Predicting Customer Churn with Machine Learning: From EDA to Classification

customer_churn_prediction.ipynb

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    "<font size=+3 color=\"#3D3D3D\"><center><b>Predicting Customer Churn with Machine Learning 🏦💰</b></center></font>\n",
    "<br>\n",
    "\n",
    "http://www.sefidian.com/2022/07/10/predicting-customer-churn-with-machine-learning-from-eda-to-classification/\n",
    "\n",
    "**Table of Contents**\n",
    "\n",
    "- [Introduction](#Introduction)\n",
    "- [Objective](#Objective)\n",
    "- [Libraries](#Libraries)\n",
    "- [Parameters and Variables](#Parameters-and-Variables)\n",
    "- [Functions](#Functions)\n",
    "- [A Quick Look at our Data](#A-Quick-Look-at-our-Data)\n",
    "- [Creating a Test Set](#Creating-a-Test-Set)\n",
    "- [Exploratory Data Analysis](#Exploratory-Data-Analysis)\n",
    "    - [Target Variable: Exited](#Target-Variable:-Exited)\n",
    "    - [Continuous Variables](#Continuous-Variables)\n",
    "    - [Categorical Variables](#Categorical-Variables)\n",
    "- [Data Preprocessing](#Data-Preprocessing)\n",
    "    - [Feature Selection](#Feature-Selection)\n",
    "    - [Encoding Categorical Features](#Encoding-Categorical-Features)\n",
    "    - [Scaling](#Scaling)\n",
    "    - [Addressing Class Imbalance](#Addressing-Class-Imbalance)\n",
    "- [Building Machine Learning Models](#Building-Machine-Learning-Models)\n",
    "    - [Baseline Models](#Baseline-Models)\n",
    "    - [Model Tuning](#Model-Tuning)\n",
    "- [Results](#Results)\n",
    "    - [Learning Curves](#Learning-Curves)\n",
    "    - [Feature Importance](#Feature-Importance)\n",
    "    - [Performance Comparison](#Performance-Comparison)\n",
    "- [Evaluating the Test Set](#Evaluating-the-Test-Set)\n",
    "- [Bibliography](#Bibliography)\n",
    "- [Future Development](#Future-Development)\n",
    "- [Conclusions](#Conclusions)\n",
    "  \n",
    "\n",
    "<br>\n",
    "\n",
    "# Introduction\n",
    "\n",
    "\n",
    "Customer churn (also known as customer attrition) occurs when a customer stops using a company's products or services. \n",
    "\n",
    "Customer churn affects profitability, especially in industries where revenues are heavily dependent on subscriptions (e.g. banks, telephone and internet service providers, pay-TV companies, insurance firms, etc.). It is estimated that acquiring a new customer can cost up to five times more than retaining an existing one.\n",
    "\n",
    "Therefore, customer churn analysis is essential as it can help a business:\n",
    "\n",
    "- identify problems in its services (e.g. poor quality product/service, poor customer support, wrong target audience, etc.), and \n",
    "- make correct strategic decisions that would lead to higher customer satisfaction and consequently higher customer retention.\n",
    "\n",
    "If you would like to know more about this topic, please refer to the references in the [Bibliography](#Bibliography) section."
   ]
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   "source": [
    "## Dataset"
   ]
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   "source": [
    "https://www.kaggle.com/datasets/shrutimechlearn/churn-modelling"
   ]
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    "<br>\n",
    "\n",
    "# Objective\n",
    "\n",
    "The goal of this notebook is to understand and predict customer churn for a bank. Specifically, we will initially perform **Exploratory Data Analysis** (**EDA**) to identify and visualise the factors contributing to customer churn. This analysis will later help us build **Machine Learning** models to predict whether a customer will churn or not. \n",
    "\n",
    "This problem is a typical **classification** task. The task does not specify which performance metric to use for optimising our machine learning models. I decided to use **recall** since correctly classifying elements of the positive class (customers who will churn) is more critical for the bank.\n",
    "\n",
    "<br>\n",
    "\n",
    "*Skills: Exploratory Data Analysis, Data Visualisation, Data Preprocessing (Feature Selection, Encoding Categorical Features, Feature Scaling), Addressing Class Imbalance (SMOTE), Model Tuning.*\n",
    "\n",
    "*Models Used: Logistic Regression, Support Vector Machines, Random Forests, Gradient Boosting, XGBoost, and Light Gradient Boosting Machine.*\n"
   ]
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   "source": [
    "<br>\n",
    "\n",
    "# Libraries"
   ]
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      "Requirement already satisfied: seaborn in /opt/conda/lib/python3.7/site-packages (0.10.0)\r\n",
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      "Requirement already satisfied: numpy>=1.15 in /opt/conda/lib/python3.7/site-packages (from seaborn) (1.18.5)\r\n",
      "Installing collected packages: seaborn\r\n",
      "  Attempting uninstall: seaborn\r\n",
      "    Found existing installation: seaborn 0.10.0\r\n",
      "    Uninstalling seaborn-0.10.0:\r\n",
      "      Successfully uninstalled seaborn-0.10.0\r\n",
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      "\u001b[33mWARNING: You are using pip version 20.3.1; however, version 21.2.4 is available.\r\n",
      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n"
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      "✔️ Libraries Imported!\n"
     ]
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   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('precision', 3)\n",
    "\n",
    "# Data Visualisation Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "!pip install seaborn --upgrade\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "# Statistics\n",
    "from scipy.stats import chi2_contingency\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Machine Learning\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, auc, roc_auc_score, roc_curve\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import scikitplot as skplt\n",
    "\n",
    "print('✔️ Libraries Imported!')"
   ]
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    "<br>\n",
    "\n",
    "# Parameters and Variables\n",
    "\n",
    "It is convenient to set some (default) parameters and variables for the whole notebook.  "
   ]
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      "✔️ Default Parameters and Variables Set!\n"
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    "font_size = 20\n",
    "plt.rcParams['axes.labelsize'] = font_size\n",
    "plt.rcParams['axes.titlesize'] = font_size + 2\n",
    "plt.rcParams['xtick.labelsize'] = font_size - 2\n",
    "plt.rcParams['ytick.labelsize'] = font_size - 2\n",
    "plt.rcParams['legend.fontsize'] = font_size - 2\n",
    "\n",
    "colors = ['#00A5E0', '#DD403A']\n",
    "colors_cat = ['#E8907E', '#D5CABD', '#7A6F86', '#C34A36', '#B0A8B9', '#845EC2', '#8f9aaa', '#FFB86F', '#63BAAA', '#9D88B3', '#38c4e3']\n",
    "colors_comp = ['steelblue', 'seagreen', 'black', 'darkorange', 'purple', 'firebrick', 'slategrey']\n",
    "\n",
    "random_state = 42\n",
    "scoring_metric = 'recall'\n",
    "comparison_dict, comparison_test_dict = {}, {}\n",
    "\n",
    "print('✔️ Default Parameters and Variables Set!')"
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    "<br>\n",
    "\n",
    "# Functions\n",
    "\n",
    "Since we will reuse parts of the code, it will be helpful to define some functions.\n",
    "\n",
    "## `plot_continuous()`"
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      "✔️ Function Defined!\n"
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   "source": [
    "def plot_continuous(feature):\n",
    "    '''Plot a histogram and boxplot for the churned and retained distributions for the specified feature.'''\n",
    "    df_func = train_df.copy()\n",
    "    df_func['Exited'] = df_func['Exited'].astype('category')\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(2,\n",
    "                                   figsize=(9, 7),\n",
    "                                   sharex=True,\n",
    "                                   gridspec_kw={'height_ratios': (.7, .3)})\n",
    "\n",
    "    for df, color, label in zip([df_retained, df_churned], colors, ['Retained', 'Churned']):\n",
    "        sns.histplot(data=df,\n",
    "                     x=feature,\n",
    "                     bins=15,\n",
    "                     color=color,\n",
    "                     alpha=0.66,\n",
    "                     edgecolor='firebrick',\n",
    "                     label=label,\n",
    "                     kde=False,\n",
    "                     ax=ax1)\n",
    "    ax1.legend()\n",
    "\n",
    "    sns.boxplot(x=feature, y='Exited', data=df_func, palette=colors, ax=ax2)\n",
    "    ax2.set_ylabel('')\n",
    "    ax2.set_yticklabels(['Retained', 'Churned'])\n",
    "\n",
    "    plt.tight_layout();\n",
    "\n",
    "\n",
    "print('✔️ Function Defined!')"
   ]
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   "source": [
    "## `plot_categorical()`"
   ]
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      "✔️ Function Defined!\n"
     ]
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   "source": [
    "def plot_categorical(feature):\n",
    "    '''For a categorical feature, plot a seaborn.countplot for the total counts of each category next to a barplot for the churn rate.'''\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "    sns.countplot(x=feature,\n",
    "                  hue='Exited',\n",
    "                  data=train_df,\n",
    "                  palette=colors,\n",
    "                  ax=ax1)\n",
    "    ax1.set_ylabel('Count')\n",
    "    ax1.legend(labels=['Retained', 'Churned'])\n",
    "\n",
    "    sns.barplot(x=feature,\n",
    "                y='Exited',\n",
    "                data=train_df,\n",
    "                palette=colors_cat,\n",
    "                ax=ax2)\n",
    "    ax2.set_ylabel('Churn rate')\n",
    "\n",
    "    if (feature == 'HasCrCard' or feature == 'IsActiveMember'):\n",
    "        ax1.set_xticklabels(['No', 'Yes'])\n",
    "        ax2.set_xticklabels(['No', 'Yes'])\n",
    "\n",
    "    plt.tight_layout();\n",
    "\n",
    "\n",
    "print('✔️ Function Defined!')"
   ]
  },
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   "source": [
    "## `plot_conf_mx()`"
   ]
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     "shell.execute_reply": "2021-08-25T08:44:18.562165Z",
     "shell.execute_reply.started": "2021-08-25T07:32:50.636349Z"
    },
    "papermill": {
     "duration": 0.119066,
     "end_time": "2021-08-25T08:44:18.562938",
     "exception": false,
     "start_time": "2021-08-25T08:44:18.443872",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✔️ Function Defined!\n"
     ]
    }
   ],
   "source": [
    "def plot_conf_mx(cm, ax):\n",
    "    '''Plot a confusion matrix in the specified axes object.'''\n",
    "    sns.heatmap(data=cm,\n",
    "                annot=True,\n",
    "                cmap='Blues',\n",
    "                annot_kws={'fontsize': 30},\n",
