHrzJkzvPHGGwwcOJBr164B8Pvvv/Pff/9ZtThR+illCljnIhOAO296sefKHg5FHsJeb8/gBoOtXab24iNMASvuMvjUh0HLweCsdVVCiBLswoULdOjQgYceeoi0tDQA7OzsqFKlisaVCSGEKHTI2rJlC40bN2bXrl2sWrWK+Ph4AA4ePMibb75p9QJF6aWUIioh1dxVMNDbmTWj299R04uvDn8FwKO1H8Xb0fs2R5cyybHw/aMQfQbcq8Hjq8CpgtZVCSFKsDVr1nDPPfewe/dujh8/zvHjx7UuSQghRDaFDlnjx4/nnXfeYf369RgMWVOZunbtys6dO61anCi9lFL0m7uDoHc2mPetGd0eG5vCB6zDEYfZeXkntjpbnmz4pBWrLAHSkmHJQLhyCJy84YmfwU3ehRZC5C09PZ3x48fTt29frl+/TsuWLdm/fz+NGzfWujQhhBDZFDpkHT58mEceeSTXfl9fXyIjI61SlCjdMkew9l24bt4XFOCJk+HOGjhkXov1wP/Zu8/oqKq3DePXpBcg1AChhS5IDR0pUiSIIkV6RxBEAZU/KCodaYpYERQELGgoSnkFUUCjNFFKUIr0TkInvc6c90NkJJBAyiSTcv/WynLm5Jwzd3DQebL3fnaFJ/DJl4sKEHMCrH4Gzm4H1wKJI1hFcvG+XyKSIRcvXqRVq1bMmTMHgNGjR7N9+3Z8fX3tG0xERO6R5u6CBQsWJDg4mPLlyyc5vn//fkqVKmWzYJIz3R7BurPA2jOhLUU8XdI1TTDgnwB+Pv8zJkw8U/MZW0a1L8OA/xsNRzeAoyv0/gZK1rZ3KhHJxgYNGsT27dvJnz8/S5YsoVu3bvaOJCIiKUjzSFavXr149dVXCQkJwWQyYbFY2LFjB2PHjmXAgAGZkVFykOh48z0jWOktsNadWMeM3TMAGF57OBW8Ktgsp10ZBvw0AYKWg8kRui8D32b2TiUi2dz8+fNp0aIF+/btU4ElIpLNpXkka+bMmbzwwguUKVMGs9lM9erVMZvN9OnThwkTJmRGRskhDMMgKs5sfZ6REawfz/zIpJ2TAOhXrR/P137eZjntbsd7sOujxMedPoKHOtg1johkT5cvX2br1q306dMHgCpVqvDrr7/aOZWIiKRGmossFxcXFi1axMSJEzl48CARERHUrVuXypUrZ0Y+ySFu74V1u5MggIeLY7oKrN8u/Mb438ZjMSw8XflpXmnwSrruky3tXQZbpiQ+bjcD6vSxZxoRyaYCAwOt26SULFmSVq1a2TuSiIikQZqLrO3bt9OsWTPKli1L2bJlMyOT5DC398K6s8CqX65QuvbD2h28m5d/eZkEI4EO5TswsfHE3FNgHV4H37+c+LjZGGg60r55RCTbsVgszJ49m4kTJ2KxWHj44YcpWbKkvWOJiEgapbnIat26NaVKlaJ3797069eP6tWrZ0YuyQEMwyA63kxUnPmevbDSM4oVdCWIUT+PIs4SR+syrXmz2Zs4OqSvI2G2c/IX+HYoGBbwGwhtJtk7kYhkM9euXaN///5s2rQJSGx0MX/+fDw8POycTERE0irNjS8uXbrE//73P3799Vdq1KhBnTp1ePvtt7lw4UJm5JNs6nYXweqTfrxnLyxPV6c0F1iHrx/m+S3PE50QTVOfprzd8m2cHZxtHds+LuyFgL5gjoNqT8GT70JuGZ0TEZvYuXMndevWZdOmTbi7u7NkyRKWLl2qAktEJIdKc5FVtGhRRo4cyY4dOzh58iTdu3fn888/x9fXl9atW2dGRsmG7u4iCOnfC+vEzRMM3zyc8Phw/Lz9eK/Ve7g4ujz4wpzg6lFY3g3iI6HCo/D0Ysgto3MiYjMHDx7kwoULVK1ald27dzN48GB7RxIRkQxI83TBO5UvX57x48dTu3ZtJk6cqK5HeURyXQQ9XBxxd07bFEHDMDgVeophm4dxK/YWDxd5mPlt5uPu5J4ZsbPerfPwZReIvgGl6kHP5eDkau9UIpINPfvss5jNZvr160f+/PntHUdERDIo3UXWjh07WL58OatXryYmJoZOnToxa9YsW2aTbCi5zYY9XBzxcEndW+lq1FV+D/6d3cG72R2ym5DIEAAqF6rMJ499Qj6XfJmSO8tFXksssMIuQtGq0GcVuOaSn01EMuzPP//k1Vdf5dtvv6VQoUKYTCZGjBhh71giImIjaS6yXnvtNQICArh06RKPPfYY77//Pp06ddK88TwiKu7ezYYf1EXwbNhZlh9Zzu7g3ZwKPXXP96sXqc78NvPxcvWyeV67iA2Hr56G68fBqwz0XwOeReydSkSyAcMwmD9/PmPGjCE+Pp433niDjz/+2N6xRETExtJcZP3222+MGzeOHj16ULRo0czIJNmUYRh0X7jL+jw1mw2HxoYyaNMgrkVfA8CEiWpFqtGoZCMal2jMw0UfpoBLgdzTpj0+BgL6QHAQeBRJLLC8Stk7lYhkA2FhYQwdOpRVq1YB0KVLF2bOnGnnVCIikhnSXGTt2LEjM3JIDhAd/1+r9uolCzywwAJ4d++7XIu+Rtn8ZXmp3ks0LNEw94xY3c2cAN8OgdO/gUs+6PctFNUm3SICQUFBdO/enRMnTuDk5MTcuXMZPXp07vkFk4iIJJGqImv9+vU8/vjjODs7s379+vue+9RTT9kkmGQ/hvHf41XPNXngh4M/Q/7k2+PfAjDtkWnUK14vM+PZl2HA9y/BP9+Dowv0/gZ86to7lYhkA5s2baJz587ExsZStmxZVqxYQePGje0dS0REMlGqiqzOnTsTEhKCt7c3nTt3TvE8k8mE2WxO8fuSc909VfBBv3yNNccybdc0ALpV6Za7CyyALVNg/5dgcoBuS6B8C3snEpFsokGDBhQvXpyaNWvy+eefU6SI1miKiOR2qSqyLBZLso8l77h7quCDml189vdnnAk7Q1H3orxc7+WsiGg/O96HHe8lPu74AVTraNc4ImJ/Fy5coFSpUphMJooUKcKOHTvw8fHBwSHN21OKiEgOlOb/2n/xxRfExsbeczwuLo4vvvjCJqEk+0nrVMGNpzcCMKbeGAq4FMjMaPa170vYPCnx8WPTwK+/ffOIiN198cUXVK1alaVLl1qPlS5dWgWWiEgekub/4g8ePJjQ0NB7joeHh2uH+lzKYjF48sPt1ucPmipoMSxcirgEgF9xv8yMZl9Hvof/G534+JEXE79EJM+Kjo5m6NChDBw4kKioKNauXYtx52+oREQkz0hzkWUYRrKjGBcuXMDLK5d2jcvDDCOxwDp9LRJI3VTBGzE3iLfE42BywNvDOytiZr3Tv8HqZ8CwQN3+0HaqvROJiB0dO3aMxo0b89lnn2EymZgyZQpr1qxR90ARkTwq1S3c69ati8lkwmQy0aZNG5yc/rvUbDZz+vRp2rdvnykhxX7uXItVvqgn349q9sAPDbdHsYq5F8PZwTnTM2a5S/vhmz5gjoWHnoQn33vw8J6I5ForVqxg6NChRERE4O3tzfLly2nbtq29Y4mIiB2lusi63VUwKCgIf39/8uXLZ/2ei4sLvr6+PP300zYPKNnH96Oa4eDw4GIiODIYgJKeJTM7Uta7dhy+ehriwsG3OTz9GTimebs5Eckljh49Sp8+fbBYLLRo0YJvvvkGHx8fe8cSERE7S/Wnw8mTJwPg6+tLz549cXNzy7RQkn3cuZwgtYM1wRH/Fln5clmRFXoBvuwCUdehZB3o9TU46++BSF5WtWpVJk+eTExMDNOmTUsyy0NERPKuNP/fYODAgZmRQ7Khu/fGSq3bI1k+nrnot7mR1xMLrNDzUKQy9PsW3HJx10QRSdHatWupUaMGlSpVAmDSpEl2TiQiItlNqoqswoULc+zYMYoWLUqhQoXuuybnxo0bNgsn9pXWvbFuuxSZuCYr10wXjA2H5d3g2jEoUAr6rwHPovZOJSJZLD4+nvHjxzNv3jzq1q3Lzp07NatDRESSlaoi69133yV//vzWx+qWlPekZm+s20IiQ4BcMl0wIRZW9INL+8C9cGKBVbCMvVOJSBY7d+4cPXv25PfffwegVatW2vdKRERSlKoi684pgoMGDcqsLJINGIZBdLwZgKg4s/V4Wurq290Fc/xIlsUM3z0LpwLB2RP6rYZiVe2dSkSy2MaNG+nfvz83btzAy8uLZcuWWZtBiYiIJCfNa7L27duHs7MzNWvWBGDdunUsXbqU6tWrM2XKFFxcXGweUrKGYRh0W7iLvWdvpvsekfGRhMUlTjHM0UWWYcCGMXB4HTi6QK/lUKqevVOJSBZKSEhg0qRJzJo1C4B69eqxcuVKKlSoYOdkIiKS3aV5rsPw4cM5duwYAKdOnaJnz554eHiwatUqXnnlFZsHlKwTHW9OtsCqX65Qqtdj3Z4qmN8lP/lc8j3g7Gzs5+mwdxmYHODpxVCxlb0TiUgWs1gsbN26FYAXXniBHTt2qMASEZFUSfNI1rFjx6hTpw4Aq1atomXLlnz99dfs2LGDXr168d5779k4omSVO9u175nQFg+XxMLK3dkx1euxbk8VzNGdBXd+BNveSXz85LtQvZN984iIXbi4uLBy5Up2795Njx497B1HRERykDQXWYZhYLFYANiyZQtPPvkkAGXKlOHatWu2TSdZ5u527R4ujni4pH2/lxy/EXHQ1/DTG4mP20yGeoPsGkdEso7ZbObNN98kNjaWmTNnAlCuXDnKlStn52QiIpLTpPlTdP369XnzzTdp27Ytv/76KwsWLADg9OnTFC9e3OYBJfMZhsH1yLh0tWu/m7XIyomdBf/ZCOtGJj5uMhKavWzfPCKSZS5fvky/fv3YsmULAL169aJWrVp2TiUiIjlVmous9957j759+7J27VreeOMN62aMq1evpmnTpjYPKJkruWYXaWnXfrccO5J1ZjusGgSGGer0hXZvpq2loojkWL/99hu9evUiODgYDw8PFi5cqAJLREQyJM1FVq1atfj777/vOf7222/j6Ji+0Q+xn6i4pM0u6pcrZF2LlR7BETlwJCv4AHzTG8yxULUDdPxABZZIHmCxWHjrrbd44403sFgsVKtWjdWrV1O9enV7RxMRkRwu7Ytu/rV3716OHDkCQPXq1fHz87NZKMkad6/D2jOhLUU8XTK02XSOG8m6fhK+ehpiw6BcM+i2BBzT/ddCRHKQ3r17s3LlSgD69+/PggUL8PT0tHMqERHJDdL8afLKlSv07NmTX3/9lYIFCwJw69YtWrVqRUBAAMWKFbN1Rskk0fHmJOuwMlpgxZvjuRJ1Bcgh3QXDLsEXnSHyKpSoBb2/Bmd3e6cSkSzSsWNH1q9fz4cffsiQIUMy9N8/ERGRO6V5n6xRo0YRERHBoUOHuHHjBjdu3ODgwYOEhYUxevTozMgomeTOlu0ZWYd1W8DRAMyGmcJuhSniXiSD6TJZ1A34siuEnoPCFaHfd+DmZe9UIpKJDMPg/Pnz1uf9+vXj+PHjDB06VAWWiIjYVJqLrE2bNvHxxx9TrVo167Hq1aszf/58fvjhB5uGk8xz91TBjH6+uBZ9jY+DPgZgVN1ROJjS/NbKOnGR8HUPuHoE8vtA/zWQTyOwIrnZrVu3ePrpp2nUqBFXrlyxHi9durQdU4mISG6V5k/CFosFZ2fne447Oztb98+S7O/uqYLpbdl+27t73yUiPoIaRWrQtXJXW0TMHAlxsKI/XPgT3AslFliFtAeOSG62d+9e/Pz8WLNmDdeuXeP333+3dyQREcnl0lxktW7dmhdffJFLly5Zj128eJGXX36ZNm3a2DScZI2MThXcf2U/60+ux4SJ1xu9nn1HsSxmWDMcTm4FZ0/ouxq8H7J3KhHJJIZh8PHHH9O0aVNOnz6Nr68vO3bs4KmnnrJ3NBERyeXS/Gn4o48+IiwsDF9fXypWrEjFihUpX748YWFhfPjhh5mRUWzEMAyi4hL+/TJbj2dkqqDZYmbm7pkAdK3clZrFamY0ZuYwDNg4Fg59Bw7O0PNLKF3f3qlEJJOEh4fTu3dvXnjhBeLi4ujUqRP79u2jQYMG9o4mIiJ5QJq7C5YpU4Z9+/axdetWawv3atWq0bZtW5uHE9tJbtPhjAqJDGFM4Bj+ufEP+V3yM9ovGzc++WUm7FkCmKDrp1BJo64iudnkyZNZsWIFTk5OzJkzh5dfflnNLUREJMukqchasWIF69evJy4ujjZt2jBq1KjMyiU2ZBgG1yPjki2w6pcrlO71WDN+n8Hf1xI3ph5ddzSF3QpnKGem+X0B/PZW4uMn3oEa2XjNmIjYxJQpUzhw4ADTp0+nadOm9o4jIiJ5TKqLrAULFvDCCy9QuXJl3N3d+e677zh58iRvv/12ZuaTDEpuBGvPhLZ4uCQWVu7Ojun67e7RG0cJvBAIwJAaQ+hRtYdN8trcgRWwaXzi49YToMEQ++YRkUwRGRnJ559/zogRIzCZTBQoUICtW7faO5aIiORRqV6T9dFHHzF58mSOHj1KUFAQn3/+OR9//HFmZhMbiIozJymw6pcrRBFPFzxcnPBwcUr39JnPDn4GQHvf9rxU76Xs2ezi2I+wdkTi48bPQ/Ox9s0jIpniyJEjNGrUiBdeeEFrg0VEJFtI9SfjU6dOMXDgQOvzPn36kJCQQHBwcKYEk4y7ey+sPRPa2mTT4XNh5/jxzI8ADK05NEP3yjRnd8LKAWCYoVYvaDcj45uBiUi2s3z5cho0aMChQ4coUaIEtWrVsnckERGR1BdZsbGxeHp6/nehgwMuLi5ER0dnSjDJuLv3wiri6WKThd9LDi7BYlhoUboFVQtXzfD9bC7kb/i6FyTEQJX20OkjcMiGI20ikm4xMTEMHz6cfv36ERkZSZs2bQgKCuLRRx+1dzQREZG0Nb6YOHEiHh4e1udxcXHMmDEDLy8v67F58+bZLp3YjC1GsAAuR15m3cl1ADxb89kM38/mbpyCL7tCbCiUbQrdl4HjvZtni0jOdeLECbp3705QUBAmk4lJkyYxceJEHB0ztqm6iIiIraS6yGrRogVHjx5Ncqxp06acOnXK+lztcbMvW/2r+fzw5yRYEqhfvD51vOvY5qa2Eh4CX3SGyCtQvCb0/gac3e2dSkRs7PLly/z9998UK1aM5cuX89hjj9k7koiISBKpLrICAwMzMYbkBDdjbrL62GogG45iRd9MHMG6dRYKlYd+34J7QXunEpFM8Mgjj7B8+XKaNWtGqVKl7B1HRETkHlqokosZhm3vt/zIcqIToqlepDpNfJrY9uYZERcFX/eEK4cgXwkYsBbyF7d3KhGxkTNnztCqVSsOHTpkPdazZ08VWCIikm2pyMql7u4smFERcRF8/c/XQOIoVraZGmqOT+wieH43uHlB/++gkK+9U4mIjaxfv566desSGBjI8OHDMWz92yMREZFMoCIrl7q7s6C7c8YWhK88tpLwuHDKe5WnddnWtoiYcRZL4j5YJzaDkzv0WQXFH7Z3KhGxgfj4eMaNG0enTp24desWjRo1Yvny5dnnFzwiIiL3kabugpJz3PnL3ox2Foy3xPPV4a8AGFJjSPbYeNgwYNOr8PcqcHCCnl9B2Ub2TiUiNnDhwgV69uzJzp07AXjppZeYM2cOLi4udk4mIiKSOiqycqG7pwpm9Be/W89u5Wr0VYq6F6VD+Q4ZTGcjv74Ff3wKmKDLJ1C5rb0TiYgN/PPPPzRr1ozr169ToEABli5dSteuXe0dS0REJE3SNSSxbds2+vXrR5MmTbh48SIAX375Jdu3b7dpOEkfW08V/OafbwDoXqU7ztlhz6k/FkHgzMTHHd6Gmt3sm0dEbKZSpUrUqFEDPz8/9u3bpwJLRERypDQXWd9++y3+/v64u7uzf/9+YmNjAQgNDWXmzJk2DygZk9Gpgv/c+Id9V/bhZHKie5XuNkyWTn+vho3jEh8/+jo0zGat5EUkzS5fvmz9f4mTkxOrV69mx44dVKxY0c7JRERE0ifNRdabb77JwoULWbRoEc7O/41qPPLII+zbt8+m4STjMjpV8PYo1mPlHqOYRzEbJMqA45thzXDAgIbDoeUr9s0jIhn2yy+/ULt2bcaOHWs9VrRoUdzc3OyYSkREJGPSXGQdPXqUFi1a3HPcy8uLW7du2SKTZIBhGETFmW1yr1sxt9hwagMAfar1sck90+3cbljRHywJULM7tJ+d8QpSROzGYrHw5ptv0rZtWy5fvsyvv/5KZGSkvWOJiIjYRJobX5QoUYITJ07g6+ub5Pj27dupUKGCrXJJOhiGQbeFu9h79qZN7rfmxBpizbFUK1yN2sVq2+Se6XL5EHzdHRKiodJj0HkBOGSDDociki5Xr16lX79+/PTTTwAMGTKEDz74AA8PDzsnExERsY00f1J99tlnefHFF9m9ezcmk4lLly6xfPlyxo4dy4gRIzIjo6RSdLw5SYFVv1yhdDe9MFvMrDi6AoDeD/W23940N07Dl10hJhTKNIYeX0B2aL4hIumyfft26taty08//YS7uzvLli1j8eLFKrBERCRXSfNI1vjx47FYLLRp04aoqChatGiBq6srY8eOZdSoUZmRUdJhz4S2FPF0SXdx9NuF37gYcREvVy8eL/+4jdOlUvhl+LILRISA98PQJwBc9EFMJKeKiIigc+fOXL9+nYceeohVq1ZRo0YNe8cSERGxuTQXWSaTiTfeeINx48Zx4sQJIiIiqF69Ovny5cuMfJJOHi6OGRp9ut3womvlrrg52WEBevQt+OppuHkaCpaD/t+Be6GszyEiNpMvXz4WLVrEt99+y8KFC/X/DRERybXSvRmxi4sL1atXt2UWySZOhZ5iV/AuHEwO9KzaM+sDxEXBN73g8t/g6Q0D1kL+ElmfQ0Qy7I8//iAiIoLWrVsD0KVLF7p06WLnVCIiIpkrzUVWq1at7jtC8vPPP2cokNhfwD8BALQs3ZJS+Upl7Yub42H1YDi3C1y9EkewCquhikhOYxgGH374IWPHjsXLy4v9+/dTunRpe8cSERHJEmkusurUqZPkeXx8PEFBQRw8eJCBAwfaKpfYSURcBOtOrAMSG15kKYsF1r0AxzaBkxv0WQElamZtBhHJsNDQUIYMGcK3334LQMuWLcmfP7+dU4mIiGSdNBdZ7777brLHp0yZQkRERIYDiX2tP7meqIQoynuVp3HJxln3woYBP74Of60Ak2NiF8FyTbLu9UXEJvbv30/37t05efIkzs7OvPPOO4wcOdJ+HUpFRETswGabDfXr148lS5bY6naSDoaR0esNVh1bBdihbfu2ubB7QeLjLguhin/WvbaIZJhhGHzyySc0adKEkydPUq5cObZv386oUaNUYImISJ5jsyJr165duLnZoQudAIkfcLov3JWhexy9eZQTt07g4uDCExWesFGyVPjzM/j5zcTH7edArR5Z99oiYhMmk4nff/+d2NhYOnbsyL59+2jYsKG9Y4mIiNhFmqcLdu3aNclzwzAIDg5mz549TJw40WbBJG2i480cDg4DoHrJAunahHjDqQ0AtCzTkgIuBWyaL0UHv4MN/0t83PJVaPxc1ryuiNiEYRjWkar58+fzyCOPMGTIEI1eiYhInpbmIsvLyyvJcwcHB6pWrcq0adNo166dzYJJ+q16rkmaP+CYLWY2ntoIkHWjWCe2wnfDAAMaDIVHX8ua1xURm1i2bBnff/89K1euxMHBAQ8PD4YOHWrvWCIiInaXpiLLbDYzePBgatasSaFC2hg2u0rPL5D/vPwnV6KvUMClAM1LNbd9qLtd3Acr+oElHmo8DY+/nb7gIpLloqKiGDlyJEuXLgUgICCAPn362DmViIhI9pGmNVmOjo60a9eOW7duZVIcsZfbUwXb+bbDxdElc1/MYob1oyA+Ciq2gc4LwcFmywNFJBMdPXqURo0asXTpUhwcHHjzzTfp1auXvWOJiIhkK2n+ZFujRg1OnTqVGVkkAzLSWTDeHM/ms5sBeLLCkzZKdB/7voDLB8HNC55eDE6ZXNSJiE1888031K9fn4MHD1K8eHG2bNnCG2+8gYN+SSIiIpJEmv/P+OabbzJ27Fi+//57goODCQsLS/IlWS+jnQUvRFwgMj4Sdyd36nrXtWGyZMSE/tdJ8NHXwKNw5r6eiNjE9OnT6dOnDxERETz66KMEBQXRqlUre8cSERHJllJdZE2bNo3IyEg6dOjAgQMHeOqppyhdujSFChWiUKFCFCxYUOu07CSjnQXPh58HoHT+0jiYMvk30r+9DVHXoEjlxGYXIpIjPPHEE7i5uTFhwgS2bNlCiRIl7B1JREQk20p144upU6fy3HPP8csvv2RmHsmg9HQWvF1klclXJjMi/ef6Sfh9YeJj/5ng6Jy5ryciGXL27FnKlSsHgJ+fHydPnsTHx8fOqURERLK/VBdZxr+Lflq2bJlpYSR97lyPlZ4GfRfCLwBQJn8mF1mbJyV2E6zUFqqo3b9IdhUXF8crr7zCwoUL2blzJ35+fgAqsERERFIpTXPDtLlk9pPR9ViQRUXWqV/hn+/B5AjtZmTe64hIhpw9e5bmzZvz/vvvExsbq9kLIiIi6ZCmfbKqVKnywELrxo0bGQokaZPR9Vhwx3TBzCqyLGbY9O9Gww2GgPdDmfM6IpIh33//PQMGDODmzZsUKlSIzz//nI4dO9o7loiISI6TpiJr6tSpeHl5ZVYWyaD0rMeyGBYuRCSOZJXOXzozYsG+z+HKIXArmNhRUESylfj4eCZMmMBbb70FQMOGDVmxYgW+vr72DSYiIpJDpanI6tWrF97e3pmVRTIoPbM5r0ZdJdYci6PJkZL5Sto+lFq2i2R7X3/9tbXAevHFF3nrrbdwcdH+dSIiIumV6iJL67Fyp9ujWCU8S+DskAnd/n57G6KuQ9EqiVMFRSTb6d+/P5s2baJbt248/fTT9o4jIiKS46W68YVxZws7yTYy+q8lU9djqWW7SLZkNpv54IMPiIqKAsDBwYFvvvlGBZaIiIiNpHoky2KxZGYOSQdbdBbM1CLrp4n/tWyv/Jjt7y8iaXb58mX69OnDzz//TFBQEEuWLLF3JBERkVwnTWuyJHuJist4Z8EzoWcAKFegnC2jwalAOLohsWW7/0zb3ltE0iUwMJDevXsTEhKCp6cnbdu2tXckERGRXClN+2RJ9nH3KFZ6OgsCnAo9BUB5r/I2y4Y54Y6W7UOhWFXb3VtE0sxisTBz5kzatGlDSEgIDz/8MHv27KFPnz72jiYiIpIraSQrh7p7fywPl7SPYpktZs6GnQVsXGTt/wKuHP63Zft4291XRNLs2rVr1sYWAIMGDWL+/Pl4eHjYOZmIiEjupSIrh7qz4UV6R7EuRlwk3hKPq6MrPp4+tgkWfeu/lu2tXlfLdhE7i4mJYc+ePbi7uzN//nwGDx5s70giIiK5noqsHOjuqYLp7a5/e6qgbwFfHB3SPhKWLGvL9qpQ/xnb3FNE0sQwDOsvXkqXLs2qVasoUqQINWvWtHMyERGRvCFbrMmaP38+vr6+uLm50ahRI/74449UXRcQEIDJZKJz586ZGzCbuXuqYHoaXgCcDj0NQAWvCrYJdv0k7P4k8bFatovYxc2bN+nSpQtr1qyxHnv00UdVYImIiGQhuxdZK1asYMyYMUyePJl9+/ZRu3Zt/P39uXLlyn2vO3PmDGPHjqV58+ZZlDT7sMVUQciEphc/Tfi3ZftjUFldy0Sy2p9//omfnx/r1q3jueeeIzo62t6RRERE8iS7F1nz5s3j2WefZfDgwVSvXp2FCxfi4eFx371bzGYzffv2ZerUqVSoYKNRmBzCVlMF4b+RrPIFbVBknfwFjm78t2X7jIzfT0RSzTAMPvroIx555BHOnDlDhQoV+OGHH3B3d7d3NBERkTzJrkVWXFwce/fuTbJXi4ODA23btmXXrpQ32Z02bRre3t4MGTLkga8RGxtLWFhYkq+czBZTBUNjQ/n80OccuHoAsMF0QXMC/Ph64uOGz6plu0gWCg0NpWfPnowaNYr4+Hi6du3Kvn378PPzs3c0ERGRPMuujS+uXbuG2WymePHiSY4XL16cf/75J9lrtm/fzmeffUZQUFCqXmPWrFlMnTo1o1GzpbROFTxy/Qjf/PMNG09vJNYcC4Cns2fGNyLe93liy3b3QtDy1YzdS0RSLSwsjPr163PixAmcnJyYO3cuo0ePTvcUYhEREbGNHNVdMDw8nP79+7No0SKKFi2aqmtee+01xowZY30eFhZGmTJlMitilkrL56j/O/l/vL79devzhwo/xOPlH6dxyca4OrqmP0T0Lfjl3+mBj6plu0hWKlCgAO3bt2f9+vWsXLmSRo0a2TuSiIiIYOciq2jRojg6OnL58uUkxy9fvkyJEiXuOf/kyZOcOXOGjh07Wo9ZLBYAnJycOHr0KBUrVkxyjaurK66uGSgicgGzxcyEHRMAqFm0Jq80eIXaxWrb5rfdSVq2a/8dkcwWERFBVFQU3t7eAMydO5epU6dSuLB+wSEiIpJd2HVNlouLC/Xq1WPr1q3WYxaLha1bt9KkSZN7zn/ooYf4+++/CQoKsn499dRTtGrViqCgoFwzQmVr/3fq/7AYicVoy9ItqeNdxzYF1rUTsHth4mO1bBfJdIcOHaJhw4b06NGDhIQEIPEXSSqwREREshe7TxccM2YMAwcOpH79+jRs2JD33nuPyMhIBg9OHBUZMGAApUqVYtasWbi5uVGjRo0k1xcsWBDgnuPyn1XHVgFQKl8pulftbrsb/zQBLAlQuZ1atotksi+++IIRI0YQFRVFaGgoZ8+evWfkXkRERLIHuxdZPXv25OrVq0yaNImQkBDq1KnDpk2brM0wzp07h4OD3TvN25VhGETHmwGIijOn6dpjN4/x19W/cDI58VWHryjsZqPfeJ/8GY79kNiyvZ1atotklujoaEaNGsVnn30GwGOPPcZXX31lnS4oIiIi2Y/diyyAkSNHMnLkyGS/FxgYeN9rly1bZvtA2YhhGHRbuIu9Z2+m6/pvj30LQKuyrSjqnrpmIQ9kToAf30h83PBZKFbFNvcVkSSOHTtG9+7d+euvvzCZTEydOpXXX38dR8e0b90gIiIiWSdbFFmSsuh4c7IFVv1yhR64R1ZMQgz/d+r/AOhWuZvtQu1bppbtIpnMMAwGDBjAX3/9hbe3N19//TVt2rSxdywRERFJBRVZ2Zxh/Pd4z4S2eLgkFlbuzo4PbF6x+exmwuPCKZWvFI19GtsmUPQt+Fkt20Uym8lkYsmSJYwbN47FixdTsmRJe0cSERGRVMrbi52yOcMw6L5wl/W5h4sjHi5OeLg4pao74OpjqwHoWrkrDiYb/av+9S2IvgHFHoL6z9jmniICwKlTp1i+fLn1efXq1dmwYYMKLBERkRxGI1nZWHS8mcPBYQBUL1nggdMD73Ty1kn2XdmHo8mRzpU62ybQtRPwxyeJj/1ngKPePiK2smbNGgYPHkxERATlypWjWbNm9o4kIiIi6aSRrBxi1XNN0rS31bfHExtetCzdEm8PG3Uhu7NleyW1bBexhbi4OMaMGUPXrl0JDQ2lUaNGlCtXzt6xREREJANUZOUQadk7OMGSwPqT6wF4usrTtglwu2W7g5NatovYyLlz52jZsiXvvvsuAGPHjiUwMFAbq4uIiORwmu+VTRmGkeY9sW67FHGJ0NhQ3BzdeMTnkYyHMSfAptcTHzdQy3YRW9i4cSP9+/fnxo0bFCxYkGXLltGpUyd7xxIREREbUJGVDWV0b6zz4ecBKJ2/NI4ONthPZ98yuHoksWX7o2rZLmILJ0+e5MaNG9SvX5+VK1dSvnx5e0cSERERG1GRlQ3dvTdWavbEutOF8AtAYpGV8TA3/2vZ3uqNxEJLRNLFMAzr2sqRI0fi6elJ3759cXV1tXMyERERsSWtycrm9kxom+amF9aRrHw2KLJ+ffu/lu31Bmf8fiJ51JYtW2jevDlhYYkdQ00mE88884wKLBERkVxIRVY25+Hy4E2H73a7yCqTP4OL568dv6Nl+0y1bBdJB7PZzJQpU2jXrh07duxg5syZ9o4kIiIimUyfmnOh8xE2KrKsLdv9oVIbGyQTyVsuX75M37592bp1KwDDhg1j8uTJdk4lIiIimU1FVjZkGBm51rCuycpQkXViKxzblNiy3V8t20XS6rfffqNXr14EBwfj4eHBJ598Qr9+/ewdS0RERLKAiqxsxjAMui/cle7rr8dcJzohGhMmSuUrlb6bmBPgxzcSHzccBkUrpzuPSF60cuVKevfujcVioXr16qxatYrq1avbO5aIiIhkERVZ2Ux0vJnDwYkL46uXLJCmroLwX2fBEp4lcHZ0Tl+IvUv/bdleGFq+kr57iORhjz76KCVKlKBNmzYsWLAAT09Pe0cSERGRLKQiK5u5c6pgWrsKgg2aXkTfhF/+XZjf6nW1bBdJpVOnTlGhQgUAvL292bdvH97e3mn+OywiIiI5n7oLZiN3TxVMz2ezf278A2SgyPr1LbVsF0kDwzB49913qVq1Kl999ZX1ePHixVVgiYiI5FEqsrKRjE4VPHT9EF8f+RqAxj6N0x7g2nH449PEx2rZLvJAt27domvXrowZM4aEhAR++eUXe0cSERGRbEBFVjaV1qmCUfFRvPrbqyQYCbQt2xb/cv5pf9Ef30hs2V6lvVq2izzAnj178PPzY+3atbi4uDB//nwWL15s71giIiKSDWioIptKyywji2Fh2u/TOBt2Fm8Pb6Y0nZL2aUontsDxHxNbtrd7M23XiuQhhmHw8ccfM2bMGOLi4ihfvjyrVq2iXr169o4mIiIi2YRGsnI4wzCY/cdsNpzagIPJgVnNZuHl6pW2myRp2T5cLdtF7mPfvn2MHDmSuLg4OnfuzL59+1RgiYiISBIaycrhPtj/Ad/88w0Abz7yJg1LNkz7TfYuhav//NuyfZyNE4rkLvXq1WPChAkUKlSIl19+Wc0tRERE5B4qsnKoozeO8tr21zh+8zgAExtPpGPFjmm/UfRN+GVG4mO1bBe5h2EYLFu2jFatWuHr6wvA9OnT7RtKREREsjVNF8yhNpzeYC2w/lfvf/So2iN9Nwqck1hoFaumlu0id4mMjGTQoEE888wz9OzZk7i4OHtHEhERkRxAI1k5lMViAaBZqWYMqjEofTe5egz+XJT4uL1atovc6ciRI3Tr1o3Dhw/j4OBA586dcXLS3xERERF5MH1iyOEqF8pAk4qfJvzXsr1ia9uFEsnhvvrqK4YPH05UVBQlSpQgICCAli1b2juWiIiI5BCaLpiNGEbqzz0RegIAd0f39L1YkpbtM9J3D5FcJiYmhmHDhtG/f3+ioqJo06YNQUFBKrBEREQkTVRkZROGYdB94a5Unfv9qe/ZcXEHDiYHnqjwRNpfzJwAm15PfNxwOBStlPZ7iORSe/bswWQyMXnyZH788UeKFy9u70giIiKSw2i6YDYRHW/mcHAYANVLFsDd2fGecyyGhY+DPuaTvz4BoFvlbpQtUDbtL7ZnCVw7+m/L9lcylFskNzAMA5PJhJubG6tWreLUqVM89thj9o4lIiIiOZSKrGxo1XNN7tl7JzohmgnbJ/DT2Z8AGFxjMC/5vZT2m0fdgMCZiY9bvwHuBTMWViQHi42NZezYsRQpUoQpU6YAULFiRSpWrGjfYCIiIpKjqcjKhu7e29RsMTN883D2X9mPk4MTk5tMpnOlzum7+a9vJbZs964OfoMyGlUkxzp9+jQ9evRgz549ODo60r9/fxVXIiIiYhNak5UDbD67mf1X9pPPOR+LHluU/gLrzpbt/jPUsl3yrHXr1uHn58eePXsoVKgQ69atU4ElIiIiNqMiK5tIqbOgYRgs+juxMBrw8ADql6if/hf56Y1/W7Y/rpbtkifFx8czduxYOnfuzK1bt2jcuDFBQUE88UQ6GsiIiIiIpEBDGdlASp0Fw+PCGbhpIMdvHsfDyYM+D/VJ/4sc3wLHfwIH58RRLJE8xjAMHn/8cbZu3QrAyy+/zOzZs3FxcbFzMhEREcltNJKVDaTUWXD1sdUcv3kcgN4P9cbL1St9L2COhx//bdneaDgU0bQoyXtMJhN9+vTBy8uL7777jnnz5qnAEhERkUyhIiubud1Z0DAM1p5YC8Djvo/zQt0X0n/TPUsTW7Z7FIEW42wTVCQHSEhI4MyZM9bngwcP5ujRo3Tp0sV+oURERCTXU5GVzdzuLPjXtb84FXoKN0c3JjWZhLODc/pueGfL9lZq2S55R3BwMG3btqVFixbcuHEDSBzN0ubCIiIiktlUZGVTa46vAaCdbzvyueRL/41+nXNHy/aBNkonkr39/PPP1KlTh19//ZWbN2/y119/2TuSiIiI5CEqsrKh6IRoNp3ZBJD+du0AV4/CH7dbts9Uy3bJ9cxmM9OmTaNt27ZcuXKFmjVrsmfPHh599FF7RxMREZE8RJ+6s6EtZ7cQGR9J6XylqVe8Xvpv9OMbYJihageo2Mp2AUWyoStXrtCvXz82b94MwJAhQ/jggw/w8PCwczIRERHJa1RkZUO3G150qtQJB1M6BxuPb4YTmxNbtrd703bhRLKpN954g82bN+Pu7s6CBQsYOFDTY0VERMQ+VGRlM+fCz/JHyB+YMNGpYqf03UQt2yUPeuutt7h48SJvvfUWNWrUsHccERERycO0JitbMXhnz1sANCvVjJL5SqbvNnuWwLVjatkuudqNGzd47733MAwDgEKFCrFx40YVWCIiImJ3GsnKRpwKBPF7yE5cHFx4pcEr6btJ1A34RS3bJXfbvXs3PXr04Ny5c7i7uzN8+HB7RxIRERGx0khWNmFyjMS1+PcADK89HF8v3/TdKHA2xNwC74fVsl1yHcMw+OCDD2jevDnnzp2jUqVKNGzY0N6xRERERJLQSFY24eq9AQenSCp6VWLww4PTd5OrR+HPxYmP26tlu+QuoaGhDBkyhG+//RaAbt26sXjxYry8vOycTERERCQpfQrPBo5cP4RzwX0YhonXGk7E2dE5fTf68fV/W7Y/ARUetWlGEXvav38/3bp149SpUzg7O/POO+8wcuRITCaTvaOJiIiI3ENFVjYQeOEXABLCa1CzaK303eT4Zjix5d+W7dNtmE7E/m7dusWZM2coV64cK1eu1BRBERERydZUZGUDO4N3AJAQXi19N1DLdsmFDMOwjlS1atWKgIAA2rZtS6FCheycTEREROT+1PjCzq5EXuHYzX8AMEdWSd9N/vzs35btRaFlOrsSimQjf//9N40aNeLYsWPWY927d1eBJSIiIjmCiiw7++XcNgDM0aWp5u2Du7Nj2m4QdQMCZyU+bv0GuKkJgORsy5Yto1GjRvz555+89NJL9o4jIiIikmYqsuzIMAze2b4egISIKqx6rknaF/IHzkps2V68hlq2S44WFRXFM888w+DBg4mOjsbf35/PP//c3rFERERE0kxFlh2Fx8YS5XgYgDJufni4pHEU68o/iVMFAfxngkMarxfJJv755x8aNWrE0qVLcXBw4M0332Tjxo0UK1bM3tFERERE0kyNL+zo4PW/MTnGYCR4sGZIr7SPYv30xh0t21tmTkiRTLZ3715atmxJZGQkxYsX55tvvqFVq1b2jiUiIiKSbiqy7Ojnc1sASIisjJNjGkehjv2klu2SK9SqVYtatWrh6urKN998Q4kSJewdSURERCRDVGTZya2YW6w7+R0A8aF+abv4zpbtjZ9Ty3bJcc6cOUOpUqVwdnbG2dmZ//u//6NgwYI4pvWXDSIiIiLZkNZk2cnyf5YTY47BHOOT9tbtfy6G68cTW7a3GJc5AUUyybfffkvt2rV5/fXXrceKFCmiAktERERyDRVZdhAZH8nyI8sBiLvWCkjDWqwkLdsnqGW75BhxcXG89NJLdOvWjbCwMHbv3k1cXJy9Y4mIiIjYnIosO1h5dCXhceGYY4uREP4w1UsWSP3+WIGzICb035btAzI3qIiNnD17lubNm/P+++8D8Morr7B161ZcXFzsnExERETE9rQmK4vFmmP5/FDi3j9x11sCDqnfH+vKkf9atrefpZbtkiN8//33DBgwgJs3b1KoUCG++OILnnzySXvHEhEREck0KrKy2Nrja7kecx1HS2ESQusCkKrO7YaR2OzCMMNDT0L5FpkbVMQGbty4QZ8+fQgPD6dhw4asXLmScuXK2TuWiIiISKZSkZWF4s3xLD20FIDIK80Ax9RPFTy+GU7+nNiy/bFpmRtUxEYKFy7MokWL2LlzJ2+//bamB4qIiEieoDVZWeTUrVP03diXixEXKeRamPhbDQBSN1UwScv2EWrZLtnaTz/9xK+//mp93rNnT95//30VWCIiIpJnqMjKApvPbqbH9z04cuMIXq5eTGkyHQxnIJVTBZO0bB+buWFF0slsNjNp0iTat29Pr169uHz5sr0jiYiIiNiFpgtmst8u/MYrv75CgpFAU5+mTH9kOvmcCgM/pu4Gkdf/a9neZqJatku2FBISQp8+ffjll18A6Ny5M15eeq+KiIhI3qQiKxOFxYUx7tdxJBgJPF7+cWY1m4WDyYHrkWnYG+jOlu11+2deWJF0CgwMpHfv3oSEhODp6cmnn35Knz597B1LRERExG5UZGWiTac3EZUQRQWvCsxoNgMHkwPdFu5i79mbqbvBlSOwZ0niY7Vsl2zGMAxmzpzJpEmTsFgsPPzww6xevZqHHnrI3tFERERE7EprsjLRmuNrAOhauSvODs5ExZmTFFj1yxVKubOgWrZLDnDo0CEsFguDBg3ijz/+UIElIiIigkayMs3xm8c5eP0gTiYnnqzwJBaLwZMfbrd+f8+EthTxdEm5s+Dxn/5r2d5uehalFnkwwzAwmUyYTCY++eQTOnXqRM+ePe0dS0RERCTb0EhWJll7Yi0ALUq3oLBbYZ78cDunr0UCUL1kgfsXWAlxSVu2F66QBYlF7s8wDObOnUuPHj0wDAOA/Pnzq8ASERERuYtGsjJBvCWe7099D0CXyl2IjjdzODgMgPJFPfl+VLP7743152K4fgI8i0GLcVkRWeS+bt68yaBBg1i/fj0AGzZs4Mknn7RzKhEREZHsSUVWJvjtwm/ciLlBUfeiNCvVjLiE/773/ahmODjcp8CKvA6/zk583HoCuBXI3LAiD/Dnn3/So0cPzpw5g4uLCx988AFPPPGEvWOJiIiIZFuaLpgJbk8V7FihI04OSevYB24+HDjz35btNdWyXezKMAw+/PBDHnnkEc6cOUOFChXYtWsXw4cPv/9IrIiIiEgepyLLxiLiIth2YRsAnSt1TtvF0Tdhz9LEx+1nqmW72NVLL73E6NGjiY+Pp2vXruzbtw8/Pz97xxIRERHJ9lRk2VjQ1SDMhpnS+UpToWAaG1ac2Z7Ysr1oFbVsF7vr3bs3Hh4evP/++6xevRovLy97RxIRERHJEbQmy8b2Xd4HgF/xdPzG/9Svif9UgSV2YBgGx48fp0qVKgA0btyYs2fPUrRoUTsnExEREclZNJJlY/uv7AfAzzsdRdbp3xL/Wb6lDROJPFhERAQDBgygdu3a/PXXX9bjKrBERERE0k5Flo2dvHUSgGpFqqXtwrBguHYUMIFvM9sHE0nBoUOHaNCgAV999RXx8fHs2bPH3pFEREREcjQVWTZ0M+YmN2NvAuBbwDdtF59JbJZByVrgUdi2wURS8Pnnn9OgQQP++ecffHx8CAwM5JlnnrF3LBEREZEcTUWWDZ0KPQWAj6cPHs4eabz49nosTRWUzBcVFcWQIUMYNGgQ0dHRPPbYY+zfv59mzTSKKiIiIpJRKrJs6HToaQDKe5VP24WGAadVZEnWWbJkCUuWLMFkMjFt2jR++OEHvL297R1LREREJFdQd0Ebuj2SdXeRZRgPuPDmaQg9Dw5OUK5JJqUT+c+IESPYtWsXQ4YMoXXr1vaOIyIiIpKraCTLhm4XWXfuj2UYBt0X7rr/hbe7CpZuAC6emRVP8rCYmBhmzZpFTEwMAI6OjixfvlwFloiIiEgm0EiWDZ0JPQNA+QL/jWRFx5s5HBwGQPWSBXB3drz3Qq3Hkkx06tQpunfvzr59+zh//jwff/yxvSOJiIiI5GoaybKR6IRoLkVcApKOZN1p1XNNMJlMSQ8axh37Y2kTYrGtNWvW4Ofnx759+yhSpAhPPfWUvSOJiIiI5HoqsmzkTOgZDAwKuhaksFvyLdjvrq8AuHIYoq6Bk3vidEERG4iLi2PMmDF07dqV0NBQmjZtyv79+2nfvr29o4mIiIjkeiqybCTdnQVvj2KVawJOLjZOJXnR+fPnadmyJe+++y4AY8eOJTAwkDJlytg5mYiIiEjeoDVZNmJteuGVdKrgAzsLaj2W2JjZbObo0aMULFiQZcuW0alTJ3tHEhEREclTVGTZSHIjWQ/sLGhOgLM7Eh9rPZZkgGEY1vV+vr6+fPfdd5QrV47y5dM4sioiIiIiGabpgjaS3B5ZUXEP6CwYHASxYeDmBSVrZ1VUyWUuXbpEq1at2LBhg/XYo48+qgJLRERExE40kmUDFsPC2bCzQOJ0QcMwiIoz8+SH263nJNtZ8FRg4j99m4NDMq3dRR5g8+bN9O3bl6tXr3L27FnatWuHs7OzvWOJiIiI5GkqsmwgwZJAvCUeAC8XL7ot3MXeszet369esgAeLskUUdbW7VqPJWljNpuZPn0606ZNwzAMateuzapVq1RgiYiIiGQDKrJsLDrBfE+B9f2oZveOYsXHwPndiY8rqMiS1Lt8+TJ9+/Zl69atAAwbNoz33nsPd3d3OycTEREREVCRlan2TGhLEU+XewssgAt/QEIM5CsBRatkfTjJka5evUrdunUJDg7Gw8ODTz75hH79+tk7loiIiIjcQUVWJvJwcUy+wII7Wre3SGGXYpF7FStWjCeffJKdO3eyatUqqlWrZu9IIiIiInIXFVm29qB9sW6zrsdS63a5v+vXr2OxWChWrBgA77//PhaLBU9PTzsnExEREZHkqIW7Daw9sdb6eMBnfzz4gpgwuLg38bHWY8l97Nq1i7p169KnTx/MZjMA7u7uKrBEREREsjEVWTaw7uQ66+N/gmOAFPbFuu3cLjDMUMgXCpbNgoSS0xiGwbx582jRogXnz5/n7NmzXL582d6xRERERCQVVGTZ0KRG04DEwirZfbFus67H0iiW3OvmzZt06dKF//3vfyQkJNCzZ0/27NmDj4+PvaOJiIiISCqoyLKhj7desj6+by8LrceSFOzZs4d69eqxbt06XFxcmD9/Pt988w0FChSwdzQRERERSSU1vrChczejgAdMFYy8Bpf/TnyskSy5g8ViYdCgQZw+fZry5cuzatUq6tWrZ+9YIiIiIpJGGsmyhbs6Ct53quCZbYn/9H4Y8hXL3FySozg4OPDVV1/Rq1cv9u3bpwJLREREJIdSkZVBhmFw7HJ4kmP3nSp45/5YkucdOHCAr776yvq8Tp06fPPNNxQsWNB+oUREREQkQ1RkZVB0vJnIWLP1ef1yhVKeKghw+t8iS63b8zTDMFi8eDGNGzfmmWee4c8//7R3JBERERGxEa3JsqF5PWrzVOX7TBW8dR5unAKTA5RrmrXhJNuIjIxkxIgRfPnllwB06NCBChUq2DmViIiIiNiKRrJsyNXJIeUCC/7rKujjB25eWRNKspXDhw/TsGFDvvzySxwdHZk1axb/93//R5EiRewdTURERERsJFsUWfPnz8fX1xc3NzcaNWrEH3/8keK5ixYtonnz5hQqVIhChQrRtm3b+56f2SyGBZNTBACODveZJghq3Z7HLV++nAYNGnD48GFKlizJzz//zPjx43FwyBZ/DUVERETERuz+6W7FihWMGTOGyZMns2/fPmrXro2/vz9XrlxJ9vzAwEB69+7NL7/8wq5duyhTpgzt2rXj4sWLWZw80baLv+LgcgPD7EbtonVSPtEwtB4rj7t06RJRUVG0bduWoKAgWrRQsS0iIiKSG5kMwzAefFrmadSoEQ0aNOCjjz4CEvcKKlOmDKNGjWL8+PEPvN5sNlOoUCE++ugjBgwY8MDzw8LC8PLyIjQ0NMMbvBqGQa/ve3P4xiFir7UiaNQ8PFxSWOZ27Th8VB8cXWH8WXB2z9BrS85gGIZ1CqnFYuGbb76hV69eODo+YNRTRERERDKdLWuDO9l1JCsuLo69e/fStm1b6zEHBwfatm3Lrl27UnWPqKgo4uPjKVy4cLLfj42NJSwsLMmXrRy6dojDNw5hWJyJv/HI/U8+FZj4zzINVWDlEStXrqRJkyZERkYCie/tvn37qsASERERyeXsWmRdu3YNs9lM8eLFkxwvXrw4ISEhqbrHq6++io+PT5JC7U6zZs3Cy8vL+lWmTJkM577tStQNACxxRanm7fOA1u2312NpqmBuFxsby6hRo+jZsye7d+/mgw8+sHckEREREclCdl+TlRGzZ88mICCANWvW4Obmluw5r732GqGhodav8+fP2+S1DcMgJt5ifb7qufu0brdY4My2xMdaj5WrnT59mmbNmlmnv7722muMGzfOzqlEREREJCvZdZ+sokWL4ujoyOXLl5Mcv3z5MiVKlLjvtXPnzmX27Nls2bKFWrVqpXieq6srrq6uNsl7m2EYdFu4i6Br+/Eom3jsfp3bufw3RN8El/yJ7dslV1q3bh2DBg3i1q1bFC5cmC+//JIOHTrYO5aIiIiIZDG7jmS5uLhQr149tm7daj1msVjYunUrTZo0SfG6t956i+nTp7Np0ybq16+fFVGTiI43s/fsTetzDxen+08VPPVvV8FyTcFR+z/nRp9++imdO3fm1q1bNG7cmP3796vAEhEREcmj7D5dcMyYMSxatIjPP/+cI0eOMGLECCIjIxk8eDAAAwYM4LXXXrOeP2fOHCZOnMiSJUvw9fUlJCSEkJAQIiIisjy7yTGxoUFlb6/UbUKsqYK5VseOHSlevDhjxozh119/pWzZsvaOJCIiIiJ2YvdhlZ49e3L16lUmTZpESEgIderUYdOmTdZmGOfOnUuyWeuCBQuIi4ujW7duSe4zefJkpkyZkpXRccp3BICGJRqmfFJCHJzdmfhYmxDnKkeOHKFatWoAlCxZksOHD6fY5VJERERE8g67F1kAI0eOZOTIkcl+LzAwMMnzM2fOZH6g1DDF45TvHwDalG2T8nkX90J8JHgUAe+HsyicZKaEhAQmT57MrFmzWLFiBd27dwdQgSUiIiIiQDaYLphTOXqexOQYRzH3YtQoWiPlE29PFfRtDg76487pgoODadu2LTNnzsQwDPbu3WvvSCIiIiKSzWSLkaycyCnfIQBalG6Fg+k+xdPpf5teaD1Wjrd161b69OnDlStXyJ8/P4sWLaJnz572jiUiIiIi2YyGVtLJ0TWx7Xw97/t0N4yLgvN/JD7WJsQ5ltlsZtq0aTz22GNcuXKFWrVqsWfPHhVYIiIiIpIsFVkZ5ORwn8HAc7vAEg8FSkPhClkXSmxq+/btTJ48GcMwGDp0KL///jtVqlSxdywRERERyaY0XTAz3V6PVb7FA3YrluysZcuWvP7661StWpUBAwbYO46IiIiIZHMqsjKT1mPlSBaLhffff5/u3btTunRpAGbMmGHnVCIiIiKSU2i6YHqZLPf/fvRNCD6Q+Fj7Y+UY169f56mnnmLMmDH07t2bhIQEe0cSERERkRxGI1npEBkfiYNrMABl8pdN/qQzO8CwQJHKUMAnC9NJev3+++/06NGD8+fP4+rqyoABA3B0dLR3LBERERHJYTSSlQ67gndgcjBjiStC+QIpNLS4vR5LUwWzPcMweO+992jevDnnz5+nUqVK/P777zz77LOYtJZORERERNJII1npEHj+ZwDiw2qk/CH89nosTRXM1sLCwhg0aBBr1qwBoFu3bixevBgvLy87JxMRERGRnEojWWkUZ45jx6XtACSEP5z8SeGX4eo/gAl8m2ddOEkzJycnjh07hrOzMx9++CErV65UgSUiIiIiGaKRrDT6/dLvRCVEYonPjyWmdPIn3Z4qWKImeBTOunCSKoZhAGAymfDw8GD16tWEh4fToEEDOycTERERkdxAI1lp9NPZLQAkRDxM9ZIFcXdOpjGCWrdnW+Hh4fTp04fZs2dbjz300EMqsERERETEZlRkpYHZYua3C4EAJIQ9zKrnmiS/Jsu6HktFVnby999/U79+fQICApg6dSrBwcH2jiQiIiIiuZCKrDTYd2UfN2NvYpjdMUdVINmeFzdOw61z4OAEZZtkeUZJ3tKlS2nYsCHHjh2jVKlSbN26lZIlS9o7loiIiIjkQiqy0uCnM1sBSAh/iOolC6UwVfDf9Vil6oNrvixMJ8mJjIxk0KBBPPPMM8TExODv78/+/ft55JFH7B1NRERERHIpNb5IJcMw+PVCYuv2hPAarBr3gKmCWo9ld2azmZYtW7J3714cHByYNm0ar732Gg4O+t2CiIiIiGQeFVmpdOT6EYIjgzEsziREVk5+qqBh/DeSpfVYdufo6MgzzzzDxYsX+eabb3j00UftHUlERERE8gD9Sj8VDMNgxNrPAUiIqAKGS/InXjkCkVfByR1K18/ChHJbTEwMJ0+etD4fMWIEhw8fVoElIiIiIllGRVYqRMebuWreCyRuQFy/3APWY5VrAk6uWZhQAE6cOEGTJk1o164doaGhQOJeWIUKFbJzMhERERHJS1RkpcL58HM4ul3GMBzY/NxzqWjd3iJrAwqrV6/Gz8+PoKAgwsLCOH78uL0jiYiIiEgepSIrFS5GXATAEluM4vkKJV9gmRPgzPbEx1qPlWViY2MZPXo03bt3Jzw8nGbNmhEUFET9+pquKSIiIiL2oSIrFWLNMf8+SmaK4G3BByA2DNy8oGTtLMmV1505c4bmzZvz4YcfAvDKK6/w888/U6pUKTsnExEREZG8TN0FU2Hv5T0AmKPv8+H99lRB3+bgcJ9iTGzmtdde488//6RQoUJ88cUXPPnkk/aOJCIiIiKiIis1dgXvAMAcWTXlk7QeK8t9+OGHxMXFMW/ePMqVK2fvOCIiIiIigKYLPtD5sPOcCz+LYTiQEFkp+ZMSYuHc74mPtR4r01y4cIG5c+danxctWpRvv/1WBZaIiIiIZCsayXqAbRe3AWCOKgcWt+RPOv8HJMRAvuJQ7D6jXZJuP/74I/369ePatWt4e3szYMAAe0cSEREREUmWRrIe4NcL/xZZkVWpXrJACvtj3TFVMLnOg5JuZrOZiRMn8vjjj3Pt2jXq1q3LI488Yu9YIiIiIiIp0kjWfcSaY61NLxIiHmLV6yntj/XvJsSaKmhTwcHB9OnTh8DAQACee+453n33XdzcUhhRFBERERHJBlRk3ceekD3EmmOwxBfAEls8+UGq2HC4uDfxsZpe2ExgYCC9evXi8uXLeHp6smjRInr37m3vWCIiIiIiD6Qi6z4OXD0AgDmyEpDCNMCzu8CSAIV8oZAaMNhKTEwMV65coUaNGqxatYqHHnrI3pFERERERFJFRdZ9xJnjADAs7imfpNbtNmOxWHBwSFwm2L59e7799lv8/f3x8PCwczIRERERkdRT44v7OBd2AQDD7JGKphdaj5URO3bsoHbt2pw6dcp6rEuXLiqwRERERCTHUZGVAsMwCLq6HwBzlC+rnkum6UXkdQj5O/GxRrLSxTAM5s6dS8uWLTl48CBvvPGGvSOJiIiIiGSIpgum4GLERa5GX8EwHDFHl0m+6cWZxPbueFeHfN5Zmi83uHHjBoMGDeL//u//AOjduzeffPKJnVOJiIiIiGSMiqwU7LuyDwBLdCkwXJI/Seux0u2PP/6gR48enD17FldXV95//32GDRuWfIt8EREREZEcREVWCvZdTiyyzNG+KZ+k/bHSJTAwkHbt2hEfH0/FihVZtWoVdevWtXcsEREReQCz2Ux8fLy9Y4ikiYuLi7W5WlZRkZWCvZcT975KiPJN/oTQi3D9BJgcwPeRrAuWCzRp0oRatWrh6+vLZ599hpeXl70jiYiIyH0YhkFISAi3bt2ydxSRNHNwcKB8+fK4uKQwOy0TqMhKRnhcOGfCzgCJTS+SdXsUy6cuuKlIeJAjR45QuXJlnJyccHV1ZcuWLXh5eWl6oIiISA5wu8Dy9vbGw8ND//+WHMNisXDp0iWCg4MpW7Zslr13VWQl43ToaQCKuRcj3JJCC3G1bk8VwzBYtGgRo0ePZuzYsbz55psAFCxY0L7BREREJFXMZrO1wCpSpIi944ikWbFixbh06RIJCQk4OztnyWuqhXsyToUm7tVUrkD55E8wjDvWY6npRUoiIiLo378/w4cPJzY2lr///huz2WzvWCIiIpIGt9dgae9KyaluTxPMys+hKrKScbvI8k2pyLp+EsIugqMLlG2chclyjkOHDtGgQQOWL1+Oo6Mjc+bMYc2aNTg6JrOhs4iIiGR7miIoOZU93ruaLpiM07cSpwumWGSdDkz8Z5lG4OyeNaFykM8//5wRI0YQHR2Nj48PK1asoFmzZvaOJSIiIiKSJTSSdZd4c7y1s2DVQlWTP0mt21N06dIlnn/+eaKjo2nXrh1BQUEqsERERCTX2bp1K9WqVdNSCBu7du0a3t7eXLhwwd5RMkRF1l3+DPmT8PhwCrsV5uEiNe89wWKB09sSH2s91j18fHxYuHAh06ZNY+PGjRQrVszekURERCSPGjRoEJ07d07x+76+vphMJkwmEx4eHtSsWZPFixen6t6vvPIKEyZMyLVLIQzDYNKkSZQsWRJ3d3fatm3L8ePH73uN2Wxm4sSJlC9fHnd3dypWrMj06dMxDCPZ85977jlMJhPvvfee9VjRokUZMGAAkydPtuWPk+VUZN1l67mtALQq0wpHh2T+0lw+CNE3wCUflPLL4nTZU0BAADt37rQ+79+/PxMnTsy1/9ERERGR3GPatGkEBwdz8OBB+vXrx7PPPssPP/xw32u2b9/OyZMnefrppzP02nFxcRm6PjO99dZbfPDBByxcuJDdu3fj6emJv78/MTExKV4zZ84cFixYwEcffcSRI0eYM2cOb731Fh9++OE9565Zs4bff/8dHx+fe743ePBgli9fzo0bN2z6M2UlFVl3sBgWfj7/MwBty7VN/qTbrdvLNQXHrGkBmV3FxMTw/PPP07t3b3r06MH169ftHUlERESygGEYRMUlZPlXSiMiGZE/f35KlChBhQoVePXVVylcuDCbN2++7zUBAQE89thjuLm5WY+dPHmSTp06Ubx4cfLly0eDBg3YsmVLkut8fX2ZPn06AwYMoECBAgwbNgxILNqaN2+Ou7s7ZcqUYfTo0URGRlqv+/LLL6lfv741a58+fbhy5YoN/xSSMgyD9957jwkTJtCpUydq1arFF198waVLl1i7dm2K1+3cuZNOnTrxxBNP4OvrS7du3WjXrh1//PFHkvMuXrzIqFGjWL58ebIt1R9++GF8fHxYs2aNrX+0LKPGF3c4fP0w16Kvkc85H41KNCI+uSm2Wo8FwKlTp+jevTv79u0DEn/j4OWlTZlFRETyguh4M9Un/Zjlr3t4mj8eLpnz8dVisbBmzRpu3rxpbfmdkm3bttGnT58kxyIiIujQoQMzZszA1dWVL774go4dO3L06FHKli1rPW/u3LlMmjTJOh3u5MmTtG/fnjfffJMlS5Zw9epVRo4cyciRI1m6dCmQ2EZ/+vTpVK1alStXrjBmzBgGDRrExo0bU8z43HPP8dVXX93354iIiEj2+OnTpwkJCaFt2/8GHby8vGjUqBG7du2iV69eyV7XtGlTPv30U44dO0aVKlU4cOAA27dvZ968edZzLBYL/fv3Z9y4cTz88MMpZmvYsCHbtm1jyJAh9/0ZsisVWXc4H34egKqFq+Ls6Ey8OSHpCeZ4OPvvtLgKebfIWrNmDYMHDyY0NJQiRYrw1Vdf0b59e3vHEhEREUmzV199lQkTJhAbG0tCQgKFCxdm6NCh973m7Nmz90xzq127NrVr17Y+nz59OmvWrGH9+vWMHDnSerx169b873//sz4fOnQoffv25aWXXgKgcuXKfPDBB7Rs2ZIFCxbg5ubGM888Yz2/QoUKfPDBBzRo0ICIiAjy5cuXbMZp06YxduzYVP853CkkJASA4sWLJzlevHhx6/eSM378eMLCwnjooYdwdHTEbDYzY8YM+vbtaz1nzpw5ODk5MXr06Ptm8PHxYf/+/enKnx2oyLrDtehrABRzT6FZw8V9EBcBHkXAO+XKO7dKSEjglVde4d133wUSf1sREBBAmTJl7JxMREREspK7syOHp/nb5XVtbdy4cQwaNIjg4GDGjRvH888/T6VKle57TXR0dJKpgpA4KjRlyhQ2bNhAcHAwCQkJREdHc+7cuSTn1a9fP8nzAwcO8Ndff7F8+XLrMcMwsFgsnD59mmrVqrF3716mTJnCgQMHuHnzJhaLBYBz585RvXr1ZDN6e3vj7e2d6j8HW1i5ciXLly/n66+/5uGHHyYoKIiXXnoJHx8fBg4cyN69e3n//ffZt2/fA/eucnd3JyoqKouS256KrDvcLrKKuhdN/oTb67F8m4ND3lvO5ujoyNmzZwEYO3YsM2fOTHYerYiIiORuJpMp06btZbWiRYtSqVIlKlWqxKpVq6hZsyb169dPsXi5fc3NmzeTHBs7diybN29m7ty5VKpUCXd3d7p163ZPcwtPT88kzyMiIhg+fHiyIztly5YlMjISf39//P39Wb58OcWKFePcuXP4+/vft3FGRqYLlihRAoDLly9TsmRJ6/HLly9Tp06dFO83btw4xo8fb51OWLNmTc6ePcusWbMYOHAg27Zt48qVK0mmT5rNZv73v//x3nvvcebMGevxGzdu5Ogu1bnjb4eNPLjIur0eK2+1brdYLDg4OGAymViyZAlDhw7l8ccft3csEREREZsqU6YMPXv25LXXXmPdunUpnle3bl0OHz6c5NiOHTsYNGgQXbp0ARILmDuLhpT4+flx+PDhFEfP/v77b65fv87s2bOts4f27NnzwPtmZLpg+fLlKVGiBFu3brUWVWFhYezevZsRI0akeF1UVBQOdw1EODo6Wkfe+vfvn2SdF4C/vz/9+/dn8ODBSY4fPHiQRx99NF35swMVWXe4b5EVHwXndyc+rvBo1oWyo4SEBCZOnMiZM2f4+uuvMZlMeHl5qcASERGRHCM0NJSgoKAkx4oUKZLicocXX3yRGjVqsGfPnnum9t3m7+/P559/nuRY5cqV+e677+jYsSMmk4mJEydai4v7efXVV2ncuDEjR45k6NCheHp6cvjwYTZv3sxHH31E2bJlcXFx4cMPP+S5557j4MGDTJ8+/YH3zch0QZPJxEsvvcSbb75J5cqVKV++PBMnTsTHxyfJvmNt2rShS5cu1jVnHTt2ZMaMGZQtW5aHH36Y/fv3M2/ePOuasiJFilCkSJEkr+Xs7EyJEiWoWrWq9VhUVBR79+5l5syZ6cqfHeS9OW/3cb81WQ4X/gBzHBQoBYUrZHW0LHfp0iVat27N7NmzCQgIYNu2bfaOJCIiIpJmgYGB1K1bN8nX1KlTUzy/evXqtGvXjkmTJqV4Tt++fTl06BBHjx61Hps3bx6FChWiadOmdOzYEX9/f/z8Hrynaq1atfj11185duwYzZs3p27dukyaNMnaWKNYsWIsW7aMVatWUb16dWbPns3cuXPT