Telecom_Customer_Churn_Prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/MarcellinusRyan20/TelecomChurnPrediction/blob/main/Telecom_Customer_Churn_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "## Import Libraries and Data"
      ],
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        "id": "slqHyldTu19Y"
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      "execution_count": 111,
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import LeaveOneOut\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('train.csv')\n",
        "df_test = pd.read_csv('test.csv')"
      ],
      "metadata": {
        "id": "5GiY2lZKutNE"
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      "execution_count": 2,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "## Data Understanding and Preprocessing"
      ],
      "metadata": {
        "id": "FT4-i1lku918"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get to know about the data\n",
        "df_train.head()"
      ],
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          "height": 226
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        "outputId": "1555f98b-bf5b-4607-dfc3-24d18716b64b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  state  account_length      area_code international_plan voice_mail_plan  \\\n",
              "0    OH             107  area_code_415                 no             yes   \n",
              "1    NJ             137  area_code_415                 no              no   \n",
              "2    OH              84  area_code_408                yes              no   \n",
              "3    OK              75  area_code_415                yes              no   \n",
              "4    MA             121  area_code_510                 no             yes   \n",
              "\n",
              "   number_vmail_messages  total_day_minutes  total_day_calls  \\\n",
              "0                     26              161.6              123   \n",
              "1                      0              243.4              114   \n",
              "2                      0              299.4               71   \n",
              "3                      0              166.7              113   \n",
              "4                     24              218.2               88   \n",
              "\n",
              "   total_day_charge  total_eve_minutes  total_eve_calls  total_eve_charge  \\\n",
              "0             27.47              195.5              103             16.62   \n",
              "1             41.38              121.2              110             10.30   \n",
              "2             50.90               61.9               88              5.26   \n",
              "3             28.34              148.3              122             12.61   \n",
              "4             37.09              348.5              108             29.62   \n",
              "\n",
              "   total_night_minutes  total_night_calls  total_night_charge  \\\n",
              "0                254.4                103               11.45   \n",
              "1                162.6                104                7.32   \n",
              "2                196.9                 89                8.86   \n",
              "3                186.9                121                8.41   \n",
              "4                212.6                118                9.57   \n",
              "\n",
              "   total_intl_minutes  total_intl_calls  total_intl_charge  \\\n",
              "0                13.7                 3               3.70   \n",
              "1                12.2                 5               3.29   \n",
              "2                 6.6                 7               1.78   \n",
              "3                10.1                 3               2.73   \n",
              "4                 7.5                 7               2.03   \n",
              "\n",
              "   number_customer_service_calls churn  \n",
              "0                              1    no  \n",
              "1                              0    no  \n",
              "2                              2    no  \n",
              "3                              3    no  \n",
              "4                              3    no  "
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\"area_code_510\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"international_plan\",\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\": \"voice_mail_plan\",\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\": \"number_vmail_messages\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 0,\n        \"max\": 52,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          49,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.01237333141773,\n        \"min\": 0.0,\n        \"max\": 351.5,\n        \"num_unique_values\": 1843,\n        \"samples\": [\n          277.7,\n          276.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 165,\n        \"num_unique_values\": 120,\n        \"samples\": [\n          70,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.182096032769186,\n        \"min\": 0.0,\n        \"max\": 59.76,\n        \"num_unique_values\": 1843,\n        \"samples\": [\n          47.21,\n          47.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_eve_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.24951818379498,\n        \"min\": 0.0,\n        \"max\": 359.3,\n        \"num_unique_values\": 1773,\n        \"samples\": [\n          177.6,\n          294.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_eve_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 170,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          102,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_eve_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.271211992240319,\n        \"min\": 0.0,\n        \"max\": 30.54,\n        \"num_unique_values\": 1572,\n        \"samples\": [\n          25.82,\n          21.31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_night_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.353548074635995,\n        \"min\": 0.0,\n        \"max\": 395.0,\n        \"num_unique_values\": 1757,\n        \"samples\": [\n          95.0,\n          210.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_night_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 0,\n        \"max\": 175,\n        \"num_unique_values\": 128,\n        \"samples\": [\n          122,\n          60\n        ],\n        \"semantic_type\": \"\",\n 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\"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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "jb-tGLwIvIq7",
        "outputId": "52c1dc9b-66d1-4251-b775-7c0af2014cb4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id state  account_length      area_code international_plan voice_mail_plan  \\\n",
              "0   1    KS             128  area_code_415                 no             yes   \n",
              "1   2    AL             118  area_code_510                yes              no   \n",
              "2   3    IA              62  area_code_415                 no              no   \n",
              "3   4    VT              93  area_code_510                 no              no   \n",
              "4   5    NE             174  area_code_415                 no              no   \n",
              "\n",
              "   number_vmail_messages  total_day_minutes  total_day_calls  \\\n",
              "0                     25              265.1              110   \n",
              "1                      0              223.4               98   \n",
              "2                      0              120.7               70   \n",
              "3                      0              190.7              114   \n",
              "4                      0              124.3               76   \n",
              "\n",
              "   total_day_charge  total_eve_minutes  total_eve_calls  total_eve_charge  \\\n",
              "0             45.07              197.4               99             16.78   \n",
              "1             37.98              220.6              101             18.75   \n",
              "2             20.52              307.2               76             26.11   \n",
              "3             32.42              218.2              111             18.55   \n",
              "4             21.13              277.1              112             23.55   \n",
              "\n",
              "   total_night_minutes  total_night_calls  total_night_charge  \\\n",
              "0                244.7                 91               11.01   \n",
              "1                203.9                118                9.18   \n",
              "2                203.0                 99                9.14   \n",
              "3                129.6                121                5.83   \n",
              "4                250.7                115               11.28   \n",
              "\n",
              "   total_intl_minutes  total_intl_calls  total_intl_charge  \\\n",
              "0                10.0                 3               2.70   \n",
              "1                 6.3                 6               1.70   \n",
              "2                13.1                 6               3.54   \n",
              "3                 8.1                 3               2.19   \n",
              "4                15.5                 5               4.19   \n",
              "\n",
              "   number_customer_service_calls  \n",
              "0                              1  \n",
              "1                              0  \n",
              "2                              4  \n",
              "3                              3  \n",
              "4                              3  "
            ],
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              "      <td>76</td>\n",
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              "      <td>203.0</td>\n",
              "      <td>99</td>\n",
              "      <td>9.14</td>\n",
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              "      <td>6</td>\n",
              "      <td>3.54</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>VT</td>\n",
              "      <td>93</td>\n",
              "      <td>area_code_510</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
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              "      <td>190.7</td>\n",
              "      <td>114</td>\n",
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              "      <td>218.2</td>\n",
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              "      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-b3d8924c-1046-46c8-878c-615705b9cc2a 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_test",
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 750,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 216,\n        \"min\": 1,\n        \"max\": 750,\n        \"num_unique_values\": 750,\n        \"samples\": [\n          507,\n          358,\n          134\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 51,\n        \"samples\": [\n          \"DC\",\n          \"CA\",\n          \"NC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"account_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 1,\n        \"max\": 238,\n        \"num_unique_values\": 175,\n        \"samples\": [\n          178,\n          122,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"area_code_415\",\n          \"area_code_510\",\n          \"area_code_408\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"international_plan\",\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\": \"voice_mail_plan\",\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\": \"number_vmail_messages\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 0,\n        \"max\": 51,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          13,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.25833652623419,\n        \"min\": 12.5,\n        \"max\": 350.8,\n        \"num_unique_values\": 619,\n        \"samples\": [\n          197.9,\n          104.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 39,\n        \"max\": 163,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          61,\n          107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.053756130672962,\n        \"min\": 2.13,\n        \"max\": 59.64,\n        \"num_unique_values\": 619,\n        \"samples\": [\n          33.64,\n          17.71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_eve_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.1854711216683,\n        \"min\": 31.2,\n        \"max\": 363.7,\n        \"num_unique_values\": 611,\n        \"samples\": [\n          252.4,\n          227.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_eve_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 37,\n        \"max\": 164,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          137,\n          58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_eve_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.435637975827544,\n        \"min\": 2.65,\n        \"max\": 30.91,\n        \"num_unique_values\": 584,\n        \"samples\": [\n          11.47,\n          13.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_night_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.53135092238578,\n        \"min\": 50.9,\n        \"max\": 364.3,\n        \"num_unique_values\": 628,\n        \"samples\": [\n          214.1,\n          200.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_night_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 12,\n        \"max\": 168,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          96,\n          58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_night_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.318920092877665,\n        \"min\": 2.29,\n        \"max\": 16.39,\n        \"num_unique_values\": 502,\n        \"samples\": [\n          10.15,\n          12.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_intl_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7703402852267365,\n        \"min\": 0.0,\n        \"max\": 18.9,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          12.5,\n          14.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_intl_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 19,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          3,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_intl_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7477036466496423,\n        \"min\": 0.0,\n        \"max\": 5.1,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          3.38,\n          3.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_customer_service_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYs_PYRkvOUe",