    "                ax=ax)\n",
    "\n",
    "    ax.set_xlabel('Predicted Label')\n",
    "    ax.set_xticks([0.5, 1.5])\n",
    "    ax.set_xticklabels(['Retained', 'Churned'])\n",
    "\n",
    "    ax.set_ylabel('True Label')\n",
    "    ax.set_yticks([0.25, 1.25])\n",
    "    ax.set_yticklabels(['Retained', 'Churned']);\n",
    "\n",
    "\n",
    "print('✔️ Function Defined!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.086234,
     "end_time": "2021-08-25T08:44:18.734178",
     "exception": false,
     "start_time": "2021-08-25T08:44:18.647944",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## `plot_learning_curve()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:16.349247Z",
     "start_time": "2021-08-25T06:37:16.337839Z"
    },
    "_kg_hide-input": true,
    "execution": {
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     "exception": false,
     "start_time": "2021-08-25T08:44:18.819923",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✔️ Function Defined!\n"
     ]
    }
   ],
   "source": [
    "def plot_learning_curve(estimator,\n",
    "                        X,\n",
    "                        y,\n",
    "                        ax,\n",
    "                        cv=None,\n",
    "                        train_sizes=np.linspace(0.1, 1.0, 5)):\n",
    "    '''Plot the learning curves for an estimator in the specified axes object.'''\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator,\n",
    "        X,\n",
    "        y,\n",
    "        cv=cv,\n",
    "        n_jobs=-1,\n",
    "        train_sizes=train_sizes,\n",
    "        scoring='accuracy')\n",
    "\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "    ax.fill_between(train_sizes,\n",
    "                    train_scores_mean - train_scores_std,\n",
    "                    train_scores_mean + train_scores_std,\n",
    "                    alpha=0.1,\n",
    "                    color='dodgerblue')\n",
    "    ax.fill_between(train_sizes,\n",
    "                    test_scores_mean - test_scores_std,\n",
    "                    test_scores_mean + test_scores_std,\n",
    "                    alpha=0.1,\n",
    "                    color='darkorange')\n",
    "\n",
    "    ax.plot(train_sizes,\n",
    "            train_scores_mean,\n",
    "            color='dodgerblue',\n",
    "            marker='o',\n",
    "            linestyle='-',\n",
    "            label='Training Score')\n",
    "    ax.plot(train_sizes,\n",
    "            test_scores_mean,\n",
    "            color='darkorange',\n",
    "            marker='o',\n",
    "            linestyle='-',\n",
    "            label='Cross-validation Score')\n",
    "\n",
    "    ax.set_xlabel('Training Examples')\n",
    "    ax.set_ylabel('Score')\n",
    "    ax.legend(loc='best', fontsize=14);\n",
    "\n",
    "\n",
    "print('✔️ Function Defined!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.086175,
     "end_time": "2021-08-25T08:44:19.094213",
     "exception": false,
     "start_time": "2021-08-25T08:44:19.008038",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## `clf_performance()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:16.367599Z",
     "start_time": "2021-08-25T06:37:16.352719Z"
    },
    "_kg_hide-input": true,
    "execution": {
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     "shell.execute_reply": "2021-08-25T08:44:19.284644Z",
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    },
    "papermill": {
     "duration": 0.104602,
     "end_time": "2021-08-25T08:44:19.285386",
     "exception": false,
     "start_time": "2021-08-25T08:44:19.180784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✔️ Function Defined!\n"
     ]
    }
   ],
   "source": [
    "def clf_performance(classifier, classifier_name, classifier_name_abv):\n",
    "    '''Display the overall performance of a classifier with this template.'''\n",
    "    print('\\n', classifier_name)\n",
    "    print('-------------------------------')\n",
    "    print('   Best Score ({}): '.format(scoring_metric) + str(np.round(classifier.best_score_, 3)))\n",
    "    print('   Best Parameters: ')\n",
    "    for key, value in classifier.best_params_.items():\n",
    "        print('      {}: {}'.format(key, value))\n",
    "\n",
    "    y_pred_pp = cross_val_predict(estimator=classifier.best_estimator_,\n",
    "                                  X=X_train,\n",
    "                                  y=y_train,\n",
    "                                  cv=5,\n",
    "                                  method='predict_proba')[:, 1]\n",
    "    y_pred = y_pred_pp.round()\n",
    "\n",
    "    cm = confusion_matrix(y_train, y_pred, normalize='true')\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_train, y_pred_pp)\n",
    "    comparison_dict[classifier_name_abv] = [\n",
    "        accuracy_score(y_train, y_pred),\n",
    "        precision_score(y_train, y_pred),\n",
    "        recall_score(y_train, y_pred),\n",
    "        roc_auc_score(y_train, y_pred_pp), fpr, tpr\n",
    "    ]\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "    plot_conf_mx(cm, ax1)\n",
    "    plot_learning_curve(classifier.best_estimator_, X_train, y_train, ax2)\n",
    "\n",
    "    plt.tight_layout();\n",
    "\n",
    "\n",
    "print('✔️ Function Defined!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.085715,
     "end_time": "2021-08-25T08:44:19.456465",
     "exception": false,
     "start_time": "2021-08-25T08:44:19.370750",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## `plot_feature_imp()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:16.379999Z",
     "start_time": "2021-08-25T06:37:16.372064Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:19.637919Z",
     "iopub.status.busy": "2021-08-25T08:44:19.637099Z",
     "iopub.status.idle": "2021-08-25T08:44:19.640243Z",
     "shell.execute_reply": "2021-08-25T08:44:19.640749Z",
     "shell.execute_reply.started": "2021-08-25T07:32:50.672984Z"
    },
    "papermill": {
     "duration": 0.09816,
     "end_time": "2021-08-25T08:44:19.640901",
     "exception": false,
     "start_time": "2021-08-25T08:44:19.542741",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✔️ Function Defined!\n"
     ]
    }
   ],
   "source": [
    "def plot_feature_imp(classifier, classifier_name, color, ax):\n",
    "    '''Plot the importance of features for a classifier as a barplot.'''\n",
    "    importances = pd.DataFrame({'Feature': X_train.columns,\n",
    "                                'Importance': np.round(classifier.best_estimator_.feature_importances_, 3)})\n",
    "\n",
    "    importances = importances.sort_values('Importance', ascending=True).set_index('Feature')\n",
    "\n",
    "    importances.plot.barh(color=color,\n",
    "                          edgecolor='firebrick',\n",
    "                          legend=False,\n",
    "                          ax=ax)\n",
    "    ax.set_title(classifier_name)\n",
    "    ax.set_xlabel('Importance');\n",
    "\n",
    "\n",
    "print('✔️ Function Defined!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.085582,
     "end_time": "2021-08-25T08:44:19.812408",
     "exception": false,
     "start_time": "2021-08-25T08:44:19.726826",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## `test_func()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:16.394878Z",
     "start_time": "2021-08-25T06:37:16.386447Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:19.997592Z",
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    },
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     "duration": 0.101981,
     "end_time": "2021-08-25T08:44:20.000916",
     "exception": false,
     "start_time": "2021-08-25T08:44:19.898935",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✔️ Function Defined!\n"
     ]
    }
   ],
   "source": [
    "def test_func(classifier, classifier_name, ax):\n",
    "    '''Assess the performance on the test set and plot the confusion matrix.'''\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    cm = confusion_matrix(y_test, y_pred, normalize='true')\n",
    "\n",
    "    comparison_test_dict[classifier_name] = [accuracy_score(y_test, y_pred),\n",
    "                                             precision_score(y_test, y_pred),\n",
    "                                             recall_score(y_test, y_pred)]\n",
    "\n",
    "    sns.heatmap(cm,\n",
    "                annot=True,\n",
    "                annot_kws={'fontsize': 24},\n",
    "                cmap='Blues',\n",
    "                ax=ax)\n",
    "\n",
    "    ax.set_title(classifier_name)\n",
    "\n",
    "    ax.set_xlabel('Predicted Label')\n",
    "    ax.set_xticks([0.5, 1.5])\n",
    "    ax.set_xticklabels(['Retained', 'Churned'])\n",
    "\n",
    "    ax.set_ylabel('True Label')\n",
    "    ax.set_yticks([0.2, 1.4])\n",
    "    ax.set_yticklabels(['Retained', 'Churned']);\n",
    "\n",
    "\n",
    "print('✔️ Function Defined!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.087573,
     "end_time": "2021-08-25T08:44:20.177305",
     "exception": false,
     "start_time": "2021-08-25T08:44:20.089732",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "\n",
    "# A Quick Look at our Data\n",
    "\n",
    "We start by importing the dataset as a Pandas DataFrame. We can also take a look at the top five rows using the `head()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:16.443025Z",
     "start_time": "2021-08-25T06:37:16.397358Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:20.367009Z",
     "iopub.status.busy": "2021-08-25T08:44:20.366281Z",
     "iopub.status.idle": "2021-08-25T08:44:20.433588Z",
     "shell.execute_reply": "2021-08-25T08:44:20.434255Z",
     "shell.execute_reply.started": "2021-08-25T07:32:50.697802Z"
    },
    "papermill": {
     "duration": 0.169903,
     "end_time": "2021-08-25T08:44:20.434409",
     "exception": false,
     "start_time": "2021-08-25T08:44:20.264506",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✔️ Dataset Imported Successfully!\n",
      "\n",
      "It contains 10000 rows and 14 columns.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../input/predicting-churn-for-bank-customers/Churn_Modelling.csv')\n",
    "\n",
    "print('✔️ Dataset Imported Successfully!\\n')\n",
    "print('It contains {} rows and {} columns.'.format(df.shape[0], df.shape[1]))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.087875,
     "end_time": "2021-08-25T08:44:20.609697",
     "exception": false,
     "start_time": "2021-08-25T08:44:20.521822",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Our DataFrame has 14 features/attributes and 10K customers/instances. The last feature, '**Exited**', is the **target variable** and indicates whether the customer has churned (0 = No, 1 = Yes). The meaning of the rest of the features can be easily inferred from their name.\n",
    "\n",
    "Features 'RowNumber', 'CustomerId', and 'Surname' are specific to each customer and can be dropped:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CreditScore', 'Geography', 'Gender', 'Age', 'Tenure', 'Balance',\n",
