Task_4.ipynb

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    "kernelspec": {
      "display_name": "Python 3",
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      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
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    "kaggle": {
      "accelerator": "none",
      "dataSources": [
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          "sourceId": 10275062,
          "sourceType": "datasetVersion",
          "datasetId": 6357669
        },
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          "sourceType": "datasetVersion",
          "datasetId": 6357896
        },
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          "sourceId": 10276497,
          "sourceType": "datasetVersion",
          "datasetId": 6358730
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      "isInternetEnabled": true,
      "language": "python",
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    },
    "colab": {
      "name": "Task_4",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/teja-1403/Forage-BCG-X-Data-Science/blob/main/Task_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "source": [
        "# IMPORTANT: SOME KAGGLE DATA SOURCES ARE PRIVATE\n",
        "# RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES.\n",
        "import kagglehub\n",
        "kagglehub.login()\n"
      ],
      "metadata": {
        "id": "k3DSb1xwlcVe"
      },
      "cell_type": "code",
      "outputs": [],
      "execution_count": null
    },
    {
      "source": [
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "teja098_bcg_x_task2_path = kagglehub.dataset_download('teja098/bcg-x-task2')\n",
        "teja098_cleandata_path = kagglehub.dataset_download('teja098/cleandata')\n",
        "teja098_dataforpredictions_path = kagglehub.dataset_download('teja098/dataforpredictions')\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "id": "MDPN2EIJlcVp"
      },
      "cell_type": "code",
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Customer Churn Prediction**"
      ],
      "metadata": {
        "id": "-R3XuJS2lcVv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1. Import Packages**"
      ],
      "metadata": {
        "id": "tS_pf6ZqlcV1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:39.841895Z",
          "iopub.execute_input": "2024-12-23T08:11:39.84229Z",
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          "shell.execute_reply": "2024-12-23T08:11:39.845283Z"
        },
        "id": "f8nolvR1lcV4"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.tree import plot_tree"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:39.941337Z",
          "iopub.execute_input": "2024-12-23T08:11:39.941718Z",
          "iopub.status.idle": "2024-12-23T08:11:39.94832Z",
          "shell.execute_reply.started": "2024-12-23T08:11:39.941685Z",
          "shell.execute_reply": "2024-12-23T08:11:39.947159Z"
        },
        "id": "bpPPCIQllcV6"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2. Load the Dataset**"
      ],
      "metadata": {
        "id": "cVvYmIFklcV-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(r\"/kaggle/input/dataforpredictions/data_for_predictions.csv\")\n",
        "df.drop(columns=[\"Unnamed: 0\"], inplace=True)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:39.976566Z",
          "iopub.execute_input": "2024-12-23T08:11:39.976885Z",
          "iopub.status.idle": "2024-12-23T08:11:40.186233Z",
          "shell.execute_reply.started": "2024-12-23T08:11:39.976862Z",
          "shell.execute_reply": "2024-12-23T08:11:40.185244Z"
        },
        "id": "bJxQFslNlcWC"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
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        "outputId": "9b046089-0da4-4c24-9959-18707fac423e"
      },
      "outputs": [
        {
          "execution_count": 33,
          "output_type": "execute_result",
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          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "\n",
        "## **3. Random Forest: Model Training**\n",
        "\n",
        "\n",
        "----"
      ],
      "metadata": {
        "id": "44h9hUFXlcWM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A random forest is a supervised learning algorithm which means that you must provide the algorithm with a set of features, as well as the outcome that you’re trying to predict, in our case churn.\n",
        "\n",
        "The way it makes predictions is by building a set of decision trees on different samples of the data and by taking a majority vote to decide what prediction to make."
      ],
      "metadata": {
        "id": "X9gxVGYIlcWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = df.copy()"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:40.210474Z",
          "iopub.execute_input": "2024-12-23T08:11:40.210781Z",
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        "id": "ekkMVDnAlcWP"
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      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['churn']  # Dependent Variable\n",
        "X = df.drop(columns=['id', 'churn'])    # Independent Variable\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:40.218389Z",
          "iopub.execute_input": "2024-12-23T08:11:40.218706Z",
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        "outputId": "32d21474-12d0-4dc2-d99e-5b169a5b7f49"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "(14606, 61)\n(14606,)\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:40.229197Z",
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        "id": "NCxEGU2GlcWT"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
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        "outputId": "2b6c6bd6-f6ce-4bbb-8ac8-4245476fa67a"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "(10954, 61)\n(10954,)\n(3652, 61)\n(3652,)\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Why Random Forest:**\n",
        "\n",
        "- **No Need to Scale:** Unlike some models that rely on distance calculations, the Random Forest uses a rule-based approach, meaning your features don’t need to be scaled.\n",
        "- **Master of Non-Linearity:** Random Forests excel at handling non-linear parameters, which is often where linear models stumble.\n",
        "- **Power Hungry:** Training a Random Forest on a large dataset demands significant computational power because you’re building an entire ensemble of models.\n",
        "- **Time-Intensive:** With great power comes great complexity-the larger the forest, the longer the training time."
      ],
      "metadata": {
        "id": "ZGFqOPGglcWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:40.247957Z",
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      "execution_count": null
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    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
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        "outputId": "3f337279-0cde-4216-fa48-cd5295ee026c"
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      "outputs": [
        {
          "execution_count": 39,
          "output_type": "execute_result",
          "data": {
            "text/plain": "RandomForestClassifier()",
            "text/html": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:43.656081Z",
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          "shell.execute_reply": "2024-12-23T08:11:43.737967Z"
        },
        "id": "Cjim3_RplcWY"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **4. Evaluate the model**"
      ],
      "metadata": {
        "id": "npq_v7_1lcWY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:43.740257Z",
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        },
        "id": "20mKg04klcWZ",
        "outputId": "9eeeab96-ee81-45a9-a473-4947aa84b716"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.9030668127053669\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:43.748462Z",
          "iopub.execute_input": "2024-12-23T08:11:43.748881Z",
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          "shell.execute_reply": "2024-12-23T08:11:43.763731Z"
        },
        "id": "trUVSBjJlcWa",
        "outputId": "3871c6e9-59a4-439a-beac-e5d2783ec653"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Classification Report:\n               precision    recall  f1-score   support\n\n           0       0.90      1.00      0.95      3286\n           1       0.73      0.05      0.10       366\n\n    accuracy                           0.90      3652\n   macro avg       0.82      0.52      0.52      3652\nweighted avg       0.89      0.90      0.86      3652\n\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5. Hyperparameter Tuning:**\n",
        "\n",
        "**Hyperparameter tuning** is the process of finding the optimal set of hyperparameters for a machine learning model to improve its performance. Hyperparameters are parameters that are not learned from the data but are set before the training process begins (e.g., the number of trees in a RandomForest, the maximum depth of each tree, etc.).\n",
        "\n",
        "### How Hyperparameter Tuning is Used in This Code:\n",
        "\n",
        "1. **Parameter Grid Definition**:\n",
        "   - A grid of possible hyperparameter values is defined using `param_grid`.\n",
        "   - Example parameters include `n_estimators` (number of trees), `max_depth` (maximum depth of each tree), `min_samples_split` (minimum samples required to split a node), etc.\n",
        "\n",
        "2. **GridSearchCV**:\n",
        "   - `GridSearchCV` is used to perform an exhaustive search over the specified hyperparameter grid.\n",
        "   - It evaluates every combination of hyperparameters using cross-validation (in this case, 3-fold CV) to determine which combination yields the best performance.\n",
        "\n",
        "3. **Model Selection**:\n",
        "   - The `best_estimator_` from `GridSearchCV` is the `RandomForestClassifier` with the best set of hyperparameters.\n",
        "   - This optimized model is then used to make predictions and evaluate performance.\n",
        "\n",
        "### Why Hyperparameter Tuning is Used:\n",
        "\n",
        "- **Improve Model Performance**: By finding the best hyperparameters, the model is better suited to the specific dataset, leading to improved accuracy and generalization.\n",
        "- **Prevent Overfitting/Underfitting**: Proper tuning can prevent overfitting (model too complex) or underfitting (model too simple), ensuring the model performs well on unseen data.\n",
        "- **Optimization**: It helps in utilizing the model’s full potential by fine-tuning parameters that directly affect model complexity and performance."