        "outputId": "da7425fe-d049-4c66-8dbf-0ff4c3a9b0fd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4250 entries, 0 to 4249\n",
            "Data columns (total 20 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
            " 0   state                          4250 non-null   object \n",
            " 1   account_length                 4250 non-null   int64  \n",
            " 2   area_code                      4250 non-null   object \n",
            " 3   international_plan             4250 non-null   object \n",
            " 4   voice_mail_plan                4250 non-null   object \n",
            " 5   number_vmail_messages          4250 non-null   int64  \n",
            " 6   total_day_minutes              4250 non-null   float64\n",
            " 7   total_day_calls                4250 non-null   int64  \n",
            " 8   total_day_charge               4250 non-null   float64\n",
            " 9   total_eve_minutes              4250 non-null   float64\n",
            " 10  total_eve_calls                4250 non-null   int64  \n",
            " 11  total_eve_charge               4250 non-null   float64\n",
            " 12  total_night_minutes            4250 non-null   float64\n",
            " 13  total_night_calls              4250 non-null   int64  \n",
            " 14  total_night_charge             4250 non-null   float64\n",
            " 15  total_intl_minutes             4250 non-null   float64\n",
            " 16  total_intl_calls               4250 non-null   int64  \n",
            " 17  total_intl_charge              4250 non-null   float64\n",
            " 18  number_customer_service_calls  4250 non-null   int64  \n",
            " 19  churn                          4250 non-null   object \n",
            "dtypes: float64(8), int64(7), object(5)\n",
            "memory usage: 664.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azL1f93XvStm",
        "outputId": "09b14c01-5ded-469d-8a36-313facb239b1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 750 entries, 0 to 749\n",
            "Data columns (total 20 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
            " 0   id                             750 non-null    int64  \n",
            " 1   state                          750 non-null    object \n",
            " 2   account_length                 750 non-null    int64  \n",
            " 3   area_code                      750 non-null    object \n",
            " 4   international_plan             750 non-null    object \n",
            " 5   voice_mail_plan                750 non-null    object \n",
            " 6   number_vmail_messages          750 non-null    int64  \n",
            " 7   total_day_minutes              750 non-null    float64\n",
            " 8   total_day_calls                750 non-null    int64  \n",
            " 9   total_day_charge               750 non-null    float64\n",
            " 10  total_eve_minutes              750 non-null    float64\n",
            " 11  total_eve_calls                750 non-null    int64  \n",
            " 12  total_eve_charge               750 non-null    float64\n",
            " 13  total_night_minutes            750 non-null    float64\n",
            " 14  total_night_calls              750 non-null    int64  \n",
            " 15  total_night_charge             750 non-null    float64\n",
            " 16  total_intl_minutes             750 non-null    float64\n",
            " 17  total_intl_calls               750 non-null    int64  \n",
            " 18  total_intl_charge              750 non-null    float64\n",
            " 19  number_customer_service_calls  750 non-null    int64  \n",
            "dtypes: float64(8), int64(8), object(4)\n",
            "memory usage: 117.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "R5GMZT9rwGOa",
        "outputId": "736a130f-19d1-46e5-e638-35ac87f23720"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       account_length  number_vmail_messages  total_day_minutes  \\\n",
              "count     4250.000000            4250.000000        4250.000000   \n",
              "mean       100.236235               7.631765         180.259600   \n",
              "std         39.698401              13.439882          54.012373   \n",
              "min          1.000000               0.000000           0.000000   \n",
              "25%         73.000000               0.000000         143.325000   \n",
              "50%        100.000000               0.000000         180.450000   \n",
              "75%        127.000000              16.000000         216.200000   \n",
              "max        243.000000              52.000000         351.500000   \n",
              "\n",
              "       total_day_calls  total_day_charge  total_eve_minutes  total_eve_calls  \\\n",
              "count      4250.000000       4250.000000        4250.000000      4250.000000   \n",
              "mean         99.907294         30.644682         200.173906       100.176471   \n",
              "std          19.850817          9.182096          50.249518        19.908591   \n",
              "min           0.000000          0.000000           0.000000         0.000000   \n",
              "25%          87.000000         24.365000         165.925000        87.000000   \n",
              "50%         100.000000         30.680000         200.700000       100.000000   \n",
              "75%         113.000000         36.750000         233.775000       114.000000   \n",
              "max         165.000000         59.760000         359.300000       170.000000   \n",
              "\n",
              "       total_eve_charge  total_night_minutes  total_night_calls  \\\n",
              "count       4250.000000          4250.000000        4250.000000   \n",
              "mean          17.015012           200.527882          99.839529   \n",
              "std            4.271212            50.353548          20.093220   \n",
              "min            0.000000             0.000000           0.000000   \n",
              "25%           14.102500           167.225000          86.000000   \n",
              "50%           17.060000           200.450000         100.000000   \n",
              "75%           19.867500           234.700000         113.000000   \n",
              "max           30.540000           395.000000         175.000000   \n",
              "\n",
              "       total_night_charge  total_intl_minutes  total_intl_calls  \\\n",
              "count         4250.000000         4250.000000       4250.000000   \n",
              "mean             9.023892           10.256071          4.426353   \n",
              "std              2.265922            2.760102          2.463069   \n",
              "min              0.000000            0.000000          0.000000   \n",
              "25%              7.522500            8.500000          3.000000   \n",
              "50%              9.020000           10.300000          4.000000   \n",
              "75%             10.560000           12.000000          6.000000   \n",
              "max             17.770000           20.000000         20.000000   \n",
              "\n",
              "       total_intl_charge  number_customer_service_calls  \n",
              "count        4250.000000                    4250.000000  \n",
              "mean            2.769654                       1.559059  \n",
              "std             0.745204                       1.311434  \n",
              "min             0.000000                       0.000000  \n",
              "25%             2.300000                       1.000000  \n",
              "50%             2.780000                       1.000000  \n",
              "75%             3.240000                       2.000000  \n",
              "max             5.400000                       9.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae7e6d1c-6ced-4de6-95ca-b97486f84f55\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>account_length</th>\n",
              "      <th>number_vmail_messages</th>\n",
              "      <th>total_day_minutes</th>\n",
              "      <th>total_day_calls</th>\n",
              "      <th>total_day_charge</th>\n",
              "      <th>total_eve_minutes</th>\n",
              "      <th>total_eve_calls</th>\n",
              "      <th>total_eve_charge</th>\n",
              "      <th>total_night_minutes</th>\n",
              "      <th>total_night_calls</th>\n",
              "      <th>total_night_charge</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</th>\n",
              "      <th>total_intl_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "      <td>4250.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.236235</td>\n",
              "      <td>7.631765</td>\n",
              "      <td>180.259600</td>\n",
              "      <td>99.907294</td>\n",
              "      <td>30.644682</td>\n",
              "      <td>200.173906</td>\n",
              "      <td>100.176471</td>\n",
              "      <td>17.015012</td>\n",
              "      <td>200.527882</td>\n",
              "      <td>99.839529</td>\n",
              "      <td>9.023892</td>\n",
              "      <td>10.256071</td>\n",
              "      <td>4.426353</td>\n",
              "      <td>2.769654</td>\n",
              "      <td>1.559059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>39.698401</td>\n",
              "      <td>13.439882</td>\n",
              "      <td>54.012373</td>\n",
              "      <td>19.850817</td>\n",
              "      <td>9.182096</td>\n",
              "      <td>50.249518</td>\n",
              "      <td>19.908591</td>\n",
              "      <td>4.271212</td>\n",
              "      <td>50.353548</td>\n",
              "      <td>20.093220</td>\n",
              "      <td>2.265922</td>\n",
              "      <td>2.760102</td>\n",
              "      <td>2.463069</td>\n",
              "      <td>0.745204</td>\n",
              "      <td>1.311434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</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",
              "      <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",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>73.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>143.325000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>24.365000</td>\n",
              "      <td>165.925000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>14.102500</td>\n",
              "      <td>167.225000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>7.522500</td>\n",
              "      <td>8.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>180.450000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>30.680000</td>\n",
              "      <td>200.700000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>17.060000</td>\n",
              "      <td>200.450000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>9.020000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.780000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>127.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>216.200000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>36.750000</td>\n",
              "      <td>233.775000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>19.867500</td>\n",
              "      <td>234.700000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>10.560000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.240000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>243.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>351.500000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>59.760000</td>\n",
              "      <td>359.300000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>30.540000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>17.770000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>5.400000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "        document.querySelector('#df-ae7e6d1c-6ced-4de6-95ca-b97486f84f55 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-ae7e6d1c-6ced-4de6-95ca-b97486f84f55');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-49e2529d-d2b0-4f45-9e3e-24bfe4c9c817')\"\n",
              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      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-49e2529d-d2b0-4f45-9e3e-24bfe4c9c817 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_train\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"account_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1469.7748803328852,\n        \"min\": 1.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          100.23623529411765,\n          100.0,\n          4250.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_vmail_messages\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1498.2015899855621,\n        \"min\": 0.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4250.0,\n          7.631764705882353,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1449.5801297434773,\n        \"min\": 0.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          180.2596,\n          180.45,\n          4250.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1473.992526287573,\n        \"min\": 0.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          99.90729411764706,\n          100.0,\n          4250.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1493.0432072255735,\n        \"min\": 0.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          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  \"num_unique_values\": 8,\n        \"samples\": [\n          99.8395294117647,\n          100.0,\n          4250.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_night_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1499.774867203779,\n        \"min\": 0.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.023891764705883,\n          9.02,\n          4250.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_intl_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1499.3907762383392,\n        \"min\": 0.0,\n        \"max\": 4250.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.256070588235294,\n          10.3,\n          4250.0\n        ],\n        \"semantic_type\": \"\",\n        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            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "ANKVyW1HwODn",
        "outputId": "8437a970-408c-4e8b-d8ca-ffe2c6c6d7b0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              id  account_length  number_vmail_messages  total_day_minutes  \\\n",
              "count  750.00000      750.000000             750.000000         750.000000   \n",
              "mean   375.50000      100.385333               8.454667         180.454933   \n",
              "std    216.65064       39.699029              14.123712          53.258337   \n",
              "min      1.00000        1.000000               0.000000          12.500000   \n",
              "25%    188.25000       74.000000               0.000000         146.625000   \n",
              "50%    375.50000      101.000000               0.000000         178.200000   \n",
              "75%    562.75000      126.000000              21.000000         215.975000   \n",
              "max    750.00000      238.000000              51.000000         350.800000   \n",
              "\n",
              "       total_day_calls  total_day_charge  total_eve_minutes  total_eve_calls  \\\n",
              "count       750.000000        750.000000         750.000000       750.000000   \n",
              "mean        100.721333         30.677920         203.258267       100.273333   \n",
              "std          19.718539          9.053756          52.185471        19.367535   \n",
              "min          39.000000          2.130000          31.200000        37.000000   \n",
              "25%          88.000000         24.925000         166.800000        87.000000   \n",
              "50%         101.000000         30.295000         203.350000       101.000000   \n",
              "75%         114.000000         36.715000         235.975000       113.000000   \n",
              "max         163.000000         59.640000         363.700000       164.000000   \n",
              "\n",
              "       total_eve_charge  total_night_minutes  total_night_calls  \\\n",
              "count        750.000000           750.000000         750.000000   \n",
              "mean          17.277080           199.619467         100.370667   \n",
              "std            4.435638            51.531351          19.185238   \n",
              "min            2.650000            50.900000          12.000000   \n",
              "25%           14.177500           164.475000          88.000000   \n",
              "50%           17.285000           199.450000         100.500000   \n",
              "75%           20.057500           234.800000         113.000000   \n",
              "max           30.910000           364.300000         168.000000   \n",
              "\n",
              "       total_night_charge  total_intl_minutes  total_intl_calls  \\\n",
              "count          750.000000          750.000000        750.000000   \n",
              "mean             8.982827           10.294133          4.485333   \n",
              "std              2.318920            2.770340          2.421901   \n",
              "min              2.290000            0.000000          0.000000   \n",
              "25%              7.402500            8.525000          3.000000   \n",
              "50%              8.975000           10.300000          4.000000   \n",
              "75%             10.565000           12.100000          6.000000   \n",
              "max             16.390000           18.900000         19.000000   \n",
              "\n",
              "       total_intl_charge  number_customer_service_calls  \n",
              "count         750.000000                     750.000000  \n",
              "mean            2.779933                       1.634667  \n",
              "std             0.747704                       1.276207  \n",
              "min             0.000000                       0.000000  \n",
              "25%             2.305000                       1.000000  \n",
              "50%             2.780000                       1.000000  \n",
              "75%             3.270000                       2.000000  \n",
              "max             5.100000                       6.000000  "
            ],
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              "  <div id=\"df-bd0cb9f4-dd39-4fa2-8197-4ab25c56f01d\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>account_length</th>\n",
              "      <th>number_vmail_messages</th>\n",
              "      <th>total_day_minutes</th>\n",
              "      <th>total_day_calls</th>\n",
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              "      <th>total_eve_minutes</th>\n",
              "      <th>total_eve_calls</th>\n",
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              "      <th>total_night_minutes</th>\n",
              "      <th>total_night_calls</th>\n",
              "      <th>total_night_charge</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</th>\n",
              "      <th>total_intl_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>750.00000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>750.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>375.50000</td>\n",
              "      <td>100.385333</td>\n",
              "      <td>8.454667</td>\n",
              "      <td>180.454933</td>\n",
              "      <td>100.721333</td>\n",
              "      <td>30.677920</td>\n",
              "      <td>203.258267</td>\n",
              "      <td>100.273333</td>\n",
              "      <td>17.277080</td>\n",
              "      <td>199.619467</td>\n",
              "      <td>100.370667</td>\n",
              "      <td>8.982827</td>\n",
              "      <td>10.294133</td>\n",
              "      <td>4.485333</td>\n",
              "      <td>2.779933</td>\n",
              "      <td>1.634667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>216.65064</td>\n",
              "      <td>39.699029</td>\n",
              "      <td>14.123712</td>\n",
              "      <td>53.258337</td>\n",
              "      <td>19.718539</td>\n",
              "      <td>9.053756</td>\n",
              "      <td>52.185471</td>\n",
              "      <td>19.367535</td>\n",
              "      <td>4.435638</td>\n",
              "      <td>51.531351</td>\n",
              "      <td>19.185238</td>\n",
              "      <td>2.318920</td>\n",
              "      <td>2.770340</td>\n",
              "      <td>2.421901</td>\n",
              "      <td>0.747704</td>\n",
              "      <td>1.276207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>2.130000</td>\n",
              "      <td>31.200000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>2.650000</td>\n",
              "      <td>50.900000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2.290000</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>188.25000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>146.625000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>24.925000</td>\n",
              "      <td>166.800000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>14.177500</td>\n",
              "      <td>164.475000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>7.402500</td>\n",
              "      <td>8.525000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.305000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>375.50000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>178.200000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>30.295000</td>\n",
              "      <td>203.350000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>17.285000</td>\n",
              "      <td>199.450000</td>\n",
              "      <td>100.500000</td>\n",
              "      <td>8.975000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.780000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>562.75000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>215.975000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>36.715000</td>\n",
              "      <td>235.975000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>20.057500</td>\n",
              "      <td>234.800000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>10.565000</td>\n",
              "      <td>12.100000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.270000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>750.00000</td>\n",
              "      <td>238.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>350.800000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>59.640000</td>\n",
              "      <td>363.700000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>30.910000</td>\n",
              "      <td>364.300000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>16.390000</td>\n",
              "      <td>18.900000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>6.000000</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-bd0cb9f4-dd39-4fa2-8197-4ab25c56f01d')\"\n",
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              "    </button>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bd0cb9f4-dd39-4fa2-8197-4ab25c56f01d');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c3d2b24-61a2-4367-a295-cab2e0b915e3')\"\n",
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              "\n",
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              "  </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",
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              "  }\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",
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              "    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-6c3d2b24-61a2-4367-a295-cab2e0b915e3 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_test\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.998900578443,\n        \"min\": 1.0,\n        \"max\": 750.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          750.0,\n          375.5,\n          562.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"account_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 241.04195478031193,\n        \"min\": 1.0,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          100.38533333333334,\n          101.0,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_vmail_messages\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.9457745671414,\n        \"min\": 0.0,\n        \"max\": 750.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          750.0,\n          8.454666666666666,\n          51.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 231.62062173539772,\n        \"min\": 12.5,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          180.45493333333334,\n          178.2,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 237.72290367679975,\n        \"min\": 19.71853880452783,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          100.72133333333333,\n          101.0,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_day_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 255.98898146405736,\n        \"min\": 2.13,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.677919999999997,\n          30.295,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_eve_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 227.22123895455127,\n        \"min\": 31.2,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          203.25826666666669,\n          203.35,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_eve_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 238.01530825141316,\n        \"min\": 19.367535313535647,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          100.27333333333333,\n          101.0,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_eve_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 259.9233345260615,\n        \"min\": 2.65,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.27708,\n          17.285,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_night_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 225.1049789689078,\n        \"min\": 50.9,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          199.61946666666668,\n          199.45,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_night_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 240.1649988731791,\n        \"min\": 12.0,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          100.37066666666666,\n          100.5,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_night_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.3291468802704,\n        \"min\": 2.29,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.982826666666668,\n          8.975000000000001,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_intl_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.05162183688645,\n        \"min\": 0.0,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.294133333333335,\n          10.3,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_intl_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 263.26281157870557,\n        \"min\": 0.0,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.485333333333333,\n          4.0,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_intl_charge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 264.3118384831731,\n        \"min\": 0.0,\n        \"max\": 750.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.779933333333333,\n          2.78,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_customer_service_calls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 264.5190178901891,\n        \"min\": 0.0,\n        \"max\": 750.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          750.0,\n          1.6346666666666667,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for any null value\n",