       "       'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary',\n",
       "       'Exited'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['RowNumber', 'CustomerId', 'Surname'], axis=1, inplace=True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "The `info()` method can give us valuable information such as the number of non-null values and the type of each feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   CreditScore      10000 non-null  int64  \n",
      " 1   Geography        10000 non-null  object \n",
      " 2   Gender           10000 non-null  object \n",
      " 3   Age              10000 non-null  int64  \n",
      " 4   Tenure           10000 non-null  int64  \n",
      " 5   Balance          10000 non-null  float64\n",
      " 6   NumOfProducts    10000 non-null  int64  \n",
      " 7   HasCrCard        10000 non-null  int64  \n",
      " 8   IsActiveMember   10000 non-null  int64  \n",
      " 9   EstimatedSalary  10000 non-null  float64\n",
      " 10  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 859.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Thankfully, there are **no missing values** in our DataFrame. The `describe()` method gives us a statistical summary of the numerical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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     "end_time": "2021-08-25T06:37:16.517921Z",
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    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>650.529</td>\n",
       "      <td>96.653</td>\n",
       "      <td>350.00</td>\n",
       "      <td>584.00</td>\n",
       "      <td>652.000</td>\n",
       "      <td>718.000</td>\n",
       "      <td>850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>38.922</td>\n",
       "      <td>10.488</td>\n",
       "      <td>18.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>37.000</td>\n",
       "      <td>44.000</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>5.013</td>\n",
       "      <td>2.892</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>76485.889</td>\n",
       "      <td>62397.405</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>97198.540</td>\n",
       "      <td>127644.240</td>\n",
       "      <td>250898.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumOfProducts</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.530</td>\n",
       "      <td>0.582</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HasCrCard</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsActiveMember</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>100090.240</td>\n",
       "      <td>57510.493</td>\n",
       "      <td>11.58</td>\n",
       "      <td>51002.11</td>\n",
       "      <td>100193.915</td>\n",
       "      <td>149388.247</td>\n",
       "      <td>199992.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exited</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count        mean        std     min       25%         50%  \\\n",
       "CreditScore      10000.0     650.529     96.653  350.00    584.00     652.000   \n",
       "Age              10000.0      38.922     10.488   18.00     32.00      37.000   \n",
       "Tenure           10000.0       5.013      2.892    0.00      3.00       5.000   \n",
       "Balance          10000.0   76485.889  62397.405    0.00      0.00   97198.540   \n",
       "NumOfProducts    10000.0       1.530      0.582    1.00      1.00       1.000   \n",
       "HasCrCard        10000.0       0.706      0.456    0.00      0.00       1.000   \n",
       "IsActiveMember   10000.0       0.515      0.500    0.00      0.00       1.000   \n",
       "EstimatedSalary  10000.0  100090.240  57510.493   11.58  51002.11  100193.915   \n",
       "Exited           10000.0       0.204      0.403    0.00      0.00       0.000   \n",
       "\n",
       "                        75%        max  \n",
       "CreditScore         718.000     850.00  \n",
       "Age                  44.000      92.00  \n",
       "Tenure                7.000      10.00  \n",
       "Balance          127644.240  250898.09  \n",
       "NumOfProducts         2.000       4.00  \n",
       "HasCrCard             1.000       1.00  \n",
       "IsActiveMember        1.000       1.00  \n",
       "EstimatedSalary  149388.247  199992.48  \n",
       "Exited                0.000       1.00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "The most important things to note are:\n",
    "\n",
    "- The age of customers ranges from 18 to 92, with a mean value approximately equal to 40,\n",
    "- The mean (and median) tenure is 5 years, so the majority of customers is loyal (tenure > 3), and\n",
    "- Approximately 50% of customers are active.\n",
    "\n",
    "EDA will help us understand our dataset better. However, before we look at the data any further, we need to create a **test set**, put it aside, and use it only to evaluate our Machine Learning models. This practice protects our models from **data snooping bias** (you can read more on page 51 of [[1](#Bibliography)]) and ensures that evaluation will be performed using unseen data. "
   ]
  },
  {
   "cell_type": "markdown",
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    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "\n",
    "# Creating a Test Set\n",
    "\n",
    "We will split our dataset into a train and test set using scikit-learn's `train_test_split()` function, which implements random sampling. Our dataset is large enough (especially relative to the number of features), so we do **not** risk introducing *sampling bias*."
   ]
  },
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   "execution_count": 14,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: 8000 rows x 11 columns\n",
      " Test set: 2000 rows x 11 columns\n"
     ]
    }
   ],
   "source": [
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=random_state)\n",
    "\n",
    "train_df.reset_index(drop=True, inplace=True)\n",
    "test_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print('Train set: {} rows x {} columns'.format(train_df.shape[0],\n",
    "                                               train_df.shape[1]))\n",
    "print(' Test set: {} rows x {} columns'.format(test_df.shape[0],\n",
    "                                               test_df.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "<br>\n",
    "\n",
    "# Exploratory Data Analysis\n",
    "\n",
    "## Target Variable: Exited\n",
    "\n",
    "As we mentioned earlier, the target variable is already encoded and can take two possible values:\n",
    "\n",
    "- Zero (0) for a customer that has **not** churned, and\n",
    "- One (1) for a customer that has churned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 395,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "sns.countplot(x='Exited', data=train_df, palette=colors, ax=ax)\n",
    "\n",
    "for index, value in enumerate(train_df['Exited'].value_counts()):\n",
    "    label = '{}%'.format(round((value / train_df['Exited'].shape[0]) * 100, 2))\n",
    "    ax.annotate(label,\n",
    "                xy=(index, value + 250),\n",
    "                ha='center',\n",
    "                va='center',\n",
    "                color=colors[index],\n",
    "                fontweight='bold',\n",
    "                size=font_size + 4)\n",
    "\n",
    "ax.set_xticklabels(['Retained', 'Churned'])\n",
    "ax.set_xlabel('Status')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_ylim([0, 7000]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.093034,
     "end_time": "2021-08-25T08:44:22.923228",
     "exception": false,
     "start_time": "2021-08-25T08:44:22.830194",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The bank kept 80% of its clientele.\n",
    "\n",
    "Notice that our dataset is **skewed/imbalanced** since the number of instances in the 'Retained' class outnumbers the number of instances in the 'Churned' class by a lot. Therefore, accuracy is probably not the best metric for model performance.\n",
    "\n",
    "\n",
    "Different visualisation techniques apply to different types of variables, so it's helpful to differentiate between continuous and categorical variables and look at them separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:16.849421Z",
     "start_time": "2021-08-25T06:37:16.841483Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:23.113271Z",
     "iopub.status.busy": "2021-08-25T08:44:23.112584Z",
     "iopub.status.idle": "2021-08-25T08:44:23.119161Z",
     "shell.execute_reply": "2021-08-25T08:44:23.118172Z",
     "shell.execute_reply.started": "2021-08-25T07:32:51.002012Z"
    },
    "papermill": {
     "duration": 0.103391,
     "end_time": "2021-08-25T08:44:23.119319",
     "exception": false,
     "start_time": "2021-08-25T08:44:23.015928",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous:  Age, CreditScore, Balance, EstimatedSalary\n",
      "Categorical:  Geography, Gender, Tenure, NumOfProducts, HasCrCard, IsActiveMember\n"
     ]
    }
   ],
   "source": [
    "continuous = ['Age', 'CreditScore', 'Balance', 'EstimatedSalary']\n",
    "categorical = ['Geography', 'Gender', 'Tenure', 'NumOfProducts', 'HasCrCard', 'IsActiveMember']\n",
    "\n",
    "print('Continuous: ', ', '.join(continuous))\n",
    "print('Categorical: ', ', '.join(categorical))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.090933,
     "end_time": "2021-08-25T08:44:23.302457",
     "exception": false,
     "start_time": "2021-08-25T08:44:23.211524",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Continuous Variables\n",
    "\n",
    "By calling the `hist()` method, we can plot a histogram for each of the four continuous numeric features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:18.05321Z",
     "start_time": "2021-08-25T06:37:16.853388Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:23.496739Z",
     "iopub.status.busy": "2021-08-25T08:44:23.496029Z",
     "iopub.status.idle": "2021-08-25T08:44:24.675758Z",
     "shell.execute_reply": "2021-08-25T08:44:24.676319Z",
     "shell.execute_reply.started": "2021-08-25T07:32:51.009255Z"
    },
    "papermill": {
     "duration": 1.279328,
     "end_time": "2021-08-25T08:44:24.676640",
     "exception": false,
     "start_time": "2021-08-25T08:44:23.397312",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 605,
       "width": 757
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df[continuous].hist(figsize=(12, 10),\n",
    "                          bins=20,\n",
    "                          layout=(2, 2),\n",
    "                          color='steelblue',\n",
    "                          edgecolor='firebrick',\n",
    "                          linewidth=1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.096261,
     "end_time": "2021-08-25T08:44:24.870010",
     "exception": false,
     "start_time": "2021-08-25T08:44:24.773749",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- 'Age' is slightly tail-heavy, i.e. it extends more further to the right of the median than to the left,\n",
