Customer_Churn_Prediction_Muammar_Yasir_202010225067.ipynb

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      "source": [
        "# 奈良うより慣れろ (narau yori narero)\n",
        "---------------------------------------------------------\n",
        "\"Better Practice Than Theory\" ^^\n",
        "credits @muamarasta ~ムアマル ヤシール"
      ],
      "metadata": {
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        "# What is Customer Churn?\n",
        "\n",
        "Customer attrition or churn, is when customers stop doing business with a company. It can have a significant impact on a company's revenue and it's crucial for businesses to find out the reasons why customers are leaving and take steps to reduce the number of customers leaving. One way to do this is by identifying customer segments that are at risk of leaving, and implementing retention strategies to keep them. Also, by using data and machine learning techniques, companies can predict which customers are likely to leave in the future and take actions to keep them before they decide to leave.\n",
        "\n",
        "We are going to build a basic model for predicting customer churn using Telco Customer Churn dataset. We are using some classification algorithm to model customers who have left, using Python tools such as pandas for data manipulation and matplotlib for visualizations."
      ],
      "metadata": {
        "id": "b4Ntmfgu6PMN"
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    {
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
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    {
      "cell_type": "markdown",
      "source": [
        "# About Dataset"
      ],
      "metadata": {
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      "source": [
        "**Internet Service Provider Customer Churn**\n",
        "\n",
        "**Context** :\n",
        "There is a big competition between Internet providers. If a providers want to increase its revenue they needs more subscriber but keep existing customer is more important than having new ones. So providers want to know which customer should cancel his service. we call this as churn. if the know who will go, maybe they can catch them with promotions.\n",
        "\n",
        "**Features**\n",
        "\n",
        "* **id** :\n",
        "unique subscriber id\n",
        "\n",
        "* **is_tv_subscriber** :\n",
        "customer has a tv subscription ?\n",
        "\n",
        "* **is_movie_package_subscriber** :\n",
        "is he/she has a sinema movie package subs\n",
        "\n",
        "* **subscription_age** :\n",
        "how many year has the customer use our service\n",
        "\n",
        "* **bill_avg** :\n",
        "last 3 months bill avg\n",
        "\n",
        "* **reamining_contract** :\n",
        "how many year remaining for customer contract. if null; customer hasnt have a contract. the customer who has a contract time have to use their service until contract end. if they canceled their service before contract time end they pay a penalty fare.\n",
        "\n",
        "* **service_failure_count** :\n",
        "customer call count to call center for service failure for last 3 months\n",
        "\n",
        "* **download_avg** :\n",
        "last 3 months internet usage (GB)\n",
        "\n",
        "* **upload_avg** :\n",
        "last 3 months upload avg (GB)\n",
        "\n",
        "* **download_over_limit** :\n",
        "most of customer has a download limit. if they reach this limit they have to pay for this. this column contain \"limit over count\" for last 9 months\n",
        "\n",
        "* **churn** :\n",
        "this is the target column. if 1 customer canceled his service"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "#Import Library"
      ],
      "metadata": {
        "id": "Awvzbs8zGAK0"
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "J9v32RLrGCeE"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "# Preprocessing"
      ],
      "metadata": {
        "id": "WSfDl3NAIgnI"
      }
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    {
      "cell_type": "code",
      "source": [
        "# initialize directory path\n",
        "path = \"/content/drive/MyDrive/Machine Learning/Asynchronous Task/data_contoh.csv\""
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      "metadata": {
        "id": "Yjxvjl4CIX_d"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "# load csv file using pandas\n",
        "df = pd.read_csv(path)\n",
        "\n",
        "# data shape (row, columns)\n",
        "df.shape"
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              "(72274, 11)"
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      "source": [
        "# show first-5 data\n",
        "df.head()"
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        "id": "FPHTlV7fIbxB",
        "outputId": "7c93d83d-46a7-41be-e06e-183cf12df7e5"
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      "execution_count": null,
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          "output_type": "execute_result",
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            "text/plain": [
              "   id  is_tv_subscriber  is_movie_package_subscriber  subscription_age  \\\n",
              "0  15                 1                            0             11.95   \n",
              "1  18                 0                            0              8.22   \n",
              "2  23                 1                            0              8.91   \n",
              "3  27                 0                            0              6.87   \n",
              "4  34                 0                            0              6.39   \n",
              "\n",
              "   bill_avg  reamining_contract  service_failure_count  download_avg  \\\n",
              "0        25                0.14                      0           8.4   \n",
              "1         0                 NaN                      0           0.0   \n",
              "2        16                0.00                      0          13.7   \n",
              "3        21                 NaN                      1           0.0   \n",
              "4         0                 NaN                      0           0.0   \n",
              "\n",
              "   upload_avg  download_over_limit  churn  \n",
              "0         2.3                    0      0  \n",
              "1         0.0                    0      1  \n",
              "2         0.9                    0      1  \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 72274,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 489102,\n        \"min\": 15,\n        \"max\": 1689744,\n        \"num_unique_values\": 72274,\n        \"samples\": [\n          643627,\n          718461,\n          967810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_tv_subscriber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_movie_package_subscriber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"subscription_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.034989872478024,\n        \"min\": -0.02,\n        \"max\": 12.8,\n        \"num_unique_values\": 1110,\n        \"samples\": [\n          1.89,\n          4.79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 0,\n        \"max\": 406,\n        \"num_unique_values\": 179,\n        \"samples\": [\n          74,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reamining_contract\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6971022671442165,\n        \"min\": 0.0,\n        \"max\": 2.92,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          1.88,\n          0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"service_failure_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 19,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"download_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.405963016601554,\n        \"min\": 0.0,\n        \"max\": 4415.2,\n        \"num_unique_values\": 2856,\n        \"samples\": [\n          155.9,\n          170.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"upload_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.818896361042711,\n        \"min\": 0.0,\n        \"max\": 453.3,\n        \"num_unique_values\": 802,\n        \"samples\": [\n          22.5,\n          44.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"download_over_limit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show information about data\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXFt1q9ZIc44",
        "outputId": "58463a49-6c46-486d-ac16-1a9fb21d406c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 72274 entries, 0 to 72273\n",
            "Data columns (total 11 columns):\n",
            " #   Column                       Non-Null Count  Dtype  \n",
            "---  ------                       --------------  -----  \n",
            " 0   id                           72274 non-null  int64  \n",
            " 1   is_tv_subscriber             72274 non-null  int64  \n",
            " 2   is_movie_package_subscriber  72274 non-null  int64  \n",
            " 3   subscription_age             72274 non-null  float64\n",
            " 4   bill_avg                     72274 non-null  int64  \n",
            " 5   reamining_contract           50702 non-null  float64\n",
            " 6   service_failure_count        72274 non-null  int64  \n",
            " 7   download_avg                 71893 non-null  float64\n",
            " 8   upload_avg                   71893 non-null  float64\n",
            " 9   download_over_limit          72274 non-null  int64  \n",
            " 10  churn                        72274 non-null  int64  \n",
            "dtypes: float64(4), int64(7)\n",
            "memory usage: 6.1 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show descriptive statistic\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "73JPD9Z3KakV",
        "outputId": "bc855869-be74-4633-b712-ca1370498e20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id  is_tv_subscriber  is_movie_package_subscriber  \\\n",
              "count  7.227400e+04      72274.000000                 72274.000000   \n",
              "mean   8.463182e+05          0.815259                     0.334629   \n",
              "std    4.891022e+05          0.388090                     0.471864   \n",
              "min    1.500000e+01          0.000000                     0.000000   \n",
              "25%    4.222165e+05          1.000000                     0.000000   \n",
              "50%    8.477840e+05          1.000000                     0.000000   \n",
              "75%    1.269562e+06          1.000000                     1.000000   \n",
              "max    1.689744e+06          1.000000                     1.000000   \n",
              "\n",
              "       subscription_age      bill_avg  reamining_contract  \\\n",
              "count      72274.000000  72274.000000        50702.000000   \n",
              "mean           2.450051     18.942483            0.716039   \n",
              "std            2.034990     13.215386            0.697102   \n",
              "min           -0.020000      0.000000            0.000000   \n",
              "25%            0.930000     13.000000            0.000000   \n",
              "50%            1.980000     19.000000            0.570000   \n",
              "75%            3.300000     22.000000            1.310000   \n",
              "max           12.800000    406.000000            2.920000   \n",
              "\n",
              "       service_failure_count  download_avg    upload_avg  download_over_limit  \\\n",
              "count           72274.000000  71893.000000  71893.000000         72274.000000   \n",
              "mean                0.274234     43.689911      4.192076             0.207613   \n",
              "std                 0.816621     63.405963      9.818896             0.997123   \n",
              "min                 0.000000      0.000000      0.000000             0.000000   \n",
              "25%                 0.000000      6.700000      0.500000             0.000000   \n",
              "50%                 0.000000     27.800000      2.100000             0.000000   \n",
              "75%                 0.000000     60.500000      4.800000             0.000000   \n",
              "max                19.000000   4415.200000    453.300000             7.000000   \n",
              "\n",
              "              churn  \n",
              "count  72274.000000  \n",
              "mean       0.554141  \n",
              "std        0.497064  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        1.000000  \n",
              "75%        1.000000  \n",