8CeQPq+88gqjRo1i2LBh1iYbmzZtuqdt/bVr16zPP/zwQ7p168bzzz9PtWrVGDt2LMOHD09VUXindevWUbZsWZo3b26znyermYzM2HAgGwsLC8PLy4vQ0FAKFChgPX49+jqPrnwUgNUdV1O1cFWi4hKs7UmPt9qN8673oXYf6LLAHtGzzObNm+nbty9Xr14lf/78fPbZZ3Tv3t3esURERMQOYmJiOH36NOXLl7+n2UNeNm7cOMLCwvjkk0/sHSXXady4MaNHj76nTX563e89nFJtkFEayfrX69tftz729rh3aNXhTO5fj2U2m5k8eTL+/v5cvXqVOnXqsG/fPhVYIiIiInd54403KFeuXKqmBErqXbt2ja5du9K7d297R8kQrckC4sxx7AlJXEDYtXJXCrkVSvL9AkTiEHIg8UkuLrIGDRpk7UIzbNgw3nvvPdzd3e2cSkRERCT7KViwIK+//vqDT5Q0KVq0KK+88oq9Y2SYRrKAg9cOEmeJo7BbYaY0mWI9fnsiZSOHI5gMCxSpBF6l7BMyCzz77LN4eXnx1Vdf8cknn6jAEhERERFJB41kAUFXgwCoV7yedWM0wzDovnAXAE0dDiWeWL6lPeJlGovFwuHDh6lRowYALVq04MyZMxQsWNC+wUREREREcjCNZAEXwy8CULFgReux6Hgzh4PDAGjlciTxYIXcU2Rdu3aNJ598ksaNG3PkyBHrcRVYIiIiIiIZoyKLO/bHcrt3f6yihOJrOZf4xDfntpG8086dO6lbty4//PADZrM5SZElIiIiIiIZoyKLO4osj3uLrMYO/+7mXbwmeBTOylg2ZxgG8+bNo2XLlly4cIEqVaqwe/duunbtau9oIiIiIiK5htZkcUeR5Z5YZBmGQVScGbijyCqfs0exbt68yeDBg1m3bh0AvXr14tNPPyV//vx2TiYiIiIikrvk+SLLMIwkRZZhGHRbuIu9Z28C0OR2kZXDpwouXLiQdevW4eLiwnvvvcdzzz1nbfIhIiIiIiK2k+enC4bFhRFniQMSi6zoeLO1wPLmJhUdgjEwQbkm9oyZYWPHjqVfv37s3LmTESNGqMASERERyUJxcXFUqlSJnTt32jtKrtO4cWO+/fZbe8dIIs8XWXsv7wUgv0t+XBxcrNMEAbY+nfjHYypZC9wLJXt9dhUWFsakSZOIi0ssIJ2dnfnyyy+pV6+enZOJiIiIZI1BgwbRuXPnJMdWr16Nm5sb77zzjvUck8nE7Nmzk5y3du3aJL+UDgwMxGQy8fDDD2M2m5OcW7BgQZYtW3bfLAsXLqR8+fI0bdo0/T9QNhcYGIifnx+urq5UqlTpgX8mU6ZMwWQy3fPl6emZ7PkBAQGYTKZ7/p1OmDCB8ePHY7FYbPSTZFyeLrIshoUXf3kRgEKuhXnig+3Uf3OL9fvuF//9TUMOmyp44MAB6tevz/Tp0xk/fry944iIiIhkC4sXL6Zv374sWLCA//3vf9bjbm5uzJkzh5s3bz7wHqdOneKLL75I0+sahsFHH33EkCFD0pz5Trd/eZ4dnT59mieeeIJWrVoRFBTESy+9xNChQ/nxxx9TvGbs2LEEBwcn+apevTrdu3e/59wzZ84wduxYmje/93P5448/Tnh4OD/88INNf6aMyPNF1m3hl1tY98UCqF+uEI7ndiQ+Kd8iq6Oli2EYLF68mMaNG3P8+HHKlClDjx497B1LREREchvDgLjIrP8yjHRHfuuttxg1ahQBAQEMHjw4yffatm1LiRIlmDVr1gPvM2rUKCZPnkxsbGyqX3vv3r2cPHmSJ554IsnxV199lSpVquDh4UGFChWYOHEi8fHx1u9PmTKFOnXqsHjxYsqXL4+bmxsAt27dYujQoRQrVowCBQrQunVrDhw4YL3u5MmTdOrUieLFi5MvXz4aNGjAli1byEy3R+reeecdqlWrxsiRI+nWrRvvvvtuitfky5ePEiVKWL8uX77M4cOH7ylGzWYzffv2ZerUqVSoUOGe+zg6OtKhQwcCAgJs/nOlV55vfHHbufPlAShf1JPvRzXDIyYE07unwOQIZbP/eqzIyEhGjBjBl19+CUCHDh344osvKFKkiJ2TiYiISK4THwUzfbL+dV+/BC7JTyW7n1dffZWPP/6Y77//njZt2tzzfUdHR2bOnEmfPn0YPXo0pUuXTvFeL730El999RUffvghY8eOTdXrb9u2jSpVqtzT1Tl//vwsW7YMHx8f/v77b5599lny58/PK6+8Yj3nxIkTfPvtt3z33Xc4OjoC0L17d9zd3fnhhx/w8vLik08+oU2bNhw7dozChQsTERFBhw4dmDFjBq6urnzxxRd07NiRo0ePUrZs2RQzPv744/f9OT755BP69u2b7Pd27dpF27Ztkxzz9/fnpZdeetAfj9XixYupUqXKPaNV06ZNw9vbmyFDhrBt27Zkr23YsOE9Uz7tSUXWXb4f1QxPVyf4Z3viAZ864FbArpke5OjRo3Tt2pXDhw/j6OjIjBkzGDduHA4OeXqgUkRERIQffviBdevWsXXrVlq3bp3ieV26dKFOnTpMnjyZzz77LMXzPDw8mDx5Mq+//jrPPvssXl5eD8xw9uxZfHzuLUonTJhgfezr68vYsWMJCAhIUmTFxcXxxRdfUKxYMQC2b9/OH3/8wZUrV3B1dQVg7ty5rF27ltWrVzNs2DBq165N7dq1rfeYPn06a9asYf369YwcOTLZjPXr1ycoKOi+P0fx4sVT/F5ISMg93y9evDhhYWFER0fj7u5+33vHxMSwfPnye5a6bN++nc8+++yB2Xx8fDh//jwWiyVbfAZWkXUX6/rG0/9Wyb7N7JYltRwdHblw4QIlS5YkICCAFi1yxvRGERERyaGcPRJHlezxumlUq1Ytrl27xuTJk2nYsCH58uVL8dw5c+bQunXrB45QDRkyhHfeeYc5c+Ywc+bMB2aIjo62TvW704oVK/jggw84efIkERERJCQkUKBA0l/ulytXzlpgQeLa+4iIiHtmK0VHR3Py5EkAIiIimDJlChs2bCA4OJiEhASio6M5d+5cihnd3d2pVKnSA3+WzLJmzRrCw8MZOHCg9Vh4eDj9+/dn0aJFFC1a9L7Xu7u7Y7FYiI2NfWBBlxXydJF18lbiG9HB5ADGXX8UZ35L/Kdv9ixY7qzSK1WqxLp166hevTre3t52TiYiIiK5nsmUrml79lCqVClWr15Nq1ataN++PT/88MM90/Zua9GiBf7+/rz22msMGjQoxXs6OTkxY8YMBg0alOLI0J2KFi3K33//neTYrl27rOuM/P398fLyIiAgwNr18La7O+1FRERQsmRJAgMD73mdggULAokNJTZv3szcuXOpVKkS7u7udOvW7b6NMzI6XfD2mqo7Xb58mQIFCqSq6Fm8eDFPPvlkktGwkydPcubMGTp27Gg9druDoJOTE0ePHqVixYoA3LhxA09Pz2xRYEEeL7K+OJzYGaZNmcf47rDLf9+4eRZunQMHJyjb2E7pUnbs2DF69erF22+/bZ1X/Oijj9o3lIiIiEg2Va5cOX799VdrobVp06YUC63Zs2dTp04dqlatet97du/enbfffpupU6c+8PXr1q3LggULMAzD2hZ+586dlCtXjjfeeMN63tmzZx94Lz8/P0JCQnBycsLX1zfZc3bs2MGgQYPo0qULkFiYnTlz5r73zeh0wSZNmrBx48YkxzZv3kyTJg/ubXD69Gl++eUX1q9fn+T4Qw89dE9xOmHCBMLDw3n//fcpU6aM9fjBgwepW7fuA18rq+TpIut8+HkAWpVpw3fc0Vf/zO31WH7gmvKQsj2sXLmSoUOHEh4ezssvv0xQUFC2mHcqIiIikp2VKVOGwMBAWrVqhb+/P5s2bbpnah5AzZo16du3Lx988MED7zl79mz8/f0feF6rVq2IiIjg0KFD1KhRA4DKlStz7tw5AgICaNCgARs2bGDNmjUPvFfbtm1p0qQJnTt35q233qJKlSpcunSJDRs20KVLF+rXr0/lypX57rvv6NixIyaTiYkTJz5wD6mMThd87rnn+Oijj3jllVd45pln+Pnnn1m5ciUbNmywnvPRRx+xZs0atm7dmuTaJUuWULJkyXtG0tzc3Kx/XrfdHq27+/i2bdto165duvPbWp7+dB5vTmyR6eRw91TBf9djlc8++2PFxsYycuRIevbsSXh4OC1atGDTpk0qsERERERSqXTp0gQGBnLt2jX8/f0JCwtL9rxp06alamPb1q1b07p1axISEu57XpEiRejSpQvLly+3Hnvqqad4+eWXGTlyJHXq1GHnzp1MnDjxga9pMpnYuHEjLVq0YPDgwVSpUoVevXpx9uxZ60jTvHnzKFSoEE2bNqVjx474+/vj5+f3wHtnRPny5dmwYQObN2+mdu3avPPOOyxevDhJEXrt2jXrurHbLBYLy5YtY9CgQdbuiWl18eJFdu7ceU9rfnsyGUYGNhzIgcLCwvDy8uLWrVs8vvFxwuPC+ar9Cjq9exqAw1Pb4TG/DoRdgP5roGLKXWiyyunTp+nevTt79+4F4LXXXmPatGk4OeXpgUgRERHJAjExMZw+fTrJPk2Sdn/99RePPfYYJ0+evG/zDUm7V199lZs3b/Lpp58m+/37vYdv1wahoaHJjmymV579lH4j5gbhceE4mBwoW6AckFhkmW6dSSywHJyhjP3XY507d466desSGhpK4cKF+fLLL+nQoYO9Y4mIiIhIGtSqVYs5c+Zw+vRpatasae84uYq3tzdjxoyxd4wk8myRdTo0sagqla8Uro6u1uMOZ/9dj1W6PrikvU2orZUpU4ZOnTpx/PhxAgICUtxATkRERESyt/t1LJT0+9///mfvCPfIs0XW2bDE7i0VvCokOe54u8jytd96rPPnz+Pp6UnhwoUxmUwsWLAAZ2dnnJ2d7ZZJRERERERSJ892TbgadRWAkp4l7zhq/DeSZaemFz/88AN169Zl4MCB1gWXHh4eKrBERERERHKIPFtk3ebo8F8Xk/KmEBwiQsDRBUo3yNIcCQkJvP7663To0IHr168THBzMrVu3sjSDiIiIiIhkXJ4vsu7UxOFw4oPSDcE563aLvnTpEm3atGHWrFkAjBw5kh07dlC4cOEsyyAiIiIiIraRZ9dkJaeJw6HEB1k4VXDr1q306dOHK1eukD9/fhYvXkyPHj2y7PVFRERERMS2VGRZGTR2OJL4MIuaXsTHxzNs2DCuXLlCrVq1WLVqFVWqVMmS1xYRERERkcyh6YL/qmi6RDFTKIaTW2L79izg7OxMQEAAw4cP5/fff1eBJSIiIiKSC+TZIivGHAOAgynxj+D2eixLqQbg5JridRn122+/sXz5cuvzBg0asHDhQtzds24NmIiIiIhkzPXr1/H29ubMmTP2jpLrNG7cmG+//dbeMTIkzxZZh64nrr+qWqgq8N96LHO5ZpnyehaLhTlz5tC6dWuGDBnCgQMHMuV1RERERCTRoEGDMJlMmEwmnJ2dKV++PK+88goxMTFJzrt9zp1fzZrd/zPhjBkz6NSpE76+vpn4E9jXqlWreOihh3Bzc6NmzZps3LjxgdcsX76c2rVr4+HhQcmSJXnmmWe4fv269fuLFi2iefPmFCpUiEKFCtG2bVv++OOPJPeYMGEC48ePt25nlBPl2SLryI3E9Vd+xf3AsFjXY1nK2X491vXr1+nYsSPjx4/HbDbTo0cPKlasaPPXEREREZGk2rdvT3BwMKdOneLdd9/lk08+YfLkyfect3TpUoKDg61f69evT/GeUVFRfPbZZwwZMiRD2eLi4jJ0fWbauXMnvXv3ZsiQIezfv5/OnTvTuXNnDh48mOI1O3bsYMCAAQwZMoRDhw6xatUq/vjjD5599lnrOYGBgfTu3ZtffvmFXbt2UaZMGdq1a8fFixet5zz++OOEh