        "print(\"Are there any null value in train data?\", df_train.isnull().any().any())\n",
        "print(\"Are there any null value in test_data?\", df_test.isnull().any().any())\n",
        "\n",
        "# We can conclude that there is no null value in the data sets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w26KjgyEzCRP",
        "outputId": "daf46b6a-2ee9-4664-8242-645b43a87763"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Are there any null value in train data? False\n",
            "Are there any null value in test_data? False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the occurences of each state in train data\n",
        "state_counts = {}\n",
        "for state in df_train['state']:\n",
        "  if state in state_counts:\n",
        "    state_counts[state] += 1\n",
        "  else:\n",
        "    state_counts[state] = 1\n",
        "\n",
        "for state, count in state_counts.items():\n",
        "  print(state,\"-\",count)\n",
        "\n",
        "# From here we can conclude that the number of state is not equal\n",
        "# From now on the focus is to train data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnIS998gwSI6",
        "outputId": "b37991e6-99f5-46ed-e443-37f840d35e6c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OH - 95\n",
            "NJ - 96\n",
            "OK - 78\n",
            "MA - 89\n",
            "MO - 80\n",
            "LA - 69\n",
            "WV - 139\n",
            "IN - 83\n",
            "RI - 87\n",
            "IA - 62\n",
            "MT - 80\n",
            "NY - 96\n",
            "ID - 106\n",
            "VA - 100\n",
            "TX - 98\n",
            "FL - 76\n",
            "CO - 80\n",
            "AZ - 77\n",
            "SC - 72\n",
            "WY - 95\n",
            "HI - 77\n",
            "NH - 78\n",
            "AK - 61\n",
            "GA - 64\n",
            "MD - 86\n",
            "AR - 71\n",
            "WI - 94\n",
            "OR - 99\n",
            "MI - 87\n",
            "DE - 80\n",
            "UT - 97\n",
            "CA - 39\n",
            "SD - 75\n",
            "NC - 80\n",
            "WA - 80\n",
            "MN - 108\n",
            "NM - 78\n",
            "NV - 83\n",
            "DC - 72\n",
            "VT - 86\n",
            "KY - 85\n",
            "ME - 89\n",
            "MS - 82\n",
            "AL - 101\n",
            "NE - 73\n",
            "KS - 87\n",
            "TN - 79\n",
            "IL - 79\n",
            "PA - 67\n",
            "CT - 88\n",
            "ND - 67\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Change binary variable to binary value 0/1 for train data\n",
        "replaced_columns = ['international_plan', 'voice_mail_plan', 'churn']\n",
        "df_train[replaced_columns] = df_train[replaced_columns].replace({'yes':1,'no':0})\n",
        "print(df_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QRd9dg0whPc",
        "outputId": "a3db41ee-2706-4b39-f65a-963d4ca2c34a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     state  account_length      area_code  international_plan  \\\n",
            "0       OH             107  area_code_415                   0   \n",
            "1       NJ             137  area_code_415                   0   \n",
            "2       OH              84  area_code_408                   1   \n",
            "3       OK              75  area_code_415                   1   \n",
            "4       MA             121  area_code_510                   0   \n",
            "...    ...             ...            ...                 ...   \n",
            "4245    MT              83  area_code_415                   0   \n",
            "4246    WV              73  area_code_408                   0   \n",
            "4247    NC              75  area_code_408                   0   \n",
            "4248    HI              50  area_code_408                   0   \n",
            "4249    VT              86  area_code_415                   0   \n",
            "\n",
            "      voice_mail_plan  number_vmail_messages  total_day_minutes  \\\n",
            "0                   1                     26              161.6   \n",
            "1                   0                      0              243.4   \n",
            "2                   0                      0              299.4   \n",
            "3                   0                      0              166.7   \n",
            "4                   1                     24              218.2   \n",
            "...               ...                    ...                ...   \n",
            "4245                0                      0              188.3   \n",
            "4246                0                      0              177.9   \n",
            "4247                0                      0              170.7   \n",
            "4248                1                     40              235.7   \n",
            "4249                1                     34              129.4   \n",
            "\n",
            "      total_day_calls  total_day_charge  total_eve_minutes  total_eve_calls  \\\n",
            "0                 123             27.47              195.5              103   \n",
            "1                 114             41.38              121.2              110   \n",
            "2                  71             50.90               61.9               88   \n",
            "3                 113             28.34              148.3              122   \n",
            "4                  88             37.09              348.5              108   \n",
            "...               ...               ...                ...              ...   \n",
            "4245               70             32.01              243.8               88   \n",
            "4246               89             30.24              131.2               82   \n",
            "4247              101             29.02              193.1              126   \n",
            "4248              127             40.07              223.0              126   \n",
            "4249              102             22.00              267.1              104   \n",
            "\n",
            "      total_eve_charge  total_night_minutes  total_night_calls  \\\n",
            "0                16.62                254.4                103   \n",
            "1                10.30                162.6                104   \n",
            "2                 5.26                196.9                 89   \n",
            "3                12.61                186.9                121   \n",
            "4                29.62                212.6                118   \n",
            "...                ...                  ...                ...   \n",
            "4245             20.72                213.7                 79   \n",
            "4246             11.15                186.2                 89   \n",
            "4247             16.41                129.1                104   \n",
            "4248             18.96                297.5                116   \n",
            "4249             22.70                154.8                100   \n",
            "\n",
            "      total_night_charge  total_intl_minutes  total_intl_calls  \\\n",
            "0                  11.45                13.7                 3   \n",
            "1                   7.32                12.2                 5   \n",
            "2                   8.86                 6.6                 7   \n",
            "3                   8.41                10.1                 3   \n",
            "4                   9.57                 7.5                 7   \n",
            "...                  ...                 ...               ...   \n",
            "4245                9.62                10.3                 6   \n",
            "4246                8.38                11.5                 6   \n",
            "4247                5.81                 6.9                 7   \n",
            "4248               13.39                 9.9                 5   \n",
            "4249                6.97                 9.3                16   \n",
            "\n",
            "      total_intl_charge  number_customer_service_calls  churn  \n",
            "0                  3.70                              1      0  \n",
            "1                  3.29                              0      0  \n",
            "2                  1.78                              2      0  \n",
            "3                  2.73                              3      0  \n",
            "4                  2.03                              3      0  \n",
            "...                 ...                            ...    ...  \n",
            "4245               2.78                              0      0  \n",
            "4246               3.11                              3      0  \n",
            "4247               1.86                              1      0  \n",
            "4248               2.67                              2      0  \n",
            "4249               2.51                              0      0  \n",
            "\n",
            "[4250 rows x 20 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "woqM1cnt1psI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Churn data visualisation\n",
        "def percentage_plot(df, column_name):\n",
        "  percentage_count = (df[column_name].value_counts(normalize=True)*100).round(3)\n",
        "  plt.figure(figsize=(8,6))\n",
        "  plt.bar(percentage_count.index, percentage_count.values, color=['lightblue','pink','lightgreen'])\n",
        "\n",
        "# Add annotations\n",
        "  for i,value in enumerate (percentage_count.values):\n",
        "    plt.text(i, value + 1, f\"{value}%\", ha='center')\n",
        "\n",
        "# Add labels\n",
        "  plt.title(str(column_name) + ' percentage')\n",
        "  plt.xlabel(str(column_name))\n",
        "  plt.ylabel('Percentage Rate')\n",
        "  plt.show()\n",
        "\n",
        "percentage_plot(df_train,'churn')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "sdtFzzn-1G6T",
        "outputId": "6ecb8461-0920-488a-a9b4-e1f8ae2238bd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "percentage_plot(df_train,'area_code')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "y1QPMdWv3kby",
        "outputId": "16538898-f1d8-497d-e945-57c3d6ecf0bc"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a correlation matrix by dropping binary variables\n",
        "cols_to_drop = ['state','area_code','international_plan','voice_mail_plan','churn']\n",
        "corr_matrix = df_train.drop(columns=cols_to_drop,axis=1).corr()\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(corr_matrix,annot=True, cmap='YlGnBu', fmt=\".2f\")\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()\n",
        "\n",
        "# From the correlation matrix, it can be concluded that the charges and minutes call have a very high correlation\n",
        "# It seems that charges are calculated based on the minutes call\n",
        "# Therefore, this variables can be dropped as they do not add any information.\n",
        "df_train.drop(['total_day_charge','total_eve_charge','total_night_charge','total_intl_charge'], axis=1, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        },
        "id": "ihp6PCPOLLp8",
        "outputId": "6109ed75-ef93-440f-8acb-584925509876"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a histograms in subplot\n",
        "# Create a blank list to get the features that wanted to be visualised\n",
        "features = []\n",
        "\n",
        "# Filling the blank list with the features that are not in the cols_to_drop\n",
        "for col in df_train.columns:\n",
        "  if col not in cols_to_drop:\n",
        "    features.append(col)\n",
        "\n",
        "# Calculating the number of rows and columns for subplot\n",
        "n_features = len(features)\n",
        "n_rows = (n_features-1) // 4 + 1\n",
        "n_cols = min(n_features, 4)\n",
        "\n",
        "# Create a function for plotting features\n",
        "def plot_features(df, features, n_rows, n_cols, plot_type, figsize=(20,10)):\n",
        "  n_features = len(features)\n",
        "  fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=figsize)\n",
        "\n",
        "  # Calculating the number of rows and columns for subplot\n",