              "max        1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c61796ba-a358-41e0-9c09-30bba5aff32e\" class=\"colab-df-container\">\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>is_tv_subscriber</th>\n",
              "      <th>is_movie_package_subscriber</th>\n",
              "      <th>subscription_age</th>\n",
              "      <th>bill_avg</th>\n",
              "      <th>reamining_contract</th>\n",
              "      <th>service_failure_count</th>\n",
              "      <th>download_avg</th>\n",
              "      <th>upload_avg</th>\n",
              "      <th>download_over_limit</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7.227400e+04</td>\n",
              "      <td>72274.000000</td>\n",
              "      <td>72274.000000</td>\n",
              "      <td>72274.000000</td>\n",
              "      <td>72274.000000</td>\n",
              "      <td>50702.000000</td>\n",
              "      <td>72274.000000</td>\n",
              "      <td>71893.000000</td>\n",
              "      <td>71893.000000</td>\n",
              "      <td>72274.000000</td>\n",
              "      <td>72274.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.463182e+05</td>\n",
              "      <td>0.815259</td>\n",
              "      <td>0.334629</td>\n",
              "      <td>2.450051</td>\n",
              "      <td>18.942483</td>\n",
              "      <td>0.716039</td>\n",
              "      <td>0.274234</td>\n",
              "      <td>43.689911</td>\n",
              "      <td>4.192076</td>\n",
              "      <td>0.207613</td>\n",
              "      <td>0.554141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.891022e+05</td>\n",
              "      <td>0.388090</td>\n",
              "      <td>0.471864</td>\n",
              "      <td>2.034990</td>\n",
              "      <td>13.215386</td>\n",
              "      <td>0.697102</td>\n",
              "      <td>0.816621</td>\n",
              "      <td>63.405963</td>\n",
              "      <td>9.818896</td>\n",
              "      <td>0.997123</td>\n",
              "      <td>0.497064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.222165e+05</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.930000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.700000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.477840e+05</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.980000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0.570000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.800000</td>\n",
              "      <td>2.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.269562e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1.310000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.500000</td>\n",
              "      <td>4.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.689744e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.800000</td>\n",
              "      <td>406.000000</td>\n",
              "      <td>2.920000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>4415.200000</td>\n",
              "      <td>453.300000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c61796ba-a358-41e0-9c09-30bba5aff32e')\"\n",
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b9492291-bc87-41a5-86ad-ed2882923f20 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 579745.3164460948,\n        \"min\": 15.0,\n        \"max\": 1689744.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          846318.2434623793,\n          847784.0,\n          72274.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_tv_subscriber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25552.454945578927,\n        \"min\": 0.0,\n        \"max\": 72274.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8152585992196364,\n          1.0,\n          0.3880903239582862\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_movie_package_subscriber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25552.576005800976,\n        \"min\": 0.0,\n        \"max\": 72274.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.33462932728228684,\n          1.0,\n          0.4718639860386069\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"subscription_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25551.532393228477,\n        \"min\": -0.02,\n        \"max\": 72274.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.4500511940670227,\n          1.98,\n          72274.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25528.228753633175,\n        \"min\": 0.0,\n        \"max\": 72274.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18.94248277388826,\n          19.0,\n          72274.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reamining_contract\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17925.550222939502,\n        \"min\": 0.0,\n        \"max\": 50702.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          50702.0,\n          0.7160392094986392,\n          1.31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"service_failure_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25551.703860355665,\n        \"min\": 0.0,\n        \"max\": 72274.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2742341644298088,\n          19.0,\n          0.8166209461172078\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"download_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25231.442049541885,\n        \"min\": 0.0,\n        \"max\": 71893.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          43.689911396102545,\n          27.8,\n          71893.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"upload_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25394.52520484576,\n        \"min\": 0.0,\n        \"max\": 71893.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.192075723644861,\n          2.1,\n          71893.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"download_over_limit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25552.303462986023,\n        \"min\": 0.0,\n        \"max\": 72274.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.20761269612862163,\n          7.0,\n          0.9971232228794198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25552.51313838191,\n        \"min\": 0.0,\n        \"max\": 72274.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5541411849351081,\n          1.0,\n          0.49706352777285445\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# count missing values each row\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtmdnY86JaLj",
        "outputId": "09bbcfad-9c52-4b31-cd78-b6ab1dc0eff6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                 0\n",
              "is_tv_subscriber                   0\n",
              "is_movie_package_subscriber        0\n",
              "subscription_age                   0\n",
              "bill_avg                           0\n",
              "reamining_contract             21572\n",
              "service_failure_count              0\n",
              "download_avg                     381\n",
              "upload_avg                       381\n",
              "download_over_limit                0\n",
              "churn                              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# input missing value with 0\n",
        "df[\"reamining_contract\"] = df[\"reamining_contract\"].fillna(0)\n",
        "\n",
        "# input missing value with mean\n",
        "df[\"download_avg\"] = df[\"download_avg\"].fillna(df[\"download_avg\"].mean())\n",
        "df[\"upload_avg\"] = df[\"upload_avg\"].fillna(df[\"upload_avg\"].mean())"
      ],
      "metadata": {
        "id": "fnJBf87GKW2F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# make sure we have handle all missing value\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OUvDhaXLTmX4",
        "outputId": "0f7ad832-8cec-440b-eb67-d678a9be99d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                             0\n",
              "is_tv_subscriber               0\n",
              "is_movie_package_subscriber    0\n",
              "subscription_age               0\n",
              "bill_avg                       0\n",
              "reamining_contract             0\n",
              "service_failure_count          0\n",
              "download_avg                   0\n",
              "upload_avg                     0\n",
              "download_over_limit            0\n",
              "churn                          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"churn\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLAnIQgqv9-R",
        "outputId": "600ef218-98cc-43a0-a02a-4877956e1870"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "churn\n",
              "1    40050\n",
              "0    32224\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define features and label\n",
        "X = df.iloc[:,:-1]\n",
        "y = df[\"churn\"]"
      ],
      "metadata": {
        "id": "IU3XV9qYly0b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modeling"
      ],
      "metadata": {
        "id": "vmr2DyE3lp95"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.33, random_state=42)\n",
        "\n",
        "print(f\"Total train data : {len(X_train)}\")\n",
        "print(f\"Total test data : {len(X_test)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gKQbDt2AUTH2",
        "outputId": "9a0eeaa5-9b36-4591-aeed-2cc5c7ab2d32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total train data : 48423\n",
            "Total test data : 23851\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# modeling using decision tree\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit(X_train,y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# show metric classification\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhgfFDqGmLBM",
        "outputId": "6d3b5720-0e6f-41cb-e4ac-d1c0ad570f2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.97      0.97     10774\n",
            "           1       0.98      0.97      0.97     13077\n",
            "\n",
            "    accuracy                           0.97     23851\n",
            "   macro avg       0.97      0.97      0.97     23851\n",
            "weighted avg       0.97      0.97      0.97     23851\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show confusion matrix\n",
        "sns.heatmap(confusion_matrix(y_test,y_pred), annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "XX3gFLmynmdO",
        "outputId": "516ddd7c-1411-4c57-e9c8-141a6511d7ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Task Modelling (step by step)"
      ],
      "metadata": {
        "id": "7ODvzZUZRnzw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize directory path\n",
        "path = \"/content/drive/MyDrive/Machine Learning/Asynchronous Task/data_tugas.csv\""
      ],
      "metadata": {
        "id": "mEYdoT8ySCK8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Libraries"
      ],
      "metadata": {
        "id": "6nLNpnJvwjhm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import sklearn\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "Z9s_sbztwneC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Dataset"
      ],
      "metadata": {
        "id": "wBSSTDoxxj4u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Machine Learning/Asynchronous Task/data_tugas.csv')\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YRB7nwo-xO8d",
        "outputId": "14d16441-f15e-4c54-b17e-a3ce7937fd34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7043, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA"
      ],
      "metadata": {
        "id": "q4GCbxUlxl_9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "b7rSPZVjxfA9",
        "outputId": "101ad106-ec79-49c2-c98f-09a2daf39697"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...               No   \n",
              "1                No             DSL            Yes  ...              Yes   \n",
              "2                No             DSL            Yes  ...               No   \n",
              "3  No phone service             DSL            Yes  ...              Yes   \n",
              "4                No     Fiber optic             No  ...               No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dd0564ea-3229-4461-a046-5e792f252533\" class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>...</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    30% {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e0825f74-da18-4275-a185-60ec7658eba3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "r02IRjPrxouc",
        "outputId": "bdec398c-a304-4cd3-dcc6-0418393873d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
              "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
              "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
              "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