4fzww8/ZOrPmJnybJGVYEmgqHtRCruUIPbSIYqYwokyXLH41LXp6/z+++/UrVuXjRs34ubmxqJFi/j888/Jly+fTV9HREREJKsYhkFUfFSWfxmGkeasrq6ulChRgjJlytC5c2fatm3L5s2b7zmvYMGClChRwvp1v610Nm7ciKurK40bN7YeM5vNDBkyhPLly+Pu7k7VqlV5//33k1w3aNAgOnfuzIwZM/Dx8aFq1cQZVefPn6dHjx4ULFiQwoUL06lTpyTTEP/8808ee+wxihYtipeXFy1btmTfvn1p/rNIi/fff5/27dszbtw4qlWrxvTp0/Hz8+Ojjz5K8Zpdu3bh6+vL6NGjKV++PM2aNWP48OFJRqqWL1/O888/T506dXjooYdYvHgxFouFrVu3Ws9xdHSkQ4cOBAQEZOrPmJnydHfBsEhXHp78EwMdf2SqM+yxVKG+o4vN7v/BBx/wv//9j4SEBCpXrsyqVauoXbu2ze4vIiIiYg/RCdE0+rpRlr/u7j678XD2SPf1Bw8eZOfOnZQrVy5DObZt20a9evWSHLNYLJQuXZpVq1ZRpEgRdu7cybBhwyhZsmSS7Xm2bt1KgQIFrIVefHw8/v7+NGnShG3btuHk5MSbb75J+/bt+euvv3BxcSE8PJyBAwfy4YcfYhgG77zzDh06dOD48ePkz58/2YzLly9n+PDh9/05fvjhB5o3T34W165duxgzZkySY/7+/qxduzbF+zVp0oTXX3+djRs38vjjj3PlyhVWr15Nhw4dUrwmKiqK+Pj4e4rahg0bMnv27Pvmz87ydJEVHWcG/mt6cc6rPs2dHW12/9DQUBISEujRoweLFi2iQIECNru3iIiIiDzY999/T758+UhISCA2NhYHB4dkR2N69+6No+N/nwO/+uorOnfunOw9z549i4+PT5Jjzs7OTJ061fq8fPny7Nq1i5UrVyYpsjw9PVm8eDEuLi7W17FYLCxevBiTyQQkTl0sWLAggYGBtGvXjtatWyd5rU8//ZSCBQvy66+/8uSTTyab8amnnqJRo/sXwqVKlUrxeyEhIRQvXjzJseLFixMSEpLiNY888gjLly+nZ8+exMTEkJCQQMeOHZk/f36K17z66qv4+PjQtm3bJMd9fHw4f/48FosFB4ecN/kuTxdZACYstPM8ATHQt2cf65s7vcxms/Uv6BtvvEGNGjXo3Llzhu8rIiIikl24O7mzu89uu7xuWrVq1YoFCxYQGRnJu+++i5OTE08//fQ957377rtJPuiXLFkyxXtGR0fj5uZ2z/H58+ezZMkSzp07R3R0NHFxcdSpUyfJOTVr1rQWWAAHDhzgxIkT94xIxcTEcPLkSQAuX77MhAkTCAwM5MqVK5jNZqKiojh37lyKGfPnz5/iKFdmOXz4MC+++CKTJk3C39+f4OBgxo0bx3PPPcdnn312z/mzZ88mICCAwMDAe/483d3dsVgsxMbG5sgu3Hm+yHqi2A0cwm+CSz5MGViPZRgGCxcuZNmyZQQGBuLu7o6DgwNdunSxYVoRERER+zOZTBmatpeVPD09qVSpEgBLliyhdu3ayTatKFGihPW8BylatCg3b95MciwgIICxY8fyzjvv0KRJE/Lnz8/bb7/N7t1Ji1FPT88kzyMiIqhXr16SLX5uK1asGAADBw7k+vXrvP/++5QrVw5XV1eaNGly38YZGZ0uWKJECS5fvpzk2OXLlylRokSK95s1axaPPPII48aNA6BWrVp4enrSvHlz3nzzzSSF69y5c5k9ezZbtmyhVq1a99zrxo0beHp65sgCC/J4keXi5MAHTcLhJ6BsE3B0Ttd9wsPDGTZsmHVx3tKlS3n++edtmFREREREMsrBwYHXX3+dMWPG0KdPn3R/gK9bty5fffVVkmM7duygadOmST4D3h6Juh8/Pz9WrFiBt7d3iktLduzYwccff2xd23T+/HmuXbt23/tmdLpgkyZN2Lp1Ky+99JL12ObNm2nSpEmK10RFReHklLS8uD3D686mJW+99RYzZszgxx9/pH79+sne6+DBg9Sta9uGdFkp501wtCHfIp44nN2R+KR8+lq3//3339SvX5+AgACcnJx45513GDFihA1TioiIiIitdO/eHUdHx/uuE3oQf39/Dh06lGQ0q3LlyuzZs4cff/yRY8eOMXHiRP78888H3qtv374ULVqUTp06sW3bNk6fPk1gYCCjR4/mwoUL1nt/+eWXHDlyhN27d9O3b98HFoj58+enUqVK9/263z1efPFFNm3axDvvvMM///zDlClT2LNnDyNHjrSe89prrzFgwADr844dO/Ldd9+xYMECTp06xY4dOxg9ejQNGza0rmGbM2cOEydOZMmSJfj6+hISEkJISAgRERFJXn/btm20a9fugX9+2VWeLrJMGHDm3yLLN+1F1tKlS2nYsCHHjh2jdOnS/Prrr4wZM0brr0RERESyKScnJ0aOHMlbb71FZGRkuu5Rs2ZN/Pz8WLlypfXY8OHD6dq1Kz179qRRo0Zcv349VTObPDw8+O233yhbtixdu3alWrVqDBkyhJiYGOvI1meffcbNmzfx8/Ojf//+jB49Gm9v73RlT62mTZvy9ddf8+mnn1K7dm1Wr17N2rVrqVGjhvWc4ODgJOvCBg0axLx58/joo4+oUaMG3bt3p2rVqnz33XfWcxYsWEBcXBzdunWjZMmS1q+5c+daz7l48SI7d+5k8ODBmfozZiaTkZ4NB3KwsLAwvLy8qLagGlWLlmPNwZ3gWgBeOQ2OqZ89OWfOHMaPHw8kbnL35ZdfUrRo0cyKLSIiImIXMTExnD59mvLlyyfb7CGv2rBhA+PGjePgwYM5svtddvbqq69y8+ZNPv30U5vc737v4du1QWhoqE07geftd0Tcv7+9KNc0TQUWQJ8+ffD29mbmzJls2LBBBZaIiIhIHvLEE08wbNgwLl68aO8ouY63tzfTp0+3d4wMydONLzzjoxMfpHKq4P79+60L8MqUKcPx48e195WIiIhIHnVnUwixnf/973/2jpBheXokq0JkaOKDBzS9iI6OZvjw4fj5+bF+/XrrcRVYIiIiIiJytzw9klU+NgrcCkLxmimec/z4cbp3786BAwcwmUwcPXo06wKKiIiIiEiOk6eLrApx8VCuBaSwWHHVqlUMGTKE8PBwihUrxvLly3nssceyOKWIiIiI/eWxXmmSi9jjvZunpwuWj09IdqpgbGwso0ePpkePHoSHh9O8eXP279+vAktERETyHGdnZyBxo1mRnCguLg74b2PkrJCnR7K8LJZkm15s3bqVDz/8EIDx48czffr0e3avFhEREckLHB0dKViwIFeuXAES93XSnqCSU1gsFq5evYqHh0eWfp7P05WDs1tB8K5+z/EOHTrw2muv8cgjj/DEE09kfTARERGRbKREiRIA1kJLJCdxcHCgbNmyWfrLgTxbZJWNi8ejXDNwcCA+Pp6ZM2cyfPhw639EZs6caeeEIiIiItmDyWSiZMmSeHt7Ex8fb+84Imni4uKS5RtG59kiq25sHPg258KFC/Ts2ZOdO3fy22+/sWXLFg2Bi4iIiCTD0dExS9e1iORU2aLxxfz58/H19cXNzY1GjRrxxx9/3Pf8VatW8dBDD+Hm5kbNmjXZuHFjml+zRmwsm05D3bp12blzJ15eXowcOVIFloiIiIiIZIjdi6wVK1YwZswYJk+ezL59+6hduzb+/v4pzvnduXMnvXv3ZsiQIezfv5/OnTvTuXNnDh48mKbXXftzLB36PMe1a9fw8/Nj7969dOnSxRY/koiIiIiI5GEmw86bHjRq1IgGDRrw0UcfAYkdQMqUKcOoUaMYP378Pef37NmTyMhIvv/+e+uxxo0bU6dOHRYuXPjA1wsLC8PLy8v6fMSIEcybNw83Nzcb/DQiIiIiIpJT3K4NQkNDKVCggM3ua9c1WXFxcezdu5fXXnvNeszBwYG2bduya9euZK/ZtWsXY8aMSXLM39+ftWvXJnt+bGwssbGx1uehoaEAuLg48vHHn9C9e3fi4uKs/fNFRERERCRvCAsLA2y/YbFdi6xr165hNpspXrx4kuPFixfnn3/+SfaakJCQZM8PCQlJ9vxZs2YxderUe47HxZkZOnQoQ4cOTWd6ERERERHJDa5fv55ktltG5frugq+99lqSka9bt25Rrlw5zp07Z9M/SJG7hYWFUaZMGc6fP2/T4WeRu+m9JllF7zXJKnqvSVYJDQ2lbNmyFC5c2Kb3tWuRVbRoURwdHbl8+XKS45cvX7buV3W3EiVKpOl8V1dXXF1d7znu5eWlv7SSJQoUKKD3mmQJvdckq+i9JllF7zXJKrbeR8uu3QVdXFyoV68eW7dutR6zWCxs3bqVJk2aJHtNkyZNkpwPsHnz5hTPFxERERERyUp2ny44ZswYBg4cSP369WnYsCHvvfcekZGRDB48GIABAwZQqlQpZs2aBcCLL75Iy5Yteeedd3jiiScICAhgz549fPrpp/b8MURERERERIBsUGT17NmTq1evMmnSJEJCQqhTpw6bNm2yNrc4d+5ckuG7pk2b8vXXXzNhwgRef/11KleuzNq1a6lRo0aqXs/V1ZXJkycnO4VQxJb0XpOsoveaZBW91ySr6L0mWSWz3mt23ydLREREREQkN7HrmiwREREREZHcRkWWiIiIiIiIDanIEhERERERsSEVWSIiIiIiIjaUK4us+fPn4+vri5ubG40aNeKPP/647/mrVq3ioYcews3NjZo1a7Jx48YsSio5XVrea4sWLaJ58+YUKlSIQoUK0bZt2we+N0VuS+t/124LCAjAZDLRuXPnzA0ouUZa32u3bt3ihRdeoGTJkri6ulKlShX9f1RSJa3vtffee4+qVavi7u5OmTJlePnll4mJicmitJJT/fbbb3Ts2BEfHx9MJhNr16594DWBgYH4+fnh6upKpUqVWLZsWZpfN9cVWStWrGDMmDFMnjyZffv2Ubt2bfz9/bly5Uqy5+/cuZPevXszZMgQ9u/fT+fOnencuTMHDx7M4uSS06T1vRYYGEjv3r355Zdf2LVrF2XKlKFdu3ZcvHgxi5NLTpPW99ptZ86cYezYsTRv3jyLkkpOl9b3WlxcHI899hhnzpxh9erVHD16lEWLFlGqVKksTi45TVrfa19//TXjx49n8uTJHDlyhM8++4wVK1bw+uuvZ3FyyWkiIyOpXbs28+fPT9X5p0+f5oknnqBVq1YEBQXx0ksvMXToUH788ce0vbCRyzRs2NB44YUXrM/NZrPh4+NjzJo1K9nze/ToYTzxxBNJjjVq1MgYPnx4puaUnC+t77W7JSQkGPnz5zc+//zzzIoouUR63msJCQlG06ZNjcWLFxsDBw40OnXqlAVJJadL63ttwYIFRoUKFYy4uLisiii5RFrfay+88ILRunXrJMfGjBljPPLII5maU3IXwFizZs19z3nllVeMhx9+OMmxnj17Gv7+/ml6rVw1khUXF8fevXtp27at9ZiDgwNt27Zl165dyV6za9euJOcD+Pv7p3i+CKTvvXa3qKgo4uPjKVy4cGbFlFwgve+1adOm4e3tzZAhQ7IipuQC6XmvrV+/niZNmvDCCy9QvHhxatSowcyZMzGbzVkVW3Kg9LzXmjZtyt69e61TCk+dOsXGjRvp0KFDlmSWvMNWtYGTLUPZ27Vr1zCbzRQvXjzJ8eLFi/PPP/8ke01ISEiy54eEhGRaTsn50vNeu9urr76Kj4/PPX+RRe6Unvfa9u3b+eyzzwgKCsqChJJbpOe9durUKX7++Wf69u3Lxo0bOXHiBM8//zzx8fFMnjw5K2JLDpSe91qfPn24du0azZo1wzAMEhISeO655zRdUGwupdogLCyM6Oho3N3dU3WfXDWSJZJTzJ49m4CAANasWYObm5u940guEh4eTv