      ],
      "metadata": {
        "id": "Ycgc9FVSlcWa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Optimize with GridSearchCV\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200],  # Reduced number of trees\n",
        "    'max_depth': [10, 20],       # Reduced maximum depth\n",
        "    'min_samples_split': [2, 5], # Reduced number of samples required to split an internal node\n",
        "    'min_samples_leaf': [1, 2],  # Reduced number of samples required to be at a leaf node\n",
        "    'bootstrap': [True]          # Keep only one bootstrap option for simplicity\n",
        "}\n"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:43.767789Z",
          "iopub.execute_input": "2024-12-23T08:11:43.768139Z",
          "iopub.status.idle": "2024-12-23T08:11:43.772605Z",
          "shell.execute_reply.started": "2024-12-23T08:11:43.768108Z",
          "shell.execute_reply": "2024-12-23T08:11:43.771548Z"
        },
        "id": "ta2c3IOalcWb"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search = GridSearchCV(estimator=model, param_grid=param_grid,\n",
        "                           cv=3, n_jobs=-1, verbose=2, scoring='accuracy')"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:43.774489Z",
          "iopub.execute_input": "2024-12-23T08:11:43.774911Z",
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          "shell.execute_reply.started": "2024-12-23T08:11:43.774878Z",
          "shell.execute_reply": "2024-12-23T08:11:43.778097Z"
        },
        "id": "HOaB0bSclcWc"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:11:43.780344Z",
          "iopub.execute_input": "2024-12-23T08:11:43.780714Z",
          "iopub.status.idle": "2024-12-23T08:12:42.647509Z",
          "shell.execute_reply.started": "2024-12-23T08:11:43.780671Z",
          "shell.execute_reply": "2024-12-23T08:12:42.646509Z"
        },
        "id": "fm9f3sHilcWc",
        "outputId": "f034b1a5-5fa1-4032-adff-42ebba21e0a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n",
          "output_type": "stream"
        },
        {
          "execution_count": 45,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n             param_grid={'bootstrap': [True], 'max_depth': [10, 20],\n                         'min_samples_leaf': [1, 2],\n                         'min_samples_split': [2, 5],\n                         'n_estimators': [100, 200]},\n             scoring='accuracy', verbose=2)",
            "text/html": "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n             param_grid={&#x27;bootstrap&#x27;: [True], &#x27;max_depth&#x27;: [10, 20],\n                         &#x27;min_samples_leaf&#x27;: [1, 2],\n                         &#x27;min_samples_split&#x27;: [2, 5],\n                         &#x27;n_estimators&#x27;: [100, 200]},\n             scoring=&#x27;accuracy&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n             param_grid={&#x27;bootstrap&#x27;: [True], &#x27;max_depth&#x27;: [10, 20],\n                         &#x27;min_samples_leaf&#x27;: [1, 2],\n                         &#x27;min_samples_split&#x27;: [2, 5],\n                         &#x27;n_estimators&#x27;: [100, 200]},\n             scoring=&#x27;accuracy&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "best_rf = grid_search.best_estimator_\n",
        "y_pred_best = best_rf.predict(X_test)"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:42.648698Z",
          "iopub.execute_input": "2024-12-23T08:12:42.649031Z",
          "iopub.status.idle": "2024-12-23T08:12:42.796748Z",
          "shell.execute_reply.started": "2024-12-23T08:12:42.648972Z",
          "shell.execute_reply": "2024-12-23T08:12:42.795655Z"
        },
        "id": "7yDybKlRlcWd"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Optimized Accuracy:\", accuracy_score(y_test, y_pred_best))\n",
        "print(\"Optimized Classification Report:\\n\", classification_report(y_test, y_pred_best))"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:42.797764Z",
          "iopub.execute_input": "2024-12-23T08:12:42.798063Z",
          "iopub.status.idle": "2024-12-23T08:12:42.815761Z",
          "shell.execute_reply.started": "2024-12-23T08:12:42.798037Z",
          "shell.execute_reply": "2024-12-23T08:12:42.814705Z"
        },
        "id": "VoMXwpnKlcWd",
        "outputId": "3e781c8f-d3ab-4ad4-bd45-a051cfae0530"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Optimized Accuracy: 0.9027929901423878\nOptimized Classification Report:\n               precision    recall  f1-score   support\n\n           0       0.90      1.00      0.95      3286\n           1       0.74      0.05      0.09       366\n\n    accuracy                           0.90      3652\n   macro avg       0.82      0.52      0.52      3652\nweighted avg       0.89      0.90      0.86      3652\n\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6. Data visualization**"
      ],
      "metadata": {
        "id": "dNObZPbjlcWe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(y_true, y_pred, title):\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    plt.figure(figsize=(8,6))\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=model.classes_, yticklabels=model.classes_)\n",
        "    plt.xlabel('Predicted')\n",
        "    plt.ylabel('True')\n",