              "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
              "7042  3186-AJIEK    Male              0      No         No      66   \n",
              "\n",
              "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
              "7038          Yes               Yes             DSL            Yes  ...   \n",
              "7039          Yes               Yes     Fiber optic             No  ...   \n",
              "7040           No  No phone service             DSL            Yes  ...   \n",
              "7041          Yes               Yes     Fiber optic             No  ...   \n",
              "7042          Yes                No     Fiber optic            Yes  ...   \n",
              "\n",
              "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
              "7038              Yes         Yes         Yes             Yes        One year   \n",
              "7039              Yes          No         Yes             Yes        One year   \n",
              "7040               No          No          No              No  Month-to-month   \n",
              "7041               No          No          No              No  Month-to-month   \n",
              "7042              Yes         Yes         Yes             Yes        Two year   \n",
              "\n",
              "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
              "7038              Yes               Mailed check          84.80        1990.5   \n",
              "7039              Yes    Credit card (automatic)         103.20        7362.9   \n",
              "7040              Yes           Electronic check          29.60        346.45   \n",
              "7041              Yes               Mailed check          74.40         306.6   \n",
              "7042              Yes  Bank transfer (automatic)         105.65        6844.5   \n",
              "\n",
              "     Churn  \n",
              "7038    No  \n",
              "7039    No  \n",
              "7040    No  \n",
              "7041   Yes  \n",
              "7042    No  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
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              "      <th>7038</th>\n",
              "      <td>6840-RESVB</td>\n",
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              "      <td>DSL</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>84.80</td>\n",
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              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>7039</th>\n",
              "      <td>2234-XADUH</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
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              "      <td>Yes</td>\n",
              "      <td>72</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>103.20</td>\n",
              "      <td>7362.9</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>4801-JZAZL</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.60</td>\n",
              "      <td>346.45</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>8361-LTMKD</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>4</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>74.40</td>\n",
              "      <td>306.6</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>3186-AJIEK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>66</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>105.65</td>\n",
              "      <td>6844.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a8d4b70-d986-4ca5-b321-59e0719c3b36')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4a8d4b70-d986-4ca5-b321-59e0719c3b36 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4a8d4b70-d986-4ca5-b321-59e0719c3b36');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bfa73a7a-9843-46fe-b973-16a3f0e8bee9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bfa73a7a-9843-46fe-b973-16a3f0e8bee9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "    30% {\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bfa73a7a-9843-46fe-b973-16a3f0e8bee9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K794hfxaxrDN",
        "outputId": "be7a61bf-a73f-40cf-d6f4-05a2045499a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7043, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Deskripsi Data:\n",
        "- customerID : Customer ID\n",
        "- Gender : Whether the customer is a male or a female\n",
        "- SeniorCitizen : Whether the customer is a senior citizen or not (1, 0)\n",
        "- Partner : Whether the customer has a partner or not (Yes, No)\n",
        "- Dependents : Whether the customer has dependents or not (Yes, No)\n",
        "- Tenure : Number of months the customer has stayed with the company\n",
        "- PhoneService : Whether the customer has a phone service or not (Yes, No)\n",
        "- MultipleLines : Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
        "- InternetService : Customer’s internet service provider (DSL, Fiber optic, No)\n",
        "- OnlineSecurity : Whether the customer has online security or not (Yes, No, No internet service)\n",
        "\n",
        "There are 7043 customers in the dataset and 19 features without customerID (non-informative) and Churn column (target variable). Most of the categorical features have 4 or less unique values.\n"
      ],
      "metadata": {
        "id": "lXHPcCjSxwYs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYjBVRmzyIg1",
        "outputId": "22258f7f-63ba-4d0d-f475-570db3ea8c74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "147903"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lFsAlX9fyMe_",
        "outputId": "828434dd-9546-4c7d-c367-cb6c363433dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID           object\n",
              "gender               object\n",
              "SeniorCitizen         int64\n",
              "Partner              object\n",
              "Dependents           object\n",
              "tenure                int64\n",
              "PhoneService         object\n",
              "MultipleLines        object\n",
              "InternetService      object\n",
              "OnlineSecurity       object\n",
              "OnlineBackup         object\n",
              "DeviceProtection     object\n",
              "TechSupport          object\n",
              "StreamingTV          object\n",
              "StreamingMovies      object\n",
              "Contract             object\n",
              "PaperlessBilling     object\n",
              "PaymentMethod        object\n",
              "MonthlyCharges      float64\n",
              "TotalCharges         object\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Totalcharges is given as object datatype but it is float datatype"
      ],
      "metadata": {
        "id": "48AXTa4CyQ29"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_5JODNHyRW9",
        "outputId": "eecbb181-cf36-4e05-a0a8-2e5509182bc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
              "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
              "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
              "       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n",
              "       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jbQJb6N_yTil",
        "outputId": "88731de3-8838-4441-cb96-33df58791aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customerID        7043 non-null   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   object \n",
            " 20  Churn             7043 non-null   object \n",
            "dtypes: float64(1), int64(2), object(18)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eagcl3NZyW8c",
        "outputId": "4270ed53-cd7d-42da-954c-381c036ac216"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID          0\n",
              "gender              0\n",
              "SeniorCitizen       0\n",
              "Partner             0\n",
              "Dependents          0\n",
              "tenure              0\n",
              "PhoneService        0\n",
              "MultipleLines       0\n",
              "InternetService     0\n",
              "OnlineSecurity      0\n",
              "OnlineBackup        0\n",
              "DeviceProtection    0\n",
              "TechSupport         0\n",
              "StreamingTV         0\n",
              "StreamingMovies     0\n",
              "Contract            0\n",
              "PaperlessBilling    0\n",
              "PaymentMethod       0\n",
              "MonthlyCharges      0\n",
              "TotalCharges        0\n",
              "Churn               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0begwCrtyZcj",
        "outputId": "488d216e-a0a0-4334-bd82-f3a15e1c9b6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Basis Data Cleaning\n",
        "\n",
        "As we have already observered in above cell that TotalCharges is given as object datatype but it is float datatype. We will fix it here"
      ],
      "metadata": {
        "id": "yOXfzwypygXi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['TotalCharges'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wq2Yq0ryzp6",
        "outputId": "458db0b1-ff57-477a-ab05-5d6db36f1f22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'],errors = 'coerce')"
      ],
      "metadata": {
        "id": "6IUPUBPKy4Ra"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['TotalCharges'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ln-8poJJy7GP",
        "outputId": "f34e5772-0f99-4b5e-c31f-8e6385f9a1d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_features = [\n",
        "    \"gender\",\n",
        "    \"SeniorCitizen\",\n",
        "    \"Partner\",\n",
        "    \"Dependents\",\n",
        "    \"PhoneService\",\n",
        "    \"MultipleLines\",\n",
        "    \"InternetService\",\n",
        "    \"OnlineSecurity\",\n",
        "    \"OnlineBackup\",\n",
        "    \"DeviceProtection\",\n",
        "    \"TechSupport\",\n",
        "    \"StreamingTV\",\n",
        "    \"StreamingMovies\",\n",
        "    \"Contract\",\n",
        "    \"PaperlessBilling\",\n",
        "    \"PaymentMethod\",\n",
        "]\n",
        "numerical_features = [\"tenure\", \"MonthlyCharges\", \"TotalCharges\"]\n",
        "target = \"Churn\""
      ],
      "metadata": {
        "id": "qja0qQUry9MZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.skew(numeric_only= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yK7ViJm_y-uJ",
        "outputId": "389c056b-bec5-4806-cf9d-5c588fa8d241"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SeniorCitizen     1.833633\n",
              "tenure            0.239540\n",
              "MonthlyCharges   -0.220524\n",
              "TotalCharges      0.961642\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr(numeric_only= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "LRZdljpyy-ry",
        "outputId": "856291d3-afc6-4e36-891d-bbfd4b1c9d60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                SeniorCitizen    tenure  MonthlyCharges  TotalCharges\n",
              "SeniorCitizen        1.000000  0.016567        0.220173      0.102411\n",
              "tenure               0.016567  1.000000        0.247900      0.825880\n",
              "MonthlyCharges       0.220173  0.247900        1.000000      0.651065\n",
              "TotalCharges         0.102411  0.825880        0.651065      1.000000"
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4512602581547341,\n        \"min\": 0.016566877681809315,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.016566877681809315,\n          0.10241060539533069,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.465982648863763,\n        \"min\": 0.016566877681809315,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          0.8258804609332019,\n          0.016566877681809315\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3701934413387694,\n        \"min\": 0.22017333857627205,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.24789985628615094,\n          0.6510648032262017,\n          0.22017333857627205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3886655247540547,\n        \"min\": 0.10241060539533069,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8258804609332019,\n          1.0,\n          0.10241060539533069\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Distribution\n",
        "\n",
        "We plot distributions for numerical and categorical features to check for outliers and compare feature distributions with target variable"
      ],
      "metadata": {
        "id": "uJFiO7AazH7M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Numerical Features Distribution\n",
        "\n",
        "Numeric summarizing techniques (mean, standard deviation, etc.) don't show us spikes, shapes of distributions and it is hard to observe outliers with it. That is the reason we use histograms."