/+/Vm0aBFFixa1dxzJ5SwWC97e3nz66afUq1ePnj178sYbb7Bw4UJ7R5NcJjAwkJkzZ/Lxxx+zb98+vvvuOzZs2MD06dPtHU0kWblqJKto0aI4Ojpy+fLlJMcvX75MiRIlkr2mRIkSaTpfBNL3Xrtt7ty5zJ49my1btlCrVq3MjCm5QFrfaydPnuTMmTN07NjResxisQDg5OTE0aNHqVixYuaGlhwpPf9dK1myJM7Ozjg6OlqPVatWjZCQEOLi4nBxccnUzJIzpee9NnHiRPr378/QoUMBqFmzJpGRkQwbNow33ngDBweNG4htpFQbFChQINWjWJDLRrJcXFyoV68eW7dutR6zWCxs3bqVJk2aJHtNkyZNkpwPsHnz5hTPF4H0vdcA3nrrLaZPn86mTZuoX79+VkSVHC6t77WHHnqIv//+m6CgIOvXU089Ze2SVKZMmayMLzlIev679sgjj3DixAlrIQ9w7NgxSpYsqQJLUpSe91pUVNQ9TwXVogAADDlJREFUhdTt4j6xn4GIbdisNkhbT47sLyAgwHB1dTWWLVtmHD582Bg2bJhRsGBBIyQkxDAMw+jfv78xfvx46/k7duwwnJycjLlz5xpHjhwxJk+ebDg7Oxt///23vX4EySHS+l6bPXu24eLiYqxevdoIDg62foWHh9vrR5AcIq3vtbupu6CkVlrfa+fOnTPy589vjBw50jh69Kjx/fffG97e3sabb75prx9Bcoi0vtcmT55s5M+f3/jmm2+MU6dOGT/99JNRsWJFo0ePHvb6ESSHCA8PN/bv32/s37/fAIx58+YZ+/fvN86ePWsYhmGMHz/e6N+/v/X8U6dOGR4eHsa4ceOMI0eOGPPnzzccHR2NTZs2pel1c12RZRiG8eGHHxply5Y1XFxcjIYNGxq///679XstW7Y0Bg4cmOT8lStXGlWqVDFcXFyMhx9+2NiwYUMWJ5acKi3vtXLlyhnAPV+TJ0/O+uCS46T1v2t3UpElaZHW99rOnTuNRo0aGa6urkaFChWMGTNmGAkJCVmcWnKitLzX4uPjjSlTphgVK1Y03NzcjDJlyhjPP/+8cfPmzawPLjnKL7/8kuznr9vvr4EDBxotW7a855o6deoYLi4uRoUKFYylS5em+XVNhqExVhEREREREVvJVWuyRERERERE7E1FloiIiIiIiA2pyBIREREREbEhFVkiIiIiIiI2pCJLRERERETEhlRkiYiIiIiI2JCKLBERERERERtSkSUiIiIiImJDKrJERCRdli1bRsGCBe0dI91MJhNr16697zmDBg2ic+fOWZJHRERyDxVZIiJ52KBBgzCZTPd8nThxwt7RWLZsmTWPg4MDpUuXZvDgwVy5csUm9w8ODubxxx8H4MyZM5hMJoKCgpKc8/7777Ns2TKbvF5KpkyZYv05HR0dKVOmDMOGDePGjRtpuo8KQhGR7MPJ3gFERMS+2rdvz9KlS5McK1asmJ3SJFWgQAGOHj2KxWLhwIEDDB48mEuXLvHjjz9m+N4lSpR44DleXl4Zfp3UePjhh9myZQtms5kjR47wzDPPEBoayooVK7Lk9UVExLY0kiUikse5urpSokSJJF+Ojo7MmzePmjVr4unpSZkyZXj++eeJiIhI8T4HDhygVatW5M+fnwIFClCvXj327Nlj/f727dtp3rw57u7ulClThtGjRxMZGXnfbCaTiRIlSuDj48Pjjz/O6NGj2bJlC9HR0VgsFqZNm0bp0qVxdXWlTp06bNq0yXptXFwcI0eOpGTJkri5uVGuXDlmzZqV5N63pwuWL18egLp162IymXj00UeBpKNDn376KT4+PlgsliQZO3XqxDPPPGN9vm7dOvz8/HBzc6NChQpMnTqVhISE+/6cTk5OlChRglKlStG2bVu6d+/O5s2brd83m80MGTKE8uXL4+7uTtWqVXn//fet358yZQqff/4569ats46KBQYGAnD+/Hl69OhBwYIFKVy4MJ06deLMmTP3zSMiIhmjIktERJLl4ODABx98wKFDh/j888/5+eefeeWVV1I8v2/fvpQuXZo///yTvXv3Mn78eJydnQE4efIk7du35+mnn+avv/5ixYoVbN++nZEjR6Ypk7u7OxaLhYSEBN5//33eeecd5s6dy19//YW/vz9PPfUUx48fB+CDDz5g/fr1rFy5kqNHj7J8+XJ8fX2Tve8ff/wBwJYtWwgODua7776755zu3btz/fp1fvnlF+uxGzdusGnTJvr27QvAtm3bGDBgAC+++CKHDx/mk08+YdmyZcyYMSPVP+OZM2f48ccfcXFxsR6zWCyULl2aVatWcfjwYSZNmsTrr7/OypUrARg7diw9evSgffv2BAcHExwcTNOmTYmPj8ff35/8+fOzbds2duzYQb58+Wjfvj1xcXGpziQiImlkiIhInjVw4EDD0dHR8PT0tH5169Yt2XNXrVplFClSxPp86dKlhpeXl/V5/vz5jWXLliV77ZAhQ4xhw4YlObZt2zbDwcHBiI6OTvaau+9/7Ngxo0qVKkb9+vUNwzAMHx8fY8aMGUmuadCggfH8888bhmEYo0aNMlq3bm1YLJZk7w8Ya9asMQzDME6fPm0Axv79+5OcM3DgQKNTp07W5506dTKeeeYZ6/NPPvnE8PHxMcxms2EYhtGmTRtj5syZSe7x5ZdfGiVLlkw2g2EYxuTJkw0HBwfD09PTcHNzMwADMObNm5fiNYZhGC+88ILx9NNPp5j19mtXrVo1yZ9BbGys4e7ubvz444/3vb+IiKSf1mSJiORxrVq1YsGCBdbnnp6eQOKozqxZs/jnn38ICwsjISGBmJgYoqKi8PDwuOc+Y8aMYejQoXz55ZfWKW8VK1YEEqcS/vXXXyxfvtx6vmEYWCwWTp8+TbVq1ZLNFhoaSr58+bBYLMTExNCsWTMWL15MWFgYly5d4pFHHkly/iOPPMKBAweAxKl+jz32GFWrVqV9+/Y8+eSTtGvXLkN/Vn379uXZZ5/l448/xtXVleXLl9OrVy8cHBysP+eOHTuSjFyZzeb7/rkBVK1alfXr1xMTE8NXX31FUFAQo0aNSnLO/PnzWbJkCefOnSM6Opq4uDjq1Klz37wHDhzgxIkT5M+fP8nxmJgYTp48mY4/ARERSQ0VWSIieZynpyeVKlVKcuzMmTM8+eSTjBgxghkzZlC4cGG2b9/OkCFDiIuLS7ZYmDJlCn369GHDhg388MMPTJ48mYCAALp06UJERATDhw9n9OjR91xXtmzZFLPlz5+fffv24eDgQMmSJXF3dwcgLCzsgT+Xn58fp0+f5ocffmDLli306NGDtm3bsnr16gdem5KOHTtiGAYbNmygQYMGbNu2jXfffdf6/YiICKZOnUrXrl3vudbNzS3F+7q4uFj/HcyePZsnnniCqVOnMn36dAACAgIYO3Ys77zzDk2aNCF//vy8/fbb7N69+755IyIiqFevXpLi9rbs0txERCQ3UpElIiL32Lt3LxaLhXfeecc6SnN7/c/9VKlShSpVqvDyyy/Tu3dvli5dSpcuXfDz8+Pw4cP3FHMP4uDgkOw1BQoUwMfHhx07dtCyZUvr8R07dtCwYcMk5/Xs2ZOePXvSrVs32rdvz40bNyhcuHCS+91e/2Q2m++bx83Nja5du7J8+XJOnDhB1apV8fPzs37fz8+Po0ePpvnnvNuECRNo3bo1I0aMsP6cTZs25fnnn7eec/dIlIuLyz35/fz8WLFiBd7e3hQoUCBDmUREJPXU+EJERO5RqVIl4uPj+fDDDzl16hRffvklCxcuTPH86OhoRo4cSWBgIGfPnmXHjh38+eef1mmAr776Kjt37mTkyJEEBQVx/Phx1q1bl+bGF3caN24cc+bMYcWKFRw9epTx48cTFBTEiy++CMC8efP45ptv+Oeffzh27BirVq2iRIkSyW6g7O3tjbu7O5s2beLy5cuEhoam+Lp9+/Zlw4YNLFmyxNrw4rZJkybxxRdfMHXqVA4dOsSRI0cICAhgwoQJafrZmjRpQq1atZg5cyYAlStXZs+ePfz4448cO3aMiRMn8ueffya5xtfXl7/++oujR49y7do14uPj6du3L0WLFqVTp05s27aN06dPExgYyOjRo7lw4UKaMomISOqpyBIRkXvUrl2befPmMWfOHGrUqMHy5cuTtD+/m6OjI9evX2fAgAH/364duyQexnEc/xw0OQYtDjqlNCgIii0tLuIkOCS4hrgF6h60ugRtQX9FSESbQX9ETUFzu5s3HBzX0HDH7zg4Xq/1GZ7vM775PqnVajk9Pc1gMMjl5WWSpNlsZrPZ5PX1NScnJ2m1Wrm4uEi5XP7jGc/Pz7NYLLJcLtNoNPLw8JC7u7scHh4m+fHVcLVapd1up9Pp5O3tLff39z83c7/a29vL9fV1bm5uUi6XMxwOv7y31+tlf38/Ly8vmUwmn876/X7W63UeHx/T6XRyfHycq6urVKvV337ffD7P7e1t3t/fM5vNMhqNMh6P0+128/Hx8WmrlSTT6TT1ej3tdjsHBwd5fn5OqVTK09NTKpVKRqNRjo6OcnZ2lu12a7MF8Bd92+12u389BAAAwP/CJgsAAKBAIgsAAKBAIgsAAKBAIgsAAKBAIgsAAKBAIgsAAKBAIgsAAKBAIgsAAKBAIgsAAKBAIgsAAKBAIgsAAKBA3wH/gw/MBdjvwQAAAABJRU5ErkJggg=="},"metadata":{}}]},{"source":"The ideal area under the ROC curve is 1. Based on the areas calculated from the three models were satisfactory in predicting customer churning tendencies.","metadata":{},"id":"afc13cc1-880d-4ebd-af74-88c58b223373","cell_type":"markdown"},{"source":"","metadata":{"executionCancelledAt":null,"executionTime":45,"lastExecutedAt":1713141659441,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":""},"id":"ef9921f7-6b08-4c33-b91b-fe71f42be0d5","cell_type":"code","execution_count":30,"outputs":[]},{"source":"","metadata":{"executionCancelledAt":null,"executionTime":47,"lastExecutedAt":1713141659488,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":""},"id":"241f47ab-3b16-4a61-adc4-28681c3dca68","cell_type":"code","execution_count":30,"outputs":[]},{"source":"","metadata":{"executionCancelledAt":null,"executionTime":48,"lastExecutedAt":1713141659536,"lastExecutedByKernel":"0776b898-98a7-419e-9e54-19bd89c11b59","lastScheduledRunId":null,"lastSuccessfullyExecutedCode":""},"id":"cf27786a-40ef-4871-aaba-d11dfacd4a4b","cell_type":"code","execution_count":30,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"editor":"DataCamp Workspace"},"nbformat":4,"nbformat_minor":5}