        "    plt.title(title)\n",
        "    plt.show()"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:42.816757Z",
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        },
        "id": "4s4o4445lcWf"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "import pydot\n",
        "from sklearn.tree import export_graphviz\n",
        "from graphviz import Source\n",
        "import os\n",
        "import subprocess\n",
        "\n",
        "def plot_decision_tree(model, title, output_file='decision_tree.html'):\n",
        "    # Check if Graphviz is installed\n",
        "    try:\n",
        "        subprocess.run(['dot', '-V'], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
        "    except (FileNotFoundError, subprocess.CalledProcessError):\n",
        "        print(\"Graphviz is not installed or not found in PATH. Please install Graphviz and add it to your PATH.\")\n",
        "        return\n",
        "\n",
        "    # Export the tree to a dot file\n",
        "    dot_data = export_graphviz(\n",
        "        model.estimators_[0],\n",
        "        out_file=None,\n",
        "        feature_names=X.columns,\n",
        "        filled=True,\n",
        "        rounded=True,\n",
        "        special_characters=True\n",
        "    )\n",
        "\n",
        "    # Create a Source object\n",
        "    graph = Source(dot_data)\n",
        "\n",
        "    # Save the graph as PNG\n",
        "    png_file = 'temp_tree.png'\n",
        "    graph.render(filename='temp_tree', format='png', cleanup=False)\n",
        "\n",
        "    # Create HTML content\n",
        "    html_content = f'''\n",
        "    <html>\n",
        "    <body>\n",
        "    <h1>{title}</h1>\n",
        "    <img src=\"{png_file}\" alt=\"Decision Tree\">\n",
        "    </body>\n",
        "    </html>\n",
        "    '''\n",
        "\n",
        "    # Write HTML content to a file\n",
        "    with open(output_file, 'w') as file:\n",
        "        file.write(html_content)\n",
        "\n",
        "    print(f'Decision tree saved to {output_file}. Open this file in your browser to view the tree.')\n"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:42.82334Z",
          "iopub.execute_input": "2024-12-23T08:12:42.823666Z",
          "iopub.status.idle": "2024-12-23T08:12:42.830336Z",
          "shell.execute_reply.started": "2024-12-23T08:12:42.82363Z",
          "shell.execute_reply": "2024-12-23T08:12:42.829333Z"
        },
        "id": "cIht8n2BlcWf"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix before hyperparameter tuning\n",
        "plot_confusion_matrix(y_test, y_pred, title='Confusion Matrix Before Hyperparameter Tuning')"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:42.831282Z",
          "iopub.execute_input": "2024-12-23T08:12:42.831576Z",
          "iopub.status.idle": "2024-12-23T08:12:43.077075Z",
          "shell.execute_reply.started": "2024-12-23T08:12:42.831551Z",
          "shell.execute_reply": "2024-12-23T08:12:43.075932Z"
        },
        "id": "qoszPaYolcWi",
        "outputId": "628a775c-865d-47a7-e573-532fc8defcf2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 800x600 with 2 Axes>",
            "image/png": 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xx+x+P1xdXdWoUaMcfz8GDRp0zWM6St++fe3mO2d93+bmO/bRRx9ViRIlrvraI0eOaNu2berdu7fdSFHz5s1Vu3btfOk/kFcMbRcy3t7ekqQzZ87kqv1ff/0lFxcXVa1a1W57YGCgfH199ddff9ltv/wLP0uJEiV0+vRpkz3O7tFHH9XHH3+s/v3764UXXlDr1q3VuXNnde3a1VaI5fQ+JNmG3C5Xs2ZNrVixItuE+SvfS9YX8OnTp23X8Xqy5lDNnTtX8fHxuvvuu1W1atUclx/JzMzU9OnTNWPGDB08eFAZGRm2fSVLlszV+SSpbNmyebqxZurUqVq0aJHi4+M1Z84c+fv75/q15cqVU5s2bWzPH3roIZUsWVIjRozQ0qVL1aFDB+3du1fSf3MyryY5OdnuP3CXM/Ozq1Spkl27vXv3yjAM3XHHHTmeI7c3J9xzzz05LlVVokSJbEPevXv31ty5c/XTTz+pWbNm+vHHH5WYmKhevXple31O/apWrZrmzZsn6b9hX8MwNGbMGI0ZMybHvh07dsyuGL3yGlzrfFWqVJGLi4vd7+Uvv/yisWPHKi4uLtvc4OTkZPn4+Fz3XHk5xpWft6x95cuXz3F71neKmWtzpazf0VatWuW4/8rPe5EiRWzzTm+2a30v3ehrsz5rV37fZ23Lyz9ogfxCIVnIeHt7KygoKMeJ/Ndy5c0uV3O1BCQrlTFzjssLKum/lGPdunVas2aNli1bpuXLl2vu3Llq1aqVVq5cedU+5NWNvJcsVqtVnTt31uzZs3XgwAGNGzfuqm0nTZqkMWPG6IknntDEiRPl5+cnFxcXDR06NE9LeeT1btnff//ddrf0tm3b9Nhjj+Xp9Vdq3bq1JGndunXq0KGDre9vvPHGVZcryus82eu58hpkZmbKYrHohx9+yPHnmt/nl/67aSkgIEBffvmlmjVrpi+//FKBgYF2hXduZV3DESNG5Jj8Sdn/45+X34MrP3v79+9X69atVaNGDb311lsqX7683Nzc9P3332vatGnZfh9zOldej3G1z9v1Podmrs2Vso7xxRdfKDAwMNv+K28cu3z04EZd63svp/d+I99L+fGdBtxsFJKF0IMPPqgPP/xQcXFxdkNJOQkODlZmZqb27t1rt9ZZYmKikpKS8nWx6BIlStjd4ZzlytRTklxcXNS6dWu1bt1ab731liZNmqSXXnpJa9asyfE/1Fn93LNnT7Z9u3fvVqlSpRy2PEqPHj306aefysXFRd27d79qu2+++UYtW7bUJ598Yrc9KSlJpUqVsj3PbVGfG+fOnVPfvn0VEhKi++67T1OmTNHDDz9suzPcjEuXLkn6b11J6b+0S/rvHzFmiqj8+NlVqVJFhmGoUqVKqlatWp77YIarq6t69Oih6OhoTZ48WQsXLrzqcHNWIna5P//803aDR+XKlSX9l5yauYY5ne/yFHHfvn3KzMy0nW/JkiVKTU3V4sWL7VKs3E4ByK9j5EZers3VPjtZv6P+/v75cn3z4lrfe1nv7WbJ+qxdeRf41bYBNwNzJAuh559/Xp6enurfv78SExOz7d+/f7+mT58u6b+hWUl6++237dq89dZbkqTw8PB861eVKlWUnJysrVu32rYdPXo0253hp06dyvbarKTryiWJspQpU0b16tXT7Nmz7b60t2/frpUrV9repyO0bNlSEydO1HvvvZdj2pHF1dU1WzIwf/78bPO7soqmnP7jk1ejRo3S4cOHNXv2bL311luqWLGiIiIirnodc2PJkiWSZFv+pkGDBqpSpYqmTp1qKy4vl7Wu6NXkx8+uc+fOcnV11fjx47NdY8MwdPLkyesew4xevXrp9OnTeuqpp3T27Nmrrh+4cOFCu5/zxo0btWHDBrVv317SfwVOixYt9MEHH+jo0aPZXn+9a3il999/3+75u+++K0m282UVu5dfq+TkZH322We5Pkd+HCM38nJtrvbZCQsLk7e3tyZNmpTtDvwrj5HfqlSpovXr1ystLc22benSpQ5Zlup6goKCVKtWLX3++ed2n9W1a9dq27ZtN70/gEQiWShVqVJFc+bM0aOPPqqaNWva/WWbX3/9VfPnz7eta1a3bl1FREToww8/VFJSkpo3b66NGzdq9uzZ6tSpk1q2bJlv/erevbtGjRqlhx9+WM8884xt+Y1q1arZzc2ZMGGC1q1bp/DwcAUHB+vYsWOaMWOGypUrZ7dm4JXeeOMNtW/fXqGhoerXr59tCRkfH59rDjnfKBcXl2zLveTkwQcf1IQJE9S3b1/dd9992rZtm7766qtsqUSVKlXk6+urWbNmycvLS56enmrUqNE158TlZPXq1ZoxY4bGjh1rW47os88+U4sWLTRmzBhNmTLlusf4888/9eWXX0r6b+Hz9evXa/bs2apataptLqCLi4s+/vhjtW/fXnfeeaf69u2rsmXL6t9//9WaNWvk7e1tKz6v5kZ/dlWqVNGrr76q0aNH69ChQ+rUqZO8vLx08OBBLViwQE8++aRGjBhx3ePk1V133aVatWpp/vz5qlmzZo7LPkn/Db02adJEgwYNUmpqqt5++22VLFlSzz//vK3N+++/ryZNmqh27doaMGCAKleurMTERMXFxemff/6xW2/0eg4ePKiHHnpI7dq1U1xcnL788kv16NHDVvy3bdtWbm5u6tChg60I/uijj+Tv759jsZaT/DhGbuX22tSrV0+urq6aPHmykpOTZbVabetczpw5U7169VL9+vXVvXt3lS5dWocPH9ayZcvUuHFjvffee/na5yz9+/fXN998o3bt2qlbt27av3+/vvzyS1tKerNNmjRJHTt2VOPGjdW3b1+dPn1a7733nmrVqpXjPwQBh7v5N4ojt/78809jwIABRsWKFQ03NzfDy8vLaNy4sfHuu+/aLaORnp5ujB8/3qhUqZJRtGhRo3z58sbo0aPt2hjGf8tYhIeHZzvPlctYXG35H8MwjJUrVxq1atUy3NzcjOrVqxtffvlltuV/Vq1aZXTs2NEICgoy3NzcjKCgIOOxxx4z/vzzz2znuHKJnB9//NFo3Lix4eHhYXh7exsdOnQwdu7cadcm63xXLi+UtazKwYMHr3pNDcN++Z+rudryP88995xRpkwZw8PDw2jcuLERFxeX47I9ixYtMkJCQowiRYrYvc/mzZvbLUNzucuPk5KSYgQHBxv169c30tPT7doNGzbMcHFxMeLi4q75HnTF8jeurq5GuXLljCeffNJITEzM1v733383OnfubJQsWdKwWq1GcHCw0a1bN2PVqlW2Nldb/scwbuxnl+Xbb781mjRpYnh6ehqenp5GjRo1jMjISGPPnj3XfK9ZP/tNmzbluP9a133KlCmGJGPSpEnZ9l3+e/Dmm28a5cuXN6xWq9G0aVO7pXiy7N+/3+jdu7cRGBhoFC1a1Chbtqzx4IMPGt98802u+pp1fXbu3Gl07drV8PLyMkqUKGEMHjzYuHDhgl3bxYsXG3Xq1DHc3d2NihUrGpMnTzY+/fTTbJ+Bq33u8+MYkozIyMirXrO8XhvDMIyPPvrIqFy5suHq6pptKaA1a9YYYWFhho+Pj+Hu7m5UqVLF6NOnj7F582Zbm9x8vq8mp+V/DMMw3nzzTaNs2bKG1Wo1GjdubGzevPmqy/9c+dnI6bvuasv/5PSdK8kYO3as3bb//e9/Ro0aNQyr1WrUqlXLWLx4sdGlSxejRo0apt43cCMshsEsXgDOa/r06Ro2bJgOHTqU7a7ZQ4cOqVKlSnrjjTcckoheady4cRo/fryOHz9uN+8WuJ569eqpdOnSiomJKeiuwMkwRxKA0zIMQ5988omaN2+e49JYQGGTnp5uu2EuS2xsrP744w+7P9kI3CzMkQTgdM6dO6fFixdrzZo12rZtmxYtWlTQXQJy5d9//1WbNm3Us2dPBQUFaffu3Zo1a5YCAwOzLfQP3AwUkgCczvHjx9WjRw/5+vrqxRdf1EMPPVTQXQJypUSJEmrQoIE+/vhjHT9+XJ6engoPD9frr7+epz+MAOQX5kgCAADAFOZIAgAAwBQKSQAAAJhCIQkAAABTbsubbTzuGlzQXQDgIKc3OeYvmAAoeO4FWJU4sna48Pvt+71FIgkAAABTbstEEgAAIE8sZGtmUEgCAABYLAXdg1sS5TcAAABMIZEEAABgaNsUrhoAAABMIZEEAABgjqQpJJIAAAAwhUQSAACAOZKmcNUAAABgCokkAAAAcyRNoZAEAABgaNsUrhoAAABMIZEEAABgaNsUEkkAAACYQiIJAADAHElTuGoAAAAwhUQSAACAOZKmkEgCAADAFBJJAAAA5kiaQiEJAADA0LYplN8AAAAwhUQSAACAoW1TuGoAAAAwhUQSAACARNIUrhoAAABMIZEEAABw4a5tM0gkAQAAYAqJJAAAAHMkTaGQBAAAYEFyUyi/AQAAYAqJJAAAAEPbpnDVAAAAYAqJJAAAAHMkTSGRBAAAKCRmzpypOnXqyNvbW97e3goNDdUPP/xg23/x4kVFRkaqZMmSKl68uLp06aLExES7Yxw+fFjh4eEqVqyY/P39NXLkSF26dMmuTWxsrOrXry+r1aqqVasqOjraVH8pJAEAACwujnvkQbly5fT6669ry5Yt2rx5s1q1aqWOHTtqx44dkqRhw4ZpyZIlmj9/vtauXasjR46oc+fOttdnZGQoPDxcaWlp+vXXXzV79mxFR0frlVdesbU5ePCgwsPD1bJlS8XHx2vo0KHq37+/VqxYkffLZhiGkedXFXIedw0u6C4AcJDTm94r6C4AcBD3Apxw59H2DYcd+8LKkTf0ej8/P73xxhvq2rWrSpcurTlz5qhr166SpN27d6tmzZqKi4vTvffeqx9++EEPPvigjhw5ooCAAEnSrFmzNGrUKB0/flxubm4aNWqUli1bpu3bt9vO0b17dyUlJWn58uV56huJJAAAgMXisEdqaqpSUlLsHqmpqdftUkZGhv73v//p3LlzCg0N1ZYtW5Senq42bdrY2tSoUUMVKlRQXFycJCkuLk61a9e2FZGSFBYWppSUFFuqGRcXZ3eMrDZZx8gLCkkAAAAHDm1HRUXJx8fH7hEVFXXVrmzbtk3FixeX1WrVwIEDtWDBAoWEhCghIUFubm7y9fW1ax8QEKCEhARJUkJCgl0RmbU/a9+12qSkpOjChQt5umzctQ0AAOBAo0eP1vDhw+22Wa3Wq7avXr264uPjlZycrG+++UYRERFau3ato7tpCoUkAACAA5f/sVqt1ywcr+Tm5qaqVatKkho0aKBNmzZp+vTpevTRR5WWlqakpCS7VDIxMVGBgYGSpMDAQG3cuNHueFl3dV/e5so7vRMTE+Xt7S0PD488vTeGtgEAAAqxzMxMpaamqkGDBipatKhWrVpl27dnzx4dPnxYoaGhkqTQ0FBt27ZNx44ds7WJiYmRt7e3QkJCbG0uP0ZWm6xj5AWJJAAAQCH5E4mjR49W+/btVaFCBZ05c0Zz5sxRbGysVqxYIR8fH/Xr10/Dhw+Xn5+fvL29NWTIEIWGhuree++VJLVt21YhISHq1auXpkyZooSEBL388suKjIy0paIDBw7Ue++9p+eff15PPPGEVq9erXnz5mnZsmV57i+FJAAAQCFx7Ngx9e7dW0ePHpWPj4/q1KmjFStW6P7775ckTZs2TS4uLurSpYtSU1MVFhamGTNm2F7v6uqqpUuXatCgQQoNDZWnp6ciIiI0YcIEW5tKlSpp2bJlGjZsmKZPn65y5crp448/VlhYWJ77yzqSAG4prCMJ3L4KdB3J8HccduwLy55x2LELWuHIcQEAAHDLYWgbAACgkMyRvNVQSAIAAFBImsJVAwAAgCkkkgAAAA5ckPx2RiIJAAAAU0gkAQAAmCNpClcNAAAAppBIAgAAMEfSFBJJAAAAmEIiCQAAwBxJUygkAQAAGNo2hfIbAAAAppBIAgAAp2chkTSFRBIAAACmkEgCAACnRyJpDokkAAAATCGRBAAAIJA0hUQSAAAAppBIAgAAp8ccSXMoJAEAgNOjkDSHoW0AAACYQiIJAACcHomkOSSSAAAAMIVEEgAAOD0SSXNIJAEAAGAKiSQAAACBpCkkkgAAADCFRBIAADg95kiaQyIJAAAAU0gkAQCA0yORNIdCEgAAOD0KSXMY2gYAAIApJJIAAMDpkUiaQyIJAAAAU0gkAQAACCRNIZEEAACAKSSSAADA6TFH0hwSSQAAAJhCIgkAAJweiaQ5FJIAAMDpUUiaw9A2AAAATCGRBAAAIJA0hUQSAAAAppBIAgAAp8ccSXNIJAEAAGAKiSQAAHB6JJLmkEgCAADAFBJJAADg9EgkzaGQBAAATo9C0hyGtgEAAGAKiSQAAACBpCkkkgAAADCFRBIAADg95kiaQyIJAAAAU0gkAQCA0yORNIdEEgAAAKaQSAIAAKdHImkOhSQAAAB1pCkMbQMAAMAUCkkAAOD0LBaLwx55ERUVpbvvvlteXl7y9/dXp06dtGfPHrs2LVq0yHaOgQMH2rU5fPiwwsPDVaxYMfn7+2vkyJG6dOmSXZvY2FjVr19fVqtVVatWVXR0dJ6vG4UkAABAIbF27VpFRkZq/fr1iomJUXp6utq2batz587ZtRswYICOHj1qe0yZMsW2LyMjQ+Hh4UpLS9Ovv/6q2bNnKzo6Wq+88oqtzcGDBxUeHq6WLVsqPj5eQ4cOVf/+/bVixYo89Zc5kgAAwOkVlpttli9fbvc8Ojpa/v7+2rJli5o1a2bbXqxYMQUGBuZ4jJUrV2rnzp368ccfFRAQoHr16mnixIkaNWqUxo0bJzc3N82aNUuVKlXSm2++KUmqWbOmfv75Z02bNk1hYWG57i+JJAAAgAOlpqYqJSXF7pGampqr1yYnJ0uS/Pz87LZ/9dVXKlWqlGrVqqXRo0fr/Pnztn1xcXGqXbu2AgICbNvCwsKUkpKiHTt22Nq0adPG7phhYWGKi4vL03sjkUSBG/BIEw3o2lTBQf99SHYdSNCkD3/Qyl92qoR3MY0ZFK7W99ZQ+cASOnH6rJbEbtX4GUuVcvaiJKlnh0b6aEKvHI9dodULOn76rCTpqW7NNPDRZgoO8tPfCac1+ZMVmrN04815kwByrf39rXTkyL/Ztj/avYdeHDO2AHoEZ+DIRDIqKkrjx4+32zZ27FiNGzfumq/LzMzU0KFD1bhxY9WqVcu2vUePHgoODlZQUJC2bt2qUaNGac+ePfruu+8kSQkJCXZFpCTb84SEhGu2SUlJ0YULF+Th4ZGr90YhiQL3b2KSxry7SPsOH5dFFvXs0Ejzpz2pe7u/LovFojKlfTR62gLtOpCgCmX89O5L3VWmtI96jPxEkvTNyt8U8+tOu2N+OL6X3K1FbUXkgEeaaMKQDoqc+LU27/hLd9eqqPfHPKaklPP6ft32m/6eAVzdV3O/UWZGhu35vn179VT/vro/rF0B9gowb/To0Ro+fLjdNqvVet3XRUZGavv27fr555/ttj/55JO2/127dm2VKVNGrVu31v79+1WlSpX86XQuUUiiwF1ZyI17f4kGPNJE99SppNkL4/TYiI9t+w7+c0Lj3luiT1/rLVdXF2VkZOpiaroupqbb2pQqUVwt7qmmgeO/sm3rEX6PPvn2F32z8jdJ0qF/T6rBnRX0XJ/7KSSBQubKIbxPP/5Q5ctXUMO77ymgHsEZODKRtFqtuSocLzd48GAtXbpU69atU7ly5a7ZtlGjRpKkffv2qUqVKgoMDNTGjfYjbomJiZJkm1cZGBho23Z5G29v71ynkVIBF5InTpzQp59+qri4OFvUGhgYqPvuu099+vRR6dKlC7J7KAAuLhZ1ub++PD3ctGHrwRzbeHu5K+XcRWVkZOa4//EH79H5i2la8GO8bZtb0SK6mJZu1+7CxXQ1rBWsIkVcdOlSzscCULDS09K0bOli9YroW2huhsBtqpD8ehmGoSFDhmjBggWKjY1VpUqVrvua+Ph4SVKZMmUkSaGhoXrttdd07Ngx+fv7S5JiYmLk7e2tkJAQW5vvv//e7jgxMTEKDQ3NU38LrJDctGmTwsLCVKxYMbVp00bVqlWT9F81/M477+j111/XihUr1LBhw2seJzU1NduEVSMzQxYXV4f1HfnvzqpBip39nNzdiujshVQ9+txH2n0gIVu7kr6eGj2gvT799terHiuiU6jm/rDZLqX8MW6X+nS6T0vWbNXvu/5W/ZAK6vPwfXIrWkSlfIsr4USKQ94XgBuzevWPOnPmjB7q9HBBdwW4KSIjIzVnzhwtWrRIXl5etqDNx8dHHh4e2r9/v+bMmaMHHnhAJUuW1NatWzVs2DA1a9ZMderUkSS1bdtWISEh6tWrl6ZMmaKEhAS9/PLLioyMtCWjAwcO1Hvvvafnn39eTzzxhFavXq158+Zp2bJleeqvxTAMI38vQe7ce++9qlu3rmbNmpXtX5mGYWjgwIHaunXrde8eGjduXLYJrK4Bd6toGYZAbiVFi7iqfJkS8inuoYfb3KU+D4eqbf/pdsWkl6e7ls0crFMp59R16Ac5poiN6lRS7OzndF+Pyfp919+27e7Wonr7hW7qEX6PLBbp2Kkz+nrZJj3X934Ftx6tY6fO3JT3iRt3etN7Bd0F3EQDB/RT0aJF9e6MWQXdFdwE7gU4Tlp5+PfXb2TSgbceyHXbqyXvn332mfr06aO///5bPXv21Pbt23Xu3DmVL19eDz/8sF5++WV5e3vb2v/1118aNGiQYmNj5enpqYiICL3++usqUuT/LnJsbKyGDRumnTt3qly5chozZoz69OmTp/dWYIWkh4eHfv/9d9WoUSPH/bt379Zdd92lCxcuXPM4OSWS/k1HkUje4pbNGqwDf5/QkNf+J0kqXsyqJTMidf5imjo/M0upaZdyfN3MsT1Ur0Z5hT42Ocf9RYq4KMDPW0dPJKtfl8Z69ZmOCmz2vAroYwATKCSdx5Ej/yo8rI3emv6uWrZqc/0X4JZHIXnrKbAfWdZE0KsVkhs3bsx2W3pOcprAShF563OxWGR1++/X08vTXUtmRCo17ZK6Dv3gqkWkp4ebutxfX6+8u/iqx710KVP/HkuSJD0S1kA//LSDIhIopBYt+E5+fiXVtFmLgu4KnABzcM0psEJyxIgRevLJJ7Vlyxa1bt3aVjQmJiZq1apV+uijjzR16tSC6h5uoglDHtKKX3bo76On5eXprkfbN1Szhneow9Mz5OXprqUzIuXh7qa+L82Wt6e7vD3dJUnHT59VZub/FYFdwxqoiKuLvl62Kds5qlbwV8Nawdq0/ZBKeBXTM71aKaRKkPqP+eKmvU8AuZeZmalFC75Th46d7IbiABQuBfbpjIyMVKlSpTRt2jTNmDFDGf9/zTBXV1c1aNBA0dHR6tatW0F1DzdRab/i+mRibwWW8lby2YvavvdfdXh6hlZv2K2mDe7QPXX+u2Nt55Jxdq+r/sArOnz0lO15n06hWrT6DyWfzT4dwtXVomd7tVK14AClX8rQus1/qmWfN+1eD6DwWB/3q44ePaJOnbsUdFfgJAgkzSmwOZKXS09P14kTJyRJpUqVUtGiRW/oeB53Dc6PbgEohJgjCdy+CnKOZNURPzjs2PumtnfYsQtaoRgvKFq0qG3tIwAAgJuNOZLmFIpCEgAAoCBRR5rjUtAdAAAAwK2JRBIAADg9hrbNIZEEAACAKSSSAADA6RFImkMiCQAAAFNIJAEAgNNzcSGSNINEEgAAAKaQSAIAAKfHHElzKCQBAIDTY/kfcxjaBgAAgCkkkgAAwOkRSJpDIgkAAABTSCQBAIDTY46kOSSSAAAAMIVEEgAAOD0SSXNIJAEAAGAKiSQAAHB6BJLmUEgCAACnx9C2OQxtAwAAwBQSSQAA4PQIJM0hkQQAAIApJJIAAMDpMUfSHBJJAAAAmEIiCQAAnB6BpDkkkgAAADCFRBIAADg95kiaQyIJAAAAU0gkAQCA0yOQNIdCEgAAOD2Gts1haBsAAACmkEgCAACnRyBpDokkAAAATCGRBAAATo85kuaQSAIAAMAUEkkAAOD0CCTNIZEEAACAKSSSAADA6TFH0hwKSQAA4PSoI81haBsAAACmkEgCAACnx9C2OSSSAAAAMIVEEgAAOD0SSXNIJAEAAGAKiSQAAHB6BJLmkEgCAADAFBJJAADg9JgjaQ6FJAAAcHrUkeYwtA0AAABTSCQBAIDTY2jbHBJJAAAAmEIiCQAAnB6BpDkkkgAAADCFRBIAADg9FyJJU0gkAQAAComoqCjdfffd8vLykr+/vzp16qQ9e/bYtbl48aIiIyNVsmRJFS9eXF26dFFiYqJdm8OHDys8PFzFihWTv7+/Ro4cqUuXLtm1iY2NVf369WW1WlW1alVFR0fnub8UkgAAwOlZLI575MXatWsVGRmp9evXKyYmRunp6Wrbtq3OnTtnazNs2DAtWbJE8+fP19q1a3XkyBF17tzZtj8jI0Ph4eFKS0vTr7/+qtmzZys6OlqvvPKKrc3BgwcVHh6uli1bKj4+XkOHDlX//v21YsWKvF03wzCMvL3Fws/jrsEF3QUADnJ603sF3QUADuJegBPuwmZscNixVzzdyPRrjx8/Ln9/f61du1bNmjVTcnKySpcurTlz5qhr166SpN27d6tmzZqKi4vTvffeqx9++EEPPvigjhw5ooCAAEnSrFmzNGrUKB0/flxubm4aNWqUli1bpu3bt9vO1b17dyUlJWn58uW57h+JJAAAgAOlpqYqJSXF7pGampqr1yYnJ0uS/Pz8JElbtmxRenq62rRpY2tTo0YNVahQQXFxcZKkuLg41a5d21ZESlJYWJhSUlK0Y8cOW5vLj5HVJusYuUUhCQAAnJ6LxXGPqKgo+fj42D2ioqKu26fMzEwNHTpUjRs3Vq1atSRJCQkJcnNzk6+vr13bgIAAJSQk2NpcXkRm7c/ad602KSkpunDhQq6vG3dtAwAAONDo0aM1fPhwu21Wq/W6r4uMjNT27dv1888/O6prN4xCEgAAOD1H/olEq9Waq8LxcoMHD9bSpUu1bt06lStXzrY9MDBQaWlpSkpKskslExMTFRgYaGuzceNGu+Nl3dV9eZsr7/ROTEyUt7e3PDw8ct1PhrYBAAAKCcMwNHjwYC1YsECrV69WpUqV7PY3aNBARYsW1apVq2zb9uzZo8OHDys0NFSSFBoaqm3btunYsWO2NjExMfL29lZISIitzeXHyGqTdYzcIpEEAABOr7CsRx4ZGak5c+Zo0aJF8vLyss1p9PHxkYeHh3x8fNSvXz8NHz5cfn5+8vb21pAhQxQaGqp7771XktS2bVuFhISoV69emjJlihISEvTyyy8rMjLSlowOHDhQ7733np5//nk98cQTWr16tebNm6dly5blqb8kkgAAAIXEzJkzlZycrBYtWqhMmTK2x9y5c21tpk2bpgcffFBdunRRs2bNFBgYqO+++86239XVVUuXLpWrq6tCQ0PVs2dP9e7dWxMmTLC1qVSpkpYtW6aYmBjVrVtXb775pj7++GOFhYXlqb+sIwnglsI6ksDtqyDXkXzwg00OO/bSp+522LELGkPbAADA6bkUkqHtWw1D2wAAADCFRBIAADg9Ry7/czsjkQQAAIApJJIAAMDpEUiaQyIJAAAAU0gkAQCA03MhkjSFRBIAAACmkEgCAACnRyBpDoUkAABweiz/Yw5D2wAAADCFRBIAADg9AklzSCQBAABgCokkAABweiz/Yw6JJAAAAEwhkQQAAE6PPNIcEkkAAACYQiIJAACcHutImkMhCQAAnJ4LdaQpDG0DAADAFBJJAADg9BjaNodEEgAAAKaQSAIAAKdHIGkOiSQAAABMIZEEAABOjzmS5pBIAgAAwBQSSQAA4PRYR9IcCkkAAOD0GNo2h6FtAAAAmEIiCQAAnB55pDkkkgAAADDFVCH5008/qWfPngoNDdW///4rSfriiy/0888/52vnAAAAbgYXi8Vhj9tZngvJb7/9VmFhYfLw8NDvv/+u1NRUSVJycrImTZqU7x0EAABA4ZTnQvLVV1/VrFmz9NFHH6lo0aK27Y0bN9Zvv/2Wr50DAAC4GSwWxz1uZ3kuJPfs2aNmzZpl2+7j46OkpKT86BMAAABuAXkuJAMDA7Vv375s23/++WdVrlw5XzoFAABwM1ksFoc9bmd5LiQHDBigZ599Vhs2bJDFYtGRI0f01VdfacSIERo0aJAj+ggAAIBCKM/rSL7wwgvKzMxU69atdf78eTVr1kxWq1UjRozQkCFDHNFHAAAAh7rNg0OHyXMhabFY9NJLL2nkyJHat2+fzp49q5CQEBUvXtwR/QMAAHC4232ZHkcx/Zdt3NzcFBISkp99AQAAwC0kz4Vky5YtrzlxdPXq1TfUIQAAgJuNQNKcPBeS9erVs3uenp6u+Ph4bd++XREREfnVLwAAABRyeS4kp02bluP2cePG6ezZszfcIQAAgJvtdl+mx1FM/a3tnPTs2VOffvppfh0OAAAAhZzpm22uFBcXJ3d39/w63A3Zu/rNgu4CAAC4heRbsuZk8lxIdu7c2e65YRg6evSoNm/erDFjxuRbxwAAAFC45bmQ9PHxsXvu4uKi6tWra8KECWrbtm2+dQwAAOBmYY6kOXkqJDMyMtS3b1/Vrl1bJUqUcFSfAAAAbioX6khT8jQlwNXVVW3btlVSUpKDugMAAIBbRZ7nltaqVUsHDhxwRF8AAAAKhIvFcY/bWZ4LyVdffVUjRozQ0qVLdfToUaWkpNg9AAAA4BxyPUdywoQJeu655/TAAw9Ikh566CG7iamGYchisSgjIyP/ewkAAOBA3GxjTq4LyfHjx2vgwIFas2aNI/sDAACAW0SuC0nDMCRJzZs3d1hnAAAACsLtPpfRUfI0R5LYFwAAAFnytI5ktWrVrltMnjp16oY6BAAAcLORlZmTp0Jy/Pjx2f6yDQAAwK3OhUrSlDwVkt27d5e/v7+j+gIAAIBbSK4LSeZHAgCA21WeF9aGpDxct6y7tgEAAAApD4VkZmYmw9oAAOC2ZLE47pFX69atU4cOHRQUFCSLxaKFCxfa7e/Tp48sFovdo127dnZtTp06pccff1ze3t7y9fVVv379dPbsWbs2W7duVdOmTeXu7q7y5ctrypQpee4rSS4AAEAhcu7cOdWtW1fvv//+Vdu0a9dOR48etT2+/vpru/2PP/64duzYoZiYGC1dulTr1q3Tk08+adufkpKitm3bKjg4WFu2bNEbb7yhcePG6cMPP8xTX/N0sw0AAMDtqDDdtd2+fXu1b9/+mm2sVqsCAwNz3Ldr1y4tX75cmzZtUsOGDSVJ7777rh544AFNnTpVQUFB+uqrr5SWlqZPP/1Ubm5uuvPOOxUfH6+33nrLruC8HhJJAAAAB0pNTVVKSordIzU19YaOGRsbK39/f1WvXl2DBg3SyZMnbfvi4uLk6+trKyIlqU2bNnJxcdGGDRtsbZo1ayY3Nzdbm7CwMO3Zs0enT5/OdT8oJAEAgNNz5BzJqKgo+fj42D2ioqJM97Vdu3b6/PPPtWrVKk2ePFlr165V+/btlZGRIUlKSEjIdl9LkSJF5Ofnp4SEBFubgIAAuzZZz7Pa5AZD2wAAwOk58m9tjx49WsOHD7fbZrVaTR+ve/futv9du3Zt1alTR1WqVFFsbKxat25t+rhmkEgCAAA4kNVqlbe3t93jRgrJK1WuXFmlSpXSvn37JEmBgYE6duyYXZtLly7p1KlTtnmVgYGBSkxMtGuT9fxqcy9zQiEJAACcnovF4rCHo/3zzz86efKkypQpI0kKDQ1VUlKStmzZYmuzevVqZWZmqlGjRrY269atU3p6uq1NTEyMqlevrhIlSuT63BSSAAAAhcjZs2cVHx+v+Ph4SdLBgwcVHx+vw4cP6+zZsxo5cqTWr1+vQ4cOadWqVerYsaOqVq2qsLAwSVLNmjXVrl07DRgwQBs3btQvv/yiwYMHq3v37goKCpIk9ejRQ25uburXr5927NihuXPnavr06dmG4K+HOZIAAMDpFaLVf7R582a1bNnS9jyruIuIiNDMmTO1detWzZ49W0lJSQoKClLbtm01ceJEu+Hyr776SoMHD1br1q3l4uKiLl266J133rHt9/Hx0cqVKxUZGakGDRqoVKlSeuWVV/K09I8kWYzb8G8f/nP6xm6pB1B4lfLKv3lFAAoX9wKMtyb+uM9hxx7TpqrDjl3QSCQBAIDTc+Rd27cz5kgCAADAFBJJAADg9CwikjSDQhIAADg9hrbNYWgbAAAAppBIAgAAp0ciaQ6JJAAAAEwhkQQAAE7PUphWJL+FkEgCAADAFBJJAADg9JgjaQ6JJAAAAEwhkQQAAE6PKZLmUEgCAACn50IlaQpD2wAAADCFRBIAADg9brYxh0QSAAAAppBIAgAAp8cUSXNIJAEAAGAKiSQAAHB6LiKSNINEEgAAAKaQSAIAAKfHHElzKCQBAIDTY/kfcxjaBgAAgCkkkgAAwOnxJxLNIZEEAACAKSSSAADA6RFImkMiCQAAAFNIJAEAgNNjjqQ5JJIAAAAwhUQSAAA4PQJJcygkAQCA02OI1hyuGwAAAEwhkQQAAE7Pwti2KSSSAAAAMIVEEgAAOD3ySHNIJAEAAGAKiSQAAHB6LEhuDokkAAAATCGRBAAATo880hwKSQAA4PQY2TaHoW0AAACYQiIJAACcHguSm0MiCQAAAFNIJAEAgNMjWTOH6wYAAABTSCQBAIDTY46kOSSSAAAAMIVEEgAAOD3ySHNIJAEAAGAKiSQAAHB6zJE0h0ISAAA4PYZozeG6AQAAwBQSSQAA4PQY2jaHRBIAAACmkEgCAACnRx5pDokkAAAATCGRBAAATo8pkuaQSAIAAMAUEkkAAOD0XJglaQqFJAAAcHoMbZvD0DYAAEAhsm7dOnXo0EFBQUGyWCxauHCh3X7DMPTKK6+oTJky8vDwUJs2bbR37167NqdOndLjjz8ub29v+fr6ql+/fjp79qxdm61bt6pp06Zyd3dX+fLlNWXKlDz3lUISAAA4PYsD/y+vzp07p7p16+r999/Pcf+UKVP0zjvvaNasWdqwYYM8PT0VFhamixcv2to8/vjj2rFjh2JiYrR06VKtW7dOTz75pG1/SkqK2rZtq+DgYG3ZskVvvPGGxo0bpw8//DBv180wDCPP77CQ++d0akF3AYCDlPKyFnQXADiIewFOuFu2/ZjDjh1ey9/0ay0WixYsWKBOnTpJ+i+NDAoK0nPPPacRI0ZIkpKTkxUQEKDo6Gh1795du3btUkhIiDZt2qSGDRtKkpYvX64HHnhA//zzj4KCgjRz5ky99NJLSkhIkJubmyTphRde0MKFC7V79+5c949EEgAAOD2LxXGP1NRUpaSk2D1SU82FXgcPHlRCQoLatGlj2+bj46NGjRopLi5OkhQXFydfX19bESlJbdq0kYuLizZs2GBr06xZM1sRKUlhYWHas2ePTp8+nev+UEgCAAA4UFRUlHx8fOweUVFRpo6VkJAgSQoICLDbHhAQYNuXkJAgf3/7FLRIkSLy8/Oza5PTMS4/R25w1zYAAHB6jlz+Z/To0Ro+fLjdNqv19pimQyEJAADgQFarNd8Kx8DAQElSYmKiypQpY9uemJioevXq2docO2Y/5/PSpUs6deqU7fWBgYFKTEy0a5P1PKtNbjC0DQAAnJ4j50jmp0qVKikwMFCrVq2ybUtJSdGGDRsUGhoqSQoNDVVSUpK2bNlia7N69WplZmaqUaNGtjbr1q1Tenq6rU1MTIyqV6+uEiVK5Lo/FJIAAMDpFaZC8uzZs4qPj1d8fLyk/26wiY+P1+HDh2WxWDR06FC9+uqrWrx4sbZt26bevXsrKCjIdmd3zZo11a5dOw0YMEAbN27UL7/8osGDB6t79+4KCgqSJPXo0UNubm7q16+fduzYoblz52r69OnZhuCve91Y/gfArYTlf4DbV0Eu/7Ny13GHHbttzdJ5ah8bG6uWLVtm2x4REaHo6GgZhqGxY8fqww8/VFJSkpo0aaIZM2aoWrVqtranTp3S4MGDtWTJErm4uKhLly565513VLx4cVubrVu3KjIyUps2bVKpUqU0ZMgQjRo1Kk99pZAEcEuhkARuXwVZSMbsOuGwY99fs5TDjl3QGNoGAACAKdy1DQAAnJ6L41b/ua2RSAIAAMAUEkkAAOD0LA5ckPx2RiIJAAAAU0gkAQCA08vvhcOdBYUkAABwegxtm8PQNgAAAEwhkQQAAE6P5X/MIZEEAACAKSSSAADA6TFH0hwSSQAAAJhCIolCZ/G3c7X4u3lKPHpEkhRcuYp6PfGUGt3X1K6dYRgaPexpbVr/i8ZPfltNmreSJC1fukhvvDomx2N/8/0alfAr6dg3ACBPtmzepOhPP9Gundt1/PhxTXvnfbVq3ca2/+SJE3r7ramK+/VnnTlzRvUbNNQLL41RcHDFgus0bjss/2MOhSQKnVL+ARoQOVRly1WQIUMrly3WK88/qw8+n6eKlava2n37vy9lyeGT37JNmO4JbWy3bcrEl5WWmkYRCRRCFy6cV/Xq1dWpcxcNf3aw3T7DMDT0mUgVKVJEb787Q8WLF9fns6P1VL+++m7xMhUrVqyAeg1AopBEIXRf0xZ2z/sNekZLFszTzu1bbYXkvj93a/6c2ZoZ/T89Et7Krr3V3V1Wd3fb86TTp/T75o0a8dJ4h/cdQN41adpcTZo2z3HfX38d0tY/4vXtoqWqWvUOSdLLr4xTq+aNtfz7Zerc9ZGb2VXcxggkzWGOJAq1jIwMrY75QRcvXFBI7bqSpIsXL+i1V17QMyNfkl/JUtc9xsrvl8jq7qFmLe93dHcB5LP0tDRJktXNatvm4uIiNzc3/f7bloLqFm5DLhaLwx63s0JdSP7999964oknrtkmNTVVKSkpdo/U1NSb1EM4yoF9fyq8ZSO1a9ZQb09+VeMnv62KlapIkma8/YburF1XjZu1zNWxfliyQK3btrdLKQHcGipWqqwyZYL0zttvKiU5Welpafr04w+VmJCg48ePF3T3AKdXqAvJU6dOafbs2ddsExUVJR8fH7vH+9Om3KQewlHKB1fSh5/P1/uffKWHOnfT5Akv69DB/fp13RrFb96oyGGjcnWcHdv+0OFDB9T+oc4O7jEARyhatKjemv6u/jp0SE3vu0eNGtbTpo0b1KRpM7mwgjTykcWBj9tZgc6RXLx48TX3Hzhw4LrHGD16tIYPH2637fj5G+oWCoGiRYuqbPkKkqRqNUK0Z+d2fTf3K1mtVh359289dL/9zTTjRw9X7br19dbMT+22f7/4O1WtVkPVaoTctL4DyF8hd9bSvO8W6cyZM0pPT5efn58e7/6I7ryzVkF3DXB6BVpIdurUSRaLRYZhXLVNTnflXs5qtcpqtdptS8lgaPt2k2lkKj0tTX0GPK0HrkgX+z/eRYOeHanQKybrXzh/XmtXrVD/Qc/ezK4CcBAvLy9J/92As3PHdkUO4bONfHS7R4cOUqCFZJkyZTRjxgx17Ngxx/3x8fFq0KDBTe4VCtrHM6brntDG8g8oo/Pnz2n1yh/0x2+b9frbs+RXslSON9j4B5ZRmaBydtvW/LhcGRkZatMu/GZ1HYAJ58+d0+HDh23P//3nH+3etUs+Pj4qExSklSt+UIkSfipTJkh79+7RlKhJatmqje5r3KQAew1AKuBCskGDBtqyZctVC8nrpZW4PZ0+fUqvj39Zp04el2fx4qpcpZpef3uWGjYKzdNxfliyQE2bt1ZxL28H9RRAftixY7v69+1tez51SpQk6aGOD2vipNd1/PhxTZ3yuk6eOKnSpUvrwYc66qmBTxdUd3Gb4k8kmmMxCrBS++mnn3Tu3Dm1a9cux/3nzp3T5s2b1bx5zuuLXc0/pxnaBm5Xpbys128E4JbkXoDx1ob9yQ47dqMqPg47dkEr0ELSUSgkgdsXhSRw+yrIQnLjAccVkvdUvn0LSf6yDQAAcHoMbJtTqNeRBAAAQOFFIgkAAEAkaQqJJAAAAEwhkQQAAE6P5X/MIZEEAACAKSSSAADA6V3nLzLjKkgkAQAAYAqJJAAAcHoEkuZQSAIAAFBJmsLQNgAAAEwhkQQAAE6P5X/MIZEEAACAKSSSAADA6bH8jzkkkgAAADCFRBIAADg9AklzSCQBAABgCokkAAAAkaQpFJIAAMDpsfyPOQxtAwAAwBQSSQAA4PRY/sccEkkAAACYQiIJAACcHoGkOSSSAAAAMIVEEgAAgEjSFBJJAAAAmEIiCQAAnB7rSJpDIgkAAABTSCQBAIDTYx1JcygkAQCA06OONIehbQAAAJhCIgkAAEAkaQqJJAAAAEwhkQQAAE6P5X/MIZEEAACAKRSSAADA6Vksjnvkxbhx42SxWOweNWrUsO2/ePGiIiMjVbJkSRUvXlxdunRRYmKi3TEOHz6s8PBwFStWTP7+/ho5cqQuXbqUH5cpG4a2AQAACpE777xTP/74o+15kSL/V64NGzZMy5Yt0/z58+Xj46PBgwerc+fO+uWXXyRJGRkZCg8PV2BgoH799VcdPXpUvXv3VtGiRTVp0qR87yuFJAAAcHqFaYZkkSJFFBgYmG17cnKyPvnkE82ZM0etWrWSJH322WeqWbOm1q9fr3vvvVcrV67Uzp079eOPPyogIED16tXTxIkTNWrUKI0bN05ubm752leGtgEAACyOe6SmpiolJcXukZqaetWu7N27V0FBQapcubIef/xxHT58WJK0ZcsWpaenq02bNra2NWrUUIUKFRQXFydJiouLU+3atRUQEGBrExYWppSUFO3YseOGL9OVKCQBAAAcKCoqSj4+PnaPqKioHNs2atRI0dHRWr58uWbOnKmDBw+qadOmOnPmjBISEuTm5iZfX1+71wQEBCghIUGSlJCQYFdEZu3P2pffGNoGAABOz5HL/4wePVrDhw+322a1WnNs2759e9v/rlOnjho1aqTg4GDNmzdPHh4eDuujWSSSAAAADmS1WuXt7W33uFoheSVfX19Vq1ZN+/btU2BgoNLS0pSUlGTXJjEx0TanMjAwMNtd3FnPc5p3eaMoJAEAgNMrLMv/XOns2bPav3+/ypQpowYNGqho0aJatWqVbf+ePXt0+PBhhYaGSpJCQ0O1bds2HTt2zNYmJiZG3t7eCgkJubHO5IChbQAAgEJixIgR6tChg4KDg3XkyBGNHTtWrq6ueuyxx+Tj46N+/fpp+PDh8vPzk7e3t4YMGaLQ0FDde++9kqS2bdsqJCREvXr10pQpU5SQkKCXX35ZkZGRuU5B84JCEgAAOL3CsvzPP//8o8cee0wnT55U6dKl1aRJE61fv16lS5eWJE2bNk0uLi7q0qWLUlNTFRYWphkzZthe7+rqqqVLl2rQoEEKDQ2Vp6enIiIiNGHCBIf012IYhuGQIxegf05f/ZZ6ALe2Ul75/y9qAIWDewHGW/uPXXDYsav4F76bZPILiSQAAEBhiSRvMRSSAADA6Tly+Z/bGXdtAwAAwBQSSQAA4PRudJkeZ0UiCQAAAFNIJAEAgNMjkDSHRBIAAACmkEgCAAAQSZpCIgkAAABTSCQBAIDTYx1JcygkAQCA02P5H3MY2gYAAIApJJIAAMDpEUiaQyIJAAAAU0gkAQCA02OOpDkkkgAAADCFRBIAAIBZkqaQSAIAAMAUEkkAAOD0mCNpDoUkAABwetSR5jC0DQAAAFNIJAEAgNNjaNscEkkAAACYQiIJAACcnoVZkqaQSAIAAMAUEkkAAAACSVNIJAEAAGAKiSQAAHB6BJLmUEgCAACnx/I/5jC0DQAAAFNIJAEAgNNj+R9zSCQBAABgCokkAAAAgaQpJJIAAAAwhUQSAAA4PQJJc0gkAQAAYAqJJAAAcHqsI2kOhSQAAHB6LP9jDkPbAAAAMIVEEgAAOD2Gts0hkQQAAIApFJIAAAAwhUISAAAApjBHEgAAOD3mSJpDIgkAAABTSCQBAIDTYx1JcygkAQCA02No2xyGtgEAAGAKiSQAAHB6BJLmkEgCAADAFBJJAAAAIklTSCQBAABgCokkAABweiz/Yw6JJAAAAEwhkQQAAE6PdSTNIZEEAACAKSSSAADA6RFImkMhCQAAQCVpCkPbAAAAMIVEEgAAOD2W/zGHRBIAAACmkEgCAACnx/I/5pBIAgAAwBSLYRhGQXcCMCs1NVVRUVEaPXq0rFZrQXcHQD7i8w0UfhSSuKWlpKTIx8dHycnJ8vb2LujuAMhHfL6Bwo+hbQAAAJhCIQkAAABTKCQBAABgCoUkbmlWq1Vjx45lIj5wG+LzDRR+3GwDAAAAU0gkAQAAYAqFJAAAAEyhkAQAAIApFJIAAAAwhUISt7T3339fFStWlLu7uxo1aqSNGzcWdJcA3KB169apQ4cOCgoKksVi0cKFCwu6SwCugkISt6y5c+dq+PDhGjt2rH777TfVrVtXYWFhOnbsWEF3DcANOHfunOrWrav333+/oLsC4DpY/ge3rEaNGunuu+/We++9J0nKzMxU+fLlNWTIEL3wwgsF3DsA+cFisWjBggXq1KlTQXcFQA5IJHFLSktL05YtW9SmTRvbNhcXF7Vp00ZxcXEF2DMAAJwHhSRuSSdOnFBGRoYCAgLstgcEBCghIaGAegUAgHOhkAQAAIApFJK4JZUqVUqurq5KTEy0256YmKjAwMAC6hUAAM6FQhK3JDc3NzVo0ECrVq2ybcvMzNSqVasUGhpagD0DAMB5FCnoDgBmDR8+XBEREWrYsKHuuecevf322zp37pz69u1b0F0DcAPOnj2rffv22Z4fPHhQ8fHx8vPzU4UKFQqwZwCuxPI/uKW99957euONN5SQkKB69erpnXfeUaNGjQq6WwBuQGxsrFq2bJlte0REhKKjo29+hwBcFYUkAAAATGGOJAAAAEyhkAQAAIApFJIAAAAwhUISAAAAplBIAgAAwBQKSQAAAJhCIQkAAABTKCQBAABgCoUkgEKrT58+6tSpk+15ixYtNHTo0Jvej9jYWFksFiUlJd30cwNAYUYhCSDP+vTpI4vFIovFIjc3N1WtWlUTJkzQpUuXHHre7777ThMnTsxVW4o/AHC8IgXdAQC3pnbt2umzzz5Tamqqvv/+e0VGRqpo0aIaPXq0Xbu0tDS5ubnlyzn9/Pzy5TgAgPxBIgnAFKvVqsDAQAUHB2vQoEFq06aNFi9ebBuOfu211xQUFKTq1atLkv7++29169ZNvr6+8vPzU8eOHXXo0CHb8TIyMjR8+HD5+vqqZMmSev7552UYht05rxzaTk1N1ahRo1S+fHlZrVZVrVpVn3zyiQ4dOqSWLVtKkkqUKCGLxaI+ffpIkjIzMxUVFaVKlSrJw8NDdevW1TfffGN3nu+//17VqlWTh4eHWrZsaddPAMD/oZAEkC88PDyUlpYmSVq1apX27NmjmJgYLV26VOnp6QoLC5OXl5d++ukn/fLLLypevLjatWtne82bb76p6Ohoffrpp/r555916tQpLViw4Jrn7N27t77++mu988472rVrlz744AMVL15c5cuX17fffitJ2rNnj44eParp06dLkqKiovT5559r1qxZ2rFjh4YNG6aePXtq7dq1kv4reDt37qwOHTooPj5e/fv31wsvvOCoywYAtzSGtgHcEMMwtGrVKq1YsUJDhgzR8ePH5enpqY8//tg2pP3ll18qMzNTH3/8sSwWiyTps88+k6+vr2JjY9W2bVu9/fbbGj16tDp37ixJmjVrllasWHHV8/7555+aN2+eYmJi1KZNG0lS5cqVbfuzhsH9/f3l6+sr6b8Ec9KkSfrxxx8VGhpqe83PP/+sDz74QM2bN9fMmTNVpUoVvfnmm5Kk6tWra9u2bZo8eXI+XjUAuD1QSAIwZenSpSpevLjS09OVmZmpHj16aNy4cYqMjFTt2rXt5kX+8ccf2rdvn7y8vOyOcfHiRe3fv1/Jyck6evSoGjVqZNtXpEgRNWzYMNvwdpb4+Hi5urqqefPmue7zvn37dP78ed1///1229PS0nTXXXdJknbt2mXXD0m2ohMAYI9CEoApLVu21MyZM+Xm5qagoCAVKfJ/Xyeenp52bc+ePasGDRroq6++ynac0qVLmzq/h4dHnl9z9uxZSdKyZctUtmxZu31Wq9VUPwDAmVFIAjDF09NTVatWzVXb+vXra+7cufL395e3t3eObcqUKaMNGzaoWbNmkqRLly5py5Ytql+/fo7ta9eurczMTK1du9Y2tH25rEQ0IyPDti0kJERWq1WHDx++apJZs2ZNLV682G7b+vXrr/8mAcAJcbMNAId7/PHHVapUKXXs2FE//fSTDh48qNjYWD3zzDP6559/JEnPPvusXn/9dS1cuFC7d+/W008/fc01ICtWrKiIiAg98cQTWrhwoe2Y8+bNkyQFBwfLYrFo6dKlOn78uM6ePSsvLy+NGDFCw4YN0+zZs7V//3799ttvevfddzV79mxJ0sCBA7V3716NHDlSe/bs0Zw5cxQdHe3oSwQAtyQKSQAOV6xYMa1bt04VKlRQ586dVbNmTfXr108XL160JZTPPfecevXqpYiICIWGhsrLy0sPP/zwNY87c+ZMde3aVU8//bRq1KihAQMG6Ny5c5KksmXLavz48XrhhRcUEBCgwYMHS5ImTpyoMWPGKCoqSjVr1lS7du20bNkyVapUSZJUoUIFffvtt1q4cKHq1q2rWbNmadKkSQ68OgBw67IYV5vJDgAAAFwDiSQAAABMoZAEAACAKRSSAAAAMIVCEgAAAKZQSAIAAMAUCkkAAACYQiEJAAAAUygkAQAAYAqFJAAAAEyhkAQAAIApFJIAAAAw5f8B1B+eAKUdCjkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix after hyperparameter tuning\n",
        "plot_confusion_matrix(y_test, y_pred_best, title='Confusion Matrix After Hyperparameter Tuning')"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:43.078069Z",
          "iopub.execute_input": "2024-12-23T08:12:43.078359Z",
          "iopub.status.idle": "2024-12-23T08:12:43.32004Z",