    "- Most values for 'CreditScore' are above 600,\n",
    "- If we ignore the first bin, 'Balance' follows a fairly normal distribution, and\n",
    "- The distribution of 'EstimatedSalary' is more or less uniform and provides little information.\n",
    "\n",
    "## Looking for Correlations\n",
    "\n",
    "We can compute the standard correlation coefficient between every pair of (continuous) features using the pandas' `corr()` method and plot it as a matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:18.563593Z",
     "start_time": "2021-08-25T06:37:18.056186Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:25.076759Z",
     "iopub.status.busy": "2021-08-25T08:44:25.075221Z",
     "iopub.status.idle": "2021-08-25T08:44:25.427647Z",
     "shell.execute_reply": "2021-08-25T08:44:25.428273Z",
     "shell.execute_reply.started": "2021-08-25T07:32:52.023405Z"
    },
    "papermill": {
     "duration": 0.463382,
     "end_time": "2021-08-25T08:44:25.428424",
     "exception": false,
     "start_time": "2021-08-25T08:44:24.965042",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 470,
       "width": 552
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "sns.heatmap(train_df[continuous].corr(),\n",
    "            annot=True,\n",
    "            annot_kws={'fontsize': 16},\n",
    "            cmap='Blues',\n",
    "            ax=ax)\n",
    "\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "ax.tick_params(axis='y', rotation=360);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.100805,
     "end_time": "2021-08-25T08:44:25.629228",
     "exception": false,
     "start_time": "2021-08-25T08:44:25.528423",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There is no significant intercorrelation between our features, so we do **not** have to worry about multicollinearity.\n",
    "\n",
    "Let's look at these features in greater detail.\n",
    "\n",
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:19.207401Z",
     "start_time": "2021-08-25T06:37:18.566576Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:25.831519Z",
     "iopub.status.busy": "2021-08-25T08:44:25.830460Z",
     "iopub.status.idle": "2021-08-25T08:44:26.757973Z",
     "shell.execute_reply": "2021-08-25T08:44:26.757335Z",
     "shell.execute_reply.started": "2021-08-25T07:32:52.319826Z"
    },
    "papermill": {
     "duration": 1.029933,
     "end_time": "2021-08-25T08:44:26.758084",
     "exception": false,
     "start_time": "2021-08-25T08:44:25.728151",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_churned = train_df[train_df['Exited'] == 1]\n",
    "df_retained = train_df[train_df['Exited'] == 0]\n",
    "\n",
    "plot_continuous('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.10189,
     "end_time": "2021-08-25T08:44:26.961974",
     "exception": false,
     "start_time": "2021-08-25T08:44:26.860084",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Interestingly, there is a clear difference between age groups since older customers are more likely to churn. This observation could potentially indicate that preferences change with age, and the bank hasn't adapted its strategy to meet the requirements of older customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.101649,
     "end_time": "2021-08-25T08:44:27.166708",
     "exception": false,
     "start_time": "2021-08-25T08:44:27.065059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Credit Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:19.798633Z",
     "start_time": "2021-08-25T06:37:19.210378Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:27.377885Z",
     "iopub.status.busy": "2021-08-25T08:44:27.376654Z",
     "iopub.status.idle": "2021-08-25T08:44:28.142922Z",
     "shell.execute_reply": "2021-08-25T08:44:28.142257Z",
     "shell.execute_reply.started": "2021-08-25T07:32:53.123132Z"
    },
    "papermill": {
     "duration": 0.874587,
     "end_time": "2021-08-25T08:44:28.143033",
     "exception": false,
     "start_time": "2021-08-25T08:44:27.268446",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_continuous('CreditScore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.103549,
     "end_time": "2021-08-25T08:44:28.353353",
     "exception": false,
     "start_time": "2021-08-25T08:44:28.249804",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There is no significant difference between retained and churned customers in terms of their credit scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.102976,
     "end_time": "2021-08-25T08:44:28.560944",
     "exception": false,
     "start_time": "2021-08-25T08:44:28.457968",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:20.387913Z",
     "start_time": "2021-08-25T06:37:19.801113Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:28.778452Z",
     "iopub.status.busy": "2021-08-25T08:44:28.771489Z",
     "iopub.status.idle": "2021-08-25T08:44:29.651202Z",
     "shell.execute_reply": "2021-08-25T08:44:29.650369Z",
     "shell.execute_reply.started": "2021-08-25T07:32:53.771615Z"
    },
    "papermill": {
     "duration": 0.987393,
     "end_time": "2021-08-25T08:44:29.651371",
     "exception": false,
     "start_time": "2021-08-25T08:44:28.663978",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 638
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_continuous('Balance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.10858,
     "end_time": "2021-08-25T08:44:29.877587",
     "exception": false,
     "start_time": "2021-08-25T08:44:29.769007",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Again, the two distributions are quite similar. There is a big percentage of non-churned customers with a low account balance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.108124,
     "end_time": "2021-08-25T08:44:30.094279",
     "exception": false,
     "start_time": "2021-08-25T08:44:29.986155",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Estimated Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:21.0466Z",
     "start_time": "2021-08-25T06:37:20.39036Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:30.318860Z",
     "iopub.status.busy": "2021-08-25T08:44:30.317870Z",
     "iopub.status.idle": "2021-08-25T08:44:31.136915Z",
     "shell.execute_reply": "2021-08-25T08:44:31.136342Z",
     "shell.execute_reply.started": "2021-08-25T07:32:54.411717Z"
    },
    "papermill": {
     "duration": 0.933108,
     "end_time": "2021-08-25T08:44:31.137030",
     "exception": false,
     "start_time": "2021-08-25T08:44:30.203922",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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oWgAAAACoW36uC9ifXHHFFfGf//mf8fHHH0cqlYply5bFJZdcEieffHKcffbZceyxx8ahhx5a45rly5fHP/7xj/jv//7vmD17dlRVVWV+1rt377jiiiua+zEAAAAAQPBXXfv27WPixIlx2WWXxaZNmyKVSkVVVVXMnj07Zs+eHRE7ZvF16NAhUqlUlJaWRjqdzly/c7ZgOp2OLl26xCOPPBLt27fP1eMAAAAA0IpZ6ruLAQMGxB//+McoLCzMBHkRO0K9dDodVVVVsWXLligpKYmqqqrM+YjIhH6FhYUxadKkzIYhAAAAANDcBH+1OPLII2Pq1Klx9dVXR8+ePWvM6ovYEfDtDAR3SqfT8bnPfS5+/OMfx9SpU+Ooo45qzpIBAAAAoAZLfevQrl27uOqqq+L73/9+vPbaa7Fw4cJ4++23Y8OGDbFp06aIiDjooIOiW7duMXjw4DjuuOPihBNOiPz85vkjLS8vj9dffz1ee+21WLx4cSxdujQ2bNgQFRUV0blz5+jTp08MHTo0zjzzzBg2bNhe3WPp0qUxbdq0mDNnTnz88cdRUlISPXv2jMLCwhg9enSceeaZceCBB+7V2G+99VZMmzYt5s6dG5988kmUlZVFQUFBDBw4MM4666w444wzol27dns1NgAAAACCvz3Ky8uLL3/5y/HlL38516VERMQ///nPuOuuu+Kvf/1rlJaW1tpn/fr1sX79+nj77bfjySefjKFDh8Zdd90Vhx9+eFb3qKysjAkTJsTEiRNj+/btNX62atWqWLVqVcyZMyceeuihuPvuu+PEE0/Muv7S0tK455574umnn97tZ8uXL4/ly5fHSy+9FF/4whdi/PjxZk4CAAAA7CVLfVuYjz/+OJ577rndQr9evXrFscceGyeccEL079+/xs/eeOONuOiii2LBggVZ3eOWW26Jhx9+OBP6pVKpGDBgQAwfPjx69+6d6bd69er49re/HXPmzMlq3IqKirjyyitrhH5t27aNI444IoYNGxaf+9znMuffe++9uOyyy+Ldd9/NamwAAAAAajLjrwU7/vjj44ILLoiTTz45evXqVeNnK1asiAceeCBmzpwZETtm2v3whz+M559/Prp3717nmI8//nhMnTo10x4+fHj88pe/jMLCwsy5V199NW644Yb45JNPorKyMq655pqYPn16HHLIIfXWe++998Zrr72WaY8aNSpuueWWOPjggyMioqqqKmbNmhW33nprlJSUxJYtW+L73/9+PPfcc3u9pBgAAACgtWp1M/62bNkS3/nOd+Lyyy+Pyy+/PL71rW/FypUr93ncFStWxBVXXJEZ9wc/+EGUl5c3QsU1tWnTJkaOHBlTp06Np556KsaOHbtb6BcR0a9fv7j//vvjiiuuyJwrLi6OiRMn1jn2hg0boqioKNM++uij47HHHqsR+kVEnHTSSTFlypTo2LFjRESUlJTEgw8+WG/dH3zwQTz11FOZ9te//vV48MEHM6Hfzmc766yz4ve//33k5eVFRMSaNWvi0UcfrXdsAAAAAHbX6oK/iRMnxpw5c2L+/Pkxf/78+OpXvxp9+/bd53H79esXp5xySsybNy/mz58fr7zySpMEVoMGDYqioqKsv3137bXX1ggGX3jhhTr7TpkyJTZv3pxp33777XVusPH5z38+rrrqqkx7xowZ9Qaov//976OysjIidizvvf3223fbGXmn448/Pi6++OJMe9KkSbF169Y6xwYAAABgd60q+Nu4cWP88Y9/zAROp512Wnzzm99stPG/+c1vxqmnnhrpdDrS6XT84Q9/iJKSkkYbf2+0a9cuvvrVr2baq1evrjNEmzVrVuZ4yJAhMWTIkHrHHjt2bLRv3z4idizTffHFF2vtV1FRES+99FKmPXLkyBoz/Wozbty4zHFpaWnMnj273v4AAAAA1NSqgr+ZM2dGWVlZpNPpyMvLi5/97GeNfo/rr78+8vPzI5VKxdatW+O5555r9Hs0VNeuXWu0awsjV6xYEe+//36m/bWvfS2rcYcOHZppv/zyy7X2W7BgQWzcuLFBYw8YMKDGTMy6xgYAAACgdq0q+JsxY0ZE7Nil9hvf+EYceuihjX6P/v37x5gxYyKdTkdExLRp0xr9Hg21atWqzHGbNm2iW7duu/VZvHhxjfbxxx+f1djV+73zzju19tn1/Be/+MUGj71rfQAAAADUr9UEf+Xl5fH2229n2meddVaT3evss8+OiIh0Oh1vvfVWbNu2rcnutSdlZWU1lskec8wxkZ+/+2bOS5YsqdHu379/VuNX71dSUhJr1qypd+y2bdvucfffnaoHs8uWLYvt27dndR0AAAAAEbsnQAm1aNGizOYSnTp1ihEjRjTZvUaMGBGdOnWKLVu2RGVlZSxevLjGktjm9MQTT9TYsGPMmDG19qs+KzAvLy8KCgqyGr9Pnz412itXrtxtl+HqYxcUFESbNtnlzdUDwm3btsW6detq3cG4IfLz20TXrh33aYxcyM//vz+ztu3yclhJ7iXx+dt89t3RbJ4tic/fEC3x+RvyfvekJT5/Y9ofn78x32999sdnb065ev7mer97kuv751pTPf/+8n73ZH+vr6nt7fO3lPe7Jy29/n1V1/Mn5f3uSdKfb09a4u/uETV/f98f7F/VNKEVK1ZExI5lvoceemits94aS35+fo2ZcMuXL2+ye9XnvffeiwkTJmTan//852Ps2LG19q3+3b9OnTpFXl52f8F07ty5RnvLli31jn3QQQdlNW62YwMAAABQu1Yz42/Tpk2Z4549ezb5/arfo/rGFs1lw4YN8aMf/SjKy8sjYscsvrvvvjvatWtXa//S0tLM8c6derOxa9/awrnqYx9wwAGNOnZDVVZWRXFx6Z477meq/5+Oim2te8lzEp9/5//Jy+bZkvj8DdESn78h73dPWuLzN6b98fkb8/3WZ3989uaUq+dvrve7J7m+f6411fPvL+93T/b3+pra3j5/S3m/e9LS699XdT1/Ut7vniT9+fakJf7uHrHj9/e2bfef2ZqtZsZf9VlnBx54YJPfr/o9mnumWllZWVx11VXx0UcfZc7927/9WwwbNqzOa3Yug46IBs2G3LVv9XFqO5ftTMLaxq6oqMj6WgAAAIDWrtUEf9Vnum3YsKHJ71f9Hk25rHhX27Ztix/96Efx+uuvZ86NGzcuvve979V7XYcOHTLHO2cJZmPXvh077r4GvynHBgAAAKB2rSb469q1a+b4n//8Z5Pfr/o9unXr1uT3i9gxI+6aa66Jv/3tb5lzF198cfy///f/9nht9VCtrKws63tu3bq1RrtTp07NOjYAAAAAtWs1wV+/fv0iIiKdTseSJUuiuLi4ye5VXFwc77///m73bkqVlZVx3XXXxcsvv5w5d+GFF8YvfvGLSH2241F9unfvnjkuLS2tsTS6PuvWravRri3krD72rv0bMnb18BYAAACA+rWa4O/oo4+ONm3aRCqViqqqqpg9e3aT3Wv27NlRVVUVETt2ET766KOb7F4REdu3b4+f/vSn8cILL2TOXXDBBXHHHXdkFfpFRBx22GE12qtXr87quur92rRpU2M349rGLi4uzjpUrD52z549G7QjMAAAAEBr12qCvwMPPDCGDBkS6XQ60ul0PPLII5lwrjFVVVXFxIkTI2JH6DdkyJAm3Uxk+/btcf3118fzzz+fOXf++efHnXfeGW3aZP96Bw4cWKO9ePHirK6r3u+QQw6pddfeAQMG1Gi/8847WY29aNGizPHhhx+e1TUAAAAA7NBqgr+IiHPPPTdz/MEHH8Rjjz3W6PeYNGlSLFmypNZ7Nradod/MmTMz577xjW/EXXfd1aDQLyJi8ODBNb7FN2/evKyumz9/fuZ4xIgRtfbZ9Xw2Y5eXl8ebb765x7EBAAAAqF2rCv4uuOCC6NatW6RSqUin0/HrX/86pk2b1mjjT58+PX71q19lltd27do1LrjggkYbv7qqqqq48cYba4R+Y8aMibvvvrvBoV9ExAEHHBAnn3xypv3iiy/utrnGrhYsWBArVqzItEeNGlVrvz59+sSgQYMy7enTp0c6na537FmzZtXYCKSusQEAAACoXasK/jp06BDXXXddpNPpzLf+brrpprjzzjsbtNvsrsrKyuKuu+6Km266KbOUOJVKxbXXXhsdOnRoxCfYoaqqKm6++eaYPn165tx5550X99xzz16FfjtdeOGFmePNmzfHpEmT6u1fVFSUOe7Tp0986UtfqrPvRRddlDletmxZzJgxo86+27ZtyyyXjogYOnTobsuFAQAAAKhfqwr+InYEUGeccUaN8O/JJ5+MUaNGxYQJE7Le1CIi4uOPP44JEybE6NGjY/LkybF9+/ZIpVKRSqXi9NNPj7FjxzZ6/el0Om677bb4r//6r8y5c889N+699959Cv0iIk455ZQaS2qLiorilVdeqbXvb37zm3j11Vcz7auvvjratWtX59hjx46NQw89NNO+4447aizl3amysjJuvfXWGsulr7vuugY9BwAAAAAR+