      ],
      "metadata": {
        "id": "VFwUAm-CzMEW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[numerical_features].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Q_de3uoUzRqs",
        "outputId": "5525760a-6520-42bf-ea0d-278ccddd85b2"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            tenure  MonthlyCharges  TotalCharges\n",
              "count  7043.000000     7043.000000   7032.000000\n",
              "mean     32.371149       64.761692   2283.300441\n",
              "std      24.559481       30.090047   2266.771362\n",
              "min       0.000000       18.250000     18.800000\n",
              "25%       9.000000       35.500000    401.450000\n",
              "50%      29.000000       70.350000   1397.475000\n",
              "75%      55.000000       89.850000   3794.737500\n",
              "max      72.000000      118.750000   8684.800000"
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              "      <th></th>\n",
              "      <th>tenure</th>\n",
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              "      <th>mean</th>\n",
              "      <td>32.371149</td>\n",
              "      <td>64.761692</td>\n",
              "      <td>2283.300441</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
              "      <td>24.559481</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>401.450000</td>\n",
              "    </tr>\n",
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              "      <th>50%</th>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-876a9515-6b15-478e-9047-d783d98ae4da button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-876a9515-6b15-478e-9047-d783d98ae4da');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e5e9340b-7cd9-42a2-88bf-ae6b9bfe3508\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e5e9340b-7cd9-42a2-88bf-ae6b9bfe3508')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e5e9340b-7cd9-42a2-88bf-ae6b9bfe3508 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[numerical_features]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2478.9752758409018,\n        \"min\": 0.0,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.37114865824223,\n          29.0,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2468.7047672837775,\n        \"min\": 18.25,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          64.76169246059918,\n          70.35,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3117.312944742373,\n        \"min\": 18.8,\n        \"max\": 8684.8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2283.3004408418656,\n          1397.475,\n          7032.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[numerical_features].hist(bins=30, figsize=(10, 7))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "odxBVn_TzWhM",
        "outputId": "ce9c049b-6560-4a94-de99-f4673a10d697"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'tenure'}>,\n",
              "        <Axes: title={'center': 'MonthlyCharges'}>],\n",
              "       [<Axes: title={'center': 'TotalCharges'}>, <Axes: >]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We look at distributions of numerical features in relation to the target variable. We can observe that the greater TotalCharges and tenure are the less is the probability of churn."
      ],
      "metadata": {
        "id": "_7_TSejpzZur"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n",
        "df[df.Churn == \"No\"][numerical_features].hist(bins=30, color=\"blue\", alpha=0.5, ax=ax)\n",
        "df[df.Churn == \"Yes\"][numerical_features].hist(bins=30, color=\"red\", alpha=0.5, ax=ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "tDBwrQa7zWeI",
        "outputId": "eda81917-ac9d-4f65-c436-6811b59454aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<Axes: title={'center': 'tenure'}>,\n",
              "       <Axes: title={'center': 'MonthlyCharges'}>,\n",
              "       <Axes: title={'center': 'TotalCharges'}>], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Categorical Feature Distribution\n",
        "\n",
        "To analyze categorical features, we use bar charts. We observe that Senior citizens and customers without phone service are less represented in the data."
      ],
      "metadata": {
        "id": "c_4FGTKZzkh-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ROWS, COLS = 4, 4\n",
        "fig, ax = plt.subplots(ROWS,COLS, figsize=(19,19))\n",
        "row, col = 0, 0,\n",
        "for i, categorical_feature in enumerate(categorical_features):\n",
        "    if col == COLS - 1:\n",
        "        row += 1\n",
        "    col = i % COLS\n",
        "    df[categorical_feature].value_counts().plot(kind='bar', ax=ax[row, col]).set_title(categorical_feature)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "IU2n1goazpup",
        "outputId": "1f7079b5-1520-4bb3-99c9-3507b3ecb0ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1900x1900 with 16 Axes>"
            ],
            "image/png": 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79nCiZsXat2+vyMhIffPNN5Kun3AnJCRo2LBh6ty5s8Hp8Ge8VsB1nL8CwIP166+/qlKlSnn25+TkKDMz04BEhcuVK1fyXIEtScnJySwOfBuTJk2SdP1L7BkzZlhMt2Zvby8/Pz/NmDHDqHhWr3Tp0jKZTDKZTKpcubJFISA7O1tpaWkWhb3ChjUBABQrtWvX1s8//6yGDRvq1Vdf1csvv6yyZcsaHQvI1xNPPKFJkybpmWeeMToKbuLSpUt68cUXtXv3bl2+fFne3t5KTExUUFCQfvzxR5UqVcroiPh/vFYAAOBhqF+/vgYOHKhXX33VYh2iyMhIRUdHa9OmTUZHtGpt2rRR/fr19cEHH8jZ2Vn79u2Tr6+vunbtqpycHH333XdGR7R6zZo105IlS1S6dGmjoxQqc+fOVW5urnr06KHJkydbTCd9o4gSFBRkYML7QxEAQLFz4MABzZ8/X4sWLdKZM2f03HPPKTQ0VB07dsz3igPAKGvXrtV7772nMWPGKDAwUCVKlLBod3FxMSgZ/mrz5s3at2+f0tLSVK9ePbVs2dLoSLgJXisAAPAg/ec//1H37t01fPhwRUZGatSoUTpy5IjmzZun5cuX67nnnjM6olX7+eef1aJFC9WrV09r165V+/btdeDAASUnJ2vLli2qWLGi0RFRxG3YsEFPPfVUns/fhR1FAADF2pYtW7RgwQJ9++23unbtWr7zRQNGsbGxkaQ88xGyMLD1OHHihAICAoyOgTvAawUAAB6WTZs2KTIyUnFxceYLD0aMGKFWrVoZHa1QuHTpkqZOnWrx/EVERKh8+fJGRys0zpw5o2XLlikhIUEZGRkWbRMnTjQoVeFz7dq1PM9fYb0YjzUBABRrpUqVkpOTk+zt7XX58mWj4wAW1q1bZ3QE3EalSpX07LPPKjw8XC+++KIcHR2NjoSb4LUCAAAPS+PGjRUdHW10jELL1dVV//jHP4yOUWjFxMSoffv2CggI0OHDh1WzZk2dPHlSubm5qlevntHxrN7Vq1c1dOhQffPNN7p48WKe9sJ6MR4jAQAUO/Hx8VqwYIEWLFigI0eO6Nlnn1W3bt304osvWsz5BgC3Exsbqzlz5mjhwoXKyMhQly5d1KNHDzVs2NDoaPgLXisAAPAwZWRk6Pz588rJybHYX6FCBYMSFR4pKSnauXNnvs9fWFiYQakKjyeffFKtW7fWqFGjzOtSeHh4KDQ0VCEhIerTp4/REa1aRESE1q1bpw8++ECvvfaapk2bpl9//VVffPGFPvroI4WGhhod8Z5QBABQrDRq1Ei7du1SrVq1FBoaqldeeUWPPvqo0bGAm0pJSdGXX36pQ4cOSZJq1KihHj16ULCyMllZWVq2bJmioqK0cuVKVa5cWT169NBrr72mcuXKGR0Pf8JrBQAAHqSjR4+qR48e2rp1q8V+pvS8Mz/88INCQ0OVlpYmFxcXi6lRTSaTkpOTDUxXODg7Oys2NlYVK1ZU6dKltXnzZtWoUUNxcXHq0KGDTp48aXREq1ahQgXNmzdPTZs2lYuLi3766SdVqlRJX331lRYuXKgff/zR6Ij3xMboAADwMLVo0UL79+/X3r179c4771AAgFXbvXu3KlasqEmTJik5OVnJycmaOHGiKlasqJ9++snoePgTOzs7derUSd9++60+/vhjHTt2TO+88458fHwUFhamc+fOGR0R/4/XCsCtrF+/XiaTSSkpKUZHMTOZTFq6dKkk6eTJkzKZTIqNjZWUN29UVJTc3NwMyQngutdff102NjZavny59uzZo59++kk//fST9u7dyzn8HRg8eLB69OihtLQ0paSk6PfffzdvFADuTKlSpczz2JcvX17Hjx83t/32229GxSo0kpOTzWuJubi4mP/dPfPMM9q4caOR0e4LRQAAxUZmZqYWLVqUZ5FVwFoNHDhQ7du318mTJ7VkyRItWbJE8fHxev755zVgwACj4+FPdu/erbfeekvly5fXxIkT9c477+j48eOKjo7W2bNn1aFDB6Mj4v/xWgHW4fXXX5fJZJLJZJK9vb0qVaqkyMhIZWVlGR3toRk5cqT5OTCZTHJ1dVXjxo21YcMGi37nzp1T69at7+g+u3Tpol9++eVBxAVwh2JjY/XFF1+odevWqlOnjmrXrm2x4dZ+/fVX9e/fXyVLljQ6SqHVqFEjbd68WZLUpk0bDR48WB9++KF69OihRo0aGZzO+gUEBCg+Pl6SVLVqVX3zzTeSro9SKcyFdhYGBlBslChRQteuXTM6BnDHdu/erX/961+ys/vfn2s7OzsNHTpUDRo0MDAZbpg4caLmzJmjI0eOqE2bNpo3b57atGkjG5vr11n4+/srKipKfn5+xgYFrxVghUJCQjRnzhylp6frxx9/VEREhEqUKKHhw4cbkiczM/OhP2aNGjW0Zs0aSdevPBw/fryef/55nTlzxjz1n5eX1x3fn5OTk5ycnB5IVgB3pnr16lxtfR+Cg4O1e/du85XYuHsTJ05UWlqaJGnUqFFKS0vT4sWL9fjjj2vixIkGp7N+b7zxhuLi4vTss8/q3XffVbt27TR16lRlZmYW6uePkQAAipWIiAh9/PHHxeoqMxReLi4uSkhIyLP/9OnTcnZ2NiAR/mr69Onq1q2bTp06paVLl+r55583f6l8g4eHh7788kuDEuIGXivA+jg4OMjLy0u+vr7q06ePWrZsqWXLlmnixIkKDAxUqVKl5OPjo7feesv8ZYb0vylvli5dqscff1yOjo4KDg7W6dOnLe7/P//5j+rVqydHR0cFBARo1KhRFueAJpNJ06dPV/v27VWqVCl9+OGH+ebcvHmzGjduLCcnJ/n4+Kh///66cuWKuf3zzz835/D09NSLL75obvvuu+8UGBgoJycnlSlTRi1btrT4XTs7O3l5ecnLy0vVq1dXZGSk0tLSLK7m//N0QLfz1+mARo4cqTp16uirr76Sn5+fXF1d1bVrV12+fNnc5/LlywoNDVWpUqVUvnx5TZo0SU2bNmXUIXAXUlNTzdvHH3+soUOHav369bp48aJFW2pqqtFRrdKyZcvMW9u2bTVkyBCNHDlS//73vy3ali1bZnTUQiEgIEC1atWSdH1qoBkzZmjfvn3697//LV9fX4PTWb+BAweqf//+kqSWLVvq8OHDWrBggfbu3au3337b4HT3jpEAAIqVXbt2KSYmRqtXrzZ/uPyzJUuWGJQMyKtLly4KDw/X+PHj9dRTT0mStmzZoiFDhuiVV14xOB2k6wu/3Y69vb26d+/+ENLgVnitAOvn5OSkixcvysbGRlOmTJG/v79OnDiht956S0OHDtXnn39u7nv16lV9+OGHmjdvnuzt7fXWW2+pa9eu2rJliyRp06ZNCgsL05QpU9S4cWMdP35cvXr1kiS9//775vsZOXKkPvroI02ePFl2dnY6ceKERabjx48rJCREo0eP1uzZs3XhwgX17dtXffv21Zw5c7R79271799fX331lZ566iklJydr06ZNkq5P4/PKK69o3LhxeuGFF3T58mVt2rRJubm5+R5/enq65syZIzc3N1WpUqXAntfjx49r6dKlWr58uX7//Xe9/PLL+uijj8xFj0GDBmnLli1atmyZPD09NWLECP3000+qU6dOgWUAijo3NzeLaWdzc3PVokULiz4sDHxzHTt2zLMvMjIyzz6ev7uXlpamnJwci30uLi4GpSmcfH19i0TxhCIAgGLFzc1NnTt3NjoGcEfGjx8vk8mksLAw85WLJUqUUJ8+ffTRRx8ZnA4AgIKRm5urmJgYrVq1Sv369bO4At3Pz0+jR49W7969LYoAmZmZmjp1qho2bChJmjt3rqpVq6adO3fqySef1KhRo/Tuu++aC3sBAQH64IMPNHToUIsiQLdu3fTGG2+Yb/+1CDB27FiFhoaaMz3++OOaMmWKnn32WU2fPl0JCQkqVaqUnn/+eTk7O8vX11d169aVdL0IkJWVpU6dOpm/PAgMDLS4//379+uRRx6RdL2w4ezsrMWLFxfoFzQ5OTmKiooyjyJ87bXXFBMTow8//FCXL1/W3LlztWDBAvMXlnPmzJG3t3eBPT5QHKxbt87oCIXaX7+kxv2Jj49X3759tX79eospkSlE3bldu3Zp3bp1On/+fJ5/n4V1SiCKAACKlTlz5hgdAbhj9vb2+vTTTzV27FgdP35cklSxYkUWyQIAFAnLly/XI488oszMTOXk5Khbt24aOXKk1qxZo7Fjx+rw4cNKTU1VVlaWrl27pqtXr5r/BtrZ2emJJ54w31fVqlXl5uamQ4cO6cknn1RcXJy2bNliMcVPdnZ2nvu53Ro7cXFx2rdvn+bPn2/el5ubq5ycHMXHx+u5556Tr6+vAgICFBISopCQEL3wwgsqWbKkateurRYtWigwMFDBwcFq1aqVXnzxRZUuXdp8X1WqVDFPb3H58mUtXrxYL730ktatW1dg6//4+flZTCNYvnx5nT9/XtL1okdmZqaefPJJc7urq2uBjkQAioNnn33W6AiA2auvvqrc3FzNnj1bnp6eFqNUcHtjxozRe++9pypVquR5/grzc0kRAECxk5WVpfXr1+v48ePq1q2bnJ2ddfbsWbm4uJivxAKsScmSJfNcOQgAQGHXrFkzTZ8+Xfb29vL29padnZ1Onjyp559/Xn369NGHH34od3d3bd68WeHh4crIyLjjQnhaWppGjRqlTp065WlzdHQ0//zXqSHzu5+//e1v5rmB/6xChQqyt7fXTz/9pPXr12v16tUaMWKERo4cqV27dsnNzU3R0dHaunWrVq9erc8++0z/+Mc/tGPHDvn7+0u6XvCvVKmS+T7r1q2rpUuXavLkyfr666/v6Fhvp0SJEha3TSYTV90CD9CcOXP0yCOP6KWXXrLY/+233+rq1atMPXgb/fv3V6VKlfK8706dOlXHjh3T5MmTjQlWiMTFxWnPnj0UdO/Rp59+qtmzZ+v11183OkqBYmFgAMXKqVOnFBgYqA4dOigiIkIXLlyQJH388cd65513DE4HWLp27Zo++eQTtWnTRg0aNFC9evUsNgAACrNSpUqpUqVKqlChguzsrl+ftmfPHuXk5GjChAlq1KiRKleurLNnz+b53aysLO3evdt8+8iRI0pJSVG1atUkSfXq1dORI0dUqVKlPNtfFwW/lXr16ungwYP53o+9vb2k66MSWrZsqXHjxmnfvn06efKk1q5dK+n6F+5PP/20Ro0apb1798re3l7ff//9LR/T1tZWf/zxxx1nvB8BAQEqUaKEdu3aZd536dIli4WJAdydsWPHqmzZsnn2e3h4aMyYMQYkKlz+/e9/6+mnn86z/6mnntJ3331nQKLC54knntDp06eNjlFo2djY5PtvsLBjJACAYuXtt99WgwYNFBcXpzJlypj3v/DCC+rZs6eByYC8wsPDtXr1ar344ot68sknC/XQw6LuwoULOnLkiKTrUzuUK1fO4ETIDyPBAOtXqVIlZWZm6rPPPlO7du20ZcsWzZgxI0+/EiVKqF+/fpoyZYrs7OzUt29fNWrUyDytzYgRI/T888+rQoUKevHFF2VjY6O4uDj9/PPPGj169B3nGTZsmBo1aqS+ffvqzTffVKlSpXTw4EFFR0dr6tSpWr58uU6cOKEmTZqodOnS+vHHH5WTk6MqVapox44diomJUatWreTh4aEdO3bowoUL5kKFdP19KTExUdL/pgM6ePCghg0bdp/P5J1xdnZW9+7dNWTIELm7u8vDw0Pvv/++bGxsOO8A7lFCQoJ5tM+f+fr6KiEhwYBEhcvFixfl6uqaZ7+Li4t+++03AxIVPrNmzVLv3r3166+/qmbNmnlGhNWqVcugZIXDwIEDNW3atCI36oQiAIBiZdOmTdq6dav5yq0b/Pz89OuvvxqUCsjf8uXL9eOPPxbJqxCKiitXrqhfv3766quvzAts2draKiwsTJ999hnrN1iRU6dOKSQkRAkJCUpPT9dzzz0nZ2dnffzxx0pPT8/3S0YAD1/t2rU1ceJEffzxxxo+fLiaNGmisWPHKiwszKJfyZIlNWzYMHXr1k2//vqrGjdurC+//NLcHhwcrOXLlysyMlIff/yxSpQooapVq+rNN9+8qzy1atXShg0b9I9//EONGzdWbm6uKlasqC5dukiS3NzctGTJEo0cOVLXrl3T448/roULF6pGjRo6dOiQNm7cqMmTJys1NVW+vr6aMGGCWrdubb7/AwcOqHz58uZjqlixoqZPn57neB+kiRMnqnfv3nr++efl4uKioUOH6vTp0xbTJgG4cx4eHtq3b5/8/Pws9v/1QjTkr1KlSlq5cqX69u1rsf+///2vAgICDEpVuFy4cEHHjx+3WPjeZDKxMPAdeuedd9S2bVtVrFhR1atXz1NEWbJkiUHJ7o8pNzc31+gQAPCwlC5dWlu2bFH16tXl7OysuLg4BQQEaPPmzercubOSkpKMjgiYVa9eXYsWLeJKDSv2t7/9TWvWrNHUqVPNxZrNmzerf//+eu655zR9+nSDE+KGjh07ytnZWV9++aXKlCljfv9fv369evbsqaNHjxodEcAdioqK0oABA5SSkmJ0lCLpypUrevTRRzVhwgSFh4cbHQcodIYNG6bFixdrzpw5atKkiSRpw4YN6tGjh1588UWNHz/e4ITWbfbs2erbt6+GDBmi5s2bS5JiYmI0YcIETZ48mRH8d6B69eqqVq2ahg4dmu/CwL6+vgYlKxz69u2rWbNmqVmzZvk+f3PmzDEo2f2hCACgWOnSpYtcXV01c+ZMOTs7a9++fSpXrpw6dOigChUqFNo3cxRN//3vfzVlyhTNmDGDEzUrVbZsWX333Xdq2rSpxf5169bp5ZdfNq87AuOVKVNGW7duVZUqVSyKwCdPnlT16tV19epVoyMCuEMUAQrW3r17dfjwYT355JO6dOmSIiMjtX79eh07dizfec0B3FpGRoZee+01ffvtt+b1TnJychQWFqYZM2bkGZWOvKZPn64PP/zQvCaMn5+fRo4c+VBHSRVmpUqVUlxcnMXC87hzzs7OWrRokdq2bWt0lALFdEAAipUJEyYoODhY1atX17Vr19StWzcdPXpUZcuW1cKFC42OB1ho0KCBrl27poCAAJUsWTLPMMTk5GSDkuGGq1evytPTM89+Dw8PvlS2Mjk5OfkOfT5z5oycnZ0NSAQA1mP8+PE6cuSI7O3tVb9+fW3atIkCAHCP7O3ttXjxYo0ePVqxsbFycnJSYGAgF/XchT59+qhPnz66cOGCnJycWLvpLjVv3pwiwH1wd3dXxYoVjY5R4BgJAKDYycrK0uLFixUXF6e0tDTVq1dPoaGhcnJyMjoaYKFly5ZKSEhQeHh4vsMQu3fvblAy3NCiRQuVKVNG8+bNM8+d/Mcff6h79+5KTk7WmjVrDE6IGxgJBgAAgOJg5syZGj16tHr06KHAwMA8F5O1b9/eoGSFw5w5c7Ry5UrNmTOnSK3xRhEAAAArVbJkSW3btk21a9c2Ogpu4ueff1ZwcLDS09PNr1NcXJwcHR21atUq1ahRw+CEuOHMmTMKDg5Wbm6ujh49qgYNGphHgm3cuFEeHh5GRwQAAADum42NzU3bWBj49urWravjx48rNzdXfn5+eYooP/30k0HJ7g/TAQEoVubOnauyZcua53YbOnSoZs6cqerVq2vhwoUM0YRVqVq1qv744w+jY+AWatasqaNHj2r+/Pk6fPiwJOmVV15hdJEVeuyxxxQXF6dFixZp3759SktLU3h4OK8VAAAAipScnByjIxRqHTt2NDrCA8FIAADFSpUqVTR9+nQ1b95c27ZtU4sWLTR