          "shell.execute_reply.started": "2024-12-23T08:12:43.078332Z",
          "shell.execute_reply": "2024-12-23T08:12:43.319027Z"
        },
        "id": "XEXTIylzlcWj",
        "outputId": "d8f8d1c9-5a0c-411b-db7c-1e27f68df679"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 800x600 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_tree(model, title='Decision Tree Before Hyperparameter Tuning', output_file='decision_tree_before.html')"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:43.320967Z",
          "iopub.execute_input": "2024-12-23T08:12:43.321244Z",
          "iopub.status.idle": "2024-12-23T08:12:48.841427Z",
          "shell.execute_reply.started": "2024-12-23T08:12:43.321212Z",
          "shell.execute_reply": "2024-12-23T08:12:48.840402Z"
        },
        "id": "_WU9qEDSlcWk",
        "outputId": "d025cd67-cff2-433e-edd3-61376bc5328d"
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.552265 to fit\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Decision tree saved to decision_tree_before.html. Open this file in your browser to view the tree.\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_tree(best_rf, title='Decision Tree After Hyperparameter Tuning', output_file='decision_tree_after.html')"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:48.842409Z",
          "iopub.execute_input": "2024-12-23T08:12:48.842728Z",
          "iopub.status.idle": "2024-12-23T08:12:54.470352Z",
          "shell.execute_reply.started": "2024-12-23T08:12:48.8427Z",
          "shell.execute_reply": "2024-12-23T08:12:54.469425Z"
        },
        "id": "mmXI_-gflcWl",
        "outputId": "a189eb0e-07e0-4323-ea16-305bb9b88b11"
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.609313 to fit\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Decision tree saved to decision_tree_after.html. Open this file in your browser to view the tree.\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importances = pd.DataFrame({\n",
        "    'features': X_train.columns,\n",
        "    'importance': model.feature_importances_\n",
        "}).sort_values(by='importance', ascending=True).reset_index()"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:54.473095Z",
          "iopub.execute_input": "2024-12-23T08:12:54.473383Z",
          "iopub.status.idle": "2024-12-23T08:12:54.486788Z",
          "shell.execute_reply.started": "2024-12-23T08:12:54.473355Z",
          "shell.execute_reply": "2024-12-23T08:12:54.485922Z"
        },
        "id": "fsg-vZy3lcWm"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 25))\n",
        "plt.title('Feature Importances')\n",
        "plt.barh(range(len(feature_importances)), feature_importances['importance'], color='b', align='center')\n",
        "plt.yticks(range(len(feature_importances)), feature_importances['features'])\n",
        "plt.xlabel('Importance')\n",
        "plt.show()"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-12-23T08:12:54.48774Z",
          "iopub.execute_input": "2024-12-23T08:12:54.487969Z"
        },
        "id": "fTva6GLYlcWn",
        "outputId": "cc282db5-3a2d-4721-d340-0f4c0fb018af"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x2500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "From this chart, we can observe the following points:\n",
        "\n",
        "- Net margin and consumption over 12 months is a top driver for churn in this model.\n",
        "  \n",
        "- Margin on power subscription also is an influential driver.\n",
        "  \n",
        "- Time seems to be an influential factor, especially the number of months they have been active, their tenure and the number of months since they updated their contract.\n",
        "\n",
        "- The feature that our colleague recommended is in the top half in terms of how influential it is and some of the features built off the back of this actually outperform it.\n",
        "\n",
        "- Our price sensitivity features are scattered around but are not the main driver for a customer churning."
      ],
      "metadata": {
        "id": "1Em26jAFlcWo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **7. Final thoughts**\n",
        "\n",
        "- **Feature Importance Scores**: Models like Random Forest provide feature importance scores. Features with higher scores contribute more to the model's predictions.\n",
        "- **Contract Type**: Customers on month-to-month contracts might be more likely to churn compared to those on long-term contracts.\n",
        "- **Tenure**: Longer-tenured customers might be less likely to churn due to established habits or satisfaction.\n",
        "- **Monthly Charges**: Higher charges could lead to higher churn if perceived as too costly.\n",
        "- **Customer Service Calls**: A high number of calls might indicate dissatisfaction, leading to higher churn.\n",
        "\n",
        "### **Why these features are important**\n",
        "\n",
        "- **Predictive Power**: These features often show a strong correlation with customer churn based on historical data.\n",
        "- **Business Relevance**: They provide actionable insights, allowing businesses to target specific areas like contract adjustments or customer service improvements.\n"
      ],
      "metadata": {
        "id": "x1e4nMHJlcWo"
      }
    }
  ]
}