bkuIBd+9atfxYYNG2L+/PmZZb9r166NoqKiKCoqij59+sTgwYOjsLAwOnfuHJ07d45UKhWbN2+OTZs2xYcffhiLFi2KVatWRURklq3uHGv48OExfvz4Jqn9+eefj2eeeSbTTqVS8emnn8Z3v/vdrMf42c9+FkceeWStP/vFL34Rl1xySRQXF0dFRUVceeWVcfbZZ8fIkSOja9eusXLlynj22WdjwYIFmWtOPfXUGDNmTL33bNu2bdx1111xxRVXxLZt22Ljxo0xbty4uOiii+Kkk06KTp06xYcffhhPP/10vPvuu5nrxo0b5/t+AAAAAHuhVQZ/7du3j0cffTTuvffemDJlSuabfDsDvFWrVu1x5l/1b9TtDPzS6XRceumlcdNNN9U7+21f7LokOZ1O15h5l43vfe97df6ssLAwioqK4oc//GEUFxfH9u3bY/r06TWWFVd34oknxq9//eusZhsOGzYs7rvvvrjhhhti69atUV5eHlOmTIkpU6bU2v+cc86Jm2++ObuHAgAAAKCGVrfUd6d27drFrbfeGhMmTIi+ffvWmLVXPQis7Z/a+vXt2zcmTJgQP//5z5ss9Gsuw4YNi5kzZ8Y555xT57P06tUrbrrppnj88ccb9B3DUaNGxdSpU+OUU06JvLy8Wvv0798/xo8fH/fff3+dfQAAAACoX6uc8VfdyJEj47TTTosXX3wxnnzyyVi4cGFUVlbWe83O8C8/Pz+OO+64uOyyy+KMM87Y52/sZeOCCy5osp2Cq+vZs2fcf//9sXnz5pg7d26sXbs2tmzZEj169IjCwsI47rjjMsFnQ/Xv3z8mTpwYn376acyfPz/Wrl0b5eXlUVBQEAMHDqyxAzAAAAAAe6fVB38RO2bvjRo1KkaNGhVlZWXxxhtvxD/+8Y/45JNPori4ODZt2hQREQcddFB06dIlCgoK4thjj42hQ4c2ya69+5POnTvHyJEjm2TsHj16xOjRo5tkbAAAAIDWTvC3iwMOOCBOPPHEOPHEE3NdCgAAAADstVb7jT8AAAAASDLBHwAAAAAkkOAPAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAIJ/gAAAAAggQR/AAAAAJBAgj8AAAAASCDBHwAAAAAkkOAPAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAIJ/gAAAAAggQR/AAAAAJBAgj8AAAAASCDBHwAAAAAkkOAPAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAIJ/gAAAAAggQR/AAAAAJBAgj8AAAAASCDBHwAAAAAkkOAPAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAIJ/gAAAAAggQR/AAAAAJBAgj8AAAAASCDBHwAAAAAkkOAPAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAIJ/gAAAAAggQR/AAAAAJBAgj8AAAAASCDBHwAAAAAkkOAPAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAIJ/gAAAAAggQR/AAAAAJBAgj8AAAAASCDBHwAAAAAkkOAPAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAIJ/gAAAAAggQR/AAAAAJBAgj8AAAAASCDBHwAAAAAkkOAPAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAIJ/gAAAAAggQR/AAAAAJBAgj8AAAAASCDBHwAAAAAkkOAPAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAIJ/gAAAAAggQR/AAAAAJBAgj8AAAAASCDBHwAAAAAkkOAPAAAAABIoP9cFAAAAdfvW976S6xIAcsLff7DvzPgDAAAAgAQy4w8AAPZj20ZfkOsScqLdrGdzXQKQY3X9/demzY45TFVVVc1ZTrPwdx+NzYw/AAAAAEggwR8AAAAAJJDgDwAAAAASSPAHAAAAAAkk+AMAAACABLKrLwDsx771va/UegwAALAnZvwBAAAAQAKZ8QcALUDfHl3ig+Gn5bqMZtdu1rO5LgEAAFoswR8AAMB+ymceANgXlvoCAAAAQAKZ8QcAALCf2jb6glyXkBM+9QDQOAR/AECLYLkbQOvj736AfSP4AwAAANjPCL5pDII/AGC/t7/uatymzY7PJVdVVTXJ+Ja6Aa3d3i51buq/n5vazr//BT/AvhL8AQAAAOxH9tf/6dnU/E/Pxif4a6FKSkpi0aJFsWjRonj77bdj0aJF8dFHH0U6nY6IiBEjRsTkyZP36R5Lly6NadOmxZw5c+Ljjz+OkpKS6NmzZxQWFsbo0aPjzDPPjAMPPHCvxn7rrbdi2rRpMXfu3Pjkk0+irKwsCgoKYuDAgXHWWWfFGWecEe3atdun+gEAAFoym7sA+0rw1wKNGjWqRsjX2CorK2PChAkxceLE2L59e42frVq1KlatWhVz5syJhx56KO6+++448cQTsx67tLQ07rnnnnj66ad3+9ny5ctj+fLl8dJLL8UXvvCFGD9+fBx11FH7/DwA0NJZ6gUAwN5ok+sCaLhly5Y1WegXEXHLLbfEww8/nAn9UqlUDBgwIIYPHx69e/fO9Fu9enV8+9vfjjlz5mQ1bkVFRVx55ZU1Qr+2bdvGEUccEcOGDYvPfe5zmfPvvfdeXHbZZfHuu+820lMBAAAAtC5m/LVgnTp1iqOPPjoGDRoUgwYNiscffzwWL168T2M+/vjjMXXq1Ex7+PDh8ctf/jIKCwsz51599dW44YYb4pNPPonKysq45pprYvr06XHIIYfUO/a9994br732WqY9atSouOWWW+Lggw+OiB0f3p01a1bceuutUVJSElu2bInvf//78dxzz+31kmIASAJLvQAA2BuCvxbovvvui0GDBkVhYWGkUqnM+WeeeWafxt2wYUMUFRVl2kcffXQ89thju31r76STToopU6bEmDFjorS0NEpKSuLBBx+M8ePH1zn2Bx98EE899VSm/fWvfz0efPDBGvW3adMmzjrrrOjVq1eMGzcutm/fHmvWrIlHH300rrnmmn16tiSx3AsAAADIhqW+LdC5554bhx12WI3QrDFMmTIlNm/enGnffvvtdW6w8fnPfz6uuuqqTHvGjBmxcuXKOsf+/e9/H5WVlRGxY3nv7bffXmf9xx9/fFx88cWZ9qRJk2Lr1q0NehYAAACA1s6MPzJmzZqVOR4yZEgMGTKk3v5jx46N3/3ud1FeXh5VVVXx4osvxre+9a3d+lVUVMRLL72UaY8cOTKzvLcu48aNy8wQLC0tjdmzZ8eoUaMa8jiJZbkXrU31Wa5mvAIAAGRP8EdERKxYsSLef//9TPtrX/vaHq/p2rVrDB06NObOnRsRES+//HKtwd+CBQti48aNDRp7wIAB0bdv38wswpdfflnwByH4AgAAIHuCPyIidtsU5Pjjj8/quuOPPz4T/L3zzju19tn1/Be/+MWsx94Z/O180cd6AAAgAElEQVTrpiUkh+Cr9erbo0t8MPy0XJfRYG3a7PiqRlVV1V5db7YrAACwtwR/RETEkiVLarT79++f1XXV+5WUlMSaNWuiV69edY7dtm3bPe7+u9Ohhx6aOV62bFls37498vLysroWkqo1LvUWfAEAAOwdwR8REbFq1arMcV5eXhQUFGR1XZ8+fWq0V65cuVvwV33sgoKCzOyXPakeEG7bti3WrVu329gNlZ/fJrp27bhPY+RCfv7//Zll++eXVJVnXZTrEhrdzm1u0vX0yf/vP0eE998Sn3/n+22M2lvi8zem/fH5G/P91md/fPbm1Npne3v/3n9rtrfP31x/Pze1ll7/vqrr+ZPyfvck6c+3Jy3xd/eImr+/7w/2r2rImZKSksxxp06dsp5Z17lz5xrtLVu21Dv2QQcdlHVN2YwNAAAAQO3M+CMiduycu1P79u2zvm7XvrWFc9XHPuCAAxp17IaqrKyK4uLSPXfcz1T/Px17+52wpEji8zfkG3BJfP6GaInPv6/f+KuuJT5/Y9ofn78x32999sdnb065+sxBc73fuuz81IH33zTvP9fvd0+8/x329vn39/ebrZZe/76q6/mT8n73JOnPtyct8Xf3iB2/v7dtu/98psyMPyIiorKyMnOcn599Hrxr3+rj1HauId/o23XsioqKrK8FAAAAaO0Ef0RERIcOHTLH5eXlWV+3a9+OHXdfg9+UYwMAAABQO8EfEVEzVCsrK8v6uq1bt9Zod+rUqVnHBgAAAKB2gj8iIqJ79+6Z49LS0hobctRn3bp1NdrdunWrd+xd+zdk7K5du2Z9LQAAAEBrJ/gjIiIOO+ywGu3Vq1dndV31fm3atIn+/fvXO3ZxcXHWoWL1sXv27NmgHYEBAAAAWjvBHxERMXDgwBrtxYsXZ3Vd9X6HHHJIrbv2DhgwoEb7nXfeyWrsRYsWZY4PP/zwrK4BAAAAYAfBHxERMXjw4Brf4ps3b15W182fPz9zPGLEiFr77Ho+m7HLy8vjzTff3OPYAAAAANRO8EdERBxwwAFx8sknZ9ovvvjibptr7GrBggWxYsWKTHvUqFG19uvTp08MGjQo054+fXqk0+l6x541a1aNjUDqGhsAAACA2gn+yLjwwgszx5s3b45JkybV27+oqChz3KdPn/jSl75UZ9+LLrooc7xs2bKYMWNGnX23bdsWEydOzLSHDh2623JhAAAAAOon+CPjlFNOqbGktqioKF555ZVa+/7mN7+JV199NdO++uqro127dnWOPXbs2Dj00EMz7TvuuKPGUt6dKisr49Zbb40lS5Zkzl133XUNeg4AAAAAIvJzXQAN99BDD8XDDz+82/mKiorM8fz58+OYY47Zrc+YMWPijjvuqHPsX/ziF3HJJZdEcXFxVFRUxJVXXhlnn312jBw5Mrp27RorV66MZ599NhYsWJC55tRTT40xY8bUW3Pbtm3jrrvuiiuuuCK2bdsWGzdujHHjxsVFF10UJ510UnTq1Ck+/PDDePrpp+Pdd9/NXDdu3Djf9wMAAADYC4K/Fmj79u2xbdu2evuk0+la+1RWVtZ7XWFhYRQVFcUPf/jDKC4uju3bt8f06dNj+vTptfY/8cQT49e//nW0abPnyaPDhg2L++67L2644YbYunVrlJeXx5QpU2LKlCm19j/nnHPi5ptv3uO4AAAAAOzOUl92M2zYsJg5c2acc845dS7f7dWrV9x0003x+OOPR4cOHbIee9SoUTF16tQ45ZRTIi8vr9Y+/fv3j/Hjx8f9999fZx8AAAAA6mfGXwv04x//OH784x836T169uwZ999/f2zevDnmzp0ba9eujS1btkSPHj2isLAwjjvuuEilUns1dv/+/WPixInx6aefxvz582Pt2rVRXl4eBQUFMXDgwBo7AAMAAACwdwR/1Ktz584xcuTIJhm7R48eMXr06CYZGwAAAKC1s9QXAAAAABJI8AcAAAAACST4AwAAAIAEEvwBAAAAQAI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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 639
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_continuous('EstimatedSalary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.110344,
     "end_time": "2021-08-25T08:44:31.357990",
     "exception": false,
     "start_time": "2021-08-25T08:44:31.247646",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Both churned and retained customers display a similar uniform distribution in their salaries. Consequently, we can conclude that salary doesn't have a significant effect on the likelihood to churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.10777,
     "end_time": "2021-08-25T08:44:31.575450",
     "exception": false,
     "start_time": "2021-08-25T08:44:31.467680",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Categorical Variables\n",
    "\n",
    "Let's plot a seaborn.countplot for each categorical feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:22.42346Z",
     "start_time": "2021-08-25T06:37:21.053016Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:31.806192Z",
     "iopub.status.busy": "2021-08-25T08:44:31.801049Z",
     "iopub.status.idle": "2021-08-25T08:44:32.984141Z",
     "shell.execute_reply": "2021-08-25T08:44:32.984663Z",
     "shell.execute_reply.started": "2021-08-25T07:32:55.097978Z"
    },
    "papermill": {
     "duration": 1.30205,
     "end_time": "2021-08-25T08:44:32.984803",
     "exception": false,
     "start_time": "2021-08-25T08:44:31.682753",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lsVgkSSaTSQ888IBbN9s+d+6cNm7cWO58RESEmjVrprp16yo9PV2pqanGZwcPHtTo0aP1zjvvuLQ6a9asWTa/FyaTSa1bt1bjxo115swZnTt3TlLJm7+PPfaY3n77bd11110Vxi0sLNSkSZO0Z88e41xgYKBuu+02hYSEKDU1VZcuXZIkHTt2TGPHjtVHH32k9u3bVxg7OztbjzzyiI4dO2acq1u3rlq3bq2goCD9/PPPxpdc+/fv19ixY7VmzRpFRERUGBsAAG/lDblNVWVlZRnH9lY2dezYUfXr1zde/Pn66691//33Vxh33759xrGjL7XKnv/6668rLGTl5+fru+++q1Lsip777NmzNqu3+DIOAOCrvCm3oZOOY3TSAQD4Mo8Xsp555hmZTKYqX2+dCFksFrVp00bTpk1z1+OV061bN40cOVJ9+vQpV5A5ffq0Xn/9dW3YsEFSyUqoKVOmaNOmTTZ7L5S1bNkym8Swe/fuevnllxUTE2Oc++qrrzRz5kxduHBBRUVFmjZtmlJSUhQVFeX0eRcuXGhTxBo0aJBmzZqlZs2aSZKKi4u1efNmzZ49W9nZ2crJydHjjz+ujRs3Vph4PfXUUzZFrAcffFDTpk0z9p4oKCjQJ598ovnz56uwsFDp6emaNGmS/vGPf8jf399pbAAAvJW35TaVYV1wuvXWW8t9XrduXfXp00dbtmyRJG3dulWzZ892+kXV/v37dfr0aWM8aNAgu/MiIyMVGxtrrIJKSUnR5MmTnf5eb968WXl5eRXGjouLU9OmTZWRkSFJWrduXYWFLOvcLTAwUP3793c6HwAAb+UNuQ2ddByjkw4A4GZQY69iWCwWl39ZK02mLBaLfv3rX2vZsmUKCQlx67P5+flpwIABWrt2rVatWqWEhAS7q4patmypxYsXa/z48ca5zMxMLV261GHsK1euKCkpyRjfcccdev/9922KWJLUu3dvJScnq379+pJKVkO98cYbTp/75MmTWrVqlTHu16+f3njjDaOIVfqzDRkyRO+8845RXDp//rzee+89p7F3796t7du3G+MHH3xQL7zwgs0G6kFBQRo7dqwWLVpknDt8+LA+++wzp7EBAPAFtTm3qYqUlBT99NNPxthR4WbUqFHGcVZWlpYvX+40rnUeFBkZqV69ejmcO3r0aOM4NTXV2K/LnoKCApscrEuXLuXaE5by8/PTiBEjjPHu3bu1f/9+h7GvXLmi5ORkYzxw4EC7K9QAAPAltTm3Ke2kU7aIFRERoc6dO+vOO+8stwdnaScdZ3/nW5s1a5b+/ve/G0Usk8mkNm3aqHv37mrevLkxr7STzq5du1yKW9pJx7qIFRgYqHbt2ik+Pl633HKLcb60k07p3p4VKe2kY13Eqlu3rmJjY9W1a1eb73BKO+lY72MGAIC3qJFCVtkkx5X5pb+aNGmie++9V++++67ee+89m7/g3SU2NlZJSUkuv/Eyffp0m0JX6VvJ9iQnJ9u06ZkzZ47DZeK33nqrJk+ebIzXr1+vM2fOOIz9zjvvqKioSFJJElTaj9qebt266YEHHjDGy5cv1/Xr1x3GfvPNN43jJk2aaMaMGQ7nDhkyRHfffbcxfuuttxzOBQDAF9T23ObAgQN68cUX9csvv7j0bGvWrNGsWbOMc02aNNHvf/97u/P79u1r02YvKSlJO3bssDv3tdde01dffWWMp06d6rRdTkJCglq1amWM586da9M6sFRRUZFmz56t48ePG+cSExMdxpWkiRMnGsUoi8WiGTNm6NSpU+Xm5ebmKjEx0WjNHBAQUGtWzAEA4Cm1Pbex1q1bN82dO1c7duzQjh07tGbNGq1YsUJbtmzR559/rmHDhhlzSzvpXL582WlMe510Nm3apI0bN2rlypX63//9Xy1btkzh4eGSZHTSsW5D7Ii9Tjrbtm1TSkqKkpOT9eWXX+q1114zuuaUdtLJzs6uMLa9Tjo7duzQp59+qo8//lhffvmlnn/+eQUGBkqS0Umn7IozAABqO4+3FlyxYkWlr/H391eDBg3UpEkThYWFeeCpqicoKEh333231qxZI6nkbZzr16/bbauzefNm47hTp07q1KmT09gJCQn629/+pvz8fBUXF2vr1q169NFHy80rLCzUtm3bjPGAAQNsVmLZM27cOGMFV25urnbu3Gm3BU9GRobNG0sjR46ssLf1uHHjtHPnTkklLRgPHTqkjh07Or0GAABv5A25TUFBgVatWqVVq1apQ4cO6tGjh9q1a6ewsDAFBweroKBAly5d0pEjR/Svf/3LpqATGBioBQsWqFGjRg7jv/TSSxozZowyMzONt4yHDh2qAQMGKDQ0VGfOnNGnn35qk0/0799fw4cPd/rcgYGBmjdvnsaPH6+CggJdvXpV48aN0+jRo9W7d28FBwfr559/1urVq23eVB43blyFe1g1atRIc+bM0fTp02WxWJSWlqZRo0ZpzJgxiouLU1BQkH788UclJyfbtEJMTEy0Ka4BAOBrvCG3Ke2k88QTTzh9Cbm0k05YWJiWLVsm6T+ddJ555hm71zjqpFP25ZvSTjrDhw9Xbm6u0UnHuktNWY466Vi/hFzaSSciIkLjxo2T2Ww2Ouk4e5nGUScda6WddBo3bqwnn3xS0n866VivhAcAoLbzeCHLVzfGtl6eLZUs5y5b7Dl9+rRNi57f/OY3LsXt0qWL9u7dK0n64osv7Bay9u/fr6tXr1Yqdps2bdSiRQtjldcXX3xht5C1fft2FRcXG+N+/fpVGLtXr16qU6eO8vPzjdgUsgAAvsjbcpsjR47oyJEjLs1t1qyZ5s2bp7vuusvpvJiYGCUlJWnKlCnKzMyU2WxWSkqKUlJS7M7v2bOnXn31VZc2GI+Pj9crr7yimTNn6vr168rPz1dycrJNqz9rw4YN03PPPVfxD6eSVeQZGRmaP3++zGazrl275rRF9IQJE+zmYQAA+BJvyG1KO+m4avr06dq0aZPRRm/Lli0OC1lV6aTzyiuvSCrppDN16lS1aNHC7vyqdNIpLXwtX75cEydOdPhicWU76Xz22WfGC8hvvfUWhSwAgFepsT2yfI318nE/Pz81bty43JzDhw/bjLt16+ZSbOt5jr54Kns+Li6u0rHLPp+92IGBgRWuIpNK3vKxLlw5ig0AADyvTZs2mjhxomJjY409Mp1p3bq1EhMT9c9//rPCIlap+Ph4bdiwQcOGDXP4ZU9ERISeffZZLVu2rMLV3dYGDRqktWvXqm/fvg6fPzo6WosWLdLixYtd+hlLPfTQQ/r4448VFxfn8IukDh066N1339XTTz/tclwAAFB7lHbSKVXaSceeqnTSqVOnjiQZnXTsqWonnVKlnXTsqWonnVKlnXQAAPAWHl+R5Yvy8vJskolf/epXCggo/1tpvW+DpHIbjzpiPS87O1vnz5+32ZOrbOzAwEBFRUW5FNu6NU5qaqrMZnO5L3+sY0dERDjdy6Js7H//+9+SpBMnTrh0DQAAcL+mTZsqMTFRiYmJun79uo4dO6ZTp04pIyNDubm5CgwMVEhIiJo3b6727dtX+KWKI2FhYVq8eLGysrK0d+9epaenKycnR02bNlVMTIy6du3qsFhUkejoaC1dulQZGRnat2+f0tPTlZ+fr/DwcLVt21axsbFViiuVfEn10UcfKS0tTQcPHtSFCxdUXFyssLAwxcbGqnXr1lWODQAAagc66diikw4AwJtRyKqCFStW2Cw7d7Tfg/WqLX9/f2NT0IpERkbajM+cOVOukGUdOzw83KVWPZJsCl4FBQW6ePGi09jNmzd3KW7Z2K5seOqKgAA/hYbWd0ss2Mfvb+3BPwvAdQEBLCp3Vb169dS5c2d17tzZY/cICQnRgAEDPBK7adOmGjx4sEdiR0VFufwyEAAA8C6e7qRTWsjyRCed0kKWJzrplL6ATCcdAIA3ueGFLIvFoqysLOMtlUaNGikkJKTKb+962rFjx7RkyRJjfOuttyohIcHu3OzsbOM4ODjY5bY3ISEhNuOcnBynsRs2bOhSXE/HbtCggXFsNpuVl5enunXrunw9AAC+wNtyGwAAAGe8Mbehk47j2HTSAQB4oxtSyPrhhx+UkpKiAwcO6MiRI8bGl8ZDBQSoQ4cO6tKli+67775as9T5ypUreuKJJ4xl2P7+/po/f77DhCE3N9c4Lu2f7Iqyc+0Vm6xjV6ZY5MnYZefm5ORUu5BVVFSszMxcp3PCwkKcfg7nKvr9dRX/HKrPXf8sgJtBaGh9BQa6vi+Sp3lrbgMAAGCPt+c2dNKpOLa7OukAAFATarSQdezYMb344os6cOCApJK3euwpLCzU999/r++//14ffvihunTpohdffFHt2rWryce1kZeXp8mTJ+uXX34xzv35z39WfHy8w2usEz17b/44UnZu2YSx7LnKbHBeNnZhYaHbYpeda++5AQDwJd6c2wAAAJTlC7kNnXQc80QnHbaEgC/g32HAlrv+TLhzW4ga22Dik08+UUJCgg4cOGAkQiaTye5SdOtzFotFBw4cUEJCglavXl1Tj2ujoKBATzzxhL755hvj3Lhx4zRx4kSn11lvIlq6issVZefWr1/+X5zaGLugoMBhHAAAfI035zYAAABl+UJuQycd5+x10gEAwBvUyIqs9evX6/nnn5fFYjGSoNLjFi1aKCYmRiEhIbJYLMrOzlZqaqpOnz5tzJFKiiRz5sxRvXr1dN9999XEY0sqecto2rRp+vLLL41zDzzwgP7f//t/FV5rXSTKy8tz+Z7Xr1+3GQcHB9d47NK3e6oT216RDAAAX+DNuQ0AAEBZvpDb0EmnYp7opOPKlhCuYssC3Ci1dasH/kzgRnHXnwl3bgvh8UJWenq6Zs+eLUlGIhQeHq4JEyZo2LBhatKkid3rLl++rA0bNuj999/X+fPnZTKZVFxcrOeff149evQo1x/YE4qKipSYmKgvvvjCODdq1Ci99NJLLm1qav2z5ebmKjs722YZtyMXL160GTdu3Nhp7LLzKxM7NDTUbuwLFy5UK3bDhg0rlQQCAOAtvDm3AQAAKMsXcpubsZNO6QvIdNIBANwMPN5a8PXXX7dZ1TN48GBt2rRJDz/8sMNkSCoppjz88MPatGmThgwZYrzlk5+fr//5n//x9GPLbDbrqaee0pYtW4xzI0eO1Ny5c10qYknSbbfdZjM+e/asS9dZz/Pz81N0dLTT2JmZmTb9kV2NHRYWZreXsnXsymz+aR277M8OAICv8NbcBgAAwB5vz21u1k467ohNJx0AgLfwaCGrsLBQW7ZsMQo/ffr00euvv273L2FH6tWrp8WLF+vuu++WxWKRxWLRli1b3LL82RGz2awZM2Zo06ZNxrkRI0boL3/5i/z8XP8ta9u2rc348OHDLl1nPS8qKspuv+M2bdrYjI8cOeJS7B9++ME4bt26td051rEzMzNdLsC5EhsAAG/mrbkNAACAPd6e23iik44rakMnnerGppMOAMCbeLSQdeDAAeXm5hpv5Tz//PNVimMymTR79mwjCcnNzbVZLu5OpUWsDRs2GOfuv/9+zZs3r1JFLEnq2LGjzdstX3/9tUvX7du3zzju0aOH3Tllz7sSOz8/X999951HYp89e9Zm9Zaj2AAAeDNvzG0AAAAc8ebchk46JeikAwC4GXi0kHXmzBlJJQnNHXfcoRYtWlQ5VsuWLdWxY0djfPr06Wo/X1nFxcV65plnbIpYw4cP1/z58ytdxJKkunXrqk+fPsZ469at5ZZxl7V//36bn23QoEF250VGRio2NtYYp6SkyGKxOI29efNmmyXnjmLHxcWpadOmxnjdunVO40rS2rVrjePAwED179+/wmsAAPA23pbbAAAAOOOtuQ2ddOikAwC4uXi0kJWRkWEct2zZstrxrBOqy5cvVzueteLiYj333HNKSUkxzt13331asGBBlYpYpUaNGmUcZ2Vlafny5U7nJyUlGceRkZHq1auXw7mjR482jlNTU7V+/XqHcwsKCrR06VJj3KVLl3JJVSk/Pz+NGDHCGO/evVv79+93GPvKlStKTk42xgMHDrT7xhAAAN7Om3IbAACAinhjbkMnHTrpAABuPh4tZAUGBhrH+fn51Y5XUFBgN3Z1WSwWPf/88/rss8+Mc7/73e+0cOHCahWxJKlv3742yUFSUpJ27Nhhd+5rr72mr776yhhPnTpVQUFBDmMnJCSoVatWxnju3Lk2CU+poqIizZ49W8ePHzfOJSYmOn3uiRMnGsUoi8WiGTNm6NSpU+Xm5ebmKjExUZcuXZIkBQQEaNq0aU5jAwDgrbwltwEAAHCFt+U2dNIpQScdAMDNxqO7Olr/pfrTTz9VO551DOuNLatr06ZN+uSTT4yxyWRSRkaG/vSnP7kc4+mnn1b79u3tfvbSSy9pzJgxyszMVGFhoSZNmqShQ4dqwIABCg0N1ZkjxCb5AAAgAElEQVQzZ/Tpp5/arHrq37+/hg8f7vSegYGBmjdvnsaPH6+CggJdvXpV48aN0+jRo9W7d28FBwfr559/1urVq3X06FHjunHjxlX45k2jRo00Z84cTZ8+XRaLRWlpaRo1apTGjBmjuLg4BQUF6ccff1RycrJNApeYmGhTXAMAwJd4S24DAADgCm/KbTzZSad0n63STjqTJk1yOL+ynXRK2/mVdtK577777M6tSiedd999V9J/OunEx8fbnU8nHQCAt/NoIau017DFYtGZM2e0d+9e3XnnnVWKtW/fPpsVQbfffrtbnlGSzdsuUsnzWq+McsXEiRMdfhYTE6OkpCRNmTJFmZmZMpvNSklJsUm+rPXs2VOvvvqqS4lYfHy8XnnlFc2cOVPXr19Xfn6+kpOTbRIUa8OGDdNzzz3n0s80ZMgQZWRkaP78+TKbzbp27ZpNUlXWhAkT9Oijj7oUGwAAb+QtuQ0AAIArvCW3qYlOOqXt+ZKSknTHHXeob9++5eZWpZPO8uXL9csvv0gq6aQTHR2tTp062cyraiedNWvW6Nq1a0YnneXLl+vWW2+1mUcnHQCAL/BoIat9+/aKiIhQenq6kXR8/PHHaty4caXiZGZm6vnnnzfG4eHhDlc/1Vbx8fHasGGDFixYoK1bt9osty8VERGh8ePH6+GHH65UIjZo0CC1a9dO8+bN065du2Q2m8vNiY6O1uTJkytc5VXWQw89pM6dO2vBggX65ptv7C6D79ChgxITE22W4wMA4IvIbQAAgC/xltyGTjrl0UkHAHAz8WghS5LGjBmj119/XSaTSb/88ov+8Ic/aNGiReXePnHkhx9+0MyZM5WamiqpJFn5wx/+4NZnHDlypEaOHOnWmPaEhYVp8eLFysrK0t69e5Wenq6cnBw1bdpUMTEx6tq1q0wmU5ViR0dHa+nSpcrIyNC+ffuUnp6u/Px8hYeHq23btjZ9mSurU6dO+uijj5SWlqaDBw/qwoULKi4uVlhYmGJjY9W6desqxwYAwNt4Q24DAADgKm/IbeikYx+ddAAANwuPF7LGjx+vNWvW6Ny5czKZTEpNTdWYMWPUp08fDR06VJ07dy73JsipU6f07bff6p///Kd27typ4uJi47PmzZtr/Pjxnn5sjwoJCdGAAQM8Ertp06YaPHiwR2JHRUUpKirKI7EBAPAW5DYAAMCXkNuUoJMOAAC1l8li7284Nzt+/LjGjh2ra9euSSp5c8Z65ZHJZFK9evVkMpmUm5tr85du6VyLxaJGjRpp5cqVRg9n+L7CQrMyM3OdzgkLC5EkHV/6Sk08ks9oM/EpSdLFi1luiVf6z+H7fTvcEu9m8qvuJb3X3fXPArgZhIbWV2Cg/w27P7kNqsqV3AZA5ZGL4kZwdx5/I/MbchtbnuikU8oTnXRK3YhOOu7Mbfh+BzXN3d+NuRv5DWpabc5tPL4iS5LatGmjDz74QNOnT9fJkyeNv/RLEx+LxaKcnJxy15lMJiMZiomJ0auvvur1yRAAAPB+5DYAAMCXkNvYopMOAAC1i+vroKupffv2Wrt2raZOnaqwsLByS51Lkx9rFotFt9xyi/77v/9ba9euVYcOHWrqcQEAAJwitwEAAL6E3AYAANRWNbIiq1RQUJAmT56sxx9/XHv27NGBAwd06NAhXblyxVi+3rBhQzVu3FgdO3ZU165ddeeddyogoEYfEwAAwCXkNgAAwJeQ2wAAgNrohmQa/v7++vWvf61f//rXN+L2AAAAbkVuAwAAfAm5DQAAqE1qrLUgAAAAAAAAAAAAUBkUsgAAAAAAAAAAAFAruaW1YE5OjqZNm6aCgoKSoAEBeumll9SiRYtqxT19+rSef/55mc1mSVL9+vX1xhtvqE6dOtV+ZgAAAEfIbQAAgC8htwEAAN7MLSuyli5dql27dmnfvn3at2+f7r777monQ5LUsmVL9e3bV19//bX27dunHTt26L333nPDEwMAADhGbgMAAHwJuQ0AAPBm1S5kXb16VR988IFMJpMk6Z577tEf//jH6oY1/PGPf1T//v1lsVhksVj07rvvKjs7223xAQAArJHbAAAAX0JuAwAAvF21C1kbNmxQXl6eLBaL/P399fTTT7vjuWzMmDFDAQEBMplMun79ujZu3Oj2ewAAAEjkNgAAwLeQ2wAAAG9X7ULW+vXrJUkmk0n333+/WrVqVe2HKis6OlrDhw+XxWKRJK1bt87t9wAAAJDIbQAAgG8htwEAAN6uWoWs/Px8HTp0yBgPGTKk2g/kyNChQyVJFotF33//vbFBKQAAgLuQ2wAAAF9CbgMAAHxBQHUu/uGHH1RUVCRJCg4OVo8ePdzyUPb06NFDwcHBysnJUVFRkQ4fPqwuXbp47H4AAODmQ24DbxIWFnKjHwE3qYsXs270IwBwEbkNAADwBdVakXX69GlJJcvTW7VqpYCAatXFnAoICFB0dLQxPnXqlMfuBQAAbk7kNgAAwJeQ2wAAAF9QrQzm2rVrxnFYWFi1H6Yi1ve4evWqx+8HAABuLuQ28EbHl75yox8BN4k2E5+60Y8AoJLIbQAAgC+o1oqs7Oxs47hBgwbVfpiKWN8jJyfH4/cDAAA3F3IbAADgS8htAACAL6hWISsoKMg4vnLlSrUfpiLW9/DkcngAAHBzIrcBAAC+hNwGAAD4gmoVskJDQ43jS5cuVfthKmJ9j8aNG3v8fgAA4OZCbgMAAHwJuQ0AAPAF1SpktWzZUpJksVh0/PhxZWZmuuWh7MnMzNRPP/1U7t4AAADuQm4DAAB8CbkNAADwBdUqZN1xxx3y8/OTyWRScXGxdu7c6a7nKmfnzp0qLi6WJJlMJt1xxx0euxcAALg5kdsAAABfQm4DAAB8QbUKWQ0aNFCnTp1ksVhksVj09ttvG0mLOxUXF2vp0qWSSpKhTp061cgmpQAA4OZCbgMAAHwJuQ0AAPAF1SpkSdLvfvc74/jkyZN6//33qxuynOXLl+v48eN27wkAAOBO5DYAAMCXkNsAAABvV+1C1siRI9W4cWOZTCZZLBa9+uqrWrdunTueTZKUkpKiv/71rzKZTJJKNiodOXKk2+IDAABYI7cBAAC+hNwGAAB4u2oXsurVq6fExERZLBaj5/Kzzz6rv/zlL8rLy6ty3Ly8PM2bN0/PPvussQTeZDJp+vTpqlevXnUfGwAAwC5yGwAA4EvIbQAAgLerdiFLkkaPHq2BAwfaJEUrV67UoEGDtGTJEp09e9blWOfOndOSJUs0ePBgffjhhzKbzTKZTDKZTPrtb3+rhIQEdzwyAACAQ+Q2AADAl5DbAAAAbxbgrkB//etfdeXKFe3bt89Yrp6enq6kpCQlJSUpMjJSHTt2VExMjEJCQhQSEiKTyaSsrCxdu3ZNP//8s3744QelpaVJkiwWiyQZsbp3765Fixa563EBAACcIrcBAAC+hNwGAAB4K7cVsurUqaP33ntPCxcuVHJystEbuTSxSUtLq/ANn9K50n8SIYvFoj/84Q969tlnFRQU5K7HBQAAcIrcBgAA+BJyGwAA4K3c0lq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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 568,
       "width": 857
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cat = train_df[categorical]\n",
    "\n",
    "fig, ax = plt.subplots(2, 3, figsize=(12, 8))\n",
    "\n",
    "for index, column in enumerate(df_cat.columns):\n",
    "\n",
    "    plt.subplot(2, 3, index + 1)\n",
    "    sns.countplot(x=column, data=train_df, palette=colors_cat)\n",
    "\n",
    "    plt.ylabel('Count')\n",
    "    if (column == 'HasCrCard' or column == 'IsActiveMember'):\n",
    "        plt.xticks([0, 1], ['No', 'Yes'])\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.112779,
     "end_time": "2021-08-25T08:44:33.212142",
     "exception": false,
     "start_time": "2021-08-25T08:44:33.099363",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Important points:\n",
    "\n",
    "- The bank has customers in three countries (France, Spain, and Germany). Most customers are in France.\n",
    "- There are more male customers than females,\n",
    "- Only a small percentage leaves within the first year. The count of customers in tenure years between 1 and 9 is almost the same,\n",
    "- Most of the customers have purchased 1 or 2 products, while a small portion has purchased 3 and 4,\n",
    "- A significant majority of customers has a credit card, and\n",
    "- Almost 50% of customers are not active.\n",
    "\n",
    "Again, we will look at these features in greater detail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.112328,
     "end_time": "2021-08-25T08:44:33.437924",
     "exception": false,
     "start_time": "2021-08-25T08:44:33.325596",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Geography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-25T06:37:23.042468Z",
     "start_time": "2021-08-25T06:37:22.427429Z"
    },
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2021-08-25T08:44:33.677033Z",
     "iopub.status.busy": "2021-08-25T08:44:33.675906Z",
     "iopub.status.idle": "2021-08-25T08:44:34.228944Z",
     "shell.execute_reply": "2021-08-25T08:44:34.228398Z",
     "shell.execute_reply.started": "2021-08-25T07:32:56.058727Z"
    },
    "papermill": {
     "duration": 0.678315,
     "end_time": "2021-08-25T08:44:34.229053",
     "exception": false,
     "start_time": "2021-08-25T08:44:33.550738",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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2cf7+AcZxmzbttHDhp7VapyMLFy4wjjt2vEAzZ86Wv79/hdfk5OTUdFlnraaetZ+fn3F8Js8hOzu7Wu4PAAAAAKg7aGb9n+LiYh04cEDbtm3T/v37dfjwYcXHxystLU35+fny9vZWUFCQ2rVrp969e2v48OEKDQ094/scOnRI33zzjTZs2KATJ04oKytLoaGhioqK0jXXXKPBgwdX+keN8vzxxx/65ptvtHnzZiUlJSkvL09hYWFq27athgwZooEDB1b5m6qbNm1STEyMduzYoaSkJFmtVoWFhalTp04aOnSorrzySrm5uVUpNwAAwJno1esi9e8/SP/73w+SpO++W6GbbrrNbq+khg3/2Q8qPj5OVqvVbsZTbUtLS9P+/XuN8bhxDznVyKrufbJqQk096+Dgf/IWFhYqJSVZjRpV/vt3QkLcWd8bAAAAAFC30Mz6PwsXLtSMGTPKfT07O1vZ2dmKj4/XmjVr9Oabb2rMmDGaMGGCUw2ioqIivfPOO3rvvfdktVrtXouPj1d8fLw2bNig2bNn65VXXlGfPn2crj0nJ0fTp0/X559/Xua1Y8eO6dixY1q1apXatWunV199VR07dnQ6999//61nn31W//vf/8q8dvToUR09elQrV65Uz549NX36dDVr1szp3AAAAFV1333jtXbtKhUVFam4uFjz58/W9OlvGq9feGEX4zg3N0e7dv2h6Oiu1XJvk8n+CzzOLEV48mSi3bhjx06VXrNr1+8qLi4+s+JcoKaedYcO9r+z7t69S5dffmWl1+3Zs/us7w0AAAAAqFuYSvN/Sv8Rwmw2q2XLlurRo4f69u2rzp072y11UlhYqLlz52r8+PEqKCioNP/kyZM1Z84co5FlMpnUpk0b9erVSxEREUZcQkKCxowZow0bNjhVd2FhocaNG2fXyDKbzWrfvr169uypRo0aGecPHDig22+/Xfv27XMqd1ZWlkaPHm3XyPL29lanTp3UrVs3BQUFGedjY2N1++23KzEx0VEqAACAahUZ2USDBw83xhs2/Kx9+/6Z+dShwwVq0KCBMf7qq7Jf+qkqHx9vu3F+fn6l1xQVFZ3xfVas+OaMr3GFmnrWTZo0VVhYY2N8eiZeRYqKirR69U/Vcn8AAAAAQN1BM+v/eHh4qE+fPnrqqaf01VdfaceOHfrhhx/06aefauHChfrqq6+0detWzZ07V61atTKuW79+vd59990Kc3/44Ydavny5Me7Vq5e+++47rVy5Up988onWrl2rDz/8UGFhYZJOfQh/+OGHFR8fX2ndM2bM0KZNm4zxoEGDtGrVKsXExGjx4sVav369Zs6caSxjk52drfvvv19ZWVmV5n7sscd04MABY3zbbbdp3bp1WrZsmT777DOtX79ezz33nMxmsyTp5MmTGjduXJmZZwAAADVh9Ogxxu8hkrRgwVzj2MPDQzfddJsxXrPmf1q3bk213NffP0Cenl7GOC7uWKXXlF4eb8eObRXGx8Zu0Zo1ZWfG10U1+awHDx5mHK9bt1p79uyqMH7ZsqVlZsEBAAAAAOo/mln/Z/To0Vq0aJHuvvtude7cWR4eZVdgdHd315VXXqnPP/9cLVu2NM5/9NFHysvLc5g3LS3Nrtl1wQUX6IMPPlBUVJRd3MUXX6zFixfL19dX0qlZUW+//XaFNR8+fFhLliwxxldeeaXefvttNW78zzdY3dzcNGTIEM2fP9/YuyAxMVELFiwok6+kjRs3as2af/4Icdttt+n555+3m43l6emp22+/Xa+++qpxbs+ePfr6668rzA0AAFAdwsPDNXToCGO8ceMv2rXrD2M8atQtatq0uaRTs/CnTHlG3367vNJlAbOzs7R8+Vd69NEHHb7u7u6utm3bGeOvv/6y0tlZ4eHhatKkqTF+9923lZ5ucRi7bVusnnnmCaeWL6wraupZ33jjTfL3D5B0ao/byZOf0F9/HXUYu379Ws2eXfHvzwAAAACA+ok9s6ogMDBQ48aN06RJkySd2rNq586duuiii8rELl68WJmZ/2zcPWXKlHL32GrevLnGjx+v119/XZL07bff6qGHHlLTpk0dxs+fP99YssZsNmvKlCkymUwOY7t3766bbrrJaH4tXLhQ9913n3x8fBzGz5492zhu2LChnnjiCYdxkjRkyBB9/fXX+vnnnyVJc+fO1ciRI8uNBwAAqC533XWv/vvfb41lnxcsmKuZM099kcjX10/Tp7+hcePGKDMzQwUF+ZoxY6o+/3yxrrjiarVv30GBgQ1UWFiojIwMHT16WLt379K2bVtVWFio8PCIcu87cOBg7d59qnG2ZcsmXXvtNWrbtp38/PyM38eiolrrvvvGG9fccssdeuON6ZKko0cP6667btH1149Up06d5eFhVmLiCW3YsE4//7xWNptNffteokOHDiop6WSNPLvqVFPPumHDED388ES9/PILkqTk5CTdc8/tGjZshHr2vEiBgYFKSkrSunWrjNlgAwZco59++r623joAAAAAoBbQzKqizp07242Tk5Mdxn3//T8fpLt06aIuXbo4jDtt1KhRmjVrlvLz81VcXKwff/xR9957b5m4wsJCrVq1yhj379/fbkaWI3fccYfRzMrJydHPP/+sQYMGlYlLTU1VbGysMb7hhhvKbXqVzH26mXX8+HHt2rWrzDMCAMCVjt9/iatLQA0IC2us4cOv01dffSFJ2rp1s3bu3KHo6K6SpJYtozR//iI99dREHTlyWJJ09OgRLVz4/lnd99prb9Cvv27Q5s2/SpKysjK1fftvdjElv9AkSdddd6N++22L1q5dLUlKTU3R++/PlSPt2nXQc89N1T333Obw9bqopp714MHDlJKSonnz3pEkFRTka9mypVq2bGmZ2L59L9HYsQ/QzAIAAACAcwzLDFZR6U28T+9JVdLx48f1559/GuMrrrii0rxBQUHq2rWrMV69erXDuNjYWKWnp59R7jZt2tjN8iov95o1a1RcXGyMr7zyykpz9+3bV15e/+wdUV5uAACA6nbnnffa7WFVcu8sSWratJk++GCxHnvsKbVo0bLCXCaTSW3bttM99/w/vfXW7HLjPDw89Nprb+n556fqssuuVEREE/n4+JQ7S/507ilTXtHYsQ/Iz8/PYUxAQKDuuONuzZv3oQICAiqstS6qiWctSXfeebfeeGOWmjdv4fD1wMAGGjv2Ac2YMVNubnzEAQAAAIBzDTOzqmjTpk3GsYeHh8MZV3v27LEbd+/e3anc3bt31+bNmyVJe/fudRhT+nyPHj2czh0XF+ewPke5zWZzpbPJpFP7Z3Xu3Fm//fZbhbkBAKhtycmZlQfBpcaMuV9jxtxf5esbNWqk1at/qTDGbDbruutu1HXX3aiTJxO1e/cupaX9raysTJnNngoMDFCTJs3UunUbBQY2cOq+bm5uGjDgGg0YcI3Ttbq7u+vuu8fqpptu086d23T8+DHl5+crKChY4eER6tath93erV9++a1TeTdsiK00JiIi0qm40pyt4bSaeNaSdNFFffXpp19p3769+vPP/bJY0hQQEKjIyCbq1q2HzGazpKq/TwAAAABA3UUzqwoOHz6suXP/+cbv9ddfr4YNG5aJO3jwoN24ZcuWTuUvGZeVlaXExESFh4eXm9tsNqtJkyZO5W7R4p9vsx49elRWq1Xu7u7l5g4PDy93jy9HuU83sw4dOuTUNQAAALWtceNwNW4cXnlgDfL19VXfvpeqb1+XllHjauJZd+jQUR06dKzWnAAAAACAuo1mlhNsNpuys7N1+PBhrVq1Sh999JFycnIkSd26ddOTTz7p8Lr4+Hjj2N3dXWFhYU7dLzIy0m4cFxdXpplVMndYWJjTy6mUbHoVFBQoOTm5wtwREeVvfF5R7pI5AAAAAAAAAAAAqopmVjnGjBmjDRs2lPt6cHCw7rrrLo0dO7bcmUtZWVnGsZ+fX5kZUOUpvT9CdnZ2hbkDAwOdylvTuUvuG2a1WpWXlydvb2+nr3fEw8NNQUG+Z5UDdQv/ngCqKjnZTVarTe7u7IcDwBVMcnc3nfXvMh4e/Aw7EwkJCfrmm2+0Zs0aJSQkyGKxKCQkRM2aNdOAAQM0fPhwh6tknK2kpCRt27ZNe/bs0YEDBxQXF6ekpCTl5OTIZDIpICBAzZs3V5cuXTR06FBFR0dXew0AAAAA/kEzqwr8/Px088036/rrr69wCb7Ts7ckycvLq9y40krHOmo4lcx9Jg2jmsxdOjY7O/usm1kAAAAAzk8ff/yxXn/9deXl5dmdT0xMVGJiorZu3arZs2fr+eef15AhQ6rtvmlpaerXr1+FMampqUpNTdX27du1aNEi9e7dWy+99JLTS8sDAAAAODM0s8rRuXNn47i4uFgZGRk6fPiwcnJylJ2drblz52rBggV64IEHNGHCBJlMpjI5ioqKjOOSG3lXpnRsyTyOzjk748tR7sLCwmrLXTrWUd1nqqioWBZLTuWBZyg0NKDyINSImvj3BHB+sFqL7f4fAGqXTVar7ax/lwkK8pXZ7Pzv2Oert956S3PmzLE717JlS4WFhSkxMVHHjh2TJFksFj3yyCPKzc3VjTfeWC33ttlsZc6FhoYqMjJSvr6+KigoUHx8vBITE43Xt2zZoptvvlkffPCBOnXqVC11AAAAAPgHzaxyPPLII2XOFRcXa+PGjXr77be1c+dOFRYWatasWUpPT9fkyZPLxPv4+BjH+fn5Tt+7dKyvb9mlTGo69+mlBs8kd0FBQbk1AgAAAIAzfvzxR7tGVps2bfTqq6/aNYn++OMPTZo0SYcOHZIkPffcc2rbtq26dOlSLTW0bNlSV111lfr06aNu3bo5XH796NGjmjNnjpYvXy7pn8ZaTEwMK1QAAAAA1YwF28+Am5ubLrnkEi1ZskSDBw82zn/00UfavHlzmfiSjaLSS2NUJDc3127s5+dXL3M7apQBAAAAQHkKCwv16quvGuPw8HB9+umnZWY7XXjhhfr000/VuHFjSadWhZgxY0a11NCwYUP98MMPmjRpki6//PJy9xFu2bKlZsyYoTFjxhjn/vrrL33//ffVUgcAAACAf9DMqgJ3d3dNnTpVQUFBxrnFixeXiSu5EXFOTo4x26kyycnJduPg4OAKc5eOP5PcJd9DdeYODAw8o6UVAQAAAGDFihU6fvy4MX7yySfVoEEDh7FBQUF68sknjXFsbKy2bt1a4zWW9tBDD9l9kW/Tpk21XgMAAABwrqOZVUX+/v52mwJv27atTEyrVq3sxgkJCU7lLhnn5ubmcBPhkrktFovTjbKSuUNDQx1+y7Bk7vj4eKfyls5d+r0DAAAAQGVKzmoKCwvTgAEDKowfOHCgwsLCHF5fW7y9vdW6dWtjfCZfCAQAAADgHJpZZyEyMtI4tlgsZV5v27at3XjPnj1O5S0Z16RJE4frrbdp08ZuvHfvXqdy79692zgu+YGrvNwWi8XpJpwzuQEAAADAkby8PP3666/GuF+/fpWu9uDh4aFLL73UGK9evbrG6qtIUVGRcexoKXcAQN3jYXb835jyzgMAXItm1lnIzMw0jh3NcOrcubPdchNbtmxxKm/JpTF69+7tMKb0eWdy5+fn6/fff6+R3AkJCXazuMrLDQAAAACOHDx4UAUFBca4e/fuTl1XMi4hIcHhFw1r0t9//62DBw86rAcAUHc1Dm0kPz/7/d79/HzVODTURRUBACpCM+sslGw6NW/evMzr3t7edksR/vjjj8rNza0wZ2xsrN0a8YMGDXIYFxkZabcJckxMjGw2W4W5v//+e+Xl5VWau0ePHgoJCTHG33zzTYV5JWn58uXGsdls1lVXXVXpNQAAAABwWsmGkCSHy607UjqudJ6aVFRUpOeee06FhYWSTu0/fP3119fa/QEAVefm5qZ7bx9lNLT8/Hx17+2j5OZmcnFlAABHmDdbRTExMfrzzz+NcXnNmxtvvFE//PCDpFMzuRYuXKhx48aVm/fdd981jiMjI9W3b99yY0eOHGks7Xf06FF9++23GjFihMPYgoICvffee8a4a9euZZYqPM3NzU3XX3+93n//fUnSxo0bFRsbq549ezqMT0tL0+LFi43xwIEDHc5UAwAAAIDylN6vN/ctt8gAACAASURBVCIiwqnrSi7/fjpPeZ9dqkNBQYESExO1detWLVq0SPv375ck+fr66u2331aDBg2q7V4eHm4KCvKtPBAAUCWtW7XQS5Mf1d9p6WoYHEQjq5bx3zjg/OPhUfX5VczMkrR9+3a98MIL+uuvvyqNtdls+uKLLzR58mTjXMOGDXXzzTc7jL/88svtltx79913tW7dOoexM2fOtFsj/qGHHpKnp2e5tYwaNUotWrQwxlOnTrVbRvC0oqIiPfvss3bfUJw4cWK5eSXpvvvuMxpSNptNTzzxhI4dO1YmLicnRxMnTlRKSoqkU2vWP/zwwxXmBgAAAIDSsrKy7MbOfkEuICDAbpydnV1tNUnSypUr1b59e+N/F154oQYMGKCnn35a+/fvl7u7uwYOHKjly5ez3DoA1ENubm5qFBJMIwsA6jhmZunUN+uWLFmiJUuWqGPHjurdu7fat2+v0NBQ+fn5qaCgQCkpKdq7d69++uknu6aO2WzW9OnTK/z23YsvvqhbbrlFFotFhYWFGjdunIYOHar+/fsrKChIcXFxWrZsmWJjY41rrrrqKl177bUV1m02mzVt2jTdc889KigoUHp6uu644w6NHDlSF198sfz8/HTkyBF9/vnn2rdvn3HdHXfcUemHrAYNGmjKlCl69NFHZbPZFB8frxtvvFG33HKLevToIU9PT+3fv1+LFy+2WxZx4sSJdg02AAAAAHBGTk6O3djLy8up67y9ve3G1d3MqswVV1yhW2+9tUY+BxUVFctiyak8EMA5IzQ0oPIg4BzBf+OA809QkK/MZvcqXUszq5S9e/dq7969TsU2btxY06ZN06WXXlphXFRUlN59911NmDBBFotFVqtVMTExiomJcRjfp08fvfnmm3Jzq3ziXM+ePfX6669r0qRJys3NVX5+vhYvXmy37F9Jw4YN09NPP135m5M0ZMgQpaam6pVXXpHValVGRobdUoWljR07Vvfee69TuQEAAACgpKKiIruxh4dzH1dLx5XOc7ZCQ0PtPvPl5OToxIkTOnHihCRp1apVWrVqlXr37q3XX39djRs3rtb7AwAAAKCZJUlq06aN7rvvPv3yyy/at2+frFZrhfGtW7fWddddp9tuu03+/v5O3aNnz55asWKFpk+frh9//FEFBQVlYsLDw3XPPfforrvucqqRddqgQYPUvn17TZs2TRs2bHBYf8uWLTV+/PhKZ3uVdueddyo6OlrTp0/Xtm3bZLPZysR07NhREydOVL9+/c4oNwAAAACc5uPjYzfOz88vc86RvLy8CvOcrd69eztc2eL48eP66KOPtHjxYlmtVm3ZskW33367vvzySwUFBVVrDQAAAMD5jmaWpJCQEE2cOFETJ05Ubm6uDhw4oGPHjik1NVU5OTkym80KCAhQRESEOnToUOVv2oWGhuqNN95QZmamNm/erJMnTyo7O1shISGKiopSt27dZDJVbX3eli1b6r333lNqaqq2bt2qkydPKj8/X2FhYWrbtq06depUpbyS1KVLF3366aeKj4/Xjh07lJSUpOLiYoWGhqpTp05q3bp1lXMDAAAAgCT5+fnZjXNzc6vUzCqdp6Y0a9ZMkydPVp8+ffTggw+quLhYx48f14wZM/TKK6/USg0AAADA+YJmVik+Pj6Kjo5WdHR0jd0jICBA/fv3r5HcISEhuuaaa2okd5MmTdSkSZMayQ0AAADg/BYcHGw3Tk5OVsOGDSu9LikpqcI8Ne3qq6/WjTfeqKVLl0qSYmJi9NRTTykwMLBW6wAAAADOZc6vZQcAAAAAQA1p1aqV3TghIcGp60rHlc5TG4YMGWIcFxUV6ffff6/1GgAAAIBzGc0sAAAAAIDLtW3b1m68e/dup67bs2ePcWw2m9WiRYtqrcsZERERduO0tLRarwEAAAA4l9HMAgAAAAC4XEREhJo1a2aMt27d6tR1W7ZsMY579Oghd3f3aq+tMpmZmXZjlhgEAAAAqhfNLAAAAABAnTBgwADjeMuWLZUuNZiQkGDX9Bo4cGCN1VaR0o235s2bu6QOAAAA4FxFMwsAAAAAUCfccMMNcnM79TG1uLhYs2fPrjD+3XffVXFxsSTJ19dXgwcPrvEaS0tLS9OHH35ojKOiohQVFVXrdQAAAADnMg9XFwAAAFCTQkMDXF1CvZCcnFl5UD328ssv6LvvVkiSBg8epsmTX3BtQagWI0cOV2LiCUnS008/ryFDhru4Ipyttm3basSIEVq+fLkkaenSpYqOjtaoUaPKxH722Wf68ssvjfGYMWPUsGFDh3k3b96su+66yxg/+OCD+te//uUwduLEiRo+fLj69etX6ZKF+/fv12OPPabk5GTj3Lhx4yq8BgAAAMCZo5kFAACAOiklJUVbtmxUbOwWHTp0UOnpFmVkpMvd3V0BAYFq3LixOnTopG7deqhPn4vl6enp6pIBVIMnnnhCsbGxiouLkyQ988wzWr16tYYOHaqwsDCdPHlSK1eu1Jo1a4xrLrzwQo0ZM6Za7r99+3atWLFCwcHB6tevny644AI1a9ZMgYGBcnNzU0ZGhg4dOqSNGzdq06ZNslqtxrUjRozQtddeWy11AAAAAPgHzSwAAHBe2DKSPy460vvLb1xdQhmJiSe0aNEC/fe/39r9kbik3NxcJSWd1B9//K6lS5fI3z9AgwcP01133aPgYMczMwDUDyEhIXrvvfc0duxYY8+s1atXa/Xq1Q7j27dvr3nz5snHx6da60hLS1NMTIxiYmIqjXV3d9fo0aP1+OOPV2sNAAAAAE5hzywAAADUGatW/aTbbx+pb79dbtfI8vT0VPPmLdSlS1dFR3dT06bN5enpZbyelZWppUuX6KabrlN8fJwrSgdQjVq3bq1vv/1Wt912m3x9fR3GBAUFady4cfryyy8VEhJSbfceP368rrjiCgUEVL5Mrb+/v0aOHKlly5Zp0qRJxn5fAAAAAKoXM7MAAABQJ3zyyULNm/eubDabce6SS/rpxhtvVnR0V3l5edvF5+fna/v237R69U/64Yf/ymq1Kjc3Rzk52bVdOoAa4O/vr+eff16TJk3S5s2blZCQoIyMDAUHB6tZs2bq1auXPDyc+0h70UUXaf/+/U7Fjhw5UiNHjpTNZtPRo0d15MgRnThxQllZWbLZbPLz81NwcLDatWun1q1bV7qvFgAAAICzRzMLAAAALrdhwzq7Rpa/f4Beemm6evW6qNxrvLy81KfPxerT52KNHj1G77zzltavX1tLFQOoLd7e3rr88str/b4mk0lRUVGKioqq9XsDAAAAsEczCwAAAC6VkpKsqVOfNxpZPj6+mj17vlq1auN0jiZNmuqVV17XkiWfyN2dX3EBAAAAADiX8EkfAAAALrVkySfKysoyxg8//OgZNbJKuvXWO84o/uDBP7VixXLFxm5VcvJJWa1WNWoUqq5du+uGG0apXbsOFV5/4kSCRo0aYYyXLo1RRERkpfcdOXK4EhNPSJKefvp5DRky3OncGRkZ+umn77Ru3RrFx8cpLe1vFRQU2OUpL//Gjb/ou+9WaP/+vUpOTpanp6ciIiLUu3df3XTTrQoJaVRp7SUVFRVp3brV+vXXDdq7d7fS0tKUl5erBg2C1KJFS110UV8NG3atAgMbnFHeXbt+14oV32j79t+UkpIsb29vhYY2NvI1a9b8jPIBAAAAAOo3mlkAAABwmczMTMXEfG2MmzZtrqFDr63x+1qtVi1YME+ffLJQxcXFdq/FxR1XXNxxrVwZo3vu+X+69977arweZ23duklTp76g1NSUM7ouPd2iadOm6Jdf1tudLyjI159/ZurPPw9o2bKlevHFaerb91Kncm7evFEzZ76muLhjZV5LSUlWSkqyfvttqz766EM9+OC/NWxY5f+uRUVFmjnzVcXEfG23d1p+fr7S09N18OABLV26RBMm/Fs33niTU3UCAAAAAOo/mlkAAABwma1bNys3N8cYX3vtDTKZTDV+35kzX9Xy5V9JOrWsYVRUK3l5eenEiQRjRpPNZtMHH7ynkJBGuvbaG2q8psrs2bNLU6c+r8LCQklS06bNFBoapuzsbB0//le51+Xl5emRRx7UgQP7JEkhISGKjGwqk8mkI0cOKzMzQ5KUm5ujp59+XAsWfFzpzLivv/5Sb731mqxWq3EuICBQzZu3kJeXl1JSknXs2KmasrIyNX36S0pNTdHo0WPKzWm1WvX8809p3bo1dudPv8+srEwdPnxIBQUFmjnzVZnN5gprBAAAAACcO2hmAQAAwGW2b//Nbtyr10U1fs+NGzfIYrGoQYMGevDBR9S//yC7xkhs7BZNmfKM0tL+liTNmfMfDRw4WD4+PjVeW0VmzHhZhYWF6tfvCk2Y8LCaNm1mvJabm6ucnGyH133wwTxZLBa1adNODz88Ud269TBes1qt+vrrpZo1a6asVqsKCwv1n/+8qbfeml1uHRs3/qI335xhzJzq2PEC3X//g+rRo5ddIzI+Pk7vvDNT69evkyS9//5cdezYSb1793GY94svltg1si64oLMef/xptW3bzjiXlva35s59RytXxuitt16Xu7t7RY8MAAAAAHCOcHN1AQAAADh/7du32zj29vZWVFSrGr+nxWKRv3+A5sxZoMGDh5WZ4dOzZ29NnfqqMc7KytK6datrvK7K5ORka8iQ4Zo27TW7RpYk+fj4lLvflcViUceOF2j27PftGlmS5O7urpEjb9E99/w/49xvv201ZqeVrSFHL7/8gtHI6tfvcs2Z84F69uxdZkZdkyZNNW3a67rmmqGSTs10mzXrTbvlA09LS0vTggVzjfEFF3TWrFlz7RpZkhQc3FBPPfWcbrrpVhUU5NvN6gMAAAAAnLtoZgEAAMBl0tLSjONGjUJrbabN+PEPqXnzluW+Hh3dVZ07dzHGO3fuqIWqKhYUFKx///vxM16G0c3NTc8++6J8fX3LjRk16hZ5enpJOtV0+v33nQ7jVqxYLovl1L9ZSEiInn32RXl4lL/Yg8lk0qOPPqGAgEBJ0pEjh7VtW2yZuO+++1Z5eXmSTjXYnnzyWXl5eZeb94EH/qXIyCblvg4AAAAAOLfQzAIAAIDLZGRkGMf+/gG1ck8fH19jtlBFunbtbhwfPXqoJktyyoAB11TYkCpPjx69KmzcSZKfn7/dLKgjRxy/3//+d4VxPGLEDfL19av0/r6+frrssiuMcWzsljIxJZcX7Nath1q1al1hTk9PzzqxjxkAAAAAoHbQzAIAAIDLFBTkG8ell/urKR06dJSnp2elcaGhYcZxZmZWTZbklK5du1XpugsvjHYqruT7zcoq+34zMzN1+PBBY9yzZ2+na2jduq1xvG/fHrvXCgsL9eef+41xnz4XO5Wzb99Lnb4/AAAAAKB+K39NEAAAAKCGBQQEKi3tb0lSdnbtNIwaNgxxKs7Hx8c4zs/Pq6lynFbVZfWq6/0ePnxQxcXFxnj+/DnG0oSVSUlJMo5PL1N42smTiSooKDDGrVu3cSpn8+Yt5OHhoaKiIqfiAQAAAAD1F80sAAAAuExg4D/NrJJLDtYkZ2ZllWaz2WqgkjPjzJJ+jlTX+01PT7cb79ixrUr1ZGVl240zM+3/3QMDg5zK4+HhIX9/f1kslirVAQAAAACoP1hmEAAAAC7TpElT4zglJVnp6TQmyuPm5tpf3fPycqslj81WbDcuLCy0G5vNzn/fzmw+80YdAAAAAKD+YWYWAAAAXKZr1+769dcNxnj37l26+OJzfy+kksv11Rf+/gF24//+d7UCAwPPOq+fn7/dOCcnx+lrs7OzKw8CAAAAANR7zMwCAACAy3Tv3stu/NNP37uokqoxm812Y2f3byq9tF59UHrvrbi4Y9WUt6HdOCEhwanr0tMtysmhmQUAAAAA5wOaWQAAAHCZDh06qkOHC4zx2rWrlJR00oUVnZnS+1g5s+9XQkK8cnOrZ8m+2tSmTVv5+Pga4y1bNlVL3uDghgoLa2yM9+zZ5dR1u3c7FwcAAAAAqP9oZgEAAMCl7rhjtHFcWFioadOmyGazVSlXRkZ6re675evrq8DABsb40KE/K71m3bo1NVlSjfHw8FDPnv/MpIuJ+VoFBQXVkrtr1+7G8dq1q5ya4fbjj99Vy70BAAAAAHUfzSwAAAC41OWXX6WLL+5njGNjt+i116bJarWeUZ69e3fr3nvvqPWZXR06dDSOV636qcJYi8Wizz77uKZLqjG33nqncZyUdFJz5syqlrxDhgw3jlNSkrV06WcVxu/fv0+rV1f8rAEAAAAA5w6aWQAAAHApk8mkZ599UZGRTYxzMTFf69//Hq8DB/ZVev2JEwmaNm2KHnjgXiUmnqjJUh264oqrjePfftui779f6TAuLe1vPfHEv5WamlpbpVW7Ll266uqrBxrjpUuXaObMV5WXl1fhdUVFRfrll/V66KEHHP4b9ejRSxde2MUYz5v3jtatW+0w17Fjf+mppyaquLi4iu8CAAAAAFDfeLi6AAAAACAgIECzZs3TU09N1IED+yVJ27f/pjFj7lSnTheqV6+L1LJllIKCgiVJf/+dqri449q06Vft3bvbpY2NAQOu0aJFC3TyZKIk6eWXX9DWrZt02WVXKigoWOnp6dq5c7tWrvxGWVlZ6tmzt44d+6te7Q1W0lNPPafjx48ZjcavvvpCq1b9qP79B6lz5y5q2DBEJpNJWVmZiouL0/79e7V580ZlZp7aT8zREpImk0mTJj2rsWPvVF5enoqKijR58hPq1+8KXXnl1QoLa6zMzAz99ttWffvtcuXn5ys6uptOnEiot88RAAAAAOA8mlkAAOC80PvLb1xdAirRuHG4Zs9eoFmz3tSKFd/IarXKZrNp167ftWvX75Ve7+Pjo5tvvl3Nm7eohWrt7/vssy/qscceUl5enmw2m3744Tv98EPZPZ3ateugF1+crnvvvb1Wa6xO3t7eeued9zRt2gtau/bU7CmLxaIvv/xcX375eZXztmwZpRkzZmrSpEeMmV7r16/V+vVry8RGRjbRCy+8rHHjxlT5fgAAAACA+oNlBgEAAFBneHt76/HHn9ann36l664bqcaNwyuMN5lMateuvSZM+Le++CJGY8c+IC8v71qq9h9du3bXO++8p44dL3D4uq+vn2655Q7NnfuBAgMDa7m66ufr66upU1/V66//R9269ZC7u3uF8RERkbruupGaPft9RURElhvXo0cvffDBYvXqdZFMJlOZ181mswYNGqIFCz5RaGjYWb8PAAAAAED9YLI5WucDqCMKC62yWHKqPW9oaIAkqdm8X6o9Nxw7fv8lkqTk5EwXVwKgvkpM/EuSFB5eu7Nu4HrHjv2lI0cOyWKxKCMjXe7u7goICFR4eIQ6dLhAAQEBri7RzuHDB7V79y5ZLGny9fVV48YR6tGjl3x8fFxdWo3JysrSH3/sUFJSkjIy0iWZ5O/vr/DwCEVFtVJ4eMQZ50xMPKEdO7YpJSVZXl7eCgsLU7duPRQY2KD634BT9VTPz6CgIF+ZzRU3/4DTaurzEIC66/TfK/7Yus7FlQA158Jel0vib0TA+ehsPg+xzCAAAADqtObNW9T60oFno1WrNmrVqo2ry6hV/v7+6tv30mrNGR4eoWuuGVqtOQEAAAAA9RPLDAIAAAAAAAAAAKDOYmYWAAAAAJxn8vPztW3bNm3btk1xcXGyWCzKyTm1nN2iRYtcXB0AAAAA2KOZBQAAAADniYyMDM2fP19ffPGFMjIy7F6z2WwymUwOr1uxYoWmT58uSQoKCtI333wjd3f2/gIAAABQO1hmEAAAAADOA1u3btWIESP0/vvvKz09XTabzelr+/fvr6KiIqWkpOjQoUNau3ZtzRUKAAAAAKXQzAIAAACAc1xsbKzGjh2rkydP2p13d3dXUFBQpY0tb29vDRs2zBj/8MMPNVInAAAAADhCMwsAAAAAzmFpaWmaMGGC8vPzJZ1aTnDgwIH65JNPtH37di1dutSpPP379zeu//XXX2usXgAAAAAojT2zAAAAAOAc9u677yo9PV2SZDKZ9NJLL2nkyJHG6+Xtk1Va9+7d5eHhoaKiIqWmpiouLk5NmzatkZoBAAAAoCRmZgEAAADAOaqoqEjLly+XyWSSyWTSmDFj7BpZZ8LT01NRUVHG+NChQ9VVJgAAAABUiGYWAAAAAJyjduzYoaysLNlsNpnNZt1///1nlS8iIsI4PnHixNmWBwAAAABOoZkFAAAAAOeov/76S9KppQSjo6Pl7+9/VvkCAgKM46ysrLPKBQAAAADOopkFAAAAABWw2WyuLqHK/v77b+M4LCzsrPMVFxc7PAYAAACAmkQzCwAA1BMmSfX7j8oA6qvTP3dMLq2iKjw9PY3jwsLCs86XlpZmHAcFBZ11PgAAAABwBs0sAABQL5hMp35tKS62urgSAOeb0zOQ3Nzq38enhg0bGsdxcXFnnW/v3r0OcwMAAABATap/n8YAAMB5ycPDLEkqLMx3cSUAzjcFBad+7ri7m11cyZlr1aqVpFOzWg8cOCCLxVLlXLGxsUpPTzfG0dHRZ10fAAAAADiDZhYAAKgXvLx8JEl5ebkurgTA+SY//9TPndM/h+qTTp06KSQkRCaTSVarVR9//HGVc82ZM0eSZDKZ1KpVK4WGhlZXmQAAAABQIZpZAACgXvDy8pZ06o/KVmuRi6sBcL6wWouMZpa3t7eLq6maoUOHymazyWaz6b333tP27dvPOMe8efP0yy+/GOObbrqpOksEAAAAgArRzAIAAPWCh4dZZrOXbLZipaUlG3vYAEBNKS4+9fPGZiuW2exVL5cZlKRx48bJ399fJpNJhYWFuvfee/XVV185da3FYtFzzz2nt956SyaTSZIUEhKiW265pSZLBgAAAAA7Hq4uAAAAwBkmk0nBwaFKTT2poqICpaQkyMfHXz4+vnJz85DJZDL+0AoAVXF69lJxcZFyc3OUm5ul4mKr3N3NCg4Orbc/Y4KDg/XCCy/o8ccfl8lkUm5urp555hnNnTtX/fv3L7NcYGxsrI4cOaJNmzZp7dq1ysnJkc1mkyR5eHjotddek5eXlyveCgAAAIDzFM0sAABQb7i5uSs4OFRpacmyWguVnZ2u7Ox0V5cF4Bx2upHl5ubu6lLOyrBhw5SSkqIZM2bIZDLJZrPp+PHjWrhwoV2czWbTnXfeaTc+3cRzc3PT5MmT1bdv39osHQAAAABoZgEAgPrFw8OsRo0iVFiYr9zcbOXn56m42CrJ5urSAJwTTHJzc5eXl7d8fPxkNnvV2xlZpd19991q06aNJk2apNTUVLv3VfL49Cys0zNebTabgoKC9Nprr6lfv361XjcAAAAA0MwCAAD1jslkkqentzw9vV1dCgDUK5deeql++uknLV26VEuWLNHRo0fLjbXZbAoPD9fNN9+sO++8U/7+/rVXKAAAAACUQDMLAAAAAM4jvr6+Gj16tEaPHq2TJ09q27ZtOnnypCwWi4qKitSgQQM1atRI0dHRatWqlavLBQAAAACaWQAAAABwvmrcuLEGDx7s6jIAAAAAoEJuri4AAAAAAAAAAAAAKA8zswAAAADgHLZ161bjODo6Wp6enlXKU1BQoJ07dxrjXr16nXVtAAAAAOAMmlkAAAAAcA678847ZTKZJEmrVq1SZGRklfIkJycbuUwmk/bs2VOdZQIAAABAuVhmEAAAAADOcTabrVpzVWc+AAAAAKgMzSwAAAAAOMednpkFAAAAAPURzSwAAAAAOMdVx0yqkjlojgEAAACoTTSzAAAAAACVysrKMo59fHxcWAkAAACA8w3NLAAAAABApXbt2iXp1Kys4OBgF1cDAAAA4Hzi4eoCAAAAAAC1oyrLA9psNv3222+aPXu2ca5t27bVWRYAAAAAVIhmFgAAAADUc1dffbVTcbfeeqvc3d2dzltYWKi0tDQVFRXZnb/sssvOqD4AAAAAOBs0swAAAACgnouPj5fJZJLNZis3xmazKTExscr3OD2rKyIiQiNGjKhyHgAAAAA4U+yZBQAAAAColM1mU5s2bTRv3jz5+fm5uhwAAAAA5xFmZgEAAABAPderV69yX9u6daukUzOrunTpIk9PT6dymkwmeXp6KiAgQFFRUerVq5f69u1bLfUCAAAAwJmgmQUAAAAA9dzHH39c7msdOnQwlgicOXOmIiMja6ssAAAAAKgWLDMIAAAAAOe4ivbSAgAAAIC6jplZAAAAAHAOe+WVV4zj4OBgF1YCAAAAAFVDMwsAAAAAzmHXX3+9q0sAAAAAgLPCMoMAAAAAAAAAAPx/9u48rMo6///467CKgAIOpAi5orlk5pZl5mimpqXm0lRa32zzm9MvK8tlHDWbcs/JGa3RyjSzqXTMrUnN9MqcSiV1MtHcQhEEXEA2Wc/9+4MvdxxkOcA5HJbn47q4rs/n5nO/7zd6ceXd+3zeHwDVFsUsAAAAAAAAAAAAVFsUswAAAAAAAAAAAFBtcWYWAAAAANQhKSkp2rVrl/773//qzJkzSklJUVpamqxWq90xLBaLdu7c6cQsAQAAAOA3FLMAAAAAoA7IyMjQm2++qQ0bNigzM9O8bhhGuWNZLBZHpgYAAAAApaKYBQAAAAC1XFxcnMaNG6dz587JMAxZLBabIlZBcargWnHFqooUvQAAAADAEShmAQAAAEAtlpWVpQkTJujs2bPmNcMwFBYWpoCAAP38889mgat79+5KT0/XpUuXlJiYKOm3wlZgYKBat27tkp8BAAAAQN1GMQsAAAAAarFPPvlEx48fN4tSt9xyi1577TW1adNG58+fV//+/c21a9asMcfx8fHavHmzVq9ercuXL+vq1avq1KmTXnnllSr/GQAAAADUbW6uTgAAAAAA4DyrV682C1kRERFatWqV2rRpI6n0s68aN26sZ555Rlu3btVtt90mq9WqlStX6tVXX62K2CnbnAAAIABJREFUtAEAAADARDELAAAAAGqpmJgYxcXFmeddTZkyRfXq1StXjMDAQC1fvlzt2rWTYRj69NNPtXPnTmekCwAAAADFcnox6+6779bdd9+t/v37KyEhocJxEhISbGIBAAAAAEp35MgRcxwYGKhevXpVKE69evU0ffp0c75y5cpK5wYAAAAA9nL6mVmxsbGS8ttX5OXlVThObm6uTSwAAAAAQOmSkpIk5b9DtWvX7rrvF323ysrKkre3d7GxunXrptDQUMXFxenQoUOKjY1V06ZNHZ80AAAAABRBm0EAAAAAqKVSU1PNcUBAwHXfL9pyMD09vdR4bdu2NcdRUVGVzA4AAAAA7EMxCwAAAABqqbLOx/Lz87OZJyYm2r2+rLUAAAAA4Cg1ppiVnZ1tjr28vFyYCQAAAADUDIV3Y6WkpFz3fS8vLzVs2NCcnzx5stR4ly9fNsdl7eICAAAAAEepMcWsc+fOmeOinx4EAAAAAFyvZcuW5vjs2bPFrmnTpo05/u6770qMlZ6ersOHD5vzBg0aOCBDAAAAAChbjShmGYahdevWSco/oLhZs2YuzggAAAAAqr+IiAh5eHjIMAydP39eaWlp16257bbbJOW/d3355ZclFr3+/ve/KyMjw5wXPj8LAAAAAJzJwxFBli5date61atXy9/f3+64OTk5unTpkiIjI212ZnXp0qXcOQIAAABAXePj46NOnTrp4MGDMgxDe/fu1aBBg2zW3H///Vq2bJkkKTMzU4899pgmT56sXr16yc/PT9HR0frggw+0YcMGWSwWGYah3/3ud+rUqZMrfiQAAAAAdZDDilkWi6XUNYZh6MMPP6xQfMMwzPju7u564IEHKhQHAAAAAOqavn376uDBg5Kk7du3X1fMatasmUaMGKF//etfslgsSkhI0Msvv3xdHMMwJOV3yxg/frzc3d2dnzwAAAAAqIa0GSz49J/FYtHLL7+sVq1auTolAAAAAKgRhgwZIim/GPXVV18pPj7+ujVTp05Vu3btzPcuwzBsviSZHzAcOHCgxo4dW3U/AAAAAIA6zyE7s6TfPqVX2TXFadKkibp166YxY8aoc+fOFYoBAAAAAHVRaGiooqKizPex4nZU+fv7a/Xq1Zo7d642btx43fcNw5CPj4/GjRun5557zuk5AwAAAEBhDilmff3118VeNwxD/fv3Nz/B99FHH6lx48Z2xbRYLPLy8pK/v7+8vb0dkSYAAAAA1ElubmU35WjQoIHmzp2riRMn6ptvvtHZs2eVlpYmf39/tWnTRnfddZcCAwOrIFsAAAAAsOWQYlbTpk1L/X5Bq4omTZooNDTUEY90iqysLB08eFA//PCDoqKidPr0aSUlJSknJ0f+/v4KDQ1V586dde+996pbt24Vesbp06e1adMm7d27VxcuXFBaWpqCg4PVokULDRo0SPfee6/8/PwqFPvIkSPatGmT9u3bp8TERGVmZiokJEQREREaPHiwBgwYIC8vrwrF/uGHH7R582YdPnxYiYmJysvLU0hIiDp06KAhQ4aob9++dr0gAwAAAKjeGjdurD/84Q+uTgMAAAAATBajor3/7PToo4+a47feekuNGjVy5uMq5NKlS5ozZ452796tjIwMu+7p3Lmz5syZY/f5Xbm5uVq6dKlWrFihvLy8EteFhoZq7ty56tmzp11xJSkjI0Pz5s3Tp59+Wuq6Nm3aaMGCBWrXrp3dsa9cuaIZM2Zo586dpa7r1q2b5s2bp/DwcLtj2yMnJ0/Jyfb9nZRHcLC/JCl8+X8cHhvFixnfS5J08WKqizMBAABwnYCA+vL0vL7NH1AcZ70PAai+Cv5/xZED37g4E8B5bu7eRxL/jwioiyrzPuT0YlZNcOTIEY0aNeq6640bN9YNN9ygevXqKSEhQdHR0Tbfr1+/vt599127dmlNmTLFpve8xWJRq1atFBgYqPPnz+vChQvm9zw8PLR8+XLdeeedZcbNycnRU089pR9++MG85unpqZYtW8rf31/R0dG6dOmS+T1fX199/PHHuummm8qMnZaWpocfflgnTpwwr9WrV0+tWrWSl5eXfv31VyUnJ5vfu+GGG/TZZ5/Z3UrSHhSzag+KWQAAAFVfzNq6datee+01SfnvIJ9//nm17pYBWxSzgLqHYhbqAopZQN1Vmfchh7QZrE26dOmiESNGqHfv3tcVZWJiYvTWW29p69atkvJ3RP3xj3/Ul19+qaCgoBJjfvDBBzaFrO7du+svf/mLWrRoYV777rvvNGXKFCUmJio3N1cTJ07U5s2by2zhOH/+fJtC1sCBAzV9+nTdcMMNkiSr1apt27ZpxowZSktLU3p6usaPH68vvviizHaGL7/8sk0h65FHHtHEiRMVEBAgScrOzta6des0d+5c5eTkKCEhQc8++6zWr19f7KHSAAAAAKpWbGysUlJSJEnt27enkAUAAACgRuKQI+Ufhty/f39t3LhR//znPzV69OhidxeFh4frzTff1Lhx48xrycnJWrFiRYmxk5KStGzZMnPevn17rVy50qaQJUl33HGH1q5dq/r160vK3xW1ZMmSUvM+c+aM/vnPf5rzvn37asmSJWYhq+BnGzx4sN59912zwBQfH6/333+/1Njff/+9du/ebc4feeQRzZo1yyxkSZKXl5fGjBmjBQsWmNeioqL0+eeflxobAAAAQNXw8fGRlL8ry9EtwQEAAACgqlDMktShQwctW7bM7rOkXnrpJZti1/bt20tcu3btWqWm/rZldvbs2fLy8ip27Y033qgJEyaY8y1btuj8+fMlxn733XeVm5srKb+14OzZs2WxWIpd26VLFz344IPmfNWqVbp27VqJsd9++21zHBQUpMmTJ5e4dvDgwbrrrrvM+T/+8Y8S1wIAAACoOsHBweaYDvMAAAAAaqoqbzN4+vRpHThwQMePH9eVK1eUnp6unJyccsWwWCxavXq1kzIsm5eXl+666y599tlnkqS4uDhdu3bN/NRjYdu2bTPHnTp1UqdOnUqNPXr0aP39739XVlaWrFarduzYoSeeeOK6dTk5Ofr666/Nef/+/W12ZBVn7Nix5k6ujIwM7dmzRwMHDrxu3eXLlxUZGWnOR4wYUezPVjT2nj17JOW3Y/z555/VsWPHUu8BAAAA4FytW7c2x7GxsS7MBAAAAAAqrsqKWT/++KMWLFign376qVJxDMMocfdRVSrcbk/KbwtYtOATExOjkydPmvPf//73dsXt3Lmz9u3bJ0natWtXscWsyMhIXb16tVyxW7durbCwMHO3165du4otZu3evVtWq9Wc9+3bt8zYt99+u7y9vZWVlWXGppgFAAAAuFZERIQiIiJ08uRJHTt2TAkJCWV+CA4AAAAAqpsqKWatWrVKCxYskGEYZmuL6lCQqozCn2p0c3NTYGDgdWuioqJs5l26dLErdpcuXcxi1rFjx4pdU/R6165d7Y5dUMwqml9xsT09PcvcTSbl71br2LGjfvzxx1JjAwAAAKhajz/+uKZPny7DMPTXv/5V8+bNc3VKdouLi9OmTZu0e/duxcXFKTk5WY0aNVJ4eLjuuece3X///QoKCnLKs0+ePKl9+/bp0KFDOnnypOLj45Weni4fHx8FBQWpQ4cOuuOOO3TfffeV2ckCAAAAQOU4vZj1zTffmC9LFotFFovFLGr5+PjI399fHh5V3u2wUjIzM82WepJ08803F/sznDp1ymbevHlzu+IXXpeWlqb4+HibM7qKxvb09FTTpk3tit2sWTNzHB0drby8PLm7u5cYu3HjxiWe8VVc7IJi1unTp+26BwAAAIBzjRw5Unv27NH27du1adMm3XDDDXrhhReq/QcM16xZo0WLFikzM9Pmenx8vOLj43XgwAG9/fbbmjVrlgYPHuyw57777rtat26dzp49W+z3U1NTlZqaqrNnz+rf//63Fi5cqMmTJ2vUqFEOywEAAACALadXkebPny9JZhErPDxcTz/9tPr06VNj21t8+OGHSk1NNefDhg0rdl3h3Vvu7u4KCQmxK35oaKjN/Pz589cVswrHDgkJkZubm12xCxe9srOzdfHixVJjN2nSxK64RWPTjx8AAACoPhYuXKjAwEB98sknWrFihb799ls9/vjj+v3vf68GDRq4Or3rvPXWW3rnnXdsrjVv3lwhISGKj4/XuXPnJEnJycl68cUXde3aNY0cOdIhz960adN1hSxfX1+FhYUpMDBQ6enpOnnypFlku3r1qqZPn64TJ07oT3/6k0NyAAAAAGDLqcWsU6dO6cyZM+Yn/nr27Knly5fL29vbmY91qhMnTmjp0qXm/MYbb9To0aOLXZuWlmaOfX19r9sBVRJ/f3+beXp6eqmxy/Py6czYfn5+5jgvL0+ZmZmqV6+e3fcXx8PDTQEB9SsVA9ULf58AAKAu8/Cw70NojvTYY4+Z4/r16ysjI0NRUVGaMmWKpPwPsAUFBal+ffv/nWaxWLR69WqH5ypJO3bssClktW7dWgsWLFCHDh3Ma0eOHNGUKVPMjhAzZ85URESEXS3S7RUSEqLhw4drwIABat++vc37XFZWltavX69FixYpIyNDkrR69Wq1adOGHVoAAACAEzi1mPXTTz9JkgzDkIeHh+bPn1+jC1lJSUl67rnnlJWVJSl/t9XcuXNLbMNX8FIjqVw/d9G1xRWcCscuT8HImbGLrk1PT690MQsAAABA5ezfv9+mpWDBuOA847i4OF24cMHueIZhOK1FYU5OjhYsWGDOGzdurI8//lgNGza0WXfzzTfr448/1tChQ5WQkKDc3FzNnz9fa9eurXQOzZs315NPPqmhQ4eW+IFEb29vjRkzRjfffLPGjh1rviMuXrxYw4cPr3Gt9AEAAIDqzqn/wr5y5Yqk/JelW265pca2FZTyz8maMGGCTbuJF154Qd26dSvxntzcXHNcnpeZomsLxynumr07voqLnZOT47DYRdcWl3d55eZalZycUfbCcgoO9i97EZzCGX+fAAAANUVAQH15etr/b2xHKlqEqo5nZm3dulUxMTHmfOrUqdcVsgoEBARo6tSpevHFFyVJkZGROnDggLp3716pHAp34ihLp06d9Oijj+q9996TJF2+fFmRkZHq2bNnpXIAAAAAYMupxazCu4DKc/ZSdZOdna3nnntOBw8eNK+NHTtWzzzzTKn3+fj4mOOCT+rZo+ja4tp9ODt2QavB8sTOzs4uMUcAAAAArlH0TN7qbNu2beY4JCRE99xzT6nrBwwYoJCQECUmJpr3V7aYVV533323WcySpNOnT1PMAgAAABzMqcWswgWsa9euOfNRTpOTk6OJEyfq22+/Na89+OCD+vOf/1zmvYULRQWHA9uj6J+Vr69vlccuKGZVJnZ5eu4DAAAAcI5du3a5OgW7ZGZm6rvvvjPnvXv3LrPDhYeHh+68805t2LBBUv7POmPGDKfmWVRAQIDNvLhW7gAAAAAqx6mnD99yyy3my0fBwbw1SW5uriZNmmTz8jdy5Ei99tprdrXkCAoKMscZGRlmgagsFy9etJkHBgaWGrvo+vLELvri5ajYDRo0oE88AAAAALudOnXKpttDly5d7Lqv8Lq4uDglJyc7PLfSxMbG2swLv08BAAAAcAynFrOCg4PVu3dvGYahs2fP6vjx4858nEPl5eXp5Zdf1vbt281rI0aM0Ouvv253b/mWLVvazOPi4uy6r/A6Nzc3NW/evNTYycnJdhfKCscODg5WgwYNSo1d9MXM3thFf3YAAAAAKM2pU6ds5sW9BxWn6LqicZyt8DujJN16661V+nwAAACgLnD61pnJkydr3759unbtmt544w2tWrVK7u6uOfDYXnl5eZo8ebK+/PJL89oDDzygN954Q25u9tf/IiIibOZRUVFq06ZNmfdFRUWZ46ZNm6pevXrXrWndurXN/NixY3b1hj969Kg5btWqVbFrCsdOTk5WXFycXX327YkNAAAAAMUp+kE6e89dLvquEhsbq27dujksr9KcP39emzdvNucdOnRw6LuQh4ebAgJo3w4AqJ34bxxQ93h4VHx/lVN3ZklSixYtNGfOHLm7uysyMlIvvvii3buIXKGgkLV161bz2vDhwzVnzpxyFbIkqWPHjjbnRu3fv9+u+w4cOGCOe/ToUeyaotftiZ2VlaWffvrJKbHj4uJsXj5Lig0AAAAAxSn6nlhcF4ni+Pv728yr6syqvLw8TZs2TVlZWea1V155pUqeDQAAANQ1VXKo0aBBgxQUFKTnn39eX331lQ4ePKiHHnpId955p1q1anXdy4erWK1WTZ061aaQNWzYMM2dO7fchSxJqlevnnr37m22ndixY4dmzJghHx+fEu+JjIxUTEyMOR84cGCx60JDQ9WhQwdzN9TmzZs1YcKEUlsgbtu2TZmZmWXG7tq1qxo1aqTLly9LkjZt2qThw4eXGFeSNm7caI49PT3Vr1+/UtcDAAAAQGEZGRk2c29vb7vuK9rJoqqKWQsWLLD54N8f/vAH3X777Q59Rm6uVcnJGWUvBFBrBAdXj/9HBlQF/hsH1D0BAfXl6Vmxzn1OL2a1a9fOZm4Yhi5duqRly5Zp2bJlFYppsVhsWvE5gtVq1Z/+9CebFhFDhw7VvHnzKlTIKjBy5EizmJWamqpVq1bp2WefLXF94T+T0NDQUl+GRo0aZRazoqOjtWXLFg0dOrTYtdnZ2VqxYoU579y583WtCgu4ubnpgQce0HvvvSdJ+v777xUZGVliq46kpCStXbvWnA8YMMDuT1ECAAAAgCTl5ubazD087HtdLbquaBxn+PDDD7Vq1Spz3q5dO02fPt3pzwUAAADqKqe3GTQMw/yS8gtRFovF5npFvhyd48yZM/X555+b1+6//37Nnz+/UoUsSerTp49Ny71ly5bpm2++KXbtX//6V3333Xfm/Pnnn5eXl1eJsUePHq1mzZqZ89dff92mjWCB3NxczZgxw+Yg5EmTJpWa9zPPPGMWpAzD0OTJk3Xu3Lnr1mVkZGjSpEm6dOmSpPwXyYkTJ5YaGwAAAACKKtrBonD7vtIU7j5RXBxH+/zzzzVnzhxz3rx5c61YscLunWQAAAAAyq9K2gwWLl5VR19++aXWrVtnzi0Wiy5fvqynn37a7hivvPKKbrrppmK/99prr+mhhx5ScnKycnJy9Oyzz2rIkCHq37+/AgICdP78eW3YsEGRkZHmPf369dOwYcNKfaanp6fmzJmjcePGKTs7W1evXtXYsWM1atQo3XHHHfL19dWvv/6qTz/9VMePHzfvGzt2bJlnWjVs2FCzZ8/WSy+9JMMwFBsbq5EjR+qhhx5S165d5eXlpV9++UVr1661aYs4adIkmwIbAAAAANjD19fXZn7t2jW7ClNFi1lF4zjS1q1b9ac//cl8tw0LC9OqVasUEhLitGcCAAAAqIJi1gMPPODsR1Ra0ZcfwzBsdkjZ45lnninxey1atNCyZcv0xz/+UcnJycrLy9PmzZttWhoW1rNnTy1evNiuXWHdunXTokWLNGXKFF27dk1ZWVlau3atTdu/wu677z796U9/sutnGjx4sC5fvqy5c+cqLy9PKSkpNq0Ki3rqqaf0xBNP2BUbAAAAAAoLDAy0mV+8eFFBQUFl3peYmFhqHEfZtm2bpkyZIqvVKklq2rSpPvzwQzVp0sQpzwMAAADwG6cXs+bOnevsR9QI3bp109atWzVv3jzt2LFD2dnZ161p3Lixxo0bp8cee6xc7Q0HDhyotm3bas6cOdq7d6/y8vKuW9O8eXNNmDChzN1eRT366KO65ZZbNG/ePB08eLDY3XXt2rXTpEmT1Lt373LFBgAAAIACLVu2tJnHxcWpbdu2Zd4XFxdXahxH+OqrrzRp0iTzPK6CQlbTpk0d/iwAAAAA16uSNoPV3YgRIzRixAinPyc4OFhvvvmmUlNTtW/fPiUkJCg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