58mQtX75cdnZ2WrJkidERAbPVq1dr1KhR+vDDD/Mdxuni4mJQMgAAAAAAUFhQBABQrJQsWVKHDx9WhQoVNGzYMJ07d07z5s3TgQMH1LRpU124cMHoiIDZjWGcf10LIDc3l2GcVqJChQpq2rSpnn32WTVr1kwBAQFGR8ItHD16VOvWrdP58+fzXCE1YsQIg1IBAAAABWvDhg0aP368Dh06JEmqXr26hgwZosaNGxucrPDYs2eP+fmrUaOG6tata3Ci+8N0QACKlUceeUQXL15UhQoVtHr1ag0aNEiS5OjoyLQrsDrr1q0zOgJuY8yYMdq4caM+/vhj9ezZU48++qieffZZPfvss2ratKkef/xxoyPi//3rX/9Snz59VLZsWXl5eVkU10wmE0UAAABQIObMmaNHHnlEL730ksX+b7/9VlevXlX37t0NSma9pkyZcsd9+/fv/wCTFA1ff/213njjDXXq1Mn8fG3ZskUtWrRQVFSUunXrZnBC63b+/Hl17dpV69evl5ubmyQpJSVFzZo106JFi1SuXDljA94jRgIAKFZCQ0N1+PBh1a1bVwsXLlRCQoLKlCmjZcuW6e9//7t+/vlnoyMCKKTOnTunDRs2aPny5Vq8eLFycnIYrWFFfH199dZbb2nYsGFGRwEAAEVY5cqV9cUXX6hZs2YW+zds2KBevXrpyJEjBiWzXv7+/ha3L1y4oKtXr1p8AVuyZEl5eHjoxIkTBiQsXKpVq6ZevXpp4MCBFvsnTpyof/3rX+ar25G/Ll266MSJE5o3b56qVasmSTp48KC6d++uSpUqaeHChQYnvDc3Xy4aAIqgadOmKSgoSBcuXNC///1vlSlTRtL1YV6vvPKKwemAmwsMDNTp06eNjoF8XL16VatXr9Znn32mTz/9VN99951q1qzJVUpW5vfff89zRR4AAEBBS0hIyPOltnT9goSEhAQDElm/+Ph48/bhhx+qTp06OnTokJKTk5WcnKxDhw6pXr16+uCDD4yOWiicOHFC7dq1y7O/ffv2io+PNyBR4bJy5Up9/vnn5gKAdH06pWnTpum///2vgcnuD9MBAShW3NzcNHXq1Dz7R40aZUAa4M6dPHlSmZmZRsfAXzz11FPau3evqlWrpqZNm+rdd99VkyZNVLp0aaOj4S9eeuklrV69Wr179zY6CgAAKMI8PDy0b98++fn5WeyPi4szX4SGm/vnP/+p7777TlWqVDHvq1KliiZNmqQXX3xRoaGhBqYrHHx8fBQTE6NKlSpZ7F+zZo18fHwMSlV45OTkqESJEnn2lyhRIs+6YoUJRQAAAIB7dPjwYZUqVUpVq1ZV1apVVa1aNQoAVqpSpUr65z//qe3btyswMDDPiT0jNwAAQEF45ZVX1L9/fzk7O6tJkyaSrk8F9Pbbb6tr164Gp7N+586dU1ZWVp792dnZSkpKMiBR4TN48GD1799fsbGxeuqppyRdXxMgKipKn376qcHprF/z5s319ttva+HChfL29pYk/frrrxo4cKBatGhhcLp7x5oAAAAUAm3atNGXX36p8uXLGx0Ff5Kbm6v9+/dr/fr12rBhgzZu3Ch7e3s9++yzatasmXr27Gl0RPy//Ibl32AymZhfFgAAFIiMjAy99tpr+vbbb2Vnd/3a25ycHIWFhWnGjBmyt7c3OKF1a9eunX799VfNmjVL9erVk3R9+t5evXrp0Ucf1bJlywxOWDh8//33mjBhgnn+/2rVqmnIkCHq0KGDwcms3+nTp9W+fXsdOHDAPHLi9OnTqlmzppYtW6bHHnvM4IT3hiIAAABAAcjNzdWePXs0depUzZ8/n4WBAQAAirFffvlFcXFxcnJyUmBgoHx9fY2OVChcuHBB3bt318qVK80jN7OyshQcHKyoqCh5eHgYnBDFQW5urtasWaPDhw9Lul5EadmypcGp7g9FAAAArFh2draWLl1qvoKjRo0aat++vWxtbQ1OVrxFRkbqnXfe0eHDh7V+/XqtX79emzdv1uXLlxUYGKimTZvq2Wef5UobK/Tbb79JksqWLWtwEgAAANzML7/8Yv4CtmrVqqpcubLBiQqPgIAA7dq1K88aFCkpKapXrx4jYG9j3rx56tKlixwcHCz2Z2RkaNGiRQoLCzMo2f2hCACgWGnevLmWLFkiNzc3i/2pqanq2LGj1q5da0wwIB/Hjh1T27ZtdebMGfPCWEeOHJGPj49WrFihihUrGpyw+LK1tdW5c+fk7e2tunXr6tlnn9Wzzz6rJk2ayNXV1eh4+IuUlBT94x//0OLFi/X7779LkkqXLq2uXbtq9OjRef4mAAAA3I1Bgwbpgw8+UKlSpTRo0KBb9p04ceJDSlW4ZWRkKD4+XhUrVjRPq4Q7Y2Njo8TExDyjJpKSklShQgWlp6cblKxwuPFZ76/P38WLF+Xh4VFoR3vzfxGAYmX9+vXKyMjIs//atWvatGmTAYmAm+vfv78CAgK0bds2ubu7S7p+4vHqq6+qf//+WrFihcEJi68b11AkJyfLxcXF4DS4leTkZAUFBenXX39VaGioqlWrJkk6ePCgoqKiFBMTo61bt7KgMwAAuGd79+5VZmam+eebMZlMDytSoXX16lX169dPc+fOlXR9REBAQID69eunRx99VO+++67BCa3Xn9dLWLVqlcXFSdnZ2YqJiZGfn58ByQqX3NzcfP9fPXPmTKG+4IuRAACKhX379kmS6tSpo7Vr15q/UJWu/zFcuXKlvvjiC508edKghEBepUqV0vbt2xUYGGixPy4uTk8//bTS0tIMSgYbGxslJSWpXLlyRkfBbQwYMEAxMTFas2aNPD09LdoSExPVqlUrtWjRQpMmTTIoIQAAAG54++23tWXLFk2ePFkhISHat2+fAgIC9J///EcjR468ZZGluLOxsZF0vdj01697S5QoIT8/P02YMEHPP/+8EfGsXt26dWUymRQXF6caNWpYjEDJzs5WfHy8QkJC9M033xiY8t4xEgBAsVCnTh2ZTCaZTCY1b948T7uTk5M+++wzA5IBN+fg4KDLly/n2Z+WliZ7e3sDEuHPKleufNuruZKTkx9SGtzM0qVL9cUXX+QpAEiSl5eXxo0bp969e1MEAAAAsAJLly7V4sWL1ahRI4tz7Ro1auj48eMGJrN+OTk5kiR/f3/t2rWLNbDuUseOHSVJsbGxCg4O1iOPPGJus7e3l5+fnzp37mxQuvtHEQBAsRAfH6/c3FwFBARo586dFlfv2tvby8PDg4VWYXWef/559erVS19++aWefPJJSdKOHTvUu3dvtW/f3uB0GDVqVKEeDlpcnDt3TjVq1Lhpe82aNZWYmPgQEwEAgKLsypUr+uijjxQTE6Pz58+bv5i9gUVZb+3ChQt55mKXrj+vTKd0Z+Lj442OUCi9//77kiQ/Pz916dJFjo6OBicqWBQBABQLvr6+kpTnBAywZlOmTFH37t0VFBSkEiVKSJKysrLUvn17TZ482dhwUNeuXfP9gALrUrZsWZ08eVKPPfZYvu3x8fEWU8QBAADcjzfffFMbNmzQa6+9pvLly/PF9V1q0KCBVqxYoX79+kn63zoKs2bNUlBQkJHRCo3IyMhbto8YMeIhJSmcunfvbnSEB4I1AQAUO1999ZVmzJih+Ph4bdu2Tb6+vpo0aZICAgLUoUMHo+MBeRw7dkyHDh2SJFWrVk2VKlUyOBFsbW117tw5igCFQI8ePXT8+HFFR0fnmUYrPT1dwcHBCggI0OzZsw1KCAAAihI3NzetWLFCTz/9tNFRCqXNmzerdevWevXVVxUVFaW//e1vOnjwoLZu3aoNGzaofv36Rke0enXr1rW4nZmZqfj4eNnZ2alixYr66aefDEpWONjY2NyyeJednf0Q0xQcRgIAKFamT5+uESNGaMCAAfrwww/Nb96lS5fW5MmTKQLAqkRGRuqdd95RpUqVLL74/+OPP/TJJ59wBYeBuIai8IiMjFSDBg30+OOPKyIiQlWrVlVubq4OHTqkzz//XOnp6frqq6+MjgkAAIqI0qVLM8rwPjzzzDOKjY3VRx99pMDAQK1evVr16tXTtm3bFBgYaHS8QiG/xZNTU1P1+uuv64UXXjAgUeGyZMkSiyJAZmam9u7dq7lz52rUqFEGJrs/jAQAUKxUr15dY8aMUceOHeXs7Ky4uDgFBATo559/VtOmTfXbb78ZHREwu9nV5hcvXpSHh0ehvQIBeNji4+P11ltvafXq1eYCjslk0nPPPaepU6cyugYAABSYr7/+Wv/5z380d+5clSxZ0ug4gNn+/fvVrl07nTx50ugohdKCBQu0ePFi/ec//zE6yj1hJACAYiU+Pj7P0DhJcnBw0JUrVwxIBNxcbm5uvsMQ4+LiuLoIuAv+/v7673//q99//11Hjx6VJFWqVIn/jwAAQIGbMGGCjh8/Lk9PT/n5+ZnX9rqBqVjySk1NveO+Li4uDzBJ0Xbp0iVdunTJ6BiFVqNGjdSrVy+jY9wzigAAihV/f3/FxsaaFwq+YeXKlapWrZpBqQBLpUuXlslkkslkUuXKlS0KAdnZ2UpLS1Pv3r0NTAgUTqVLl9aTTz5pdAwAAFCEdezY0egIhY6bm9ttF1C+cYEUo6Fvb8qUKRa3c3Nzde7cOX311Vdq3bq1QakKtz/++ENTpkzRo48+anSUe0YRAECxMmjQIEVEROjatWvKzc3Vzp07tXDhQo0dO1azZs0yOh4gSZo8ebJyc3PVo0cPjRo1Sq6uruY2e3t7+fn5KSgoyMCEAAAAAPLz/vvvGx2h0Fm3bp3REYqUSZMmWdy2sbFRuXLl1L17dw0fPtygVIXHjYvybsjNzdXly5dVsmRJff311wYmuz+sCQCg2Jk/f75Gjhyp48ePS5K8vb01atQohYeHG5wMsLRhwwY9/fTTsrOjZg8AAAAUJnv27NGhQ4ckSTVq1Mh3WloA1mfu3LkWt28UURo2bKjSpUsblOr+UQQAUGxdvXpVaWlpeRZdBaxR27ZtNWvWLJUvX97oKAAAAABu4vz58+ratavWr18vNzc3SVJKSoqaNWumRYsWqVy5csYGtEL79u1TzZo1ZWNjo3379t2yb61atR5SKqBooQgAAEAh4OzsrLi4OAUEBBgdBQAAAMBNdOnSRSdOnNC8efPM684dPHhQ3bt3V6VKlbRw4UKDE1ofGxsbJSYmysPDQzY2NjKZTMrv60rWBLhzu3fv1jfffKOEhARlZGRYtC1ZssSgVIXL1atX833+CmshivkFABQrSUlJeueddxQTE6Pz58/nObHghAIAAAAAcK9WrlypNWvWmAsAklS9enVNmzZNrVq1MjCZ9YqPjzePkIiPjzc4TeG3aNEihYWFKTg4WKtXr1arVq30yy+/KCkpSS+88ILR8azehQsX9Prrr2vlypX5thfW740oAgAoVl5//XUlJCTon//8p8qXL2+x2AtgzXx9fVWiRAmjYwAAAAC4hZycnHzP20uUKKGcnBwDElk/X1/ffH/GvRkzZowmTZqkiIgIOTs769NPP5W/v7/+9re/Mb3sHRgwYIAuXbqkHTt2qGnTpvr++++VlJSk0aNHa8KECUbHu2dMBwSgWHF2dtamTZtUp04do6MAAAAAAIqYDh06KCUlRQsXLpS3t7ck6ddff1VoaKhKly6t77//3uCEhcPBgwfznYqlffv2BiUqPEqVKqUDBw7Iz89PZcqU0fr16xUYGKhDhw6pefPmOnfunNERrVr58uX1n//8R08++aRcXFy0e/duVa5cWcuWLdO4ceO0efNmoyPeE0YCAChWfHx88p1bELBWKSkp2rlzp86fP5/nyqGwsDCDUgEAAADIz9SpU9W+fXv5+fnJx8dHknT69GnVrFlTX3/9tcHprN+JEyf0wgsvaP/+/RZrA9wYxV9Yp2J5mEqXLq3Lly9Lkh599FH9/PPPCgwMVEpKiq5evWpwOut35coVeXh4SLr+XF64cEGVK1dWYGCgfvrpJ4PT3TuKAACKlcmTJ+vdd9/VF198IT8/P6PjALf0ww8/KDQ0VGlpaXJxcbGYvspkMlEEAAAAAKyMj4+PfvrpJ61Zs0aHDx+WJFWrVk0tW7Y0OFnh8Pbbb8vf318xMTHy9/fXzp07dfHiRQ0ePFjjx483Ol6h0KRJE0VHRyswMFAvvfSS3n77ba1du1bR0dFq0aKF0fGsXpUqVXTkyBH5+fmpdu3a5u+PZsyYUainU2I6IADFSunSpXX16lVlZWWpZMmSeeZqTE5ONigZkFflypXVpk0bjRkzRiVLljQ6DgAAAIBbyMzMlJOTk2JjY1WzZk2j4xRKZcuW1dq1a1WrVi25urpq586dqlKlitauXavBgwdr7969Rke0esnJybp27Zq8vb2Vk5OjcePGaevWrXr88cf13nvvqXTp0kZHtGpff/21srKy9Prrr2vPnj0KCQlRcnKy7O3tFRUVpS5duhgd8Z4wEgBAsTJ58mSjIwB37Ndff1X//v0pAAAAAACFQIkSJVShQgWmrLkP2dnZcnZ2lnS9IHD27FlVqVJFvr6+OnLkiMHpCgd3d3fzzzY2Nnr33XcNTFP4vPrqq+af69evr1OnTunw4cOqUKGCypYta2Cy+0MRAECxkZmZqQ0bNuif//yn/P39jY4D3FZwcLB2796tgIAAo6MAAAAAuAP/+Mc/9Pe//11fffWVxZexuDM1a9ZUXFyc/P391bBhQ40bN0729vaaOXMmn4tu4cqVKypVqtQD61+clSxZUvXq1TM6xn1jOiAAxYqrq6tiY2MpAsBqLVu2zPzzhQsXFBkZqTfeeEOBgYF5pq9q3779w44HAAAA4Bbq1q2rY8eOKTMzU76+vnm+aC3MC4s+DKtWrdKVK1fUqVMnHTt2TM8//7x++eUXlSlTRosXL1bz5s2NjmiVypcvr7ffflvdu3e/6bz1ubm5WrNmjSZOnKgmTZpo+PDhDzml9froo4/09ttvy8nJ6bZ9d+zYod9++01t27Z9CMkKDkUAAMVK9+7dVadOHQ0cONDoKEC+bGxs7qifyWRimDEAAABgZUaOHCmTyXTT9vfff/8hpikakpOTVbp06Vs+r8XdkSNH9Pe//10rVqxQ7dq11aBBA3l7e8vR0VG///67Dh48qG3btsnOzk7Dhw/X3/72N9na2hod22qEhYXpv//9r1566SW1a9dODRo0ULly5SRJWVlZOnjwoDZv3qyvv/5aZ8+e1bx589SkSRODU98digAAipXRo0drwoQJatGiherXr5/nqoz+/fsblAwAAAAAAODeJSQk6Ntvv9WmTZt06tQp/fHHHypbtqzq1q2r4OBgtW7dmi//byIuLk5Tp07Vd999p9TUVNna2srBwUFXr16VdH2Uz5tvvqnXX39djo6OBqe9exQBABQrt5oGyGQy6cSJEw8xDXBr8+bNU5cuXeTg4GCxPyMjQ4sWLVJYWJhByQAAAADkJyAgQLt27VKZMmUs9qekpKhevXp85ryJHj163FG/2bNnP+AkKO5ycnK0b98+iyJKnTp1CvWiwBJFAAAArJatra3OnTsnDw8Pi/0XL16Uh4cH0wEBAAAAVsbGxkaJiYl5zuGTkpLk4+OjjIwMg5JZNxsbG/n6+qpu3bq61VeV33///UNMBRQddkYHAAAjZGRkKD4+XhUrVpSdHW+FsE65ubn5znt55swZubq6GpAIAAAAQH6WLVtm/nnVqlUW5+vZ2dmKiYm55cj04q5Pnz5auHCh4uPj9cYbb+jVV1+Vu7u70bGAIoORAACKlatXr6pfv36aO3euJOmXX35RQECA+vXrp0cffVTvvvuuwQmB63MNmkwmxcXFqUaNGhaFquzsbMXHxyskJETffPONgSkBAAAA3GBjYyPp+jSzf/2qrUSJEvLz89OECRP0/PPPGxGvUEhPT9eSJUs0e/Zsbd26VW3btlV4eLhatWrFosDAfeLyVwDFyvDhwxUXF6f169crJCTEvL9ly5YaOXIkRQBYhY4dO0qSYmNjFRwcrEceecTcZm9vLz8/P3Xu3NmgdAAAAAD+KicnR9L1deh27dpV6OcPN4KDg4NeeeUVvfLKKzp16pSioqL01ltvKSsrSwcOHLD4XATg7lAEAFCsLF26VIsXL1ajRo0sriSoUaOGjh8/bmAy4H/ef/99SZKfn5+6dOkiR0dHgxMBAAAAuBPx8fFGRygSbGxszKMqWAvtzmVlZWnMmDHq0aOHHnvsMaPjwIrYGB0AAB6mCxcu5FmgSZKuXLnC8EJYne7du8vR0VEZGRk6c+aMEhISLDYAAAAA1qV///6aMmVKnv1Tp07VgAEDHn6gQiQ9PV0LFy7Uc889p8qVK2v//v2aOnWqEhISGAVwh+zs7PTJJ58oKyvL6CiwMowEAFCsNGjQQCtWrFC/fv0kyfzF/6xZsxQUFGRkNCCPo0ePqkePHtq6davF/hsLBnNFDAAAAGBd/v3vf1ssEnzDU089pY8++kiTJ09++KEKgbfeekuLFi2Sj4+PevTooYULFzKl0j1q3ry5NmzYID8/P6OjFBqdOnW6475Llix5gEkeHIoAAIqVMWPGqHXr1jp48KCysrL06aef6uDBg9q6das2bNhgdDzAwuuvvy47OzstX75c5cuXZ7QKAAAAYOUuXrwoV1fXPPtdXFz022+/GZCocJgxY4YqVKiggIAAbdiw4aafzwvrF7APU+vWrfXuu+9q//79ql+/vkqVKmXR3r59e4OSWa8//z+bm5ur77//Xq6urmrQoIEkac+ePUpJSbmrYoG1MeX+dclyACjijh8/ro8++khxcXFKS0tTvXr1NGzYMAUGBhodDbBQqlQp7dmzR1WrVjU6CgAAAIA7ULNmTfXu3Vt9+/a12P/ZZ59p+vTpOnjwoEHJrNvrr79+Rxc9zZkz5yGkKdxsbG4++zsjym9v2LBhSk5O1owZM2RraytJys7O1ltvvSUXFxd98sknBie8NxQBAACwUk888YQmTZqkZ555xugoAAAAAO7A7Nmz1bdvXw0ZMkTNmzeXJMXExGjChAmaPHmyevbsaXBCALdSrlw5bd68WVWqVLHYf+TIET311FO6ePGiQcnuD9MBAShWbG1tde7cuTyLA1+8eFEeHh5UxGFVPv74Yw0dOlRjxoxRYGCgSpQoYdHu4uJiUDIAAAAA+enRo4fS09P14Ycf6oMPPpAk+fn5afr06QoLCzM4HYqba9euydHR0egYhUpWVpYOHz6cpwhw+PBh5eTkGJTq/jESAECxYmNjo8TExDxFgLNnz6pixYr6448/DEoG5HVjGOdfh8WyMDAAAABg/S5cuCAnJyc98sgjRkdBMZKdna0xY8ZoxowZSkpK0i+//KKAgAD985//lJ+fn8LDw42OaNUGDRqkefPm6e9//7uefPJJSdKOHTv00Ucf6bXXXtPEiRMNTnhvGAkAoFiYMmWKpOtfps6aNcviJCw7O1sbN25k3nVYnXXr1hkdAQAAAMBdysrK0vr163X8+HF169ZN0vULz1xcXCgI4IH78MMPNXfuXI0bN85i+qmaNWtq8uTJFAFuY/z48fLy8tKECRN07tw5SVL58uU1ZMgQDR482OB0946RAACKBX9/f0nSqVOn9Nhjj5kXd5Eke3t7+fn5KTIyUg0bNjQqIgAAAACgkDt16pRCQkKUkJCg9PR081XYb7/9ttLT0zVjxgyjI6KIq1Spkr744gu1aNFCzs7OiouLU0BAgA4fPqygoCD9/vvvRke0WllZWVqwYIGCg4Pl6emp1NRUSUVjKl5GAgAoFuLj4yVJzZo105IlS1S6dGmDEwF3JiUlRV9++aUOHTokSapRo4Z69OghV1dXg5MBAAAA+Ku3335bDRo0UFxcnMqUKWPe/8ILL7AoMB6KX3/9VZUqVcqzPycnR5mZmQYkKjzs7OzUu3dv8+fvovDl/w02RgcAgIdp3bp1FABQaOzevVsVK1bUpEmTlJycrOTkZE2cOFEVK1bUTz/9ZHQ8AAAAAH+xadMmvffee7K3t7fY7+fnp19//dWgVChOqlevrk2bNuXZ/91336lu3boGJCpcnnzySe3du9foGAWOkQAAipXs7GxFRUUpJiZG58+fz7Oy+9q1aw1KBuQ1cOBAtW/fXv/6179kZ3f9T3ZWVpbefPNNDRgwQBs3bjQ4IQAAAIA/y8nJUXZ2dp79Z86ckbOzswGJUNyMGDFC3bt316+//qqcnBwtWbJER44c0bx587R8+XKj41m9t956S4MHD9aZM2dUv359lSpVyqK9Vq1aBiW7P6wJAKBY6du3r6KiotS2bVuVL19eJpPJon3SpEkGJQPycnJy0t69e/MsWn3w4EE1aNBAV69eNSgZAAAAgPx06dJFrq6umjlzppydnbVv3z6VK1dOHTp0UIUKFTRnzhyjI6IY2LRpkyIjIxUXF6e0tDTVq1dPI0aMUKtWrYyOZvVsbPJOnGMymZSbmyuTyZRvka8woAgAoFgpW7as5s2bpzZt2hgdBbgtT09PffXVV3lO1FatWqWwsDAlJSUZlAwAAABAfs6cOaPg4GDl5ubq6NGjatCggY4ePaqyZctq48aN8vDwMDoiiqApU6aoV69ecnR0VEJCgnx8fPJc9Ig7c+rUqVu2+/r6PqQkBYsiAIBixdvbW+vXr1flypWNjgLcVv/+/fX9999r/PjxeuqppyRJW7Zs0ZAhQ9S5c2dNnjzZ2IAAAAAA8sjKytKiRYu0b98+81XYoaGhcnJyMjoaiig7OzudPXtWHh4esrW11blz5yg4wQJFAADFyoQJE3TixAlNnTqVqjisXkZGhoYMGaIZM2YoKytLubm5sre3V58+ffTRRx/JwcHB6IgAAAAAAINVqFBBw4cPV5s2beTv76/du3erbNmyN+2L2zt48KASEhKUkZFhsb99+/YGJbo/FAEAFCsvvPCC1q1bJ3d3d9WoUUMlSpSwaF+yZIlByYCbu3r1qo4fPy5JqlixokqWLGlwIgAAAAA3LFu27I77FtYvEGHdZs6cqX79+ikrK+umfQr7nPYPy4kTJ/TCCy9o//795rUAJJkvJC2szx9FAADFyhtvvHHLdhZpgjXo0aPHHfWbPXv2A04CAAAA4HbyW0g0P3wBiwfp8uXLOnXqlGrVqqU1a9aoTJky+farXbv2Q05WuLRr1062traaNWuW/P39tXPnTl28eFGDBw/W+PHj1bhxY6Mj3hOKAAAAWBkbGxv5+vqqbt26utWf6e+///4hpgIAAAAAWLu5c+eqa9euTB97j8qWLau1a9eqVq1acnV11c6dO1WlShWtXbtWgwcP1t69e42OeE/sjA4AAAAs9enTRwsXLlR8fLzeeOMNvfrqq3J3dzc6FgAAAADAyjVv3lwXLlzQY489JknauXOnFixYoOrVq6tXr14Gp7N+2dnZcnZ2lnS9IHD27Nn/a+/e43suH/+PP9/bzA7vzUbYaDZsY9osU3zwzfkcUR/lMIaVTxGdHD7cahiVUSHloz75tFFUEj5ImDnEPpIcZmkoGW55s5iNTbHD6/dHP+9b74bksNc2j/vt9r7d9rqu63Vdz712u7G9r/d1XWrQoIECAwN18OBBk9PduOtbrwQA5VhUVJTOnj0rSWrSpImioqKu+gLKgrlz58pms2ncuHFatWqVAgIC9Nhjj2ndunXXXBkAAAAAwBzdu3dXbm6u/TohIUE5OTn26zNnzqhRo0YmJMOdZsCAAdq0aZMk6eTJk+rYsaO+/vprvfjii5oyZYrJ6cq+8PBwpaWlSZKaN2+uGTNmKDU1VVOmTFG9evVMTnfj2A4IQIUXHx+vsWPHysPDQ/Hx8ddsO2nSpFJKBVy/o0ePKikpSQsXLlRhYaH2798vq9VqdiwAAAAA/5+zs7NsNptq1KghSfL29tbevXvtbxqeOnVKtWrV4kwA3Ha+vr766quv1KBBA82ZM0effPKJUlNTtX79ej311FP68ccfzY5Ypq1bt075+fl65JFH9MMPP6hHjx46dOiQqlWrpk8++UTt27c3O+INYTsgABXe79/Y501+lEdOTk6yWCwyDIM/GgAAAIAy6I+fseUztzBLQUGB/TyADRs26KGHHpIkNWzYUDabzcxo5UKXLl3sXwcHB+vAgQPKzs6Wr6+vLBaLicluDtsBAQBQBl28eFEfffSROnXqpND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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature = 'Contract'\n",
        "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
        "df[df.Churn == \"No\"][feature].value_counts().plot(kind='bar', ax=ax[0]).set_title('not churned')\n",
        "df[df.Churn == \"Yes\"][feature].value_counts().plot(kind='bar', ax=ax[1]).set_title('churned')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "IIBhj-x7z1qU",
        "outputId": "b4c18424-78f9-4ded-b8c6-f08c7230f40b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'churned')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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KrVu3Tm3atFFMTIyCg4PVo0cPh9PXMzMzdfbsWUVHRxvz2rVrp2bNmikjI0OSlJGRoc6dOyskJMToExMTo+LiYu3bt8+09QEAwBUI4gAAoMry8/NVUlKiF154QQMHDtTGjRt1++2364477lBaWpokKTc3V76+vgoMDHR4b0hIiHJzc40+vw7h59rPtV1IaWmpiouLHSYAADwRp6YDAIAqq6yslCTddttteuSRRyRJ1157rbZu3apFixapT58+NbbsmTNnavr06TX2+QAAmIUj4gAAoMoaN24sHx8fdejQwWF++/btjbumh4aGqqysTIWFhQ598vLyFBoaavT57V3Uz70+1+e3Jk+erKKiImM6duxYdawSAACmI4gDAIAq8/X11fXXX6+DBw86zP/qq68UEREhSerevbvq1Kmj1NRUo/3gwYPKyclRVFSUJCkqKkp79+5Vfn6+0SclJUVWq/W8kH+On5+frFarwwQAgCfi1HQAAOCgpKREhw8fNl5nZ2crKytLQUFBatasmSZNmqS7775bvXv3Vr9+/bR+/Xp98MEH2rJliyTJZrMpMTFREydOVFBQkKxWq8aPH6+oqCjdeOONkqQBAwaoQ4cOGjFihGbNmqXc3FxNmTJFSUlJ8vPzc8VqAwBgGoI4AABwsHPnTvXr1894PXHiRElSQkKCli5dqttvv12LFi3SzJkz9fDDD6tt27b6z3/+o549exrvefnll+Xl5aW4uDiVlpYqJiZGr732mtHu7e2ttWvXaty4cYqKilJAQIASEhI0Y8YM81YUAAAXsdjtdruri6gJxcXFstlsKioqcotT15o/uc7VJbitoy/EuroEADCFu41Nns7d/p6M9RfHWA+gtqjq2MQ14gAAAAAAmIggDgAAAACAiQjiAAAAAACYiCAOAAAAAICJCOIAAAAAAJiIIA4AAAAAgIkI4gAAAAAAmIggDgAAAACAiQjiAAAAAACYyKkgPnPmTF1//fVq0KCBgoODNWzYMB08eNChz5kzZ5SUlKRGjRqpfv36iouLU15enkOfnJwcxcbGql69egoODtakSZNUXl7u0GfLli3q1q2b/Pz81Lp1ay1duvTS1hAAAAAAADfiVBBPS0tTUlKSPvvsM6WkpOjs2bMaMGCATp06ZfR55JFH9MEHH+jdd99VWlqavvvuO91xxx1Ge0VFhWJjY1VWVqatW7dq2bJlWrp0qaZOnWr0yc7OVmxsrPr166esrCxNmDBBo0eP1oYNG6phlQEAAAAAcB2L3W63X+qbv//+ewUHBystLU29e/dWUVGRmjRpopUrV+rOO++UJH355Zdq3769MjIydOONN+p///ufbr31Vn333XcKCQmRJC1atEhPPPGEvv/+e/n6+uqJJ57QunXr9MUXXxjLGj58uAoLC7V+/foq1VZcXCybzaaioiJZrdZLXcVq0/zJda4uwW0dfSHW1SUAgCncbWzydO7292SsvzjGegC1RVXHpsu6RryoqEiSFBQUJEnKzMzU2bNnFR0dbfRp166dmjVrpoyMDElSRkaGOnfubIRwSYqJiVFxcbH27dtn9Pn1Z5zrc+4zLqS0tFTFxcUOEwAAAAAA7uaSg3hlZaUmTJigm2++WZ06dZIk5ebmytfXV4GBgQ59Q0JClJuba/T5dQg/136u7ff6FBcX6+eff75gPTNnzpTNZjOm8PDwS101AAAAAABqzCUH8aSkJH3xxRd6++23q7OeSzZ58mQVFRUZ07Fjx1xdEgAAAAAA5/G5lDclJydr7dq1Sk9P19VXX23MDw0NVVlZmQoLCx2Oiufl5Sk0NNTos337dofPO3dX9V/3+e2d1vPy8mS1WlW3bt0L1uTn5yc/P79LWR0AAAAAAEzj1BFxu92u5ORkrV69Wps2bVKLFi0c2rt37646deooNTXVmHfw4EHl5OQoKipKkhQVFaW9e/cqPz/f6JOSkiKr1aoOHToYfX79Gef6nPsMAAAAAAA8lVNHxJOSkrRy5Ur997//VYMGDYxrum02m+rWrSubzabExERNnDhRQUFBslqtGj9+vKKionTjjTdKkgYMGKAOHTpoxIgRmjVrlnJzczVlyhQlJSUZR7QffPBBvfrqq3r88cc1atQobdq0Se+8847WreNupAAAAAAAz+bUEfGFCxeqqKhIffv2VdOmTY1p1apVRp+XX35Zt956q+Li4tS7d2+FhobqvffeM9q9vb21du1aeXt7KyoqSvfdd59GjhypGTNmGH1atGihdevWKSUlRV27dtXf/vY3/eMf/1BMTEw1rDIAAAAAAK7j1BHxqjxy3N/fXwsWLNCCBQsu2iciIkIffvjh735O37599fnnnztTHgAAAAAAbu+yniMOAAAAAACcQxAHAAAAAMBEBHEAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAADtLT0zVkyBCFhYXJYrFozZo1F+374IMPymKxaO7cuQ7zCwoKFB8fL6vVqsDAQCUmJqqkpMShz549e9SrVy/5+/srPDxcs2bNqoG1AQDA/RDEAQCAg1OnTqlr166/+yhSSVq9erU+++wzhYWFndcWHx+vffv2KSUlRWvXrlV6errGjh1rtBcXF2vAgAGKiIhQZmamXnrpJU2bNk2vv/56ta8PAADuxqnniAMAgCvfoEGDNGjQoN/t8+2332r8+PHasGGDYmNjHdoOHDig9evXa8eOHYqMjJQkzZ8/X4MHD9bs2bMVFhamFStWqKysTIsXL5avr686duyorKwszZkzxyGwAwBwJeKIOAAAcEplZaVGjBihSZMmqWPHjue1Z2RkKDAw0AjhkhQdHS0vLy9t27bN6NO7d2/5+voafWJiYnTw4EGdPHnygsstLS1VcXGxwwQAgCciiAMAAKe8+OKL8vHx0cMPP3zB9tzcXAUHBzvM8/HxUVBQkHJzc40+ISEhDn3OvT7X57dmzpwpm81mTOHh4Ze7KgAAuARBHAAAVFlmZqZeeeUVLV26VBaLxdRlT548WUVFRcZ07NgxU5cPAEB1IYgDAIAq+/jjj5Wfn69mzZrJx8dHPj4++uabb/Too4+qefPmkqTQ0FDl5+c7vK+8vFwFBQUKDQ01+uTl5Tn0Off6XJ/f8vPzk9VqdZgAAPBEBHEAAFBlI0aM0J49e5SVlWVMYWFhmjRpkjZs2CBJioqKUmFhoTIzM433bdq0SZWVlerRo4fRJz09XWfPnjX6pKSkqG3btmrYsKG5KwUAgMm4azoAAHBQUlKiw4cPG6+zs7OVlZWloKAgNWvWTI0aNXLoX6dOHYWGhqpt27aSpPbt22vgwIEaM2aMFi1apLNnzyo5OVnDhw83HnV27733avr06UpMTNQTTzyhL774Qq+88opefvll81YUAAAXIYgDAAAHO3fuVL9+/YzXEydOlCQlJCRo6dKlVfqMFStWKDk5Wf3795eXl5fi4uI0b948o91ms2njxo1KSkpS9+7d1bhxY02dOpVHlwEAagWCOAAAcNC3b1/Z7fYq9z969Oh584KCgrRy5crffV+XLl308ccfO1seAAAej2vEAQAAAAAwEUEcAAAAAAATEcQBAAAAADARQRwAAAAAABMRxAEAAAAAMBFBHAAAAAAAExHEAQAAAAAwEUEcAAAAAAATEcQBAAAAADARQRwAAAAAABMRxAEAAAAAMBFBHAAAAAAAExHEAQAAAAAwEUEcAAAAAAATEcQBAAAAADARQRwAAAAAABMRxAEAAAAAMBFBHAAAAAAAExHEAQAAAAAwEUEcAAAAAAATEcQBAAAAADARQRwAAAAAABMRxAEAAAAAMBFBHAAAAAAAE/m4ugAAF9b8yXWuLsEtHX0h1tUlAFe89PR0vfTSS8rMzNSJEye0evVqDRs2TJJ09uxZTZkyRR9++KG+/vpr2Ww2RUdH64UXXlBYWJjxGQUFBRo/frw++OADeXl5KS4uTq+88orq169v9NmzZ4+SkpK0Y8cONWnSROPHj9fjjz9u9uoCAGA6jogDAAAHp06dUteuXbVgwYLz2k6fPq1du3bpqaee0q5du/Tee+/p4MGDGjp0qEO/+Ph47du3TykpKVq7dq3S09M1duxYo724uFgDBgxQRESEMjMz9dJLL2natGl6/fXXa3z9AABwNY6IAwAAB4MGDdKgQYMu2Gaz2ZSSkuIw79VXX9UNN9ygnJwcNWvWTAcOHND69eu1Y8cORUZGSpLmz5+vwYMHa/bs2QoLC9OKFStUVlamxYsXy9fXVx07dlRWVpbmzJnjENgBALgScUQcAABclqKiIlksFgUGBkqSMjIyFBgYaIRwSYqOjpaXl5e2bdtm9Ondu7d8fX2NPjExMTp48KBOnjxpav0AAJiNI+IAAOCSnTlzRk888YTuueceWa1WSVJubq6Cg4Md+vn4+CgoKEi5ublGnxYtWjj0CQkJMdoaNmx43rJKS0tVWlpqvC4uLq7WdQEAwCwcEQcAAJfk7Nmzuuuuu2S327Vw4cIaX97MmTNls9mMKTw8vMaXCQBATSCIAwAAp50L4d98841SUlKMo+GSFBoaqvz8fIf+5eXlKigoUGhoqNEnLy/Poc+51+f6/NbkyZNVVFRkTMeOHavOVQIAwDQEcQAA4JRzIfzQoUP66KOP1KhRI4f2qKgoFRYWKjMz05i3adMmVVZWqkePHkaf9PR0nT171uiTkpKitm3bXvC0dEny8/OT1Wp1mAAA8EQEcQAA4KCkpERZWVnKysqSJGVnZysrK0s5OTk6e/as7rzzTu3cuVMrVqxQRUWFcnNzlZubq7KyMklS+/btNXDgQI0ZM0bbt2/Xp59+quTkZA0fPtx41vi9994rX19fJSYmat++fVq1apVeeeUVTZw40VWrDQCAabhZGwAAcLBz507169fPeH0uHCckJGjatGl6//33JUnXXnutw/s2b96svn37SpJWrFih5ORk9e/fX15eXoqLi9O8efOMvjabTRs3blRSUpK6d++uxo0ba+rUqTy6DABQKxDEAQCAg759+8put1+0/ffazgkKCtLKlSt/t0+XLl308ccfO10fAACejlPTAQAAAAAwEUEcAAAAAAATEcQBAAAAADARQRwAAAAAABM5HcTT09M1ZMgQhYWFyWKxaM2aNQ7t999/vywWi8M0cOBAhz4FBQWKj4+X1WpVYGCgEhMTVVJS4tBnz5496tWrl/z9/RUeHq5Zs2Y5v3YAAAAAALgZp4P4qVOn1LVrVy1YsOCifQYOHKgTJ04Y01tvveXQHh8fr3379iklJUVr165Venq6w+NKiouLNWDAAEVERCgzM1MvvfSSpk2bptdff93ZcgEAAAAAcCtOP75s0KBBGjRo0O/28fPzU2ho6AXbDhw4oPXr12vHjh2KjIyUJM2fP1+DBw/W7NmzFRYWphUrVqisrEyLFy+Wr6+vOnbsqKysLM2ZM4fniwIAAAAAPFqNXCO+ZcsWBQcHq23btho3bpx+/PFHoy0jI0OBgYFGCJek6OhoeXl5adu2bUaf3r17y9fX1+gTExOjgwcP6uTJkxdcZmlpqYqLix0mAAAAAADcTbUH8YEDB2r58uVKTU3Viy++qLS0NA0aNEgVFRWSpNzcXAUHBzu8x8fHR0FBQcrNzTX6hISEOPQ59/pcn9+aOXOmbDabMYWHh1f3qgEAAAAAcNmcPjX9jwwfPtz4d+fOndWlSxe1atVKW7ZsUf/+/at7cYbJkydr4sSJxuvi4mLCOAAAAADA7dT448tatmypxo0b6/Dhw5Kk0NBQ5efnO/QpLy9XQUGBcV15aGio8vLyHPqce32xa8/9/PxktVodJgAAAAAA3E2NB/Hjx4/rxx9/VNOmTSVJUVFRKiwsVGZmptFn06ZNqqysVI8ePYw+6enpOnv2rNEnJSVFbdu2VcOGDWu6ZAAAAAAAaozTQbykpERZWVnKysqSJGVnZysrK0s5OTkqKSnRpEmT9Nlnn+no0aNKTU3VbbfdptatWysmJkaS1L59ew0cOFBjxozR9u3b9emnnyo5OVnDhw9XWFiYJOnee++Vr6+vEhMTtW/fPq1atUqvvPKKw6nnAAAAAAB4IqeD+M6dO3XdddfpuuuukyRNnDhR1113naZOnSpvb2/t2bNHQ4cOVZs2bZSYmKju3bvr448/lp+fn/EZK1asULt27dS/f38NHjxYPXv2dHhGuM1m08aNG5Wdna3u3bvr0Ucf1dSpU3l0GQAAAADA4zl9s7a+ffvKbrdftH3Dhg1/+BlBQUFauXLl7/bp0qWLPv74Y2fLAwAAAADArdX4NeIAAAAAAOD/EMQBAAAAADARQRwAAAAAABMRxAEAAAAAMBFBHAAAAAAAExHEAQAAAAAwEUEcAAAAAAATEcQBAAAAADARQRwAAAAAABMRxAEAAAAAMBFBHAAAOEhPT9eQIUMUFhYmi8WiNWvWOLTb7XZNnTpVTZs2Vd26dRUdHa1Dhw459CkoKFB8fLysVqsCAwOVmJiokpIShz579uxRr1695O/vr/DwcM2aNaumVw0AALdAEAcAAA5OnTqlrl27asGCBRdsnzVrlubNm6dFixZp27ZtCggIUExMjM6cOWP0iY+P1759+5SSkqK1a9cqPT1dY8eONdqLi4s1YMAARUREKDMzUy+99JKmTZum119/vcbXDwAAV/NxdQEAAMC9DBo0SIMGDbpgm91u19y5czVlyhTddtttkqTly5crJCREa9as0fDhw3XgwAGtX79eO3bsUGRkpCRp/vz5Gjx4sGbPnq2wsDCtWLFCZWVlWrx4sXx9fdWxY0dlZWVpzpw5DoEdAIArEUfEAQBAlWVnZys3N1fR0dHGPJvNph49eigjI0OSlJGRocDAQCOES1J0dLS8vLy0bds2o0/v3r3l6+tr9ImJidHBgwd18uTJCy67tLRUxcXFDhMAAJ6IIA4AAKosNzdXkhQSEuIwPyQkxGjLzc1VcHCwQ7uPj4+CgoIc+lzoM369jN+aOXOmbDabMYWHh1/+CgEA4AIEcQAA4BEmT56soqIiYzp27JirSwIA4JIQxAEAQJWFhoZKkvLy8hzm5+XlGW2hoaHKz893aC8vL1dBQYFDnwt9xq+X8Vt+fn6yWq0OEwAAnoggDgAAqqxFixYKDQ1VamqqMa+4uFjbtm1TVFSUJCkqKkqFhYXKzMw0+mzatEmVlZXq0aOH0Sc9PV1nz541+qSkpKht27Zq2LChSWsDAIBrEMQBAICDkpISZWVlKSsrS9IvN2jLyspSTk6OLBaLJkyYoGeffVbvv/++9u7dq5EjRyosLEzDhg2TJLVv314DBw7UmDFjtH37dn366adKTk7W8OHDFRYWJkm699575evrq8TERO3bt0+rVq3SK6+8ookTJ7porQEAMA+PLwOAK0TzJ9e5ugS3dPSFWFeX4HF27typfv36Ga/PheOEhAQtXbpUjz/+uE6dOqWxY8eqsLBQPXv21Pr16+Xv72+8Z8WKFUpOTlb//v3l5eWluLg4zZs3z2i32WzauHGjkpKS1L17dzVu3FhTp07l0WUAgFqBIA4AABz07dtXdrv9ou0Wi0UzZszQjBkzLtonKChIK1eu/N3ldOnSRR9//PEl1wkAgKfi1HQAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAOCUiooKPfXUU2rRooXq1q2rVq1a6ZlnnpHdbjf62O12TZ06VU2bNlXdunUVHR2tQ4cOOXxOQUGB4uPjZbVaFRgYqMTERJWUlJi9OgAAmI4gDgAAnPLiiy9q4cKFevXVV3XgwAG9+OKLmjVrlubPn2/0mTVrlubNm6dFixZp27ZtCggIUExMjM6cOWP0iY+P1759+5SSkqK1a9cqPT1dY8eOdcUqAQBgKh9XFwAAADzL1q1bddtttyk2NlaS1Lx5c7311lvavn27pF+Ohs+dO1dTpkzRbbfdJklavny5QkJCtGbNGg0fPlwHDhzQ+vXrtWPHDkVGRkqS5s+fr8GDB2v27NkKCwtzzcoBAGACjogDAACn3HTTTUpNTdVXX30lSdq9e7c++eQTDRo0SJKUnZ2t3NxcRUdHG++x2Wzq0aOHMjIyJEkZGRkKDAw0QrgkRUdHy8vLS9u2bbvgcktLS1VcXOwwAQDgiTgiDgAAnPLkk0+quLhY7dq1k7e3tyoqKvTcc88pPj5ekpSbmytJCgkJcXhfSEiI0Zabm6vg4GCHdh8fHwUFBRl9fmvmzJmaPn16da8OAACm44g4AABwyjvvvKMVK1Zo5cqV2rVrl5YtW6bZs2dr2bJlNbrcyZMnq6ioyJiOHTtWo8sDAKCmcEQcAAA4ZdKkSXryySc1fPhwSVLnzp31zTffaObMmUpISFBoaKgkKS8vT02bNjXel5eXp2uvvVaSFBoaqvz8fIfPLS8vV0FBgfH+3/Lz85Ofn18NrBEAAObiiDgAAHDK6dOn5eXl+BXC29tblZWVkqQWLVooNDRUqampRntxcbG2bdumqKgoSVJUVJQKCwuVmZlp9Nm0aZMqKyvVo0cPE9YCAADX4Yg4AABwypAhQ/Tcc8+pWbNm6tixoz7//HPNmTNHo0aNkiRZLBZNmDBBzz77rK655hq1aNFCTz31lMLCwjRs2DBJUvv27TVw4ECNGTNGixYt0tmzZ5WcnKzhw4dzx3QAwBWPIA4AAJwyf/58PfXUU3rooYeUn5+vsLAw/b//9/80depUo8/jjz+uU6dOaezYsSosLFTPnj21fv16+fv7G31WrFih5ORk9e/fX15eXoqLi9O8efNcsUoAAJjK6VPT09PTNWTIEIWFhclisWjNmjUO7Xa7XVOnTlXTpk1Vt25dRUdH69ChQw59CgoKFB8fL6vVqsDAQCUmJqqkpMShz549e9SrVy/5+/srPDxcs2bNcn7tAABAtWvQoIHmzp2rb775Rj///LOOHDmiZ599Vr6+vkYfi8WiGTNmKDc3V2fOnNFHH32kNm3aOHxOUFCQVq5cqZ9++klFRUVavHix6tevb/bqAABgOqeD+KlTp9S1a1ctWLDggu2zZs3SvHnztGjRIm3btk0BAQGKiYnRmTNnjD7x8fHat2+fUlJStHbtWqWnp2vs2LFGe3FxsQYMGKCIiAhlZmbqpZde0rRp0/T6669fwioCAAAAAOA+nD41fdCgQRo0aNAF2+x2u+bOnaspU6botttukyQtX75cISEhWrNmjYYPH64DBw5o/fr12rFjhyIjIyX9corb4MGDNXv2bIWFhWnFihUqKyvT4sWL5evrq44dOyorK0tz5sxxCOwAAAAAAHiaar1renZ2tnJzcxUdHW3Ms9ls6tGjhzIyMiRJGRkZCgwMNEK4JEVHR8vLy0vbtm0z+vTu3dvhFLeYmBgdPHhQJ0+evOCyS0tLVVxc7DABAAAAAOBuqjWI5+bmSpJCQkIc5oeEhBhtubm5Cg4Odmj38fFRUFCQQ58Lfcavl/FbM2fOlM1mM6bw8PDLXyEAAAAAAKrZFfMc8cmTJ6uoqMiYjh075uqSAAAAAAA4T7UG8dDQUElSXl6ew/y8vDyjLTQ0VPn5+Q7t5eXlKigocOhzoc/49TJ+y8/PT1ar1WECAAAAAMDdVGsQb9GihUJDQ5WammrMKy4u1rZt2xQVFSVJioqKUmFhoTIzM40+mzZtUmVlpXr06GH0SU9P19mzZ40+KSkpatu2rRo2bFidJQMAAAAAYCqng3hJSYmysrKUlZUl6ZcbtGVlZSknJ0cWi0UTJkzQs88+q/fff1979+7VyJEjFRYWpmHDhkmS2rdvr4EDB2rMmDHavn27Pv30UyUnJ2v48OEKCwuTJN17773y9fVVYmKi9u3bp1WrVumVV17RxIkTq23FAQAAAABwBacfX7Zz507169fPeH0uHCckJGjp0qV6/PHHderUKY0dO1aFhYXq2bOn1q9fL39/f+M9K1asUHJysvr37y8vLy/FxcVp3rx5RrvNZtPGjRuVlJSk7t27q3Hjxpo6dSqPLgMAAAAAeDyng3jfvn1lt9sv2m6xWDRjxgzNmDHjon2CgoK0cuXK311Oly5d9PHHHztbHgAAAAAAbu2KuWs6AAAAAACegCAOAAAAAICJCOIAAAAAAJiIIA4AAAAAgIkI4gAAAAAAmIggDgAAAACAiQjiAAAAAACYiCAOAAAAAICJCOIAAAAAAJiIIA4AAAAAgIkI4gAAAAAAmIggDgAAAACAiQjiAAAAAACYiCAOAAAAAICJCOIAAMBp3377re677z41atRIdevWVefOnbVz506j3W63a+rUqWratKnq1q2r6OhoHTp0yOEzCgoKFB8fL6vVqsDAQCUmJqqkpMTsVQEAwHQEcQAA4JSTJ0/q5ptvVp06dfS///1P+/fv19/+9jc1bNjQ6DNr1izNmzdPixYt0rZt2xQQEKCYmBidOXPG6BMfH699+/YpJSVFa9euVXp6usaOHeuKVQIAwFQ+ri4AAAB4lhdffFHh4eFasmSJMa9FixbGv+12u+bOnaspU6botttukyQtX75cISEhWrNmjYYPH64DBw5o/fr12rFjhyIjIyVJ8+fP1+DBgzV79myFhYWZu1IAAJiII+IAAMAp77//viIjI/XnP/9ZwcHBuu666/TGG28Y7dnZ2crNzVV0dLQxz2azqUePHsrIyJAkZWRkKDAw0AjhkhQdHS0vLy9t27bNvJUBAMAFCOIAAMApX3/9tRYuXKhrrrlGGzZs0Lhx4/Twww9r2bJlkqTc3FxJUkhIiMP7QkJCjLbc3FwFBwc7tPv4+CgoKMjo81ulpaUqLi52mAAA8EScmg4AAJxSWVmpyMhIPf/885Kk6667Tl988YUWLVqkhISEGlvuzJkzNX369Br7fAAAzMIRcQAA4JSmTZuqQ4cODvPat2+vnJwcSVJoaKgkKS8vz6FPXl6e0RYaGqr8/HyH9vLychUUFBh9fmvy5MkqKioypmPHjlXL+gAAYDaCOAAAcMrNN9+sgwcPOsz76quvFBERIemXG7eFhoYqNTXVaC8uLta2bdsUFRUlSYqKilJhYaEyMzONPps2bVJlZaV69OhxweX6+fnJarU6TAAAeCJOTQcAAE555JFHdNNNN+n555/XXXfdpe3bt+v111/X66+/LkmyWCyaMGGCnn32WV1zzTVq0aKFnnrqKYWFhWnYsGGSfjmCPnDgQI0ZM0aLFi3S2bNnlZycrOHDh3PHdADAFY8gDgAAnHL99ddr9erVmjx5smbMmKEWLVpo7ty5io+PN/o8/vjjOnXqlMaOHavCwkL17NlT69evl7+/v9FnxYoVSk5OVv/+/eXl5aW4uDjNmzfPFasEAICpCOIAAMBpt956q2699daLtlssFs2YMUMzZsy4aJ+goCCtXLmyJsoDAMCtcY04AAAAAAAmIogDAAAAAGAigjgAAAAAACYiiAMAAAAAYCKCOAAAAAAAJiKIAwAAAABgIoI4AAAAAAAmIogDAAAAAGAigjgAAAAAACYiiAMAAAAAYCKCOAAAAAAAJiKIAwAAAABgIoI4AAAAAAAmIogDAAAAAGAigjgAAAAAACYiiAMAAAAAYCKCOAAAAAAAJiKIAwAAAABgIoI4AAAAAAAmIogDAAAAAGAigjgAAAAAACYiiAMAAAAAYCKCOAAAAAAAJiKIAwAAAABgIoI4AAAAAAAmIogDAIDL8sILL8hisWjChAnGvDNnzigpKUmNGjVS/fr1FRcXp7y8PIf35eTkKDY2VvXq1VNwcLAmTZqk8vJyk6sHAMB8BHEAAHDJduzYob///e/q0qWLw/xHHnlEH3zwgd59912lpaXpu+++0x133GG0V1RUKDY2VmVlZdq6dauWLVumpUuXaurUqWavAgAApiOIAwCAS1JSUqL4+Hi98cYbatiwoTG/qKhIb775pubMmaM//elP6t69u5YsWaKtW7fqs88+kyRt3LhR+/fv17/+9S9de+21GjRokJ555hktWLBAZWVlrlolAABMQRAHAACXJCkpSbGxsYqOjnaYn5mZqbNnzzrMb9eunZo1a6aMjAxJUkZGhjp37qyQkBCjT0xMjIqLi7Vv374LLq+0tFTFxcUOEwAAnsjH1QUAAADP8/bbb2vXrl3asWPHeW25ubny9fVVYGCgw/yQkBDl5uYafX4dws+1n2u7kJkzZ2r69OnVUD0AAK5V7UfEp02bJovF4jC1a9fOaOfmLQAAeLZjx47pL3/5i1asWCF/f3/Tljt58mQVFRUZ07Fjx0xbNgAA1alGTk3v2LGjTpw4YUyffPKJ0cbNWwAA8GyZmZnKz89Xt27d5OPjIx8fH6WlpWnevHny8fFRSEiIysrKVFhY6PC+vLw8hYaGSpJCQ0PP+yH+3OtzfX7Lz89PVqvVYQIAwBPVSBD38fFRaGioMTVu3FgSN28BAOBK0L9/f+3du1dZWVnGFBkZqfj4eOPfderUUWpqqvGegwcPKicnR1FRUZKkqKgo7d27V/n5+UaflJQUWa1WdejQwfR1AgDATDUSxA8dOqSwsDC1bNlS8fHxysnJkVRzN2+RuIELAABmadCggTp16uQwBQQEqFGjRurUqZNsNpsSExM1ceJEbd68WZmZmXrggQcUFRWlG2+8UZI0YMAAdejQQSNGjNDu3bu1YcMGTZkyRUlJSfLz83PxGgIAULOqPYj36NFDS5cu1fr167Vw4UJlZ2erV69e+umnn2rs5i3SLzdwsdlsxhQeHl69KwYAAKrs5Zdf1q233qq4uDj17t1boaGheu+994x2b29vrV27Vt7e3oqKitJ9992nkSNHasaMGS6sGgAAc1T7XdMHDRpk/LtLly7q0aOHIiIi9M4776hu3brVvTjD5MmTNXHiRON1cXExYRwAAJNs2bLF4bW/v78WLFigBQsWXPQ9ERER+vDDD2u4MgAA3E+NP0c8MDBQbdq00eHDhxUaGlojN2+RuIELAAAAAMAz1HgQLykp0ZEjR9S0aVN1796dm7cAAAAAAGq1aj81/bHHHtOQIUMUERGh7777Tk8//bS8vb11zz33ONy8JSgoSFarVePHj7/ozVtmzZql3Nxcbt4CAAAAALhiVHsQP378uO655x79+OOPatKkiXr27KnPPvtMTZo0kfTLzVu8vLwUFxen0tJSxcTE6LXXXjPef+7mLePGjVNUVJQCAgKUkJDAzVsAAAAAAFeEag/ib7/99u+2c/MWAAAAAEBtVuPXiAMAAAAAgP9DEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAAAAAExEEAcAAE6ZOXOmrr/+ejVo0EDBwcEaNmyYDh486NDnzJkzSkpKUqNGjVS/fn3FxcUpLy/PoU9OTo5iY2NVr149BQcHa9KkSSovLzdzVQAAcAmCOAAAcEpaWpqSkpL02WefKSUlRWfPntWAAQN06tQpo88jjzyiDz74QO+++67S0tL03Xff6Y477jDaKyoqFBsbq7KyMm3dulXLli3T0qVLNXXqVFesEgAApvJxdQEAAMCzrF+/3uH10qVLFRwcrMzMTPXu3VtFRUV68803tXLlSv3pT3+SJC1ZskTt27fXZ599phtvvFEbN27U/v379dFHHykkJETXXnutnnnmGT3xxBOaNm2afH19XbFqAACYgiPiAADgshQVFUmSgoKCJEmZmZk6e/asoqOjjT7t2rVTs2bNlJGRIUnKyMhQ586dFRISYvSJiYlRcXGx9u3bZ2L1AACYjyPiAADgklVWVmrChAm6+eab1alTJ0lSbm6ufH19FRgY6NA3JCREubm5Rp9fh/Bz7efaLqS0tFSlpaXG6+Li4upaDQAATMURcQAAcMmSkpL0xRdf6O23367xZc2cOVM2m82YwsPDa3yZAADUBII4AAC4JMnJyVq7dq02b96sq6++2pgfGhqqsrIyFRYWOvTPy8tTaGio0ee3d1E/9/pcn9+aPHmyioqKjOnYsWPVuDYAAJiHIA4AAJxit9uVnJys1atXa9OmTWrRooVDe/fu3VWnTh2lpqYa8w4ePKicnBxFRUVJkqKiorR3717l5+cbfVJSUmS1WtWhQ4cLLtfPz09Wq9VhAgDAE3GNOAAAcEpSUpJWrlyp//73v2rQoIFxTbfNZlPdunVls9mUmJioiRMnKigoSFarVePHj1dUVJRuvPFGSdKAAQPUoUMHjRgxQrNmzVJubq6mTJmipKQk+fn5uXL1AACocQRxAADglIULF0qS+vbt6zB/yZIluv/++yVJL7/8sry8vBQXF6fS0lLFxMTotddeM/p6e3tr7dq1GjdunKKiohQQEKCEhATNmDHDrNUAAMBlCOIAAMApdrv9D/v4+/trwYIFWrBgwUX7RERE6MMPP6zO0gAA8AgEcQAAAMAFmj+5ztUluK2jL8S6ugSgRnGzNgAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARARxAAAAAABMRBAHAAAAAMBEBHEAAAAAAExEEAcAAAAAwEQEcQAAAAAATEQQBwAAAADARD6uLgAAAAAA8MeaP7nO1SW4raMvxLq6BKdwRBwAAAAAABMRxAEAAAAAMBFBHAAAAAAAE7l1EF+wYIGaN28uf39/9ejRQ9u3b3d1SQAAoJox3gMAahu3DeKrVq3SxIkT9fTTT2vXrl3q2rWrYmJilJ+f7+rSAABANWG8BwDURm4bxOfMmaMxY8bogQceUIcOHbRo0SLVq1dPixcvdnVpAACgmjDeAwBqI7d8fFlZWZkyMzM1efJkY56Xl5eio6OVkZFxwfeUlpaqtLTUeF1UVCRJKi4urtliq6iy9LSrS3Bb7vLfyN2wzVwY28vFsc1cmDttM+dqsdvtLq7EPTg73jPWey53+W/kbthmLo5t5sLYZi7OXbaZqo71bhnEf/jhB1VUVCgkJMRhfkhIiL788ssLvmfmzJmaPn36efPDw8NrpEZUH9tcV1cAT8L2Ame54zbz008/yWazuboMl3N2vGes91zu+P8h3BvbDJzlbtvMH431bhnEL8XkyZM1ceJE43VlZaUKCgrUqFEjWSwWF1bmXoqLixUeHq5jx47JarW6uhx4ALYZOItt5uLsdrt++uknhYWFuboUj8RYX3X8fwhnsL3AWWwzF1fVsd4tg3jjxo3l7e2tvLw8h/l5eXkKDQ294Hv8/Pzk5+fnMC8wMLCmSvR4VquV/2ngFLYZOItt5sI4Ev5/nB3vGeudx/+HcAbbC5zFNnNhVRnr3fJmbb6+vurevbtSU1ONeZWVlUpNTVVUVJQLKwMAANWF8R4AUFu55RFxSZo4caISEhIUGRmpG264QXPnztWpU6f0wAMPuLo0AABQTRjvAQC1kdsG8bvvvlvff/+9pk6dqtzcXF177bVav379eTd0gXP8/Pz09NNPn3dqH3AxbDNwFtsMnMF4XzP4/xDOYHuBs9hmLp/FzjNUAAAAAAAwjVteIw4AAAAAwJWKIA4AAAAAgIkI4gAAAAAAmIggDgAAAACAiQjiAAxnz57VqFGjlJ2d7epS4CHYZgDAs7DfhrPYZmoGQRyAoU6dOvrPf/7j6jLgQdhmAMCzsN+Gs9hmagZBHICDYcOGac2aNa4uAx6EbQYAPAv7bTiLbab6+bi6AJinsLBQ27dvV35+viorKx3aRo4c6aKq4G6uueYazZgxQ59++qm6d++ugIAAh/aHH37YRZXBXbHNAO6DsR5VwX4bzmKbqX4Wu91ud3URqHkffPCB4uPjVVJSIqvVKovFYrRZLBYVFBS4sDq4kxYtWly0zWKx6OuvvzaxGngCthnAPTDWo6rYb8NZbDPVjyBeS7Rp00aDBw/W888/r3r16rm6HAAAUM0Y6wHAcxDEa4mAgADt3btXLVu2dHUpAACgBjDWA4Dn4BrxWiImJkY7d+5kcEaVHD9+XO+//75ycnJUVlbm0DZnzhwXVQV3xjYDuB5jPZzBfhvOYpupXgTxK9j7779v/Ds2NlaTJk3S/v371blzZ9WpU8eh79ChQ80uD24qNTVVQ4cOVcuWLfXll1+qU6dOOnr0qOx2u7p16+bq8uCG2GYA12Gsx6Vgvw1nsc1UP05Nv4J5eVXt6XQWi0UVFRU1XA08xQ033KBBgwZp+vTpatCggXbv3q3g4GDFx8dr4MCBGjdunKtLhJthmwFch7Eel4L9NpzFNlP9COIAHDRo0EBZWVlq1aqVGjZsqE8++UQdO3bU7t27ddttt+no0aOuLhFuhm0GADwL+204i22m+lXtZ1R4vOXLl6u0tPS8+WVlZVq+fLkLKoK7CggIMK77adq0qY4cOWK0/fDDD64qC26MbQZwD4z1qCr223AW20z1I4jXEg888ICKiorOm//TTz/pgQcecEFFcFc33nijPvnkE0nS4MGD9eijj+q5557TqFGjdOONN7q4OrgjthnAPTDWo6rYb8NZbDPVj5u11RJ2u10Wi+W8+cePH5fNZnNBRXBXc+bMUUlJiSRp+vTpKikp0apVq3TNNddwR0xcENsM4B4Y61FV7LfhLLaZ6sc14le46667ThaLRbt371bHjh3l4/N/v71UVFQoOztbAwcO1DvvvOPCKgEAwKVirAcAz8MR8SvcsGHDJElZWVmKiYlR/fr1jTZfX181b95ccXFxLqoO7qqwsFD//ve/deTIEU2aNElBQUHatWuXQkJCdNVVV7m6PLghthnAdRjrcSnYb8NZbDPViyPitcSyZct09913y9/f39WlwM3t2bNH0dHRstlsOnr0qA4ePKiWLVtqypQpysnJ4YY/OA/bDOAeGOtRVey34Sy2merHzdpqiYSEBPn7+6usrEzHjx9XTk6OwwScM3HiRN1///06dOiQw5e5wYMHKz093YWVwV2xzQDugbEeVcV+G85im6l+nJpeSxw6dEijRo3S1q1bHeafu7FLRUWFiyqDu9mxY4f+/ve/nzf/qquuUm5urgsqgrtjmwHcA2M9qor9NpzFNlP9COK1xP333y8fHx+tXbtWTZs2veBdVQFJ8vPzU3Fx8Xnzv/rqKzVp0sQFFcHdsc0A7oGxHlXFfhvOYpupflwjXksEBAQoMzNT7dq1c3UpcHOjR4/Wjz/+qHfeeUdBQUHas2ePvL29NWzYMPXu3Vtz5851dYlwM2wzgHtgrEdVsd+Gs9hmqh/XiNcSHTp00A8//ODqMuAB/va3v6mkpETBwcH6+eef1adPH7Vu3VoNGjTQc8895+ry4IbYZgD3wFiPqmK/DWexzVQ/jojXEps2bdKUKVP0/PPPq3PnzqpTp45Du9VqdVFlcFeffPKJ9uzZo5KSEnXr1k3R0dGuLglujm0GcC3GejiL/TacxTZTfQjitYSX1y8nP/z2ejFu4ILf+vrrr9WyZUtXlwEPwjYDuAfGelQV+204i22m+hHEa4m0tLTfbe/Tp49JlcDdeXl5qU+fPkpMTNSdd97J82jxh9hmAPfAWI+qYr8NZ7HNVD+COAAHWVlZWrJkid566y2VlZXp7rvv1qhRo9SjRw9XlwY3xTYDAJ6F/TacxTZT/QjitUhhYaHefPNNHThwQJLUsWNHjRo1SjabzcWVwR2Vl5fr/fff19KlS7V+/Xq1adNGo0aN0ogRI3hMBS6IbQZwPcZ6OIP9NpzFNlN9COK1xM6dOxUTE6O6devqhhtukCTt2LFDP//8szZu3Khu3bq5uEK4q9LSUr322muaPHmyysrK5Ovrq7vuuksvvviimjZt6ury4IbYZgDXYKzHpWK/DWexzVw+gngt0atXL7Vu3VpvvPGGfHx8JP3yi9bo0aP19ddfKz093cUVwt3s3LlTixcv1ttvv62AgAAlJCQoMTFRx48f1/Tp01VcXKzt27e7uky4EbYZwLUY6+Es9ttwFttM9SGI1xJ169bV559/rnbt2jnM379/vyIjI3X69GkXVQZ3M2fOHC1ZskQHDx7U4MGDNXr0aA0ePNi4G68kHT9+XM2bN1d5ebkLK4W7YJsB3ANjPaqK/TacxTZT/XxcXQDMYbValZOTc97gfOzYMTVo0MBFVcEdLVy4UKNGjdL9999/0VOLgoOD9eabb5pcGdwV2wzgHhjrUVXst+EstpnqxxHxWuLhhx/W6tWrNXv2bN10002SpE8//VSTJk1SXFyc5s6d69oCAQDAZWGsBwDPwRHxWmL27NmyWCwaOXKkcbpInTp1NG7cOL3wwgsurg4AAFwuxnoA8BwcEa9lTp8+rSNHjkiSWrVqpXr16rm4IgAAUJ0Y6wHA/RHEAQAAAAAwEaem1xJnzpzR/PnztXnzZuXn56uystKhfdeuXS6qDAAAVAfGegDwHATxWiIxMVEbN27UnXfeqRtuuEEWi8XVJcHNff/99zp48KAkqW3btmrSpImLK4I7Ky8v15YtW3TkyBHde++9atCggb777jtZrVbVr1/f1eUBtQJjPZzBfhuXgu+H1YdT02sJm82mDz/8UDfffLOrS4GbO3XqlMaPH69//vOfqqiokCR5e3tr5MiRmj9/Ptca4jzffPONBg4cqJycHJWWluqrr75Sy5Yt9Ze//EWlpaVatGiRq0sEagXGelQV+204i++H1c/rj7vgSnDVVVfxDFFUycSJE5WWlqb3339fhYWFKiws1H//+1+lpaXp0UcfdXV5cEN/+ctfFBkZqZMnT6pu3brG/Ntvv12pqakurAyoXRjrUVXst+Esvh9WP46I1xL/+9//NG/ePC1atEgRERGuLgdurHHjxvr3v/+tvn37OszfvHmz7rrrLn3//feuKQxuq1GjRtq6davatm2rBg0aaPfu3WrZsqWOHj2qDh066PTp064uEagVGOtRVey34Sy+H1Y/rhGvJSIjI3XmzBm1bNlS9erVU506dRzaCwoKXFQZ3M3p06cVEhJy3vzg4GAGZlxQZWWlcZrarx0/fpyjc4CJGOtRVey34Sy+H1Y/jojXEtHR0crJyVFiYqJCQkLOu4FLQkKCiyqDu+nfv78aNWqk5cuXy9/fX5L0888/KyEhQQUFBfroo49cXCHczd133y2bzabXX39dDRo00J49e9SkSRPddtttatasmZYsWeLqEoFagbEeVcV+G87i+2H1I4jXEvXq1VNGRoa6du3q6lLg5r744gvFxMSotLTU2F52794tf39/bdiwQR07dnRxhXA3x48fV0xMjOx2uw4dOqTIyEgdOnRIjRs3Vnp6uoKDg11dIlArMNajqthvw1l8P6x+BPFaolu3bnrttdd04403uroUeIDTp09rxYoV+vLLLyVJ7du3V3x8vMMNXYBfKy8v19tvv609e/aopKRE3bp1Y5sBTMZYD2ew34az+H5YvQjitcTGjRs1ffp0Pffcc+rcufN5141ZrVYXVQYAAKoDYz0AeA6CeC3h5fXLk+p+e72Y3W6XxWK54A07UDs1a9ZMffv2VZ8+fdSvXz+1bNnS1SXBAxw6dEibN29Wfn6+KisrHdqmTp3qoqqA2oWxHs5gvw1n8P2w+hHEa4m0tLTfbe/Tp49JlcDd/etf/1J6erq2bNmiw4cP66qrrlKfPn3Up08f9e3bV9dcc42rS4SbeeONNzRu3Dg1btxYoaGhDiHAYrFo165dLqwOqD0Y61FV7LfhLL4fVj+COICLOnHihNLS0rR27VqtWrXqoo87Qe0WERGhhx56SE888YSrSwEAVAH7bVwOvh9WD54jXgt17txZH374ocLDw11dCtzU6dOn9cknn2jLli3avHmzPv/8c3Xq1El9+/Z1dWlwQydPntSf//xnV5cB4FcY6/F72G/jUvD9sHp5uboAmO/o0aM6e/asq8uAm7rpppvUqFEjPfnkkzpz5oyefPJJnThxQp9//rlefvllV5cHN/TnP/9ZGzdudHUZAH6FsR6/h/02nMX3w+rHEXEADr788ksFBASoXbt2ateundq3b6+GDRu6uiy4sdatW+upp57SZ599dsE7NT/88MMuqgwAcCHst+Esvh9WP64Rr4UGDx6sN998U02bNnV1KXBDdrtde/fu1ZYtW5SWlqb09HT5+voad8kcM2aMq0uEm2nRosVF2ywWi77++msTqwEgMdbj97HfhrP4flj9COIALsputyszM1OvvvqqVqxYwc04AAAAajm+H1YPTk2vRSoqKrRmzRodOHBAktSxY0cNHTpU3t7eLq4M7mDGjBl67LHH9OWXX2rLli3asmWLPvnkE/3000/q3Lmzxo8fz6Nv8Lt++OEHSVLjxo1dXAlQezHWwxnst/FH+H5YczgiXkscPnxYsbGxOn78uNq2bStJOnjwoMLDw7Vu3Tq1atXKxRXC1by9vXXixAmFhYXpuuuuM54N2bt3b9lsNleXBzdVWFiov/71r1q1apVOnjwpSWrYsKGGDx+uZ599VoGBga4tEKhFGOtRFey34Qy+H9YcgngtMXjwYNntdq1YsUJBQUGSpB9//FH33XefvLy8tG7dOhdXCFfz8vJSbm6u/P39ZbVaXV0OPEBBQYGioqL07bffKj4+Xu3bt5ck7d+/XytXrlR4eLi2bt3KzVwAkzDW44+w34az+H5YcwjitURAQIBxZ8xf2717t26++WaVlJS4qDK4Cy8vL+Xl5alJkyauLgUeYsKECUpNTdVHH32kkJAQh7bc3FwNGDBA/fv357EmgEkY6/FH2G/DWXw/rDlcI15L+Pn56aeffjpvfklJiXx9fV1QEdxRmzZtZLFYfrdPQUGBSdXA3a1Zs0Z///vfz/syJ0mhoaGaNWuWHnzwQb7QASZhrMcfYb+NS8H3w5pBEK8lbr31Vo0dO1ZvvvmmbrjhBknStm3b9OCDD2ro0KEurg7uYvr06Vzvgyo7ceKEOnbseNH2Tp06KTc318SKgNqNsR5/hP02LgXfD2sGQbyWmDdvnhISEhQVFaU6depIksrLyzV06FDNnTvXtcXBbQwfPlzBwcGuLgMeonHjxjp69KiuvvrqC7ZnZ2cb16kCqHmM9fgj7LdxKfh+WDO4RryWOXz4sPFIk/bt26t169Yurgju4txdMdnRoqpGjRqlI0eOKCUl5bzTXktLSxUTE6OWLVtq8eLFLqoQqJ0Y63Ex7LfhLL4f1hyCeC1x7hmA9erVc5j/888/66WXXtLUqVNdVBncxbm7YrKjRVUdP35ckZGR8vPzU1JSktq1aye73a4DBw7otddeU2lpqXbu3Knw8HBXlwrUCoz1+CPst+Esvh/WHIJ4LXGxX7N+/PFHBQcHq6KiwkWVAfBk2dnZeuihh7Rx40adG04sFotuueUWvfrqqxyJA0zEWI+qYL8NuAeuEa8l7Hb7Be92uHv3bq4FAnDJWrRoof/97386efKkDh06JElq3bo1+xXABRjrURXstwH3QBC/wjVs2FAWi0UWi+W8Rw9UVFSopKREDz74oAsrBHAlaNiwoXGXZgDmYqzHpWC/DbgWp6Zf4ZYtWya73a5Ro0Zp7ty5Do8e8PX1VfPmzRUVFeXCCgEAwOVgrAcAz0MQryXS0tJ08803y8eHkyAAALgSMdYDgOfwcnUBMEefPn2MgTk2NlYnTpxwcUUAAKA6MdYDgOcgiNdC6enp+vnnn11dBgAAqCGM9QDg3gjiAAAAAACYiCBeC0VERKhOnTquLgMAANQQxnoAcG/crA0AAAAAABNxW81apLCwUNu3b1d+fr4qKysd2kaOHOmiqgAAQHVhrAcAz8AR8Vrigw8+UHx8vEpKSmS1WmWxWIw2i8WigoICF1YHAAAuF2M9AHgOgngt0aZNGw0ePFjPP/+86tWr5+pyAABANWOsBwDPQRCvJQICArR37161bNnS1aUAAIAawFgPAJ6Du6bXEjExMdq5c6erywAAADWEsR4APAc3a7uCvf/++8a/Y2NjNWnSJO3fv1+dO3c+75EmQ4cONbs8AABwmRjrAcAzcWr6FczLq2onPFgsFlVUVNRwNQAAoLox1gOAZyKIAwAAAABgIq4RryWWL1+u0tLS8+aXlZVp+fLlLqgIAABUJ8Z6APAcHBGvJby9vXXixAkFBwc7zP/xxx8VHBzM6WoAAHg4xnoA8BwcEa8l7Ha7LBbLefOPHz8um83mgooAAEB1YqwHAM/BXdOvcNddd50sFossFov69+8vH5//+09eUVGh7OxsDRw40IUVAgCAy8FYDwCehyB+hRs2bJgkKSsrSzExMapfv77R5uvrq+bNmysuLs5F1QEAgMvFWA8AnodrxGuJZcuW6e6775a/v7+rSwEAADWAsR4APAdBvJYpKytTfn6+KisrHeY3a9bMRRUBAIDqxFgPAO6PU9NriUOHDmnUqFHaunWrw/xzN3bhTqoAAHg2xnoA8BwE8Vri/vvvl4+Pj9auXaumTZte8K6qAADAczHWA4Dn4NT0WiIgIECZmZlq166dq0sBAAA1gLEeADwHzxGvJTp06KAffvjB1WUAAIAawlgPAJ6DIF5LvPjii3r88ce1ZcsW/fjjjyouLnaYAACAZ2OsBwDPwanptYSX1y+/ufz2ejFu4AIAwJWBsR4APAc3a6slNm/e7OoSAABADWKsBwDPwRFxAAAAAABMxBHxWqSwsFBvvvmmDhw4IEnq2LGjRo0aJZvN5uLKAABAdWCsBwDPwBHxWmLnzp2KiYlR3bp1dcMNN0iSduzYoZ9//lkbN25Ut27dXFwhAAC4HIz1AOA5COK1RK9evdS6dWu98cYb8vH55USI8vJyjR49Wl9//bXS09NdXCEAALgcjPUA4DkI4rVE3bp19fnnn6tdu3YO8/fv36/IyEidPn3aRZUBAIDqwFgPAJ6D54jXElarVTk5OefNP3bsmBo0aOCCigAAQHVirAcAz0EQryXuvvtuJSYmatWqVTp27JiOHTumt99+W6NHj9Y999zj6vIAAMBlYqwHAM/BXdNridmzZ8tisWjkyJEqLy+X3W6Xr6+vxo0bpxdeeMHV5QEAgMvEWA8AnoNrxGuZ06dP68iRI5KkVq1aqV69ei6uCAAAVCfGegBwfxwRv8KNGjWqSv0WL15cw5UAAICawFgPAJ6HI+JXOC8vL0VEROi6667T7/2nXr16tYlVAQCA6sJYDwCehyPiV7hx48bprbfeUnZ2th544AHdd999CgoKcnVZAACgmjDWA4Dn4Yh4LVBaWqr33ntPixcv1tatWxUbG6vExEQNGDBAFovF1eUBAIDLxFgPAJ6FIF7LfPPNN1q6dKmWL1+u8vJy7du3T/Xr13d1WQAAoJow1gOA++M54rWMl5eXLBaL7Ha7KioqXF0OAACoZoz1AOD+COK1QGlpqd566y3dcsstatOmjfbu3atXX31VOTk5/EIOAMAVgLEeADwLN2u7wj300EN6++23FR4erlGjRumtt95S48aNXV0WAACoJoz1AOB5uEb8Cufl5aVmzZrpuuuu+92btbz33nsmVgUAAKoLYz0AeB6OiF/hRo4cyd1SAQC4gjHWA4Dn4Yg4AAAAAAAm4mZtAAAAAACYiCAOAAAAAICJCOIAAAAAAJiIIA4AAAAAgIkI4oCHys3N1fjx49WyZUv5+fkpPDxcQ4YMUWpqarUto2/fvpowYUK1fd7v2bJliywWiwoLC01ZHgAA7o6xHrhy8fgywAMdPXpUN998swIDA/XSSy+pc+fOOnv2rDZs2KCkpCR9+eWXptVit9tVUVEhHx92JwAAVBfGeuAKZwfgcQYNGmS/6qqr7CUlJee1nTx50m632+3ffPONfejQofaAgAB7gwYN7H/+85/tubm5Rr+nn37a3rVrV/vy5cvtERERdqvVar/77rvtxcXFdrvdbk9ISLBLcpiys7Ptmzdvtkuyf/jhh/Zu3brZ69SpY9+8ebP98OHD9qFDh9qDg4PtAQEB9sjISHtKSopDbWfOnLE//vjj9quvvtru6+trb9Wqlf0f//iHPTs7+7xlJSQk1NjfDwAAd8dYD1zZODUd8DAFBQVav369kpKSFBAQcF57YGCgKisrddttt6mgoEBpaWlKSUnR119/rbvvvtuh75EjR7RmzRqtXbtWa9euVVpaml544QVJ0iuvvKKoqCiNGTNGJ06c0IkTJxQeHm6898knn9QLL7ygAwcOqEuXLiopKdHgwYOVmpqqzz//XAMHDtSQIUOUk5NjvGfkyJF66623NG/ePB04cEB///vfVb9+fYWHh+s///mPJOngwYM6ceKEXnnllZr48wEA4PYY64ErH+eXAB7m8OHDstvtateu3UX7pKamau/evcrOzjYG1OXLl6tjx47asWOHrr/+eklSZWWlli5dqgYNGkiSRowYodTUVD333HOy2Wzy9fVVvXr1FBoaet4yZsyYoVtuucV4HRQUpK5duxqvn3nmGa1evVrvv/++kpOT9dVXX+mdd95RSkqKoqOjJUktW7Z0eL8kBQcHKzAw8BL/OgAAeD7GeuDKxxFxwMPY7fY/7HPgwAGFh4c7/KrdoUMHBQYG6sCBA8a85s2bGwOzJDVt2lT5+flVqiMyMtLhdUlJiR577DG1b99egYGBql+/vg4cOGD8Sp6VlSVvb2/16dOnSp8PAEBtxVgPXPk4Ig54mGuuuUYWi6VabtJSp04dh9cWi0WVlZVVeu9vT5V77LHHlJKSotmzZ6t169aqW7eu7rzzTpWVlUmS6tate9n1AgBQGzDWA1c+jogDHiYoKEgxMTFasGCBTp06dV57YWGh2rdvr2PHjunYsWPG/P3796uwsFAdOnSo8rJ8fX1VUVFRpb6ffvqp7r//ft1+++3q3LmzQkNDdfToUaO9c+fOqqysVFpa2kWXJanKywMA4ErFWA9c+QjigAdasGCBKioqdMMNN+g///mPDh06pAMHDmjevHmKiopSdHS0OnfurPj4eO3atUvbt2/XyJEj1adPn/NOM/s9zZs317Zt23T06FH98MMPv/sL+jXXXKP33ntPWVlZ2r17t+69916H/s2bN1dCQoJGjRqlNWvWKDs7W1u2bNE777wjSYqIiJDFYtHatWv1/fffq6Sk5NL/QAAAeDjGeuDKRhAHPFDLli21a9cu9evXT48++qg6deqkW265RampqVq4cKEsFov++9//qmHDhurdu7eio6PVsmVLrVq1yqnlPPbYY/L29laHDh3UpEkTh7ui/tacOXPUsGFD3XTTTRoyZIhiYmLUrVs3hz4LFy7UnXfeqYceekjt2rXTmDFjjF/6r7rqKk2fPl1PPvmkQkJClJyc7PwfBgCAKwRjPXBls9ircjcIAAAAAABQLTgiDgAAAACAiQjiAAAAAACYiCAOAAAAAICJCOIAAAAAAJiIIA4AAAAAgIkI4gAAAAAAmIggDgAAAACAiQjiAAAAAACYiCAOAAAAAICJCOIAAAAAAJiIIA4AAAAAgIkI4gAAAAAAmOj/BznksdABCaMQAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Target Variable Distribution"
      ],
      "metadata": {
        "id": "FpPs0Wioz5vB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Target variable distribution shows that we are dealing with an imbalanced problem as there are many more non-churned as compare to churned users. The model would achieve high accuracy as it would mostly predict majority class - users who didn't churn in our example.\n",
        "\n",
        "Few things we can do to minimize the influence of imbalanced dataset:\n",
        "- resample data,\n",
        "- collect more samples,\n",
        "- use precision and recall as accuracy metrics."
      ],
      "metadata": {
        "id": "34bitGB60CnM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[target].value_counts().plot(kind='bar').set_title('churned')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "yj04A6R_z87a",
        "outputId": "0ecdaba3-b616-4735-eacd-f6db317943d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'churned')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Outliers Analysis with IQR Method"
      ],
      "metadata": {
        "id": "2arhPsBh0NLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = ['tenure','MonthlyCharges']\n",
        "def count_outliers(data,col):\n",
        "        q1 = data[col].quantile(0.25,interpolation='nearest')\n",
        "        q2 = data[col].quantile(0.5,interpolation='nearest')\n",
        "        q3 = data[col].quantile(0.75,interpolation='nearest')\n",
        "        q4 = data[col].quantile(1,interpolation='nearest')\n",
        "        IQR = q3 -q1\n",
        "        global LLP\n",
        "        global ULP\n",
        "        LLP = q1 - 1.5*IQR\n",
        "        ULP = q3 + 1.5*IQR\n",
        "        if data[col].min() > LLP and data[col].max() < ULP:\n",
        "            print(\"No outliers in\",i)\n",
        "        else:\n",
        "            print(\"There are outliers in\",i)\n",
        "            x = data[data[col]<LLP][col].size\n",
        "            y = data[data[col]>ULP][col].size\n",
        "            a.append(i)\n",
        "            print('Count of outliers are:',x+y)\n",
        "global a\n",
        "a = []\n",
        "for i in x:\n",
        "    count_outliers(df,i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GT62-UoB0RXC",
        "outputId": "2670957e-76cd-47af-e45d-c45c40b2b6ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No outliers in tenure\n",
            "No outliers in MonthlyCharges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cleaning and Transforming Data"
      ],
      "metadata": {
        "id": "PizL3JHE0W44"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['customerID'],axis = 1,inplace = True)"
      ],
      "metadata": {
        "id": "I03RXG2U0U2S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "gA1zNqD_0bdW",
        "outputId": "471fab83-9a8b-4af2-cfff-89d1e53fb861"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  Female              0     Yes         No       1           No   \n",
              "1    Male              0      No         No      34          Yes   \n",
              "2    Male              0      No         No       2          Yes   \n",
              "3    Male              0      No         No      45           No   \n",
              "4  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
              "0  No phone service             DSL             No          Yes   \n",
              "1                No             DSL            Yes           No   \n",
              "2                No             DSL            Yes          Yes   \n",
              "3  No phone service             DSL            Yes           No   \n",
              "4                No     Fiber optic             No           No   \n",
              "\n",
              "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
              "0               No          No          No              No  Month-to-month   \n",
              "1              Yes          No          No              No        One year   \n",
              "2               No          No          No              No  Month-to-month   \n",
              "3              Yes         Yes          No              No        One year   \n",
              "4               No          No          No              No  Month-to-month   \n",
              "\n",
              "  PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  \\\n",
              "0              Yes           Electronic check           29.85         29.85   \n",
              "1               No               Mailed check           56.95       1889.50   \n",
              "2              Yes               Mailed check           53.85        108.15   \n",
              "3               No  Bank transfer (automatic)           42.30       1840.75   \n",
              "4              Yes           Electronic check           70.70        151.65   \n",
              "\n",
              "  Churn  \n",
              "0    No  \n",
              "1    No  \n",
              "2   Yes  \n",
              "3    No  \n",
              "4   Yes  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13cdfb06-7b95-40cc-aa3a-51d4481950c8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.50</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13cdfb06-7b95-40cc-aa3a-51d4481950c8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-13cdfb06-7b95-40cc-aa3a-51d4481950c8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-13cdfb06-7b95-40cc-aa3a-51d4481950c8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0727236a-2e4c-4fc5-8644-bf44b7f7e8b8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0727236a-2e4c-4fc5-8644-bf44b7f7e8b8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0727236a-2e4c-4fc5-8644-bf44b7f7e8b8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7043,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Partner\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 72,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          8,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PhoneService\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MultipleLines\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No phone service\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InternetService\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"DSL\",\n          \"Fiber optic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineSecurity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineBackup\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DeviceProtection\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TechSupport\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingTV\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingMovies\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Contract\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Month-to-month\",\n          \"One year\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaperlessBilling\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaymentMethod\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Mailed check\",\n          \"Credit card (automatic)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.090047097678493,\n        \"min\": 18.25,\n        \"max\": 118.75,\n        \"num_unique_values\": 1585,\n        \"samples\": [\n          48.85,\n          20.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2266.771361883145,\n        \"min\": 18.8,\n        \"max\": 8684.8,\n        \"num_unique_values\": 6530,\n        \"samples\": [\n          5594.0,\n          6840.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropped customerID because it's not needed"
      ],
      "metadata": {
        "id": "QNBejKGT0gOh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## On Hot Encoding"
      ],
      "metadata": {
        "id": "D60b4F3t0rTc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.get_dummies(data=df,columns=['gender', 'Partner', 'Dependents',\n",
        "       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',\n",
        "       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',\n",
        "       'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'Churn'], drop_first=True)"
      ],
      "metadata": {
        "id": "kpIOVy600kkH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "HNeI2GEq0wSA",
        "outputId": "e4e648a4-4cb0-4c98-c325-d5fc7acc9b3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SeniorCitizen  tenure  MonthlyCharges  TotalCharges  gender_Male  \\\n",
              "0              0       1           29.85         29.85        False   \n",
              "1              0      34           56.95       1889.50         True   \n",
              "2              0       2           53.85        108.15         True   \n",
              "3              0      45           42.30       1840.75         True   \n",
              "4              0       2           70.70        151.65        False   \n",
              "\n",
              "   Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
              "0         True           False             False   \n",
              "1        False           False              True   \n",
              "2        False           False              True   \n",
              "3        False           False             False   \n",
              "4        False           False              True   \n",
              "\n",
              "   MultipleLines_No phone service  MultipleLines_Yes  ...  StreamingTV_Yes  \\\n",
              "0                            True              False  ...            False   \n",
              "1                           False              False  ...            False   \n",
              "2                           False              False  ...            False   \n",
              "3                            True              False  ...            False   \n",
              "4                           False              False  ...            False   \n",
              "\n",
              "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
              "0                                False                False   \n",
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              "2                                False                False   \n",
              "3                                False                False   \n",
              "4                                False                False   \n",
              "\n",
              "   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
              "0              False              False                  True   \n",
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              "3               True              False                 False   \n",
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              "\n",
              "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
              "0                                  False                            True   \n",
              "1                                  False                           False   \n",
              "2                                  False                           False   \n",
              "3                                  False                           False   \n",
              "4                                  False                            True   \n",
              "\n",
              "   PaymentMethod_Mailed check  Churn_Yes  \n",
              "0                       False      False  \n",
              "1                        True      False  \n",
              "2                        True       True  \n",
              "3                       False      False  \n",
              "4                       False       True  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "<p>5 rows × 31 columns</p>\n",
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              "    0% {\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1"
            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5X-KI5w0yvR",
        "outputId": "723df793-9a4d-493e-bad4-ed44f5114014"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges',\n",
              "       'gender_Male', 'Partner_Yes', 'Dependents_Yes', 'PhoneService_Yes',\n",
              "       'MultipleLines_No phone service', 'MultipleLines_Yes',\n",
              "       'InternetService_Fiber optic', 'InternetService_No',\n",
              "       'OnlineSecurity_No internet service', 'OnlineSecurity_Yes',\n",
              "       'OnlineBackup_No internet service', 'OnlineBackup_Yes',\n",
              "       'DeviceProtection_No internet service', 'DeviceProtection_Yes',\n",
              "       'TechSupport_No internet service', 'TechSupport_Yes',\n",
              "       'StreamingTV_No internet service', 'StreamingTV_Yes',\n",
              "       'StreamingMovies_No internet service', 'StreamingMovies_Yes',\n",
              "       'Contract_One year', 'Contract_Two year', 'PaperlessBilling_Yes',\n",
              "       'PaymentMethod_Credit card (automatic)',\n",
              "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check',\n",
              "       'Churn_Yes'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Rearranging Columns"
      ],
      "metadata": {
        "id": "ZyqP00oV04I1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df1[['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges',\n",
        "        'gender_Male', 'Partner_Yes', 'Dependents_Yes',\n",
        "       'PhoneService_Yes', 'MultipleLines_No phone service',\n",
        "       'MultipleLines_Yes', 'InternetService_Fiber optic',\n",
        "       'InternetService_No', 'OnlineSecurity_No internet service',\n",
        "       'OnlineSecurity_Yes', 'OnlineBackup_No internet service',\n",
        "       'OnlineBackup_Yes', 'DeviceProtection_No internet service',\n",
        "       'DeviceProtection_Yes', 'TechSupport_No internet service',\n",
        "       'TechSupport_Yes', 'StreamingTV_No internet service', 'StreamingTV_Yes',\n",
        "       'StreamingMovies_No internet service', 'StreamingMovies_Yes',\n",
        "       'Contract_One year', 'Contract_Two year', 'PaperlessBilling_Yes',\n",
        "       'PaymentMethod_Credit card (automatic)',\n",
        "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check','Churn_Yes']]"
      ],
      "metadata": {
        "id": "Hrp8lo7y1D_0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "9cnBUGFg1FxE",
        "outputId": "19724067-2e1f-46cf-c844-3f819385e51d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SeniorCitizen  tenure  MonthlyCharges  TotalCharges  gender_Male  \\\n",
              "0              0       1           29.85         29.85        False   \n",
              "1              0      34           56.95       1889.50         True   \n",
              "2              0       2           53.85        108.15         True   \n",
              "3              0      45           42.30       1840.75         True   \n",
              "4              0       2           70.70        151.65        False   \n",
              "\n",
              "   Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
              "0         True           False             False   \n",
              "1        False           False              True   \n",
              "2        False           False              True   \n",
              "3        False           False             False   \n",
              "4        False           False              True   \n",
              "\n",
              "   MultipleLines_No phone service  MultipleLines_Yes  ...  StreamingTV_Yes  \\\n",
              "0                            True              False  ...            False   \n",
              "1                           False              False  ...            False   \n",
              "2                           False              False  ...            False   \n",
              "3                            True              False  ...            False   \n",
              "4                           False              False  ...            False   \n",
              "\n",
              "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
              "0                                False                False   \n",
              "1                                False                False   \n",
              "2                                False                False   \n",
              "3                                False                False   \n",
              "4                                False                False   \n",
              "\n",
              "   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
              "0              False              False                  True   \n",
              "1               True              False                 False   \n",
              "2              False              False                  True   \n",
              "3               True              False                 False   \n",
              "4              False              False                  True   \n",
              "\n",
              "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
              "0                                  False                            True   \n",
              "1                                  False                           False   \n",
              "2                                  False                           False   \n",
              "3                                  False                           False   \n",
              "4                                  False                            True   \n",
              "\n",
              "   PaymentMethod_Mailed check  Churn_Yes  \n",
              "0                       False      False  \n",
              "1                        True      False  \n",
              "2                        True       True  \n",
              "3                       False      False  \n",
              "4                       False       True  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th>tenure</th>\n",
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              "      <th>TotalCharges</th>\n",
              "      <th>gender_Male</th>\n",
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              "      <th>PhoneService_Yes</th>\n",
              "      <th>MultipleLines_No phone service</th>\n",
              "      <th>MultipleLines_Yes</th>\n",
              "      <th>...</th>\n",
              "      <th>StreamingTV_Yes</th>\n",
              "      <th>StreamingMovies_No internet service</th>\n",
              "      <th>StreamingMovies_Yes</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>PaperlessBilling_Yes</th>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
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              "      <th>Churn_Yes</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.85</td>\n",
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              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.50</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTHoDaiR1IXi",
        "outputId": "82089c91-7cba-4317-a649-cec39be71158"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7043, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "# The imputer will replace missing values with the mean of the non-missing values for the respective columns\n",
        "\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy=\"mean\")\n",
        "\n",
        "df1.TotalCharges = imputer.fit_transform(df1[\"TotalCharges\"].values.reshape(-1, 1))"
      ],
      "metadata": {
        "id": "9Qj4IGzL1SPy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Future Scaling"
      ],
      "metadata": {
        "id": "_MT_rX3z2SKk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "scaler.fit(df1.drop(['Churn_Yes'],axis = 1))\n",
        "scaled_features = scaler.transform(df1.drop('Churn_Yes',axis = 1))"
      ],
      "metadata": {
        "id": "rjOd8XWf2VAb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Future Selection"
      ],
      "metadata": {
        "id": "kbObLBBm4251"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X = scaled_features\n",
        "Y = df1['Churn_Yes']\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.3,random_state=44)"
      ],
      "metadata": {
        "id": "R1IfzWJz2qFe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prediction using Logistic Regression"
      ],
      "metadata": {
        "id": "ozKAdW8K48bw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report,accuracy_score ,confusion_matrix\n",
        "\n",
        "logmodel = LogisticRegression()\n",
        "logmodel.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "2AeqUiXR5KdH",
        "outputId": "88415ff3-395b-4aef-a647-ed7f0867500b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predLR = logmodel.predict(X_test)"
      ],
      "metadata": {
        "id": "wixhVk7W5XgL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predLR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "toKV8ppl5Xdg",
        "outputId": "e4b8a326-3dfb-4e25-aeb5-9dbf4dc22da9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False, ..., False, False, False])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5teZ-Ido5a_j",
        "outputId": "b3b0a50d-cc40-4a6f-f40b-b9ba4b0c8307"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5616    False\n",
              "2937    False\n",
              "1355    False\n",
              "5441     True\n",
              "3333    False\n",
              "        ...  \n",
              "2797     True\n",
              "412     False\n",
              "174     False\n",
              "5761    False\n",
              "5895    False\n",
              "Name: Churn_Yes, Length: 2113, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, predLR))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luVOhPNG5a9o",
        "outputId": "ce122002-3753-4cb0-9649-75c7ef37c834"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.84      0.90      0.87      1557\n",
            "        True       0.65      0.53      0.58       556\n",
            "\n",
            "    accuracy                           0.80      2113\n",
            "   macro avg       0.74      0.71      0.73      2113\n",
            "weighted avg       0.79      0.80      0.79      2113\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate the classification report\n",
        "report = classification_report(Y_test, predLR, target_names=['Churn_No', 'Churn_Yes'])\n",
        "\n",
        "# split the report into lines\n",
        "lines = report.split('\\n')\n",
        "\n",
        "# split each line into parts\n",
        "parts = [line.split() for line in lines[2:-5]]\n",
        "\n",
        "# extract the metrics for each class\n",
        "class_metrics = dict()\n",
        "for part in parts:\n",
        "    class_metrics[part[0]] = {'precision': float(part[1]), 'recall': float(part[2]), 'f1-score': float(part[3]), 'support': int(part[4])}\n",
        "\n",
        "# create a bar chart for each metric\n",
        "fig, ax = plt.subplots(1, 4, figsize=(12, 4))\n",
        "metrics = ['precision', 'recall', 'f1-score', 'support']\n",
        "for i, metric in enumerate(metrics):\n",
        "    ax[i].bar(class_metrics.keys(), [class_metrics[key][metric] for key in class_metrics.keys()])\n",
        "    ax[i].set_title(metric)\n",
        "\n",
        "# display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "-K061_u65fq2",
        "outputId": "9118d394-7f5f-44ff-8b4b-340e135e6d06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix_LR = confusion_matrix(Y_test, predLR)"
      ],
      "metadata": {
        "id": "4ZkaiYBa5iYg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a heatmap of the matrix using matshow()\n",
        "\n",
        "plt.matshow(confusion_matrix(Y_test, predLR))\n",
        "\n",
        "# add labels for the x and y axes\n",
        "plt.xlabel('Predicted Class')\n",
        "plt.ylabel('Actual Class')\n",
        "\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        plt.text(j, i, confusion_matrix_LR[i, j], ha='center', va='center')\n",
        "\n",
        "\n",
        "# Add custom labels for x and y ticks\n",
        "plt.xticks([0, 1], [\"Not Churned\", \"Churned\"])\n",
        "plt.yticks([0, 1], [\"Not Churned\", \"Churned\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "NQjOytei5kXz",
        "outputId": "557872fb-4ab2-4f38-8493-6e203bb376b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 480x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logmodel.score(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hn7vph0z5m9p",
        "outputId": "6881530f-7633-4bf9-ef71-0e9f41ab1e98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8062880324543611"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(Y_test, predLR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GwkTiafu5peD",
        "outputId": "fb047e16-c5de-425d-90b6-5f6c99309594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8002839564600095"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion\n",
        "\n",
        "Logistic regression is a powerful and easy-to-use tool for churn prediction. This algorithm can help companies identify customers at risk of switching and take proactive measures to prevent it."
      ],
      "metadata": {
        "id": "1QrMlEBz7XXG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Summary (概要 gaiyou)\n",
        "---\n",
        "Customer churn prediction is an important task for many companies. By understanding which customers are at risk of churning, companies can take proactive measures to prevent it. One popular method for churn prediction is logistic regression, a powerful and easy-to-use machine learning algorithm. And many other ML algorithm to be used for this case, likes KNN, SVC, decision tree classifier, etc."
      ],
      "metadata": {
        "id": "IzFD5WeW75xX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import time\n",
        "\n",
        "# Fungsi animasi teks\n",
        "def animasi_teks(teks):\n",
        "  for char in teks:\n",
        "    print(char, end='', flush=True)  # Menambahkan `flush=True` untuk memastikan teks dicetak secara langsung\n",
        "    time.sleep(0.5)  # Kecepatan animasi (sesuaikan dengan keinginan)\n",
        "  print()\n",
        "\n",
        "# Variasi ucapan terima kasih dalam bahasa jepang\n",
        "ucapan_terima_kasih = [\n",
        "  \"Arigatou gozaimashita!\", # terima kasih\n",
        "  \"Shukudai arigatou Na!\", # atas tugasnya\n",
        "  \"Daigen arigatou gozaimasu!\", # luar biasa terima kasih\n",
        "  \"Otetsudai dekite ureshii desu!\", # sengan bisa ngerjain\n",
        "  \"Yoi kekka ga deru koto o negatte imasu! ww\", # semoga dapet nilai bagus! hehe\n",
        "]\n",
        "\n",
        "# Memilih ucapan terima kasih secara acak\n",
        "ucapan = (ucapan_terima_kasih)\n",
        "\n",
        "# Menjalankan animasi teks\n",
        "animasi_teks(ucapan)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERqV_e4R-Y7q",
        "outputId": "bd741c70-9139-484b-da15-14ad0d083ad1"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Arigatou gozaimashita!Shukudai arigatou Na!Daigen arigatou gozaimasu!Otetsudai dekite ureshii desu!Yoi kekka ga deru koto o negatte imasu!\n"
          ]
        }
      ]
    }
  ]
}