        "  for i, feature in enumerate(features):\n",
        "    row = i // 4\n",
        "    col = i % 4\n",
        "\n",
        "    if plot_type == 'histogram':\n",
        "      df[feature].hist(ax=axes[row, col])\n",
        "    elif plot_type == 'boxplot':\n",
        "      colors=['lightblue','pink']\n",
        "      for j, category in enumerate(df['churn'].unique()):\n",
        "        sns.boxplot(data=df[df['churn'] == category], x='churn', y=feature, ax=axes[row, col], color=colors[j])\n",
        "        axes[row, col].set_xlabel('churn')\n",
        "    axes[row, col].set_ylabel(feature)\n",
        "    axes[row, col].set_title(feature)\n",
        "\n",
        "  # Remove empty subplot\n",
        "  for i in range(n_features, n_rows*n_cols):\n",
        "    fig.delaxes(axes.flatten()[i])\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "OrSyui4g8hZY"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create histogram for features\n",
        "plot_features(df_train, features, n_rows, n_cols, plot_type='histogram')\n",
        "\n",
        "# Features are normally distributed, except number_vmail_messages,\n",
        "# total_intl_calls, and number_customer_service_calls (left-skewed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "ZQwUa1IRDFYe",
        "outputId": "588a7739-8f62-46f4-81da-6500c71dbfe1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 11 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create boxplot for features and churn\n",
        "plot_features(df_train, features, n_rows, n_cols, plot_type='boxplot')\n",
        "\n",
        "# Most boxplots between churned customer and not are identical except for:\n",
        "# number_vmail_messages: it seems that churned customer is less using voice mall messages\n",
        "# total_day_minutes: churned customer spent more minutes on call\n",
        "# number_customer_service_calls: churned customer often having a call with customer service"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "98iKxlm8DZr0",
        "outputId": "1d2524ba-fcb5-48d5-b9a1-89984743ceaf"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 11 Axes>"
            ],
            "image/png": 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QEABw9OhRFBYWlpmDoaEhcnJyEBISUm6eGhoakMtffFUqLi5GSkoKdHV14ezsrPT+BAcHo3Hjxhg0aJDYpqmpiYkTJyrt701+fwwNDREeHo4nT5688r0kImqISsZlPz8/pfZZs2YBAP73v/+9dh8V/Rtf2fysrKwwbNgwsU1bW1ucTe1lL4/DOTk5ePbsGTp27AhBEJSmS/X29saTJ0+UZiHZtWsXtLS04OXl9cY5vs1nh/LUxPmCgwcPQqFQYMSIEUqfBSwtLdG0aVPx/Sm5E/z48eNKU7gTEVHdxfH/hcqM/xWpO6ioqIjn1RUKBVJTU1FUVIT33nvvjd+binwnJ5ISi+JE1ej58+f45ptvxPW9TU1NYWZmhvT0dKW1rO7fv48WLVq8cl/379+Hs7OzUkH3nx4+fIhGjRopndQHXkwJXtJfWba2tkrbJYXkf67jWRWSk5ORnp6On376CWZmZko/48aNA/BiKpY3ya/k2J2cnJTiVFVVYW9v/0b5WVtbK51EKHm96ngviIjqq6oeV/65PwMDA2hqaorTrb3cXtZrNG3aVGlbJpPByclJXMPy3r17AAAfH59SY9PPP/+M/Pz8UutUOjg4vPFxmJqaolevXti7d6/Y9t///heqqqoYOnRoqfiyjhsAbGxsSrX/87iPHj2KDh06QFNTE8bGxjAzM8OmTZuqdb3NyuZ77949ZGRkwNzcvNT7n52dLX4u6NatG7y8vLBo0SKYmppi8ODBCAgIUFrL7bPPPsM777wDT09PWFtbY/z48QgODlZ6fYVCgdWrV6Np06ZKn+H+/vtvpffn4cOHcHR0LPW54J+fN97k92fFihW4efMmbGxs0K5dOyxcuJAX3hER/X8PHz6EXC4v9XfW0tIShoaGFfrOW9G/8ZXNz8nJqdS44OzsXCo2Li4OY8eOhbGxMXR1dWFmZoZu3boBgFIeffr0gZWVFXbt2iXm/+uvv2Lw4MGlvvtXxNt8dqjoPqvjfMG9e/cgCAKaNm1aaiy9ffu2+FnAwcEBfn5++Pnnn2FqagoPDw9s2LCB64kTEdVhHP8rP/5XpO4AANu3b4ebmxs0NTVhYmICMzMz/O9//3vj96Yi38mJpMQ1xYmq0bRp0xAQEIAZM2bA3d0dBgYGkMlkGDlyZKm7yWrSPwfoEsXFxeU+R0VFpcx2oQJrpL6pkvfmo48+go+PT5kxbm5uSts1mV9NvhYRUX31qr+lVTVOVeXf65Kx6fvvvy93PS1dXV2l7ZevAH8TI0eOxLhx43Dt2jW0atUKe/fuRa9evUoV+IHyj7Gs9peP+48//sCgQYPQtWtXbNy4EVZWVlBTU0NAQAB2795dqbwrorL5KhQKmJubiycE/qlkrVGZTIb9+/fj4sWLOHLkCI4fP47x48dj5cqVuHjxInR1dWFubo5r167h+PHjCAoKQlBQEAICAuDt7Y3t27cDAJYtW4avv/4a48ePx7fffgtjY2PI5XLMmDGjUp/h3uT3Z8SIEejSpQsOHTqEEydO4Pvvv8d3332HgwcPwtPT841fm4ioPirvs0JFVPXf+MooLi5Gnz59kJqairlz56JZs2bQ0dHB48ePMXbsWKU8VFRUMHr0aGzduhUbN27EuXPn8OTJE6XZy95EZcfiyuyz5PmV+Wz3TwqFAjKZDEFBQWW+3sufw1auXImxY8fit99+w4kTJ/D555/D398fFy9ehLW1dYVfk4iIaheO/5Uf/19l586dGDt2LIYMGYI5c+bA3NwcKioq8Pf3x/37999oXxX5Tk4kJRbFiarR/v374ePjg5UrV4pteXl5SE9PV4pzdHTEzZs3X7kvR0dHhIeHo7CwEGpqamXG2NnZ4eTJk8jKylK6YuzOnTtiP/B/V23/M4+3uZMceLsPJi8zMzODnp4eiouL0bt37yrZZ8mxR0dHo0ePHmJ7UVERYmNjlYrsVXUcRERUOdU1Tr1KyZ28JQRBQHR0tDg+ODo6AgD09fWrbGwqz5AhQ/Dpp5+KU6jfvXsX8+bNq9LXOHDgADQ1NXH8+HFoaGiI7QEBAVX6OlXF0dERJ0+eRKdOnSp0sUGHDh3QoUMHLF26FLt378aYMWOwZ88ecZp9dXV1DBw4EAMHDoRCocBnn32GLVu24Ouvv4aTkxP279+PHj16YNu2bUr7TU9PV7o4wc7ODpGRkaUu5oiOji6VP1Dx3x8rKyt89tln+Oyzz/D06VO0adMGS5cuZVGciBqUsr6X2dnZQaFQ4N69e+KMaACQlJSE9PR08Xtfec8HUOG/8ZVhZ2eHmzdvlhoXoqKilOJu3LiBu3fvYvv27fD29hbby1vaw9vbGytXrsSRI0cQFBQEMzMzeHh4vFWuNelNPtuV9+/m6OgIQRDg4OCAd95557Wv6erqCldXV8yfPx/nz59Hp06dsHnzZixZsuTND4CIiGoMx///UxXjf0XqDvv370eTJk1w8OBBpfwXLFjwRq/1std9JyeSCqdPJ6pGKioqpa6sXrduXamrob28vHD9+nUcOnSo1D5Knu/l5YVnz55h/fr15cb0798fxcXFpWJWr14NmUwmnkjV19eHqakpzp49qxS3cePGNzxCZTo6OqW+5FaGiooKvLy8cODAgTIH7eTk5Dfe53vvvQcTExNs3boVRUVFYvuuXbtKTemmo6MDoPQXdiIiqhnVNU69yi+//IKsrCxxe//+/UhISBDHzrZt28LR0RE//PADsrOzSz2/MmNTeQwNDeHh4YG9e/diz549UFdXx5AhQ6ps/8CLsVYmkyl9JomNjcXhw4er9HWqyogRI1BcXIxvv/22VF9RUZE4ZqelpZX67FVyZ3bJdG0pKSlK/XK5XLz4oSSmrM9w+/btw+PHj5XaPDw88PjxY/z+++9iW15eHrZu3aoUV9Hfn+Li4lLT05mbm6NRo0acbo6IGpyyvpf1798fALBmzRql2FWrVgEABgwYoPT8sr7TVfRvfGX0798fT548wf79+8W23Nxc/PTTT6VyAJTvxBYEAWvXri1zv25ubnBzc8PPP/+MAwcOYOTIka9cWq22eZPPduV9Hx86dChUVFSwaNGiUv9+giCI43tmZqbSd37gRYFcLpdzLCUiqgM4/v+fqhj/K1J3KCuv8PBwXLhw4Y1eC6jYd3IiKdWdT9BEddC//vUv7NixAwYGBnBxccGFCxdw8uRJmJiYKMXNmTMH+/fvx/DhwzF+/Hi0bdsWqamp+P3337F582a0bNkS3t7e+OWXX+Dn54dLly6hS5cuyMnJwcmTJ/HZZ59h8ODBGDhwIHr06IGvvvoKsbGxaNmyJU6cOIHffvsNM2bMEO9SAoBPPvkEy5cvxyeffIL33nsPZ8+exd27d9/qeNu2bYuTJ09i1apVaNSoERwcHNC+fftK7Wv58uUIDQ1F+/btMXHiRLi4uCA1NRURERE4efIkUlNT32h/6urqWLhwIaZNm4aePXtixIgRiI2NRWBgYKm1QB0dHWFoaIjNmzdDT08POjo6aN++faXWhiUiosqpjnHqVYyNjdG5c2eMGzcOSUlJWLNmDZycnDBx4kQALwqnP//8Mzw9PdG8eXOMGzcOjRs3xuPHjxEaGgp9fX0cOXKkyvL58MMP8dFHH2Hjxo3w8PCAoaFhle0beHHSYNWqVejXrx9Gjx6Np0+fYsOGDXBycsLff/9dpa9VFbp164ZPP/0U/v7+uHbtGvr27Qs1NTXcu3cP+/btw9q1azFs2DBs374dGzduxAcffABHR0dkZWVh69at0NfXF0+kfPLJJ0hNTUXPnj1hbW2Nhw8fYt26dWjVqpV418G//vUvLF68GOPGjUPHjh1x48YN7Nq1C02aNFHK69NPP8X69esxatQoTJ8+XVzzTVNTE8D/3aVQ0d+frKwsWFtbY9iwYWjZsiV0dXVx8uRJXL58WWnmISKihqBt27YAgK+++gojR46EmpoaBg4cCB8fH/z0009IT09Ht27dcOnSJWzfvh1DhgxRmhWsbdu22LRpE5YsWQInJyeYm5ujZ8+eFf4bXxkTJ07E+vXr4e3tjatXr8LKygo7duyAtra2UlyzZs3g6OiI2bNn4/Hjx9DX18eBAwdeuQa3t7c3Zs+eDQDVMnVqdavoZ7vy/t0dHR2xZMkSzJs3D7GxsRgyZAj09PQQExODQ4cOYdKkSZg9ezZOnz6NqVOnYvjw4XjnnXdQVFSEHTt2iBffExFR7cbxX9nbjv8VqTv861//wsGDB/HBBx9gwIABiImJwebNm+Hi4lLmRd2vUpHv5ESSEoio2qSlpQnjxo0TTE1NBV1dXcHDw0O4c+eOYGdnJ/j4+CjFpqSkCFOnThUaN24sqKurC9bW1oKPj4/w7NkzMSY3N1f46quvBAcHB0FNTU2wtLQUhg0bJty/f1+MycrKEmbOnCk0atRIUFNTE5o2bSp8//33gkKhUHq93NxcYcKECYKBgYGgp6cnjBgxQnj69KkAQFiwYIEYt2DBAgGAkJycrPT8gIAAAYAQExMjtt25c0fo2rWroKWlJQAodYyv8s/XFQRBSEpKEnx9fQUbGxvxeHv16iX89NNPYkxoaKgAQNi3b5/Sc2NiYgQAQkBAgFL7jz/+KNjZ2QkaGhpCu3bthHPnzglt27YV+vXrpxT322+/CS4uLoKqqqrSfrp16yY0b968VP4+Pj6CnZ1dhY+XiKihqui48rbjlI+Pj6Cjo1Pq9f/5d7xkHPn111+FefPmCebm5oKWlpYwYMAA4eHDh6We/9dffwlDhw4VTExMBA0NDcHOzk4YMWKEcOrUqdfm9CYyMzPF8XTnzp2l+kver8uXLyu1v8n7sW3bNqFp06aChoaG0KxZMyEgIEB8/sv++bml5D0LDQ2t8PFURb6CIAg//fST0LZtW0FLS0vQ09MTXF1dhS+++EJ48uSJIAiCEBERIYwaNUqwtbUVNDQ0BHNzc+Ff//qXcOXKFXEf+/fvF/r27SuYm5sL6urqgq2trfDpp58KCQkJYkxeXp4wa9YswcrKStDS0hI6deokXLhwQejWrZvQrVs3pZwePHggDBgwQNDS0hLMzMyEWbNmCQcOHBAACBcvXlSKfd3vT35+vjBnzhyhZcuWgp6enqCjoyO0bNlS2LhxY4XfayKi+uTbb78VGjduLMjlcvFzQmFhobBo0SLxe7GNjY0wb948IS8vT+m5iYmJwoABAwQ9PT0BgPj3u6J/48v7Tvk6Dx8+FAYNGiRoa2sLpqamwvTp04Xg4OBSY2dkZKTQu3dvQVdXVzA1NRUmTpwoXL9+vdzXTEhIEFRUVIR33nnnjfIpURVj8ducL6joZztBKPvfvcSBAweEzp07Czo6OoKOjo7QrFkzwdfXV4iKihIE4cW4PH78eMHR0VHQ1NQUjI2NhR49eggnT558w3eMiIikwvH//7zt+C8Ir687KBQKYdmyZeI589atWwtHjx4t83z3P8ftf475FflOTiQlmSD8Yy4DIqIGRKFQwMzMDEOHDi011SkRERFRZaxZswYzZ87Eo0eP0LhxY6nTISKieuDZs2ewsrLCN998g6+//lrqdIiIiKgGcPwnqlpcU5yIGoy8vLxSa5r88ssvSE1NRffu3aVJioiIiOq058+fK23n5eVhy5YtaNq0KQviRERUZQIDA1FcXIyPP/5Y6lSIiIiohnD8J6paXFOciKpNcXExkpOTXxmjq6sLXV3dGsnn4sWLmDlzJoYPHw4TExNERERg27ZtaNGiBYYPH14jORARUcNUUFCA1NTUV8YYGBhAS0urhjKqGs+fP0dGRsYrY4yNjaGurl5DGdW8oUOHwtbWFq1atUJGRgZ27tyJO3fuYNeuXVKnRkREVUTKcfz06dOIjIzE0qVLMWTIENjb2yv1cywmIiKqHhz/ieofTp9ORNUmNjYWDg4Or4xZsGABFi5cWGP5fP7557h06RJSU1NhbGyM/v37Y/ny5TA3N6+RHIiIqGE6c+YMevTo8cqYgIAAjB07tmYSqiKBgYEYN27cK2NCQ0Pr9Ywsa9aswc8//4zY2FgUFxfDxcUFX3zxBT788EOpUyMioioi5TjevXt3nD9/Hp06dcLOnTtLzULCsZiIiKh6cPwnqn9YFCeiapOXl4c///zzlTFNmjRBkyZNaigjIiIiaaSlpeHq1auvjGnevDmsrKxqKKOqkZCQgFu3br0ypm3btjAyMqqhjIiIiKpebR7HORYTERFVD47/RPUPi+JERERERERERERERERERFRvcU1xAAqFAk+ePIGenh5kMpnU6RAREYkEQUBWVhYaNWoEuVwudTq1CsdvIiKqrTh+l4/jNxER1WYcw8vHMZyIiGqrio7fLIoDePLkCWxsbKROg4iIqFzx8fGwtraWOo1aheM3ERHVdhy/S+P4TUREdQHH8NI4hhMRUW33uvGbRXEAenp6AF68Wfr6+hJnQ0RE9H8yMzNhY2MjjlX0fzh+ExFRbcXxu3wcv4mIqDbjGF4+juFERFRbVXT8lrQo7u/vj4MHD+LOnTvQ0tJCx44d8d1338HZ2VmM6d69O8LCwpSe9+mnn2Lz5s3idlxcHKZMmYLQ0FDo6urCx8cH/v7+UFWt2OGVTPeir6/PAZ2IiGolTk1WGsdvIiKq7Th+l8bxm4iI6gKO4aVxDCciotrudeO3pEXxsLAw+Pr64v3330dRURG+/PJL9O3bF5GRkdDR0RHjJk6ciMWLF4vb2tra4uPi4mIMGDAAlpaWOH/+PBISEuDt7Q01NTUsW7asRo+HiIiIiIiIiIiIiIiIiIhqF0mL4sHBwUrbgYGBMDc3x9WrV9G1a1exXVtbG5aWlmXu48SJE4iMjMTJkydhYWGBVq1a4dtvv8XcuXOxcOFCqKurl3pOfn4+8vPzxe3MzMwqOiIiIiIiIiIiIiIiIiIiIqpN5FIn8LKMjAwAgLGxsVL7rl27YGpqihYtWmDevHnIzc0V+y5cuABXV1dYWFiIbR4eHsjMzMStW7fKfB1/f38YGBiIPzY2NtVwNEREREREREREREREREREJDVJ7xR/mUKhwIwZM9CpUye0aNFCbB89ejTs7OzQqFEj/P3335g7dy6ioqJw8OBBAEBiYqJSQRyAuJ2YmFjma82bNw9+fn7idskC7EREREREREREREREREREVL/UmqK4r68vbt68iT///FOpfdKkSeJjV1dXWFlZoVevXrh//z4cHR0r9VoaGhrQ0NB4q3ypZikUCsTGxiIrKwt6enqwt7eHXF6rJjogIiKif+D4TUREREREREREdZFCoUB8fDyys7Ohq6sLGxsbnteq42pFUXzq1Kk4evQozp49C2tr61fGtm/fHgAQHR0NR0dHWFpa4tKlS0oxSUlJAFDuOuRUt9y8eRNBQUFIS0sT24yMjODp6ak0qwARERHVHhy/iYiIiIiIiIioLoqKisKpU6fEZZ8BwMDAAL169YKzs7OEmdHbkPSSBkEQMHXqVBw6dAinT5+Gg4PDa59z7do1AICVlRUAwN3dHTdu3MDTp0/FmJCQEOjr68PFxaVa8qaac/PmTfz666+wsLDA5MmTsWDBAkyePBkWFhb49ddfcfPmTalTJCIion/g+E1EVPts2rQJbm5u0NfXh76+Ptzd3REUFCT2d+/eHTKZTOln8uTJSvuIi4vDgAEDoK2tDXNzc8yZMwdFRUU1fShERERERETVpmQJZzMzM3h7e2PWrFnw9vaGmZkZDh48iKioKKlTpEqStCju6+uLnTt3Yvfu3dDT00NiYiISExPx/PlzAMD9+/fx7bff4urVq4iNjcXvv/8Ob29vdO3aFW5ubgCAvn37wsXFBR9//DGuX7+O48ePY/78+fD19eUU6XWcQqFAUFAQnJ2d8dFHH8HW1hYaGhqwtbXFRx99BGdnZwQFBUGhUEidKhEREf1/HL+JiGona2trLF++HFevXsWVK1fQs2dPDB48GLdu3RJjJk6ciISEBPFnxYoVYl9xcTEGDBiAgoICnD9/Htu3b0dgYCC++eYbKQ6HiIiIiIioyikUCpw6dQpOTk4YNmwYGjduDHV1dTRu3BjDhg2Dk5MTTp8+zfNadZSk06dv2rQJwIsr0l8WEBCAsWPHQl1dHSdPnsSaNWuQk5MDGxsbeHl5Yf78+WKsiooKjh49iilTpsDd3R06Ojrw8fHB4sWLa/JQqBrExsYiLS0NH374Yal1GuRyObp3747NmzcjNjYWTZo0kShLIiIiehnHbyKi2mngwIFK20uXLsWmTZtw8eJFNG/eHACgra1d7jJkJ06cQGRkJE6ePAkLCwu0atUK3377LebOnYuFCxdCXV292o+BiOifCgsLkZKSInUa9ZKJiQnU1NSkToOIiKhGxcfHIyMjA4MHD4ZMJlPqk8lkcHd3x44dOxAfHw87OzuJsqTKkrQoLgjCK/ttbGwQFhb22v3Y2dnh2LFjVZUW1RJZWVkAAAsLizL7S9pL4oiIiEh6HL+JiGq/4uJi7Nu3Dzk5OXB3dxfbd+3ahZ07d8LS0hIDBw7E119/DW1tbQDAhQsX4OrqqvT33cPDA1OmTMGtW7fQunXrMl8rPz8f+fn54nZmZmY1HRURNUQpKSkICAiQOo16ady4ceVeKEVERFRfZWdnAwDMzMzK7C9pL4mjukXSojjRq+jp6QEAkpKSYGtrW6o/KSlJKY6IiIikx/GbiKj2unHjBtzd3ZGXlwddXV0cOnQILi4uAIDRo0fDzs4OjRo1wt9//425c+eKa+kBQGJiYqkLnkq2ExMTy31Nf39/LFq0qJqOiIgaOhMTE4wbN07qNF7r2bNnOHLkCAYOHAhTU1Op06kQExMTqVMgojekUCgQGxuLrKws6Onpwd7evtQMbkT0arq6ugCA5ORkWFlZIT4+HtnZ2dDV1YWNjQ2Sk5OV4qhuYVGcai17e3sYGRnhzJkz+Oijj5QGcIVCgTNnzsDIyAj29vbSJUlERERKOH4TEdVezs7OuHbtGjIyMrB//374+PggLCwMLi4umDRpkhjn6uoKKysr9OrVC/fv34ejo2OlX3PevHnw8/MTtzMzM2FjY/NWx0FEVEJNTa1O3c1sampap/Ilorrj5s2bCAoKQlpamthmZGQET09PtGjRQsLMiOoWGxsbGBgY4MSJE3j+/DkyMjLEPgMDA2hpacHQ0JDfaeooXiZEtZZcLoenpyeioqKwc+dOxMXFIT8/H3Fxcdi5cyeioqLg6enJq92IiIhqEY7fRES1l7q6OpycnNC2bVv4+/ujZcuWWLt2bZmx7du3BwBER0cDACwtLcXZPkqUbL+qwKOhoQF9fX2lHyIiIiKqOjdv3sSvv/4KCwsLTJ48GQsWLMDkyZNhYWGBX3/9FTdv3pQ6RaI6Qy6Xo1mzZkhMTERhYSH69euHqVOnol+/figsLERiYiKcnZ15XquO4p3iVKu1aNECo0aNQlBQEDZv3iy2GxkZYdSoUbzKjYiIqBbi+E1EVDcoFAql9b5fdu3aNQCAlZUVAMDd3R1Lly7F06dPYW5uDgAICQmBvr6+OAU7EREREdUshUKBoKAgODs7Y+TIkbh06RKuXbsGY2NjjBw5Env27EFQUBBcXFxYxCOqAIVCgTt37sDS0hK5ubkIDg4W+wwMDGBpaYmoqCh0796d/0/VQSyKU63XokULvPPOOwgKCkJKSgpMTEzg6ekJdXV1qVMjIqJyLFy4sNT6oc7Ozrhz5w4AIC8vD7NmzcKePXuQn58PDw8PbNy4sdRapVR3tWjRAi4uLlzPjIiolpg3bx48PT1ha2uLrKws7N69G2fOnMHx48dx//597N69G/3794eJiQn+/vtvzJw5E127doWbmxsAoG/fvnBxccHHH3+MFStWIDExEfPnz4evry80NDQkPjoiIiKihik2NhZpaWmwtbXF4sWLoVAoxL7g4GC4uroiLS0NsbGxaNKkiYSZEtUN8fHxyMjIwODBg8tcU/zJkyfYsWMH4uPjYWdnJ3W69IZYFKdaLygoCOfOnRMH9OjoaFy+fBmdOnWCp6enxNkREVF5mjdvjpMnT4rbqqr/97Fj5syZ+N///od9+/bBwMAAU6dOxdChQ3Hu3DkpUqVqIpfL+aWbiKiWePr0Kby9vZGQkAADAwO4ubnh+PHj6NOnD+Lj43Hy5EmsWbMGOTk5sLGxgZeXF+bPny8+X0VFBUePHsWUKVPg7u4OHR0d+Pj4YPHixRIeFREREVHDlpWVBQC4fv06tLW10aRJE6irq6OgoAAPHjzA9evXleKI6NWys7MBAGZmZpDL5aUK32ZmZkpxVLewKE61WlBQEP744w/o6uqiT58+aNasGe7cuYOQkBD88ccfAMDCOBFRLaWqqlrmGqMZGRnYtm0bdu/ejZ49ewIAAgIC8O677+LixYvo0KFDmfvLz89XmuI1MzOzehKvIwoKCpCcnCx1GvWSmZkZZ6Qhonpn27Zt5fbZ2NggLCzstfuws7PDsWPHqjItIiIiInoL2traAF6cg8nLy1NaP1wul0NVVRVFRUViHBG9mq6uLgAgOTkZZmZmCA0NRWpqKoyNjdGjRw/xXFxJHNUtLIpTrVVUVIRz585BV1cXX3zxhXiH4fvvv4/WrVtjxYoVOHfuHPr06aN09yEREdUO9+7dQ6NGjaCpqQl3d3f4+/vD1tYWV69eRWFhIXr37i3GNmvWDLa2trhw4UK5RXF/f/9SU7I3ZMnJydiwYYPUadRLvr6+aNy4sdRpEBEREREREb1SYmIigBfn0su6sazkbtbExEQ0bdpUylSJ6gQbGxsYGBjgwIEDyMnJEdtjY2MREREBHR0dGBoawsbGRsIsqbJYSaRa6+LFi1AoFGUWvVVVVdG7d28cPnwYFy9eROfOnSXKkoiIytK+fXsEBgbC2dkZCQkJWLRoEbp06YKbN28iMTER6urqMDQ0VHqOhYWF+GWuLPPmzYOfn5+4nZmZ2aA/gJqZmcHX11fqNF4rOTkZe/fuxYgRI8Qppmq7upInERERERERNWwpKSni40aNGsHCwgLq6uqwsLBAo0aNcPfu3VJxRFQ+uVwObW1tJCQkAHixPGS7du1w6dIl3Lp1Czk5ObCysoJcLpc4U6oMFsWp1kpNTQXw4u7BspS0l8QREVHt8fLSFm5ubmjfvj3s7Oywd+9eaGlpVWqfGhoa0NDQqKoU6zx1dfU6dTezmZlZncqXiIiIiIiIqLYrWSvc3t4eycnJ2Lx5s9hnZGQEOzs7PHz4kGuKE1VQQUEBEhISIJfLoaenh1u3buHWrVsAAAMDA2RlZSEhIQEFBQVceq8OYlGcai1jY2MAwJ07d9C2bVvExsYiKysLenp6sLe3x507d5TiiKjiFAoF4uPjkZ2dDV1dXdjY2PDqNqpWhoaGeOeddxAdHY0+ffqgoKAA6enpSneLJyUllbkGORERERERERERlaanpwcAePr0Kb744gs8evRIPIdubW2NFStWKMUR0auFhoYCANq1a4du3bqVOod+5swZhIeHIzQ0FB4eHhJnS2+KRXGqtTp06IDg4GAEBQXh1KlTyMzMFPv09fVRUFAAuVxe7tqzRFS2qKgonDp1ChkZGWKbgYEBevXqBWdnZwkzo/osOzsb9+/fx8cff4y2bdtCTU0Np06dgpeXF4AXv5dxcXFwd3eXOFMiotqrqKgIERERSEtLg5GREdq0aVNqmSEiIiIiImo4TE1NAQC5ubn44Ycf0Lt3b3FN8T179iA3N1cpjoherWRm4pYtW0Iul8POzk6pv2XLlggPD+cMxnUUz6BQraWqqgpnZ2fcvn0beXl5Sn0lBfJ3332XJwKJ3kBUVBQOHjxY6v+bnJwcHDx4EEOHDmVhnKrE7NmzMXDgQNjZ2eHJkydYsGABVFRUMGrUKBgYGGDChAnw8/ODsbEx9PX1MW3aNLi7u/NCJyKicpw+fRqXLl2CIAhKbe3atUPPnj0lzIyIiIiIiKRScmOZiooKsrOzcfjwYaV+NTU1FBcX83wLUQUZGxsjNjYW169fR48ePUr1X79+XYyjuofVRKq1FAoF4uLiXhkTFxcHhULBaZ+JKkChUCA4OBgAYGtrCycnJ6iqqqKoqAjR0dF48OABgoOD0bRpU/4/RW/t0aNHGDVqFFJSUmBmZobOnTvj4sWLMDMzAwCsXr0acrkcXl5eyM/Ph4eHBzZu3Chx1kREtdPp06cRHh4ObW1tdOvWDU5OToiOjkZYWBjCw8MBgIVxIiIiIqIGSFVVFZ06dcIff/wBHR0dODg4QF1dHQUFBYiJiUFOTg66dOnCG8uIKqhHjx6IiIjA5cuX0bVrV6ioqIh9xcXFuHLlihhHdQ//ElKt9eDBA+Tk5MDMzAwFBQWlpnpWV1dHcnIyHjx4ACcnJwkzJaob4uLikJubCxMTE6SkpODBgwdin4GBAYyNjZGamoq4uDjY29tLlyjVC3v27Hllv6amJjZs2IANGzbUUEZERHVTUVERLl26BG1tbUybNk28cK1Vq1Zwc3PDunXrxC/rPNFFRERERNTweHp6AgDOnTuHmzdviu1yuRxdunQR+4no9dTV1dG0aVPcu3cPK1euxHvvvYeWLVvi+vXruHLlCoqLi9G0aVOoq6tLnSpVAs+aUK0VExMDAEhOToaamppSX25urlgkj4mJYVGcqAIePnwIAEhJSYGTkxMGDx4MMzMzJCcn4/z584iOjhbjWBQnIiKqHSIiIiAIArp161ZqJhe5XI6uXbsiODgYERERaNeunURZEhERERHVXwUFBUhOTpY6jVdyc3ODi4sLbt68iYyMDBgYGKBFixZQVVXF48ePpU6vXGZmZiwuUq0zbNgw7N+/H/fu3UN4eLg4QxsANG3aFMOGDZMwO3obLIpTrfXyeolNmjRBjx49YGFhgaSkJISGhiIqKqpUHBGVr+T/lcaNG2PYsGGQyWRK27/88guePHnC/6eIiIhqkbS0NAAo9yLQkvaSOCIiIiKqX86ePYvvv/8eV69eRUJCAg4dOoQhQ4aI/WPHjsX27duVnuPh4SEuoQcAqampmDZtGo4cOSIuZbZ27Vro6urW1GHUacnJyXVyprtz585JncJr+fr6onHjxlKnQVTKsGHDUFBQgNDQUKSmpsLY2Bg9evTgRRx1HIviVGtpaGiI/x0zZow4HaStrS3GjBmDJUuWoKCgQIwjolfT1NQE8OLq1rIUFhYqxREREZH0jIyMAADR0dFo1apVqf6SmV5K4oiIiIiofsnJyUHLli0xfvx4DB06tMyYfv36ISAgQNz+5/nSMWPGICEhASEhISgsLMS4ceMwadIk7N69u1pzry/MzMzg6+srdRoVkpycjL1792LEiBEwMzOTOp3Xqgs5UtUrLCxESkqK1GlUSMuWLcXHqampEmZSMSYmJqVmXqb/w6I41Vp5eXkAgPz8fOzYsQNqamrIy8uDpqYmCgsLxcJeSRwRvVrJ1b/JycnYv38/3N3dxenTL1y4IE4DxauEiYiIao82bdrg9OnTCAsLg5ubm9IU6gqFAmfPnoVcLkebNm0kzJKI6huFQoHY2FhkZWVBT08P9vb2pZZwICKimuHp6fnaNaE1NDRgaWlZZt/t27cRHByMy5cv47333gMArFu3Dv3798cPP/yARo0alfm8/Px85Ofni9uZmZmVPIK6T11dvc7dzWxmZlbncqaGIyUlRelCHqo648aNK3c8IBbFqRZ7+Qv3vXv3KhRHROXT09MTH8fGxop3lgEQZ2L4ZxwRERFJS1VVFe3atUN4eDjWrVuHrl27wsnJCdHR0Th79ixyc3PRvn17pbGciOht3Lx5E0FBQUrLMhgZGcHT0xMtWrSQMDMiIirPmTNnYG5uDiMjI/Ts2RNLliyBiYkJAODChQswNDQUC+IA0Lt3b8jlcoSHh+ODDz4oc5/+/v5YtGhRjeRPRA2LiYkJxo0bJ3UaFfLs2TMcOXIEAwcOhKmpqdTpvFbJ334qG8+cUK3l4OCA0NDQCsUR0evZ2NjAwMAAWlpayMnJQVZWltinra0NbW1t5OXlwcbGRsIsiYiI6J969uwJALh06ZLS2pByuRzt27cX+4mI3tbNmzfx66+/wtnZGR9++CEsLCyQlJSEM2fO4Ndff8WoUaNYGCciqmX69euHoUOHwsHBAffv38eXX34JT09PXLhwASoqKkhMTIS5ubnSc1RVVWFsbIzExMRy9ztv3jz4+fmJ25mZmTxnRERVQk1Nrc7dzWxqalrncqbSWBSnWsva2rpK44gaOrlcjl69euHgwYNwcnJChw4doKamhsLCQsTExCA6OhpDhw7l7AtERES1UM+ePdG1a1dEREQgLS0NRkZGaNOmDe8QJ6Iqo1AoEBQUBGdnZ3z00Ufi9wJbW1t89NFH2LlzJ4KCguDi4sLvDEREtcjIkSPFx66urnBzc4OjoyPOnDmDXr16VXq/GhoapdYmJyIiqsv4LYZqrZfvgpHJZEp9L2+/HEdEr+bs7IyhQ4ciOTkZISEhOHbsGEJCQpCcnIyhQ4fC2dlZ6hSJiIioHCVTqXt4eKBdu3YsiBNRlYqNjUVaWhq6d+9equgtl8vRvXt3pKWlITY2VpoEiYioQpo0aQJTU1Nx2TxLS0s8ffpUKaaoqAipqam865GIiBoUnkWhWuvRo0cAXqxdplAokJGRIfYZGBgAANLT08U4Iqo4QRBeuU1ERERERFWvoKAAycnJUqdRpocPHwIAiouL8fjx41L9xcXFYlxtvHPQzMwM6urqUqdBRCS5R48eISUlBVZWVgAAd3d3pKen4+rVq2jbti0A4PTp01AoFGjfvr2UqRIREdUoFsWp1rOxscGwYcNw8eJFpKamwtjYGB06dMC+ffuQnp4udXpEdUpUVJQ4ffqQIUNgZmaG5ORknD9/HgcPHuTd4kRERLWYQqFAfHw8srOzoaurCxsbG05hTFTHJCcnY8OGDVKn8Upbt259ZX9ISAhCQkJqKJuK8/X1RePGjaVOg4ioymVnZ4t3fQNATEwMrl27BmNjYxgbG2PRokXw8vKCpaUl7t+/jy+++AJOTk7w8PAAALz77rvo168fJk6ciM2bN6OwsBBTp07FyJEj0ahRI6kOi4iIqMaxKE61lqOjI548eYIbN24gJiYGWVlZYt8ff/yB7OxsMY6IXk+hUODUqVNwcnLC0KFD8ejRI9y7dw+6uroYOnQoDh48iNOnT6Np06Y8wU5ERFTLREVF4dSpU6VmT+rVqxcvaCOqQ8zMzODr6yt1GmVSKBTYuXMnTExM0L9/fzx79gx79+7FiBEjYGpqimPHjiE1NRVjxoypld8XzMzMpE6BiKhaXLlyBT169BC3/fz8AAA+Pj7YtGkT/v77b2zfvh3p6elo1KgR+vbti2+//VZpVo9du3Zh6tSp6NWrF+RyOby8vPDjjz/W+LEQERFJiUVxqrWaNm2KP/74A4IgKBXEAShtN23atKZTI6qT4uPjkZGRgVatWmHLli2lTqq3atUK0dHRiI+Ph52dnYSZEhER0ctenull8ODBnOmFqA5TV1ev1XczDxw4EL/++itOnz6N5s2bA3gxbfrp06fx8OFDjBo1CjY2NhJnSUTUsHTv3v2Vy94dP378tfswNjbG7t27qzItIiKiOqf2XdpL9P/Z29tXaRxRQ1cyu0JYWBhycnKU+nJychAWFqYUR0RERNJ7eaaXYcOGoXHjxmJRbdiwYXBychLXhCQielstWrTAqFGjkJSUhAMHDgAADhw4gKSkJIwaNQotWrSQOEMiIiIiIqLKYVGcaq0HDx6Ij1VUVJT6Xt5+OY6IyqetrS0+trOzg7e3N2bNmgVvb2+lO8NfjqOGZ/v27fjf//4nbn/xxRcwNDREx44d8fDhQwkzIyJqmEpmeunYsSOKi4tx6dIlHD9+HJcuXUJxcTHc3d2Rnp6O+Ph4qVOlWm7Tpk1wc3ODvr4+9PX14e7ujqCgILE/Ly8Pvr6+MDExga6uLry8vJCUlKS0j7i4OAwYMADa2towNzfHnDlzUFRUVNOHQtWsRYsWmDVrFoYMGQIAGDJkCGbNmsWCOBERERER1WksilOtde3aNQBA27Ztoaenp9Snp6eHNm3aKMUR0auVTLWloaFR5p1mJWtNvWpKLqr/li1bBi0tLQDAhQsXsGHDBqxYsQKmpqaYOXOmxNkRETU8JTO4REZG4ocffsCpU6cQERGBU6dO4YcffsDt27eV4ojKY21tjeXLl+Pq1au4cuUKevbsicGDB+PWrVsAgJkzZ+LIkSPYt28fwsLC8OTJEwwdOlR8fnFxMQYMGICCggKcP38e27dvR2BgIL755hupDomqkVwuF6d5b9y4ca1cQ5yIiIiIiOhNcE1xqrUKCgoAAM2bN8cHH3yA2NhYZGVlQU9PD/b29oiKikJERIQYR0SvVnIHWX5+Pg4cOAB3d3dxTdILFy4gPz9fjGvSpImUqZKE4uPj4eTkBAA4fPgwvLy8MGnSJHTq1Andu3eXNjkiogZIV1cXAHDlyhVoa2ujW7ducHJyQnR0NMLCwnDlyhWlOKLyDBw4UGl76dKl2LRpEy5evAhra2ts27YNu3fvRs+ePQEAAQEBePfdd3Hx4kV06NABJ06cQGRkJE6ePAkLCwu0atUK3377LebOnYuFCxdCXV1disMiIiIiIiIiqhBe6ku1Vsl0zidOnAAANGnSBC1bthSLdSEhIUpxRFQxnTt3RnJyMnbs2IFVq1Zhx44dePbsGTp16iR1alQL6OrqIiUlBcCLv799+vQBAGhqauL58+dSpkZE1CBZWVkBeLF80NSpU9GqVSvo6uqiVatWmDp1qrisUEkcUUUUFxdjz549yMnJgbu7O65evYrCwkL07t1bjGnWrBlsbW1x4cIFAC9mkHF1dYWFhYUY4+HhgczMTPFu87Lk5+cjMzNT6YeIiIiIiIiopvFOcaq13N3dERwcjMTEROzYsQM9evSAhYUFkpKSEBoaisTERMhkMri7u0udKlGdYGtri/PnzyM2NhaffvopHj16hOzsbOjq6sLa2hq7d+8W46jh6tOnDz755BO0bt0ad+/eRf/+/QEAt27dgr29vbTJERE1QCVLBRUXF+PgwYOlZnopLi4W49q1aydhplQX3LhxA+7u7sjLy4Ouri4OHToEFxcXXLt2Derq6jA0NFSKt7CwQGJiIgAgMTFRqSBe0l/SVx5/f38sWrSoag+EiIiIiIiI6A2xKE61lqqqKjp37ow//vgDd+/eRVRUlNgnk8kAvLjjVVWVv8ZEFWFnZwdtbW08evQIBw4cQJMmTaCmpobk5GSEh4fj0aNH0NbW5uwLDdyGDRswf/58xMfH48CBAzAxMQEAXL16FaNGjZI4OyKihictLQ0A4OnpifPnz2PHjh1in6GhITw9PREUFCTGEb2Ks7Mzrl27hoyMDOzfvx8+Pj4ICwur1tecN28e/Pz8xO3MzEzY2NhU62sSERERERER/ROriYSCggIkJydLnUaZ3NzckJWVhevXr5fqa9WqFdzc3PD48WMJMqsYMzMzrq1HtYZcLke/fv1w8OBB3L9/H/fv3y8V069fP8jlXFmjITM0NMT69etLtfMOLyIiaRgZGYmPJ0+ejPj4eHGmFxsbG/Fz8stxROVRV1eHk5MTAKBt27a4fPky1q5diw8//BAFBQVIT09Xuls8KSkJlpaWAABLS0tcunRJaX9JSUliX3k0NDSgoaFRxUdCRERERERE9GYkLYr7+/vj4MGDuHPnDrS0tNCxY0d89913cHZ2FmPy8vIwa9Ys7NmzB/n5+fDw8MDGjRuVpm2Li4vDlClTEBoaCl1dXfj4+MDf3593EFdQcnIyNmzYIHUab0QQBFy7dk2cTrK28vX1RePGjaVOg4jojfzxxx/YsmULHjx4gH379qFx48bYsWMHHBwc0LlzZ6nTIyJqUNq0aYPTp08jLCwMbm5uSjO6KBQKnD17FnK5HG3atJEwS6qrFAoF8vPz0bZtW6ipqeHUqVPw8vICAERFRSEuLk5crsrd3R1Lly7F06dPYW5uDgAICQmBvr4+XFxcJDsGIiIiIiIiooqQtGocFhYGX19fvP/++ygqKsKXX36Jvn37IjIyEjo6OgCAmTNn4n//+x/27dsHAwMDTJ06FUOHDsW5c+cAvFhbb8CAAbC0tMT58+eRkJAAb29vqKmpYdmyZVIeXp1hZmYGX19fqdN4reTkZOzduxcjRoyAmZmZ1OlUSF3JkxoGhUKB4OBgAICjoyMcHR2hqqqKoqIi8c7x4OBgNG3alHeLN2AHDhzAxx9/jDFjxiAiIgL5+fkAgIyMDCxbtgzHjh2TOEMiooZFVVUV7dq1Q3h4ONatW4euXbvCyckJ0dHROHv2LHJzc9G+fXteEEyvNW/ePHh6esLW1hZZWVnYvXs3zpw5g+PHj8PAwAATJkyAn58fjI2Noa+vj2nTpsHd3R0dOnQAAPTt2xcuLi74+OOPsWLFCiQmJmL+/Pnw9fXlneBERERERERU60l65qSkOFMiMDAQ5ubmuHr1Krp27YqMjAxs27YNu3fvRs+ePQEAAQEBePfdd3Hx4kV06NABJ06cQGRkJE6ePAkLCwu0atUK3377LebOnYuFCxdy6uoKUFdXr1N3M5uZmdWpfIlqi4cPHyI3NxfW1tYYPnw4ZDKZ2NemTRvs3LkTjx49wsOHD+Hg4CBhpiSlJUuWYPPmzfD29saePXvE9k6dOmHJkiUSZkZE1HCVfBcKDw8v9R2qffv2Yj/Rqzx9+hTe3t5ISEiAgYEB3NzccPz4cfTp0wcAsHr1asjlcnh5eSnN0lZCRUUFR48exZQpU+Du7g4dHR34+Phg8eLFUh0SERERERERUYXVqtsJMjIyAADGxsYAgKtXr6KwsBC9e/cWY5o1awZbW1tcuHABHTp0wIULF+Dq6qo0nbqHhwemTJmCW7duoXXr1qVeJz8/X7zzDQAyMzOr65CIiGqNuLg4AECXLl2UCuIAIJPJ0LlzZ+zZswdxcXEsijdgUVFR6Nq1a6l2AwMDpKen13xCREQE4P/G8Yq2E/3Ttm3bXtmvqamJDRs2vHJpLTs7O84aQ0RERERERHVSrZkfV6FQYMaMGejUqRNatGgBAEhMTIS6ujoMDQ2VYi0sLJCYmCjGvFwQL+kv6SuLv78/DAwMxB8bG5sqPhoiIqK6ydLSEtHR0aXa//zzTzRp0kSCjIiIKDAwEAkJCQCA5s2bY/z48WjevDkAICEhAYGBgRJmR0RERERERERU+9WaO8V9fX1x8+ZN/Pnnn9X+WvPmzYOfn5+4nZmZycI4EVWJwsJCpKSkSJ1GmfT09AAAp06dQv/+/ZXuFhcEAadPnxbjyruoSEomJiZQU1OTOo16b+LEiZg+fTr+85//QCaT4cmTJ7hw4QJmz56Nr7/+Wur0iIganLy8PLEgPmfOHHHt8EGDBqF///74/vvvkZCQgLy8PGhqakqZKhERERERERFRrVUriuJTp07F0aNHcfbsWVhbW4vtlpaWKCgoQHp6utLd4klJSbC0tBRjLl26pLS/pKQksa8sGhoa0NDQqOKjICICUlJSEBAQIHUar/T06dNX3lF2/PjxmkvmDYwbN67cv+tUdf79739DoVCgV69eyM3NRdeuXaGhoYHZs2dj2rRpUqdHRNTgHD16FMCLO8QVCgWOHz+O1NRUGBsbo0ePHnBxcUFkZCSOHj2KYcOGSZwtEREREREREVHtJGlRXBAETJs2DYcOHcKZM2dKrWHbtm1bqKmp4dSpU/Dy8gLwYq3TuLg4uLu7AwDc3d2xdOlSPH36FObm5gCAkJAQ6Ovrw8XFpWYPiIgaPBMTE4wbN07qNMoVGxuL0NBQqKiooLi4WGwv2e7Rowfs7e2lS/AVTExMpE6hQZDJZPjqq68wZ84cREdHIzs7Gy4uLtDV1ZU6NSKiBiktLQ3Ai9mtVq5cKbbHxsYiIiJCnPGqJI6IiIiIiIiIiEqTtCju6+uL3bt347ffflOartfAwABaWlowMDDAhAkT4OfnB2NjY+jr62PatGlwd3dHhw4dAAB9+/aFi4sLPv74Y6xYsQKJiYmYP38+fH19eTc4EdU4NTW1Wn03s6WlJYyMjHDq1ClkZGSI7bq6uujVqxecnZ0lzI5qE3V1dV5cRkRUCxgZGeHZs2eIj4+HiooK3n//fbRs2RLXr1/H5cuXER8fL8YREREREREREVHZJC2Kb9q0CQDQvXt3pfaAgACMHTsWALB69WrI5XJ4eXkhPz8fHh4e2LhxoxiroqKCo0ePYsqUKXB3d4eOjg58fHywePHimjoMIqI6xdnZGU2bNsX169cRHByMfv36oWXLlpDL5VKnRrXABx98oLTefAmZTAZNTU04OTlh9OjRvICCiKiG9O3bF/fu3QMATJ8+Xbzwt0ePHujYsSNWrVolxhERERERERERUdkknz79dTQ1NbFhwwZs2LCh3Bg7OzscO3asKlMjIqrX5HI5rKysAABWVlYsiJPIwMAAhw8fhqGhIdq2bQsAiIiIQHp6Ovr27Yv//ve/+O6773Dq1Cl06tRJ4myJiOq/CxcuiI9XrVoFFxcXtGvXDpcuXUJkZKRSnIeHhxQpEhERERERERHVepIWxYmIiKh2sbS0xOjRo7F+/XrxYgmFQoHp06dDT08Pe/bsweTJkzF37lz8+eefEmdLRFT/paamAgBMTU3x7NkzREZGKhXDS9pL4oiIiIiIiIiIqDTeGkhERESibdu2YcaMGUqzB8jlckybNg0//fQTZDIZpk6dips3b77RfpcvXw6ZTIYZM2aIbXl5efD19YWJiQl0dXXh5eWFpKSkqjoUIqJ6wdjYWPyvnp6eUp+enp64lnhJHBERERERERERlcaiOBEREYmKiopw586dUu137txBcXExgBdLm5S17nh5Ll++jC1btsDNzU2pfebMmThy5Aj27duHsLAwPHnyBEOHDn27AyAiqmd69OgBALh79y7Mzc3h7e2NWbNmwdvbG+bm5uJ64yVxRERERERERERUGqdPJyIiItHHH3+MCRMm4Msvv8T7778P4EVRe9myZfD29gYAhIWFoXnz5hXaX3Z2NsaMGYOtW7diyZIlYntGRga2bduG3bt3o2fPngCAgIAAvPvuu7h48SI6dOhQxUdGRFQ3qaqqQlVVFUVFRYiJiYGpqSk0NTURFRWFmJgYpRgiIiIiIiIiIiob7xQnIiIi0erVqzFjxgysWLECXbt2RdeuXbFixQrMnDkTq1atAgD07dsXe/bsqdD+fH19MWDAAPTu3Vup/erVqygsLFRqb9asGWxtbXHhwoUy95Wfn4/MzEylHyKi+i4+Ph5FRUWwtraGQqFAeHg4fvrpJ4SHh0OhUMDa2hpFRUWIj4+XOlWqQenp6VKnQERERDXk7NmzGDhwIBo1agSZTIbDhw+LfYWFhZg7dy5cXV2ho6ODRo0awdvbG0+ePFHah729PWQymdLP8uXLa/hIiIiIpMWiOBEREYlUVFTw1VdfISEhAenp6UhPT0dCQgK+/PJLqKioAABsbW1hbW392n3t2bMHERER8Pf3L9WXmJgIdXV1GBoaKrVbWFggMTGxzP35+/vDwMBA/LGxsXnzAyQiqmOys7MBAG3atIG+vr5Sn76+Plq3bq0UR/XPd999h//+97/i9ogRI2BiYoLGjRvj+vXrEmZGRERENSEnJwctW7bEhg0bSvXl5uYiIiICX3/9NSIiInDw4EFERUVh0KBBpWIXL16MhIQE8WfatGk1kT4REVGtwTn2iIiIqEz/LL68ifj4eEyfPh0hISHQ1NSsknzmzZsHPz8/cTszM5OFcSKq93R1dQEAv//+O5ycnDBkyBCYmZkhOTkZ58+fx5EjR5TiqP7ZvHkzdu3aBQAICQlBSEgIgoKCsHfvXsyZMwcnTpyQOEMiqo8yMjLw/PlzqdOoN549e6b0X6oaWlpaMDAwkDqNaufp6QlPT88y+wwMDBASEqLUtn79erRr1w5xcXGwtbUV2/X09GBpaVnh183Pz0d+fr64zdnaiIiormNRnIiIiJTs378fe/fuRVxcHAoKCpT6IiIiKrSPq1ev4unTp2jTpo3YVlxcjLNnz2L9+vU4fvw4CgoKkJ6ernS3eFJSUrlf0jU0NKChofHmB0REVIc1btwYMpkMWlpa8PLyglwuF9u9vLywbt065OXloXHjxhJnStUlMTFRvAjs6NGjGDFiBPr27Qt7e3u0b99e4uyIqD7KyMjA1p9+QmFRkdSp1DslF7NR1VBTVcXESZMaRGH8TWRkZEAmk5WamW358uX49ttvYWtri9GjR2PmzJlQVS2/PODv749FixZVc7ZEREQ1h0VxIiIiEv3444/46quvMHbsWPz2228YN24c7t+/j8uXL8PX17fC++nVqxdu3Lih1DZu3Dg0a9YMc+fOhY2NDdTU1HDq1Cl4eXkBAKKiohAXFwd3d/cqPSYiorrs8ePHEAQBubm52LdvH1RVVZGfnw8NDQ0UFRUhNzdXjLOzs5M4W6oORkZGiI+Ph42NDYKDg7FkyRIAgCAIKC4uljg7IqqPnj9/jsKiIvRzaQljHc5EQrVTak42giOv4/nz5yyKvyQvLw9z587FqFGjlGZ/+/zzz9GmTRsYGxvj/PnzmDdvHhISErBq1apy98XZ2oiIqL5hUZyIiIhEGzduxE8//YRRo0YhMDAQX3zxBZo0aYJvvvkGqampFd6Pnp4eWrRoodSmo6MDExMTsX3ChAnw8/ODsbEx9PX1MW3aNLi7u6NDhw5VekxERHVZyVrhFhYWePDgQal+CwsLJCUlcU3xemzo0KEYPXo0mjZtipSUFHH61L/++gtOTk4SZ0dE9Zmxji4s9FhsJKorCgsLMWLECAiCgE2bNin1vVzcdnNzg7q6Oj799FP4+/uXOyMbZ2sjIqL6hkVxIiIiEsXFxaFjx44AXqzPlpWVBQD4+OOP0aFDB6xfv77KXmv16tWQy+Xw8vJCfn4+PDw8sHHjxirbPxFRfVCyVnhSUhJUVFTg7OwMS0tLJCYmIioqCklJSUpxVP+sXr0a9vb2iI+Px4oVK8R/64SEBHz22WcSZ0dERES1QUlB/OHDhzh9+rTSXeJlad++PYqKihAbGwtnZ+caypKIiEhaLIoTERGRyNLSEqmpqbCzs4OtrS0uXryIli1bIiYmBoIgvNW+z5w5o7StqamJDRs2YMOGDW+1XyKi+szCwkJ87Ofnp7TuY1FREb7//vtScVS/qKmpYfbs2aXaZ86cKUE2REREVNuUFMTv3buH0NBQmJiYvPY5165dg1wuh7m5eQ1kSEREVDvIpU6AiIiIao+ePXvi999/B/BiDfCZM2eiT58++PDDD/HBBx9InB0RUcMTFhYmPj506BAePXqE/Px8PHr0CIcOHSozjuqfHTt2oHPnzmjUqBEePnwIAFizZg1+++03iTMjIiKi6padnY1r167h2rVrAICYmBhcu3YNcXFxKCwsxLBhw3DlyhXs2rULxcXFSExMRGJiIgoKCgAAFy5cwJo1a3D9+nU8ePAAu3btwsyZM/HRRx/ByMhIwiMjIiKqWbxTnIiIiEQ//fQTFAoFAMDX1xcmJiY4f/48Bg0ahE8//VTi7IiIGp7U1FQAQN++fREeHo4dO3aIfQYGBujTpw9CQkLEOKp/Nm3ahG+++QYzZszA0qVLUVxcDAAwNDTEmjVrMHjwYIkzJCIioup05coV9OjRQ9wuWR/cx8cHCxcuFC9sb9WqldLzQkND0b17d2hoaGDPnj1YuHAh8vPz4eDggJkzZyqtM05ERNQQ8E5xIiIiEsnlcqWpeUeOHIkff/wR06ZNg7q6uoSZERE1TMbGxgCA2NjYUstYCIKA2NhYpTiqf9atW4etW7fiq6++goqKitj+3nvv4caNGxXej7+/P95//33o6enB3NwcQ4YMQVRUlFJM9+7dIZPJlH4mT56sFBMXF4cBAwZAW1sb5ubmmDNnDoqKit7uIImIiKhc3bt3hyAIpX4CAwNhb29fZp8gCOjevTsAoE2bNrh48SLS09Px/PlzREZGYt68edDQ0JD2wIiIiGoYi+JEREQkCg4Oxp9//ilub9iwAa1atcLo0aORlpYmYWZERA1TyV1Bd+/ehYmJCby9vTFr1ix4e3vDxMQE9+7dU4qj+icmJgatW7cu1a6hoYGcnJwK7ycsLAy+vr64ePEiQkJCUFhYiL59+5bax8SJE5GQkCD+rFixQuwrLi7GgAEDUFBQgPPnz2P79u0IDAzEN998U/kDJCIiIiIiIqoBlZ4+/d69ewgNDcXTp0/FaVZL8AsxERFR3TRnzhx89913AIAbN27Az88Ps2bNQmhoKPz8/BAQECBxhkREDYuqqipUVVVRVFSEuLg4REVFQVNTE1FRUYiLi1OKofrJwcEB165dg52dnVJ7cHAw3n333QrvJzg4WGk7MDAQ5ubmuHr1Krp27Sq2a2trw9LSssx9nDhxApGRkTh58iQsLCzQqlUrfPvtt5g7dy4WLlzIWWWIiIiIiIio1qrUmZOtW7diypQpMDU1haWlJWQymdgnk8lYFCciIqqjYmJi4OLiAgA4cOAABg4ciGXLliEiIgL9+/eXODsiooYnPj4eRUVFsLa2xqNHjxAeHo7w8HCxv6Q9Pj6+VNGU6gc/Pz/4+voiLy8PgiDg0qVL+PXXX+Hv74+ff/650vvNyMgAUHrq/V27dmHnzp2wtLTEwIED8fXXX0NbWxsAcOHCBbi6usLCwkKM9/DwwJQpU3Dr1q0y72jPz89Hfn6+uJ2ZmVnpnImIiOqq4uJi3LhxA3Z2djAyMpI6HSIiogapUkXxJUuWYOnSpZg7d25V50NEREQSUldXR25uLgDg5MmT8Pb2BvDihDlPYhMR1bzs7GwAQGFhYZn9Je0lcVT/fPLJJ9DS0sL8+fORm5uL0aNHo1GjRli7di1GjhxZqX0qFArMmDEDnTp1QosWLcT20aNHw87ODo0aNcLff/+NuXPnIioqCgcPHgQAJCYmKhXEAYjbiYmJZb6Wv78/Fi1aVKk8iYiI6qoZM2bA1dUVEyZMQHFxMbp164bz589DW1sbR48eFdf7JiIioppTqaJ4Wloahg8fXtW5EBERkcQ6d+4MPz8/dOrUCZcuXcJ///tfAC/WsrW2tpY4OyKihkdXVxcAkJSUBABo3rw52rdvj/DwcNy6dUtsL4mj+mnMmDEYM2YMcnNzkZ2dDXNz87fan6+vL27evIk///xTqX3SpEniY1dXV1hZWaFXr164f/8+HB0dK/Va8+bNg5+fn7idmZkJGxubyiVORERUR+zfvx8fffQRAODIkSOIiYnBnTt3sGPHDnz11Vc4d+6cxBkSERE1PPLKPGn48OE4ceJEVedCREREElu/fj1UVVWxf/9+bNq0CY0bNwYABAUFoV+/fhJnR0TU8JiZmYmPZ8+ejUGDBsHCwgKDBg3C7Nmzy4yj+qVnz55IT08H8GK975KCeGZmJnr27PnG+5s6dSqOHj2K0NDQ117w1r59ewBAdHQ0AMDS0lK8EKNEyXZ565BraGhAX19f6YeIiKi+e/bsmTg2Hjt2DMOHD8c777yD8ePH48aNGxJnR0RE1DBV+E7xH3/8UXzs5OSEr7/+GhcvXoSrqyvU1NSUYj///POqy5CIiIhqjK2tLY4ePVqqffXq1RJkQ0REx44dEx8fOHAAampqyM/Ph4aGhtKU6seOHcOwYcOkSJGq2ZkzZ1BQUFCqPS8vD3/88UeF9yMIAqZNm4ZDhw7hzJkzcHBweO1zrl27BgCwsrICALi7u2Pp0qV4+vSpWJwPCQmBvr4+XFxcKpwLERFRfWdhYYHIyEhYWVkhODgYmzZtAgDk5uZCRUVF4uyIqD7LyMjA8+fPpU6j3nj27JnSf6lqaGlpwcDAoMZft8JF8X+eDNfV1UVYWBjCwsKU2mUyGYviREREdVRERATU1NTg6uoKAPjtt98QEBAAFxcXLFy4EOrq6hJnSETUsKSlpQEATExMEBMTU6rfxMQEKSkpYhzVH3///bf4ODIyUmnN7uLiYgQHB4szulSEr68vdu/ejd9++w16enri/gwMDKClpYX79+9j9+7d6N+/P0xMTPD3339j5syZ6Nq1K9zc3AAAffv2hYuLCz7++GOsWLECiYmJmD9/Pnx9faGhoVFFR05ERFT3jRs3DiNGjICVlRVkMhl69+4NAAgPD0ezZs0kzo6I6quMjAxs/eknFBYVSZ1KvXPkyBGpU6hX1FRVMXHSpBovjFe4KF7WCRgiIiKqXz799FP8+9//hqurKx48eICRI0figw8+wL59+5Cbm4s1a9ZInSIRUYNiZGSEZ8+eISUlBSoqKnB2doalpSUSExMRFRWFlJQUMY7ql1atWkEmk0Emk5U5TbqWlhbWrVtX4f2V3KHWvXt3pfaAgACMHTsW6urqOHnyJNasWYOcnBzY2NjAy8sL8+fPF2NVVFRw9OhRTJkyBe7u7tDR0YGPjw8WL15cuYMkIiKqpxYuXIgWLVogPj4ew4cPFy8eU1FRwb///W+Js6td0tPTkZOTI3Ua9UZycrLSf6lq6OjowNDQUOo0Xuv58+coLCpCP5eWMNbRlTodojKl5mQjOPI6nj9/XnuL4i9bvHgxZs+eDW1tbaX258+f4/vvv8c333xTJckRERFRzbp79y5atWoFANi3bx+6du2K3bt349y5cxg5ciSL4kRENaxv3764d+8eAGD69OlKd+Pm5+dj1apVYhzVLzExMRAEAU2aNMGlS5eU1o1XV1eHubn5G02/KgjCK/ttbGxKzQRXFjs7O6Vp/YmIiKhsZS1t4+PjI0EmtVd6ejpWrV6NopeWBaKqsXfvXqlTqFdU1dTgN3NmnSiMA4Cxji4s9Gp+amqi2q5SRfFFixZh8uTJpYriubm5WLRoEYviREREdZQgCFAoFACAkydP4l//+heAFyfKuXYOEVHNu3Dhgvh41apVcHFxQbt27XDp0iVERkYqxXl4eEiRIlUTOzs7ABDHZSIiIqrdfvzxxwrHcvnRF3JyclBUWIimHXtA24AzH1HtlJuRhnvnQ5GTk1NniuJEVLZKFcUFQYBMJivVfv36dRgbG791UkRERCSN9957D0uWLEHv3r0RFhYmTrUaExMDCwsLibMjImp4UlNTAQCmpqZ49uwZIiMjlYrhJe0lcVT//PLLL6/s9/b2rqFMiIiI6FVWr15doTiZTMai+D9oGxhB19hU6jSIiKiee6OiuJGRkbim2TvvvKNUGC8uLkZ2djYmT55c5UkSERFRzVizZg3GjBmDw4cP46uvvoKTkxMAYP/+/ejYsaPE2RERNTzGxsaIjY1FUVFRmf0l7bw4uf6aPn260nZhYSFyc3Ohrq4ObW1tFsWJiIhqiZiYGKlTICIiold4o6L4mjVrIAgCxo8fj0WLFiktgK6urg57e3u4u7tXeZJERERUM9zc3HDjxo1S7d9///0brVtKRERVo0ePHoiIiEB6ejrkcjnatWuHli1b4vr167h06RLS09PFOKqf0tLSSrXdu3cPU6ZMwZw5cyTIiIiIiIiIiKjueaOiuI+PDwDAwcEBHTt2hJqaWrUkRURERNJJT0/H/v37cf/+fcyZMwfGxsaIjIyEhYUFGjduLHV6REQNmiAI4g81XE2bNsXy5cvx0Ucf4c6dO1KnQ0RERAD8/PwqHLtq1apqzISIiIjKUqk1xVu3bo3nz5/j+fPnSu0ymQwaGhpQV1evkuSIiIioZv3999/o1asXDA0NERsbi4kTJ8LY2BgHDx5EXFzca9c1JSKiqhUaGgrgxVJWaWlpCA8PR3h4uNhvaGiI9PR0hIaGwsPDQ6o0SQKqqqp48uSJ1GkQERHR//fXX39VKO7lJUmJiIio5lSqKG5oaPjKwdva2hpjx47FggULIJfLK50cERER1Sw/Pz+MGzcOK1asgJ6entjev39/jB49WsLMiIgaptTUVABAdnZ2mf05OTlKcVT//P7770rbgiAgISEB69evR6dOnSTKioiIiP6p5GJGIiIiqp0qVRQPDAzEV199hbFjx6Jdu3YAgEuXLmH79u2YP38+kpOT8cMPP0BDQwNffvlllSZMRERE1efy5cvYsmVLqfbGjRsjMTFRgoyIiBo2Y2NjxMbGorCwEABgYmKC7t2748yZM0hJSRHbjY2NpUyTqtGQIUOUtmUyGczMzNCzZ0+sXLlSmqSIiIiIiIiI6phKFcW3b9+OlStXYsSIEWLbwIED4erqii1btuDUqVOwtbXF0qVLWRQnIiKqQzQ0NJCZmVmq/e7duzAzM5MgIyKihq1t27aIiIgAAEyfPh3a2toAgHfeeQe5ublYu3atGEf1k0KhkDoFIiIiqoQrV65g7969iIuLQ0FBgVLfwYMHJcqKiIio4arU3Obnz59H69atS7W3bt0aFy5cAAB07twZcXFxb5cdERER1ahBgwZh8eLF4p2HMpkMcXFxmDt3Lry8vCTOjoio4fnvf/8rPl63bh22bduGX375Bdu2bcO6devKjCMiIiIiae3ZswcdO3bE7du3cejQIRQWFuLWrVs4ffo0DAwMpE6PiIioQarUneI2NjbYtm0bli9frtS+bds22NjYAABSUlJgZGT09hkSERFRjVm5ciWGDRsGc3NzPH/+HN26dUNiYiLc3d2xdOlSqdMjImpwnj9/DgBQV1dHQUEBnj59qtRf0l4SR/VPcXExAgMDcerUKTx9+rTUneOnT5+WKDMiqu9Sc7KlToGoXLX993PZsmVYvXo1fH19oaenh7Vr18LBwQGffvoprKyspE6PiIioQapUUfyHH37A8OHDERQUhPfffx/Ai+lg7ty5g/379wN4sSbphx9++Mr9nD17Ft9//z2uXr2KhIQEHDp0SGm9tLFjx2L79u1Kz/Hw8EBwcLC4nZqaimnTpuHIkSOQy+Xw8vLC2rVroaurW5lDIyIiatAMDAwQEhKCP//8E3///Teys7PRpk0b9O7dW+rUiIgaJC0tLRQWFqKgoAByuRxmZmZQVVVFUVERkpOTxak4tbS0JM6Uqsv06dMRGBiIAQMGoEWLFpDJZFKnREQNRHDkdalTIKqz7t+/jwEDBgB4cRFjTk4OZDIZZs6ciZ49e2LRokUSZ0hERNTwVKooPmjQINy5cwdbtmzB3bt3AQCenp44fPgw7O3tAQBTpkx57X5ycnLQsmVLjB8/HkOHDi0zpl+/fggICBC3NTQ0lPrHjBmDhIQEhISEoLCwEOPGjcOkSZOwe/fuyhwaERER4cUyKJ07d5Y6DSKiBu/DDz/E1q1bAQDTpk0T1xQHoLSm+OsuSKa6a8+ePdi7dy/69+8vdSpE1MD0c2kJYx3edEK1U2pOdq2+cMPIyAhZWVkAgMaNG+PmzZtwdXVFeno6cnNzJc6OiIioYapUURwAHBwcSk2f/qY8PT3h6en5yhgNDQ1YWlqW2Xf79m0EBwfj8uXLeO+99wC8WGevf//++OGHH9CoUaMyn5efn4/8/HxxOzMzs5JHQEREVP9cvnwZoaGhZU7RumrVKomyIiJqmK5evSo+Xrt2LYyNjdGtWzeEhYUhNTVVKc7Dw0OKFKmaqaurw8nJSeo0iKgBMtbRhYUe1z4mqoyuXbsiJCQErq6uGD58OKZPn47Tp08jJCQEvXr1eqN9vW62VUEQsGDBAmzduhXp6eno1KkTNm3ahKZNm4oxnG2ViIjoLYri6enpuHTpUpknzL29vd86sRJnzpyBubk5jIyM0LNnTyxZsgQmJiYAgAsXLsDQ0FAsiANA7969IZfLER4ejg8++KDMffr7+3OKGiIiojIsW7YM8+fPh7OzMywsLJSmaOV0rURENa+k8K2mpobCwkKkpqbi0KFDYv/L7VQ/zZo1C2vXrsX69es5FhMREdUR69evR15eHgDgq6++gpqaGs6fPw8vLy/Mnz//jfb1utlWV6xYgR9//BHbt2+Hg4MDvv76a3h4eCAyMhKampoAONsqUUOTmpMtdQpE5ZLy97NSRfEjR45gzJgxyM7Ohr6+fqkT5lVVFO/Xrx+GDh0KBwcH3L9/H19++SU8PT1x4cIFqKioIDExEebm5krPUVVVhbGxMRITE8vd77x58+Dn5yduZ2ZmwsbGpkpyJiIiqsvWrl2L//znPxg7dqzUqRAREQBjY2PExsaWWwwtaTc2Nq7JtKgG/fnnnwgNDUVQUBCaN28ONTU1pf6DBw9KlBkRERGV5+XPZnK5HP/+978rva9XzbYqCALWrFmD+fPnY/DgwQCAX375BRYWFjh8+DBGjhxZJ2Zbzc1Iq7Z9E72tuvj7WZuXlyCSUqWK4rNmzcL48eOxbNkypTXtqtrIkSPFx66urnBzc4OjoyPOnDnzxtPMvExDQ6PU2uRERET04st6p06dpE6DiIj+vx49eiAiIgIFBQUAXixj1blzZ/z555+IiYkR23v06CFlmlSNDA0Ny50FjYiIiGqnY8eOQUVFpdTyNidOnEBxcfFrlxStqJiYGCQmJqJ3795im4GBAdq3b48LFy5g5MiRdWK21XvnQ2vkdYgain4uLWGsw+URqHZKzcmW7MKNShXFHz9+jM8//7xaC+JladKkCUxNTREdHY1evXrB0tIST58+VYopKipCampqueuQExERUflmzpyJDRs2YM2aNVKnQkREePH9poRcLoe5uTm0tLRgbm6Ohw8fiktZFRUVQV1dXao0qRoFBARInUKdkZ6ejpycHKnTqDeSk5OV/ktVQ0dHB4aGhlKnQUTV7N///jeWL19eql2hUODf//53lRXFS2ZLtbCwUGq3sLAQ++rCbKtNO/aAtoFRteyb6G3lZqTVuQs3jHV0YaFnIHUaRLVOpYriHh4euHLlCpo0aVLV+bzSo0ePkJKSAisrKwCAu7s70tPTcfXqVbRt2xYAcPr0aSgUCrRv375GcyMiIqoPZs+ejQEDBsDR0REuLi6copWISGL79+8HAGhpaeH58+cIDw9HeHi42F/Svn///ipbxoqoLkpPT8eq1atRVFgodSr1zt69e6VOoV5RVVOD38yZLIwT1XP37t2Di4tLqfZmzZohOjpagozeXE3OtqptYARdY9MaeS0iImq4KlUUHzBgAObMmYPIyEi4urqWOmE+aNCgCu0nOztb6UNATEwMrl27BmNjYxgbG2PRokXw8vKCpaUl7t+/jy+++AJOTk7itDPvvvsu+vXrh4kTJ2Lz5s0oLCzE1KlTMXLkyHLXQiEiIqLyff755wgNDUWPHj1gYmJS7hq2r7Np0yZs2rQJsbGxAIDmzZvjm2++Ea+Gz8vLw6xZs7Bnzx7k5+fDw8MDGzduLHV1OxFRQ1eydqO9vT1u375dqr+kvTrXeKSa16ZNG5w6dQpGRkZo3br1K8fjiIiIGsys9srJyUFRYSHvNKNareROs5ycHBbFieo5AwMDPHjwAPb29krt0dHR0NHRqbLXKZktNSkpSbyRrGS7VatWYgxnWyUiIqpkUXzixIkAgMWLF5fqk8lkKC4urtB+rly5orT2Xcl0LD4+Pti0aRP+/vtvbN++Henp6WjUqBH69u2Lb7/9VukKtV27dmHq1Kno1asX5HI5vLy88OOPP1bmsIiIiBq87du348CBAxgwYMBb7cfa2hrLly9H06ZNIQgCtm/fjsGDB+Ovv/5C8+bNMXPmTPzvf//Dvn37YGBggKlTp2Lo0KE4d+5cFR0JEVH9oK+vj6ysLNy+fRva2tro1q0bnJycEB0djbCwMLFQrq+vL3GmVJUGDx4sfu8dMmSItMnUMbzTjIiIaoPBgwdjxowZOHToEBwdHQG8KIjPmjWrwjeUVYSDgwMsLS1x6tQpsQiemZmJ8PBwTJkyBQBnWyUiIipRqaJ4ybp1b6t79+4QBKHc/uPHj792H8bGxti9e3eV5ENERNTQGRsbi1/Y38bAgQOVtpcuXYpNmzbh4sWLsLa2xrZt27B792707NkTwIv1Ut99911cvHgRHTp0eOvXJyKqL4YMGYINGzYAACZPniwWSlu1aoV3330Xq1atEuOo/liwYEGZj9+Gv78/Dh48iDt37kBLSwsdO3bEd999B2dnZzGmIjO5xMXFYcqUKQgNDYWuri58fHzg7+8PVdVKnV4gIiKql1asWIF+/fqhWbNmsLa2BvBiadAuXbrghx9+eKN9vWq2VVtbW8yYMQNLlixB06ZN4eDggK+//hqNGjUSPx9ytlUiIqIX3vpba15eHjQ1NasiFyIiIpLYwoULsWDBAgQEBEBbW7tK9llcXIx9+/YhJycH7u7uuHr1KgoLC9G7d28xplmzZrC1tcWFCxfKLYrn5+cjPz9f3OZUwUTUENy5c0d8vGrVKqVptF++wPjOnTto165djeZGNS87O7vUReoVnSUgLCwMvr6+eP/991FUVIQvv/wSffv2RWRkpDiN6+tmcikuLsaAAQNgaWmJ8+fPIyEhAd7e3lBTU8OyZcuq9mCJiIjqMAMDA5w/fx4hISG4fv06tLS04Obmhq5du77xvl4122pgYCC++OIL5OTkYNKkSUhPT0fnzp0RHBysdM6es60SERFVsiheXFyMZcuWYfPmzUhKSsLdu3fRpEkTfP3117C3t8eECROqOk8iIiKqAT/++CPu378PCwsL2NvbQ01NTan/TdYtvXHjBtzd3ZGXlwddXV0cOnQILi4uuHbtGtTV1Uuto2hhYYHExMRy9+fv749Fixa90fEQEdV1aWlpStvlzbT1zziqP2JiYjB16lScOXMGeXl5YrsgCG+0fFlwcLDSdmBgIMzNzXH16lV07doVGRkZr53J5cSJE4iMjMTJkydhYWGBVq1a4dtvv8XcuXOxcOFCqKurV92BExER1XEymQx9+/ZF3759y41xdXXFsWPHYGNjU27M62ZblclkWLx4cZlLnZbgbKtERESVLIovXboU27dvx4oVK8T1xQGgRYsWWLNmDYviREREdVRVTr/r7OyMa9euISMjA/v374ePjw/CwsIqvb958+aJV8QDL+4Uf9WJAyKi+sDIyEhpWyaTQV1dHQUFBUonR/8ZR/XHRx99BEEQ8J///AcWFhZKswW8jYyMDAAvTpIDqNBMLhcuXICrq6vSdOoeHh6YMmUKbt26hdatW5d6Hc70QkREVL7Y2FgUFhZKnQYREVGDUKmi+C+//IKffvoJvXr1wuTJk8X2li1bKk3vR0RERHVLRdct/fXXXzFo0CBxutWyqKurw8nJCQDQtm1bXL58GWvXrsWHH36IgoICpKenK90tnpSUBEtLy3L3p6GhIa6lS0TUUDRp0gSnTp0CAEyfPl1paYvc3FysXbtWjKP66fr167h69arS2t9vS6FQYMaMGejUqRNatGgBAEhMTHztTC6JiYlKBfGS/pK+snCmFyIiIiIiIqoN5JV50uPHj8WT3C9TKBS8so2IiKgB+PTTT5GUlPRGz1EoFMjPz0fbtm2hpqYmFnkAICoqCnFxcXB3d6/qVImI6rRdu3aJj7du3Yq//voLWVlZ+Ouvv7B169Yy46h+ef/99xEfH1+l+/T19cXNmzexZ8+eKt1vWebNm4eMjAzxp6qPhYiIiIiIiKgiKnWnuIuLC/744w/Y2dkpte/fv7/M6dKIiIiofnnVembAixPgnp6esLW1RVZWFnbv3o0zZ87g+PHjMDAwwIQJE+Dn5wdjY2Po6+tj2rRpcHd3R4cOHWroCIiI6oaSaaednJwQHR1dal3oJk2a4MGDB0rTU1P98vPPP2Py5Ml4/PgxWrRoATU1NaV+Nze3N9rf1KlTcfToUZw9exbW1tZiu6Wl5WtncrG0tMSlS5eU9ldykVx5s71wphciIiIiIiKqDSpVFP/mm2/g4+ODx48fQ6FQ4ODBg4iKisIvv/yCo0ePVnWOREREVMc8ffoU3t7eSEhIgIGBAdzc3HD8+HH06dMHALB69WrI5XJ4eXkhPz8fHh4e2Lhxo8RZExHVPhoaGsjNzUV0dHSZ/Q8ePBDjqH5KTk7G/fv3MW7cOLFNJpNBEATIZDIUFxdXaD+CIGDatGk4dOgQzpw5AwcHB6X+l2dy8fLyAlB6Jhd3d3csXboUT58+hbm5OQAgJCQE+vr6cHFxqYrDJSIiIiIiIqoWlSqKDx48GEeOHMHixYuho6ODb775Bm3atMGRI0fEk91ERETUcG3btu2V/ZqamtiwYQM2bNhQQxkREdVNPj4+2LRpk7htYmKC7t2748yZM0hJSVGKo/pp/PjxaN26NX799VdYWFhAJpNVaj++vr7YvXs3fvvtN+jp6YlrgBsYGEBLS6tCM7n07dsXLi4u+Pjjj7FixQokJiZi/vz58PX15YUZREREREREVKtVqigOAF26dEFISEhV5kJEREREREQvkcvlStsFBQXIz89HQUHBK+Oo/nj48CF+//13ODk5vdV+Si6u6N69u1J7QEAAxo4dC+D1M7moqKjg6NGjmDJlCtzd3aGjowMfHx8sXrz4rXIjIiKqbx48eIAmTZq8Nm7Lli2wsLCogYyIiIio0kVxIiIiIiIiql47duxQ2s7KyipzyaodO3bA19e3ptKiGtSzZ09cv379rYvigiC8NqYiM7nY2dnh2LFjb5ULERFRfefk5IRu3bphwoQJGDZsGDQ1NcuMGz16dA1nRkRE1HBVuChuZGRU4WnaUlNTK50QERER1X52dnZQU1OTOg0ionrv+fPnAAALCwskJSWV6i9pL4mj+mfgwIGYOXMmbty4AVdX11Lj76BBgyTKjIiIiMoTERGBgIAA+Pn5YerUqfjwww8xYcIEtGvXTurUiIiIGqwKF8XXrFlTjWkQERFRXXLz5k2pUyAiahC0tLRQWFiIpKQkqKio4P3330fLli1x/fp1XL58WSyUa2lpSZwpVZfJkycDQJlTlMtkMhQXF9d0SkRERPQarVq1wtq1a7Fy5Ur8/vvvCAwMROfOnfHOO+9g/Pjx+Pjjj2FmZiZ1mrVGbkaa1CkQlasu/n6m5mRLnQJRuaT8/axwUdzHx+eNd758+XJMnjwZhoaGb/xcIiIiqhmcDYaIqPb68MMPsXXrVgDA1KlToa2tDQDo0aMH2rdvj7Vr14pxVD8pFAqpUyAiIqJKUlVVxdChQzFgwABs3LgR8+bNw+zZs/Hll19ixIgR+O6772BlZSV1mpLR0dGBqpoa7p0PlToVoldSVVODjo6O1Gm8lpaWFtRUVREceV3qVIheSU1VVZKL+6t1TfFly5ZhxIgRDbYonp6ejpycHKnTqDeSk5OV/ktVQ0dHp8H+P0pEL3A2GCKi2uvq1avi47Vr10JFRQWqqqooKipSukP46tWr8PDwkCJFqiVcXV1x7Ngx2NjYSJ0KERER/X9XrlzBf/7zH+zZswc6OjqYPXs2JkyYgEePHmHRokUYPHgwLl26JHWakjE0NITfzJk8h16FkpOTsXfvXowYMYKzEVShunIO3cDAABMnTeLyWlXo2bNnOHLkCAYOHAhTU1Op06k3tLS0YGBgUOOvW61FcUEQqnP3tVp6ejpWrV6NosJCqVOpd/bu3St1CvWKqpoa/GbOrBODekZGBgf0KvTs2TOl/1LVkGpAfxuVmQ2GiIhqxj9n6CguLi5zumzO5EGxsbEo5PdPIiKiWmHVqlUICAhAVFQU+vfvj19++QX9+/eHXC4HADg4OCAwMBD29vbSJloLGBoa1onzknWNmZkZGjduLHUaJAEDA4M6d26yLjA1NYWlpaXUadBbqtaieEOWk5ODosJCNO3YA9oGRlKnQ1Sm3Iw03DsfipycnFr/4TMjIwNbf/oJhUVFUqdS7xw5ckTqFOoVNVVVTJw0qU59+MzMzIS+vr74+FVK4oiIqGYYGxsjNjZW3JbL5ZDJZBAEQWlabWNjYwmyIyIiIqKybNq0CePHj8fYsWPLnR7d3Nwc27Ztq+HMiIiIGi4WxauZtoERdI05pQLR23r+/DkKi4rQz6UljHV0pU6HqEypOdkIjryO58+f16miuJGRERISEmBubg5DQ8My1xcXBAEymazMuxOJiKj6tG/fHhEREQCAmTNnQlNTU+zLy8vD6tWrxTgienHhL1Ftxd9Poobj3r17r41RV1fnzG1EREQ1iEVxIqpTjHV0YaFXd4qNRHXB6dOnxTsMQ0NDJc6GiIhe9vvvv4uPf/zxR7z33nto2bIlrl+/jitXrijFeXt7S5EiUa1y7zw/yxARUe2Rm5uLuLg4FBQUKLW7ublJlBEREVHDxaI4ERFRA9etW7cyHxMRkfRKlrVo3LgxHj9+jPDwcISHh4v9Je2vW/6CqKHgEmZUm5UsYUZE9V9ycjLGjh2L4ODgMvs5CxsREVHNq9aieJcuXaClpVWdL0FERETVgFezExHVDvr6+sjKykJWVpa4lngJmUyGrKwsMY6IuIQZERHVDjNmzEBGRgbCw8PRvXt3HDp0CElJSViyZAlWrlwpdXpEREQNUqWK4ioqKuLaoy9LSUmBubm5eKXbsWPH3j5DIiIiqjHJyckYN24cgoKCyuzn1exERDVr2LBhWLt2LTIzM6GtrY1u3brByckJ0dHRCAsLE+8QHzZsmMSZUnV58OABmjRp8tq4LVu2wMLCogYyIiIiotc5ffo0fvvtN7z33nuQy+Wws7NDnz59oK+vD39/fwwYMEDqFImIiBoceWWe9PLdCS/Lz8+Hurr6WyVERERE0pkxYwbS09MRHh4OLS0tBAcHY/v27WjatKnSurZERFQzXv5+lZubi9u3byM1NRW3b99Gbm5umXFUvzg5OaFHjx7YuXMn8vLyyo0bPXo0dHR0ajAzIiIiKk9OTo54Q5mRkRGSk5MBAK6uroiIiJAyNSIiogbrje4U//HHHwG8mKbv559/hq6urthXXFyMs2fPolmzZlWbIREREdUYXs1ORFS7lJw01dLSwvPnzxEbG4vY2Fixv6Q9IiIC7dq1kyhLqk4REREICAiAn58fpk6dig8//BATJkzgvzcREVEt5uzsjKioKNjb26Nly5bYsmUL7O3tsXnzZlhZWUmdHhERUYP0RkXx1atXA3hxp/jmzZuhoqIi9qmrq4sDOxEREdVNZV3N/s477/BqdiIiiaSlpQEAmjVrhmvXrpVaU7xZs2b466+/xDiqf1q1aoW1a9di5cqV+P333xEYGIjOnTvjnXfewfjx4/Hxxx/DzMxM6jSJiIjoJdOnT0dCQgIAYMGCBejXrx927doFdXV1BAYGSpscERFRA/VGRfGYmBgAQI8ePXDw4EEYGRlVS1JEREQkDV7NTkRUu5R85/rrr7/KXFP8r7/+Uoqj+ktVVRVDhw7FgAEDsHHjRsybNw+zZ8/Gl19+iREjRuC7777jWE1ERFRLfPTRR+Ljtm3b4uHDh7hz5w5sbW1hamoqYWZEREQNV6XWFA8NDeVJFyIionron1ezBwUFwdbWFj/++COWLVsmcXZERA2Pm5ub+NjX1xetWrWCrq4uWrVqBV9f3zLjqH66cuUKPvvsM1hZWWHVqlWYPXs27t+/j5CQEDx58gSDBw+WOkUiIiIqh7a2Ntq0aVMtBXF7e3vIZLJSPyWfFbt3716qb/LkyVWeBxERUW33RneKlyguLkZgYCBOnTqFp0+fQqFQKPWfPn26SpIjIiKimsWr2YmIape///5bfLxhwwZ07dpVvFP87NmzSnFcY7p+WrVqFQICAhAVFYX+/fvjl19+Qf/+/SGXv7jG3cHBAYGBgbC3t5c2USIiogbOz8+vwrGrVq2qste9fPkyiouLxe2bN2+iT58+GD58uNg2ceJELF68WNzW1taustcnIiKqKypVFJ8+fToCAwMxYMAAtGjRAjKZrKrzIiIiolqg5Gp2IiKSRsla4W3atMFff/2F4OBgsU8ul6N169ZcU7ye27RpE8aPH4+xY8eWOz26ubk5tm3bVsOZERER0ctKlrUpERERgaKiIjg7OwMA7t69CxUVFbRt27ZKX9fMzExpe/ny5XB0dES3bt3ENm1tbVhaWr7RfvPz85Gfny9uZ2Zmvl2iREREEqtUUXzPnj3Yu3cv+vfvX9X5EBERkYQEQcD+/fsRGhpa5mwwBw8elCgzIqKGqWTZKgsLC8yePRsRERFIS0uDkZER2rRpgxs3bijFUf1z796918aoq6vDx8enBrIhIiKi8oSGhoqPV61aBT09PWzfvl38nJaWloZx48ahS5cu1ZZDQUEBdu7cCT8/P6Ub2Xbt2oWdO3fC0tISAwcOxNdff/3au8X9/f2xaNGiasuViIioplWqKK6urg4nJ6eqzoWIiIgkNmPGDGzZsgU9evSAhYUFZ4MhIpJYmzZtcPr0aYSFhcHNzU1pinSFQoGzZ89CLpdzVo8GIDc3F3FxcSgoKFBq53ryREREtc/KlStx4sQJpQsXjYyMsGTJEvTt2xezZs2qltc9fPgw0tPTMXbsWLFt9OjRsLOzQ6NGjfD3339j7ty5iIqKeu1F7/PmzVOaEj4zMxM2NjbVkjcREVFNqFRRfNasWVi7di3Wr1/Pk+VERET1yI4dO3Dw4EHOBkNEVEuoqqqiXbt2CA8Px7p160qtKZ6bm4v27dtDVbVSX+2oDkhOTsbYsWOVps5/2ctriBIREVHtkJmZieTk5FLtycnJyMrKqrbX3bZtGzw9PdGoUSOxbdKkSeJjV1dXWFlZoVevXrh//z4cHR3L3ZeGhgY0NDSqLVciIqKaJq/Mk/7880/s2rULjo6OGDhwIIYOHar0Q0RERHWTgYEBmjRpInUaRET0kp49e6J9+/Z4/vw5goODsX79egQHB+P58+do3749evbsKXWKVI1mzJiBjIwMhIeHQ0tLC8HBwdi+fTuaNm2K33///Y32dfbsWQwcOBCNGjWCTCbD4cOHlfrHjh0LmUym9NOvXz+lmNTUVIwZMwb6+vowNDTEhAkTkJ2d/baHSUREVK988MEHGDduHA4ePIhHjx7h0aNHOHDgACZMmFBt588fPnyIkydP4pNPPnllXPv27QEA0dHR1ZIHERFRbVWp2wkMDQ3xwQcfVHUuREREJLGFCxdi0aJF+M9//gMtLS2p0yEiopcIgvDKbaqfTp8+jd9++w3vvfce5HI57Ozs0KdPH+jr68Pf3x8DBgyo8L5ycnLQsmVLjB8/vtwT8v369UNAQIC4/c87xMaMGYOEhASEhISgsLAQ48aNw6RJk7B79+7KHSAREVE9tHnzZsyePRujR49GYWEhgBczAE2YMAHff/99tbxmQEAAzM3NX/vZ4Nq1awAAKyurasmDiIiotqpUUfzlL8hERERUf4wYMQK//vorzM3NYW9vDzU1NaX+iIgIiTIjImq4Tp8+jfDwcGhra6Nbt27i9OlhYWEIDw8HAN4tXo/l5OTA3NwcwIu1SJOTk/HOO+/A1dX1jcdlT09PeHp6vjJGQ0MDlpaWZfbdvn0bwcHBuHz5Mt577z0AwLp169C/f3/88MMPSlO1ElHdl5rDWSCo9qrtv5/a2trYuHEjvv/+e9y/fx8A4OjoCB0dHaW4R48e4f+xd+9xVVX5/8ff53CXO4qgyM1LeNeyVNLKWxk5poEzqVlmTVajNnmZGitLu9lUVlZepqa0scjSMZsyrSQvU6KlZWYXDBVFAyQVEExuZ//+8Mf+egIUETjnwOv5ePCIvdY6+3zOSfiwz2evtVq3bi2rtVYLuppsNpuWLFmi8ePH222rs3fvXiUnJ+u6665T8+bNtWvXLk2dOlVXXnmlunfvfkHPCQCAq2HjOQAAYBo/frx27NihcePGKSwsTBaLxdEhAUCTVlZWpi+//FLNmjXTlClTzA9Me/bsqe7du+ull17SV199pSuvvJJ9xRupuLg4paWlKSYmRj169NA///lPxcTEaPHixfUyw2vjxo1q2bKlgoODNWjQID3++ONq3ry5JCk1NVVBQUFmQVyShgwZIqvVqm3btlW5olxxcbGKi4vN44KCgjqPGUDd8vHxkYe7u9b98K2jQwHOysPd3elXOPP19T1r8blz587auXPnBW9jtn79eh08eFC33XabXbunp6fWr1+vF154QUVFRYqMjFRSUpIeeuihC3o+AABcUa0+NYmNjT3rh+T79u2r0Xk2b96sZ555Rjt27FBWVpbee+89jRw50uw3DEOPPPKIXn31VeXl5alfv35atGiROnToYI45duyYpkyZog8++EBWq1VJSUmaP3++/Pz8avPSAABo0tasWaOPP/5Y/fv3d3QoAACdXqHDMAxdddVVkk7vFVlYWCg/Pz9FRkbqyiuv1Lp16/T111+rd+/eDo4W9eGvf/2rsrKyJEmPPPKIrr32Wr311lvy9PTU0qVL6/S5rr32WiUmJio2NlZ79+7VAw88oISEBKWmpsrNzU3Z2dnmrPUK7u7uCgkJUXZ2dpXnnDt3rubMmVOncQKoX4GBgbpj4kT99ttvjg6l0fj111/1wQcfaPjw4WrRooWjw2k0fHx8FBgY6OgwLkhdbYdzzTXXVHmuyMhIbdq0qU6eAwAAV1erovi9995rd1xaWqpvvvlG69at09/+9rcan+dc+5k9/fTTevHFF/XGG28oNjZWs2bN0tChQ/XDDz/I29tbEvuZAQBQlyIjIxUQEODoMAAA/9/x48clSRaLRYsXL1Z+fr7ZFxgYqMsvv9xuHBqfcePGmd/36tVLBw4c0E8//aSoqKg6L6yMHj3a/L5bt27q3r272rVrp40bN2rw4MG1OufMmTM1bdo087igoECRkZEXHCuA+hUYGOjyxUZn1KJFi2q3qAAAAED9qlVR/K9//WuV7QsWLND27dtrfJ6z7WdmGIZeeOEFPfTQQxoxYoQk6d///rfCwsK0evVqjR49mv3MgCbI2feMQtPWGP59zps3T/fdd58WL16smJgYR4cDAE1ecHCwJOmjjz5S+/btNWLECIWGhio3N1dbtmzR2rVr7cah8WvWrJkuueSSBnmutm3bqkWLFkpPT9fgwYMVHh6uI0eO2I0pKyvTsWPHqi3yeHl5ycvLqyHCBQAAAACgWnW66VxCQoJmzpypJUuWXPC59u/fr+zsbA0ZMsRsCwwMVJ8+fZSamqrRo0fXaj8ziT3NAFfGnmZA/Ro3bpxOnjypdu3aqVmzZvLw8LDrP3bsmIMiA4CmqWfPnkpJSZGbm5sSExPl5uYmSYqIiFBiYqLmzZun8vJy9ezZ07GBok6dObP6XJ577rl6i+PQoUM6evSouXd5fHy88vLytGPHDvXq1UuS9Nlnn8lms6lPnz71Fsf5OJnPqglwXvz7BAAAABynToviK1euVEhISJ2cq2I/srCwMLv2sLAws682+5lJDbunGRc8cGau+O/z2s49FOLr5+gwgCodKyp0+Rs3nn/+eVksFkeHAQD4/yr2ki4vL9fLL7+syMhIeXp6qqSkRJmZmSovLzfHRUdHOzJU1KFvvvnG7vjrr79WWVmZ4uLiJEl79uyRm5ubWZiuqcLCQqWnp5vH+/fv186dOxUSEqKQkBDNmTNHSUlJCg8P1969e3Xfffepffv2Gjp0qCSpU6dOuvbaa3XHHXdo8eLFKi0t1eTJkzV69GiHr9Tm6+srdw8P/bxlg0PjAM7F3cNDvr6+jg4DgJPg+hsAgIZTq6L4xRdfbJewDcNQdna2cnNztXDhwjoLrr405J5mXJADdSvE109h/uxrBtSXW2+91dEhAADOUFh4emuOsLAw5eTkKC0tza6/or1iHBqHDRv+7zryueeek7+/v9544w1zmfzjx49rwoQJuuKKK87rvNu3b9fAgQPN44rr4vHjx2vRokXatWuX3njjDeXl5al169a65ppr9Nhjj9ktf/7WW29p8uTJGjx4sKxWq5KSkvTiiy9eyMutE0FBQZo2daqKioocHUqjkZubq3fffVd/+tOfFBoa6uhwGg1fX18FBQU5OgwATsIwDEeHAABAk1GrovjIkSPtjq1Wq0JDQzVgwAB17NixLuIy9yPLyckxl2qrOK5YGrA2+5lJDbunWYfLB6pZIPv7wTmdzD/OjRsA7AwZMkTjxo1TYmKiAgICan2euXPnatWqVfrpp5/k4+Ojyy+/XP/4xz/MGW6SdOrUKU2fPl3Lly9XcXGxhg4dqoULF1ZaJQYAmjI/v9Mr5OTk5MjHx0fR0dHy8PBQaWmpDhw4oJycHLtxaHzmzZunTz75xG7f+ODgYD3++OO65pprNH369Bqfa8CAAWf98P3jjz8+5zlCQkKUnJxc4+dsSEFBQRQb60FoaKgiIiIcHQYANEo//PCDw1dbAQCgqahVUfyRRx6p6zgqiY2NVXh4uFJSUswieEFBgbZt26a7775bkmvsZ9YsMFh+IS0cHQYAADXSpUsXzZw5U3/5y180bNgwjRs3Ttddd12lvcXPZdOmTZo0aZIuu+wylZWV6YEHHtA111yjH374wVwucurUqVqzZo1WrFihwMBATZ48WYmJifriiy/q46UBgEuquEHYzc1NU6ZMMfcUl04vqV6xp/iZNxKjcSkoKFBubm6l9tzcXJ04ccIBEQEAgKokJibWeOyqVaskqd5WLwUAAJXVek/x8vJyrV69Wj/++KOk0x+iX3/99XYf0pzL2fYzi4qK0r333qvHH39cHTp0UGxsrGbNmqXWrVubM9WdeT8zAABc0fz58/X8889r/fr1Sk5O1i233CI3NzeNGjVKN910k6666qoanWfdunV2x0uXLlXLli21Y8cOXXnllcrPz9drr72m5ORkDRo0SJK0ZMkSderUSVu3blXfvn0rnbO4uFjFxcXmcUFBwQW80url5eWx9GodqijkVFXQQe2x9GrTsXPnTkmnr79WrVql+Ph4hYaGKjc3V6mpqeae4jt37lTv3r0dGCnqyw033KAJEyZo3rx55v/jbdu26W9/+9t5ffgOAADqV2Ag2/0BAODMalUUT09P13XXXafDhw+by6DOnTtXkZGRWrNmjdq1a1ej85xtP7OlS5fqvvvuU1FRkSZOnKi8vDz1799f69atk7e3t/kYZ93PDAAAV2W1WnXNNdfommuu0eLFi/XBBx/oiSee0GuvvWYWX85Xfn6+pNNLrkrSjh07VFpaqiFDhphjOnbsqKioKKWmplZZFJ87d67mzJlTq+evqby8PD33/PMqKy2t1+dpit59911Hh9CouHt4aNrUqRTGm4Djx49Lkq677jp9/vnnWrZsmdkXGBiohIQErV271hyHxmfx4sWaMWOGxo4dq9L/n5/c3d11++2365lnnnFwdAAAoMKSJUscHQIAADiLWhXF77nnHrVr105bt241P9w+evSoxo0bp3vuuUdr1qyp0XnOtZ+ZxWLRo48+qkcffbTaMc68nxkAAK4sOztby5cv15tvvqldu3bVegaizWbTvffeq379+qlr167muT09PSsV9MLCwpSdnV3leWbOnGneQCednile10vNFRUVqay0VB0uH6hmgcHnfgDgACfzj+vnLRtUVFREUbwJqNhHOisrSxaLpVJ/VlaW3Tg0Ps2aNdPChQv1zDPPaO/evZKkdu3amduRVDh06JBat24tq9XqiDABAAAAAHBqtSqKb9q0ya4gLknNmzfXU089pX79+tVZcAAAoGEVFBToP//5j5KTk7Vx40a1bdtWN910k955550arwTze5MmTdLu3bv1+eefX1BsXl5e8vLyuqBz1FSzwGD5hbRokOcCgLO55JJLlJKSom+++Ubt27fXiBEjzOXTt2zZop07d8piseiSSy5xdKioZ76+vurevXu1/Z07d9bOnTvVtm3bBowKAABUZ+XKlXr33Xd18OBBlZSU2PV9/fXXDooKAICmq1a3kHt5eenEiROV2gsLC+Xp6XnBQQEAAMcICwvTgw8+qK5duyo1NVVpaWl6+OGHa10Qnzx5sj788ENt2LBBbdq0MdvDw8NVUlKivLw8u/E5OTkKDw+/kJcAAI2K1Wo1bwg6fPiwjhw5ouLiYh05ckSHDx+WJHl6ejI7GGddhQ0AADSsF198URMmTFBYWJi++eYb9e7dW82bN9e+ffuUkJDg6PAAAGiSavXJyR/+8AdNnDhR27Ztk2EYMgxDW7du1V133aXrr7++rmMEAAAN5L///a8OHTqk559/Xpdeemm147744gsVFxdX228YhiZPnqz33ntPn332mWJjY+36e/XqJQ8PD6WkpJhtaWlpOnjwoOLj4y/8hQBAI5GZmani4mJ17txZv/32m9atW6eXX35Z69at02+//abOnTuruLhYmZmZjg4VAAAA/9/ChQv1yiuv6KWXXpKnp6fuu+8+ffrpp7rnnnuUn5/v6PAAAGiSalUUf/HFF9WuXTvFx8fL29tb3t7e6tevn9q3b6/58+fXdYwAAKCBXH311TWabZiQkGDOUKzKpEmT9Oabbyo5OVn+/v7Kzs5Wdna2fvvtN0lSYGCgbr/9dk2bNk0bNmzQjh07NGHCBMXHx6tv37519noAwNUVFhZKOr2v9O/3FLdYLGrWrJndOAAAADjewYMHdfnll0uSfHx8zFVXb775Zr399tuODA0AgCarVnuKBwUF6f3331d6erp+/PFHSVKnTp3Uvn37Og0OAAA4p3Mt0bpo0SJJ0oABA+zalyxZoltvvVWS9Pzzz8tqtSopKUnFxcUaOnSoFi5cWB/hAoDL8vPzkyRt375dzZo101VXXaX27dsrPT1dmzZt0vbt2+3GAQAAwPHCw8N17NgxRUdHKyoqSlu3blWPHj20f/9+tjwBAMBBalUUr9C+fXsK4QAAoJKaXOR7e3trwYIFWrBgQQNEBACuqVWrVpIkNzc3TZ48WW5ubpKknj17qlu3bpo3b57Ky8vNcWi6fr+SAAAAcJxBgwbpv//9ry6++GJNmDBBU6dO1cqVK7V9+3YlJiY6OjwAAJqkWhXFk5KS1Lt3b91///127U8//bS++uorrVixok6CAwAAAICmbOfOnZKk8vJyrVq1SvHx8QoNDVVubq5SU1NVXl5ujuvdu7cDI4WjMesMAADn8corr8hms0k6vb1Y8+bNtWXLFl1//fW68847HRwdAABNU62K4ps3b9bs2bMrtSckJGjevHkXGhMAAAAAQNLx48clnb7W2rJli5YtW2b2BQUFKSEhQWvXrjXHoen64Ycf1Lp1a0eHAQAAJB06dEiRkZHm8ejRozV69GgZhqHMzExFRUU5MDoAAJqmWhXFCwsL5enpWandw8NDBQUFFxwUAABwbizRCgANIzg42Pz+rrvuUmZmpgoLC+Xn56fIyEh9++23lcbB9Z3PsqqrVq2SJLsP3gEAgGPFxsYqKytLLVu2tGs/duyYYmNjzdV+AABAw7HW5kHdunXTO++8U6l9+fLl6ty58wUHBQAAGp5hGDp48KBOnTpVo7EAgPp3ySWXyGKxaNOmTZKk6OhodenSRdHR0ZJOr+JltVp1ySWXODJM1LHAwMAafwEAAOdjGEaVN5MXFhbK29vbAREBAIBazRSfNWuWEhMTtXfvXg0aNEiSlJKSorfffpv9xAEAcFGGYah9+/b6/vvv1aFDh7OOPXHiRANFBQBNm7u7u3r37q1t27bppZde0pVXXqn27dsrPT1dmzdv1smTJ9WnTx+5u9fq0g5OasmSJY4OAQAA1MK0adMknV5dbdasWWrWrJnZV15erm3btqlnz54Oig4AgKatVp+cDB8+XKtXr9aTTz6plStXysfHR927d9f69et11VVX1XWMAACgAVitVnXo0EFHjx49Z1EcANBwKm5E/vLLL7Vu3Tqz3WKxqE+fPmY/AAAAHOubb76RdPqm8++++85uC1JPT0/16NFDM2bMcFR4AAA0abWeTjBs2DANGzasLmMBAAAO9tRTT+lvf/ubFi1apK5duzo6HADA/xcRESF/f38VFBSYbf7+/oqIiHBgVGgoK1eu1LvvvquDBw+qpKTEru/rr792UFQAAOD3NmzYIEmaMGGC5s+fr4CAAAdHBAAAKtRqT/GvvvpK27Ztq9S+bds2bd++/YKDAgAAjnHLLbfoyy+/VI8ePeTj46OQkBC7LwBAw0tLS9OqVavUsmVL3XLLLZo+fbpuueUWtWzZUqtWrVJaWpqjQ0Q9evHFFzVhwgSFhYXpm2++Ue/evdW8eXPt27dPCQkJjg4PAABUYcmSJWZB/NChQzp06FC9Pdfs2bNlsVjsvjp27Gj2nzp1SpMmTVLz5s3l5+enpKQk5eTk1Fs8AAA4q1rNFJ80aZLuu+8+9enTx6798OHD+sc//lFlwRwA6sKxokJHhwBUqzH8+3zhhRccHQIA4Aw2m00pKSlq3769Ro0aJYvFIun0zPFRo0Zp5cqV+uyzz9ShQwdZrbW65xlObuHChXrllVc0ZswYLV26VPfdd5/atm2rhx9+WMeOHXN0eAAAoAo2m02PP/645s2bp8LC058V+Pv7a/r06XrwwQfr/O+2Ll26aP369eaxu/v/few/depUrVmzRitWrFBgYKAmT56sxMREffHFF3UaAwAAzq5WRfEffvhBl1xySaX2iy++WD/88MMFBwUAv+fj4yMPd3et++FbR4cCnJWHu7t8fHwcHUatjR8/3tEhAADOkJmZqfz8fI0YMUKGYejgwYMqLCyUn5+fIiMjFR8fr2XLlikzM1PR0dGODhf14ODBg7r88sslnf6b+MSJE5Kkm2++WX379tXLL7/syPAAAEAVHnzwQb322mt66qmn1K9fP0nS559/rtmzZ+vUqVN64okn6vT53N3dFR4eXqk9Pz9fr732mpKTkzVo0CBJp2exd+rUSVu3blXfvn3rNA4AAJxZrYriXl5eysnJUdu2be3as7Ky7O5CA4C6EhgYqDsmTtRvv/3m6FAajV9//VUffPCBhg8frhYtWjg6nEbDx8dHgYGBjg7jguzdu1dLlizR3r17NX/+fLVs2VJr165VVFSUunTp4ujwAKBJqZhZlJeXp/fff1/5+flmX2BgoK688kq7cWh8wsPDdezYMUVHRysqKkpbt25Vjx49tH//fhmG4ejwAABAFd544w3961//0vXXX2+2de/eXREREfrLX/5S50Xxn3/+Wa1bt5a3t7fi4+M1d+5cRUVFaceOHSotLdWQIUPMsR07dlRUVJRSU1PPWhQvLi5WcXGxeVxQUFCnMaPu2Ww2HT58WNLpVX1btWrFalIAcIZaVbCvueYazZw5U++//775wX9eXp4eeOABXX311XUaoKs7mX/c0SEA1XK1f5+BgYEuX2x0Ri1atKjybmI0TZs2bVJCQoL69eunzZs364knnlDLli317bff6rXXXtPKlSsdHSIANCl+fn6SpP/+979q3769RowYodDQUOXm5mrLli364IMP7Mah8Rk0aJD++9//6uKLL9aECRM0depUrVy5Utu3b1diYuJ5nWvz5s165plntGPHDmVlZem9997TyJEjzX7DMPTII4/o1VdfVV5envr166dFixapQ4cO5phjx45pypQp+uCDD2S1WpWUlKT58+fzbxAAgDMcO3bMbl/vCh07dqzz7U/69OmjpUuXKi4uTllZWZozZ46uuOIK7d69W9nZ2fL09FRQUJDdY8LCwpSdnX3W886dO1dz5syp01hRf3bv3q2PPvpIeXl5kqTVq1dr48aNuu6669S1a1fHBgcATqJWRfFnn31WV155paKjo3XxxRdLknbu3KmwsDAtW7asTgN0Vb6+vnL38NDPWzY4OhTgrNw9POTr6+voMAA4ib///e96/PHHNW3aNPn7+5vtgwYNYnlWAHCAiIgIWSwW+fj4KCkpyZzpERERoaSkJL300ks6deqUIiIiHBwp6ssrr7wim80mSZo0aZKaN2+uLVu26Prrr9edd955XucqKipSjx49dNttt1VZUH/66af14osv6o033lBsbKxmzZqloUOH6ocffpC3t7ck6aabblJWVpY+/fRTlZaWasKECZo4caKSk5Mv/MUCANBI9OjRQy+//LJefPFFu/aXX35ZPXr0qNPnSkhIML/v3r27+vTpo+joaL377rsXtL3bzJkzNW3aNPO4oKBAkZGRFxSrqyopKVFubq6jw6jW3r17tW7dOrm5udm1nzhxQsnJybr22mvVrl07B0V3dqGhofL09HR0GGhgpaWlOnr0qKPDOCebzaY9e/ZIkn766SfZbDanX32hefPm8vDwcHQYTqtWRfGIiAjt2rVLb731lr799lv5+PhowoQJGjNmDG/2/xcUFKRpU6eqqKjI0aE0Grm5uXr33Xf1pz/9SaGhoY4Op9Hw9fWtdLcogKbru+++q/JD7ZYtW+rXX391QEQA0LQdPnxYhmHo5MmT+s9//qP4+HhzpnhqaqpOnjxpjmNP8cbp0KFDdh9Ajx49WqNHj5ZhGMrMzFRUVFSNz5WQkGD3wfmZDMPQCy+8oIceekgjRoyQJP373/9WWFiYVq9erdGjR+vHH3/UunXr9NVXX+nSSy+VJL300ku67rrr9Oyzz6p169YX8EoBAGg8nn76aQ0bNkzr169XfHy8JCk1NVWZmZn66KOP6vW5g4KCdNFFFyk9PV1XX321SkpKlJeXZ/f5X05OzjlXDfTy8pKXl1e9xuoqcnNztWDBAkeHcU7l5eVVHq9bt84R4dTIpEmTuMG3CTp69KiWLFni6DDOS2pqqlJTUx0dxjlNmDCBVWHPotYbgPv6+qp///6KiopSSUmJJGnt2rWSZLdXSlMWFBREsbEehIaGkigBoJ4EBQUpKytLsbGxdu3ffPMNv3sBwAEq9gq//vrrtWnTJruVuYKCgjR8+HB98MEH7CneiMXGxiorK0stW7a0az927JhiY2MrffhZW/v371d2drbdnqOBgYHq06ePUlNTNXr0aKWmpiooKMgsiEvSkCFDZLVatW3bNt1www2Vzst+pACApig2NlZ79uzRggUL9NNPP0mSEhMT9Ze//EVlZWX1+tyFhYXau3evbr75ZvXq1UseHh5KSUlRUlKSJCktLU0HDx40i/U4t9DQUE2aNMnRYVTp0KFDev/99yVJnp6eioyMlIeHh0pLS5WZmWnWbkaMGKE2bdo4MtQqMfmtaWrevLkmTJjg6DCqlZGRoQ0bNigiIkIBAQEqLy+Xm5ubCgoKdPjwYQ0cOFAxMTGODrNKzZs3d3QITq1WRfF9+/bphhtu0HfffSeLxSLDMGSxWMz+urooBwAADWv06NG6//77tWLFClksFtlsNn3xxReaMWOGbrnlFkeHBwBNTsU+zUFBQbrrrruUmZmpwsJC+fn5KTIyUr/88ovdODQ+v7/erlBYWGguaV4XKvYVDQsLs2s/c8/R7OzsSsV5d3d3hYSEVLsvKfuRAgCaooqb2p544gm79qNHjyoyMrJOPz+fMWOGhg8frujoaP3yyy965JFH5ObmpjFjxigwMFC33367pk2bppCQEAUEBGjKlCmKj49X37596yyGxs7T09NpJwp89913kiQ3NzeVlZVp7969Zp/VapWbm5vKy8t1/Phx9enTx1FhAnY8PDycdjazzWbTqlWrFBQUpF9++UWHDx82+ywWi4KCgvTNN9+od+/eTr+UOiqrVVH8r3/9q2JjY5WSkqLY2Fht27ZNx44d0/Tp0/Xss8/WdYwAAKCBPPnkk5o0aZJ5kd65c2eVl5dr7NixeuihhxwdHgA0OZGRkQoMDNSWLVs0atQouyXSDcMwZ+421f0dG7OKPTwtFotmzZqlZs2amX3l5eXatm2bevbs6aDoao79SAEATZFhGFW21/VNbdLpmcJjxozR0aNHFRoaqv79+2vr1q3mDNznn39eVqtVSUlJKi4u1tChQ7Vw4cI6jQGOU1GwKy8vl5+fn66++mp17NhRP/30kz799FNzRakzC3sAqpeZman8/HxJUrNmzdS1a1cFBQUpLy9Pu3fvVl5enjmOLcxcT62K4qmpqfrss8/UokUL826j/v37a+7cubrnnnv0zTff1HWcAACgAXh6eurVV1/VrFmztHv3bhUWFuriiy9Whw4dHB0aADRJVqtVgwcP1qpVq7Ry5cpKe4qnp6crMTGRO9QboYrrasMw9N1338nT09Ps8/T0VI8ePTRjxow6e76KmRo5OTlq1aqV2Z6Tk2MW38PDw3XkyBG7x5WVlenYsWPVzvRgP1IAQFNy5k1tDz/8cIPc1LZ8+fKz9nt7e2vBggUusSc2zp+7++kSj9Vq1YwZM8y/GS+77DL16NFDjz76qGw2mzkOwNlVbPfk6ekpNzc3ffnll2afv7+/PD09VVJSwrZQLqpWvwnLy8vl7+8vSWrRooV++eUXxcXFKTo6WmlpaXUaIAAAaHhRUVHmLK6qlmwFADScuLg4JSYmKiUlpdKe4omJiYqLi3NgdKgvGzZskCRNmDBB8+fPV0BAQL0+X2xsrMLDw5WSkmJ+WF9QUKBt27bp7rvvliTFx8crLy9PO3bsUK9evSRJn332mWw2G8txAgCghr+pDaj4zMZms+ntt9/WwIEDFRYWppycHG3YsEE2m81uHICzq9iirKSkRFFRUbrhhhvMG9O3bNmi9PR0c1y3bt0cGSpqoVZF8a5du+rbb79VbGys+vTpo6efflqenp565ZVX1LZt27qOEQAANKDXXntNzz//vH7++WdJUocOHXTvvffqz3/+s4MjA4CmKy4uTh06dKi0pzgzxBu/JUuWmN8fOnRIktSmTZtanauwsND8EEeS9u/fr507dyokJERRUVG699579fjjj6tDhw6KjY3VrFmz1Lp1a40cOVKS1KlTJ1177bW64447tHjxYpWWlmry5MkaPXq0WrduXfsXCQBAI9HQN7UBZ/4bS09Pt5u06ObmVuU4ANWr2P7C29tbSUlJ5jV3RESEkpKSNH/+fJ06darabTLg3GpVFH/ooYdUVFQkSXr00Uf1hz/8QVdccYWaN2+ud955p04DBAAADefhhx/Wc889pylTpig+Pl7S6W1Tpk6dqoMHD+rRRx91cIQA0HRZrVb2LGuCbDabHn/8cc2bN8/cE9Lf31/Tp0/Xgw8+eF43Rmzfvl0DBw40jyuWeB0/fryWLl2q++67T0VFRZo4caLy8vLUv39/rVu3zm7v07feekuTJ0/W4MGDzf1JX3zxxTp6tQAANA5n3tQG1KcWLVqY35eXl9v1nXl85jgA1atYVeHUqVP6z3/+U2kLs1OnTtmNg2upVVF86NCh5vft27fXTz/9pGPHjik4OJh/CAAAuLBFixbp1Vdf1ZgxY8y266+/Xt27d9eUKVMoigMA0MAefPBBvfbaa3rqqafUr18/SdLnn3+u2bNn69SpU3riiSdqfK4BAwacdUaDxWLRo48+etZ8HxISouTk5Jq/AAAAANSbvn376qOPPqrROADn1rp1a3399dfy9PRUTk6O3RZmAQEB5p7irJTlmmpVFK9KSEhIXZ0KAAA4SGlpqS699NJK7b169VJZWZkDIgIAoGl744039K9//UvXX3+92da9e3dFREToL3/5y3kVxQEAANB4+fr6KjY21iza7d+/31zxF0DNVGw1UFJSInd3d1122WUKDg7W8ePH9f3336ukpMRuHFxLnRXFAQCA67v55pu1aNEiPffcc3btr7zyim666SYHRQUAQNN17NgxdezYsVJ7x44ddezYMQdEBAAAAGexdetWSadX9N23b592795t9lmtVrVv317p6enaunWr+vfv76gwAZcRGRmpwMBAWSwW5efn66uvvjL7LBaLgoKCzHFwPRTFAQBo4ir2E5VO/3H3r3/9S5988om5tNa2bdt08OBB3XLLLY4KEQCAJqtHjx56+eWXK+3b/fLLL6tHjx4OigoAAADOoOImyT/+8Y/y8fHR1q1bdezYMYWEhKhv3746efKknnrqKW6mBGrIarVq8ODBWrVqldq2bavg4GCVl5fLzc1Nx48f1759+5SYmCir1eroUFELFMUBAGjivvnmG7vjXr16SZL27t0rSWrRooVatGih77//vsFjAwCgqXv66ac1bNgwrV+/XvHx8ZKk1NRUZWZm1mj/SAAAADReFdva/vTTT+rVq5dat24tf39/+fv7y2q16qeffrIbB+Dc4uLilJiYqJSUFO3bt89sDwoKUmJiouLi4hwYHS4ERXEAAJq4DRs2ODoEAABQjdjYWO3Zs0cLFiwwP9RMTEzUX/7yF5WVlTk4OgAAADhS3759tW7dOq1du1YpKSkqKCgw+wICAlRSUiKr1WquBgigZuLi4tShQwdlZmaqsLBQfn5+ioyMZIa4i6MoDgAAAACAk4qNjVVWVpaeeOIJu/ajR48qMjJS5eXlDooMAAAAjubu7q64uDj9+OOPOnXqlF1fRYG8U6dOcnenFAScL6vVqujoaEeHgTrEb0IAAGA6deqUXnrpJW3YsEFHjhyRzWaz6//6668dFBkAAE2TYRhVthcWFsrb27uBowEAAIAzsdlsOnjw4FnHHDx4UDabjRmuAJo8iuIAAMB0++2365NPPtGoUaPUu3dvWSwWR4cEAECTNG3aNEmSxWLRww8/rGbNmpl95eXl2rZtm3r27Omg6AAAAOAM9u3bp6KiIknSRRddpObNm6usrEzu7u46evSo9uzZo6KiIu3bt0/t27d3cLQA4FhOXxSfPXu25syZY9cWFxdn7qV26tQpTZ8+XcuXL1dxcbGGDh2qhQsXKiwszBHhAgDg0j788EN99NFH6tevn6NDAQCgSfvmm28knZ4p/t1338nT09Ps8/T0VI8ePTRjxgxHhQcAAAAnsG/fPklSVFSUbrnlFrvZ4DabTf/85z+VmZlJURwA5AJFcUnq0qWL1q9fbx6fuf/F1KlTtWbNGq1YsUKBgYGaPHmyEhMT9cUXXzgiVAAAXFpERIT8/f0dHQYAAE3ehg0bJEkTJkzQ/PnzFRAQ4OCIAAAA4Gzy8vIkST169Ki0PLrValWPHj2UmZlpjgOApswlNpFwd3dXeHi4+dWiRQtJUn5+vl577TU999xzGjRokHr16qUlS5Zoy5Yt2rp1a7XnKy4uVkFBgd0XAACQ5s2bp/vvv18HDhy4oPNs3rxZw4cPV+vWrWWxWLR69Wq7fsMw9PDDD6tVq1by8fHRkCFD9PPPP1/QcwIA0BgtWbKEgjgAAACqFBgYKEnauXOnbDabXZ/NZtO3335rNw4AmjKXKIr//PPPat26tdq2baubbrpJBw8elCTt2LFDpaWlGjJkiDm2Y8eOioqKUmpqarXnmzt3rgIDA82vyMjIen8NAAC4gksvvVSnTp1S27Zt5e/vr5CQELuvmioqKlKPHj20YMGCKvuffvppvfjii1q8eLG2bdsmX19fDR06VKdOnaqrlwIAAAAAANCotWvXTpKUmZmpf//730pNTdX27duVmpqqf//738rMzLQbBwBNmdMvn96nTx8tXbpUcXFxysrK0pw5c3TFFVdo9+7dys7Olqenp4KCguweExYWpuzs7GrPOXPmTE2bNs08LigooDAOAICkMWPG6PDhw3ryyScVFhYmi8VSq/MkJCQoISGhyj7DMPTCCy/ooYce0ogRIyRJ//73vxUWFqbVq1dr9OjRVT6uuLhYxcXF5jErvQAAAAAAgKasbdu28vX1VVFRkfbs2aM9e/ZUGuPr66u2bds6IDoAcC5OXxQ/8wP17t27q0+fPoqOjta7774rHx+fWp3Ty8tLXl5edRUiAACNxpYtW5SamqoePXrU23Ps379f2dnZdiu9BAYGqk+fPkpNTa22KD537lzNmTOn3uICAAAAAABwJVarVZdccon+97//yWKxyDAMs6/i+JJLLqm03zgANEUu95swKChIF110kdLT0xUeHq6SkhLl5eXZjcnJyVF4eLhjAgQAwIV17NhRv/32W70+R8VqLmFhYXbtNVnpJT8/3/yqWAIMAAAAAACgKbLZbNq9e7ciIiIq7RseFBSkiIgI7d69u9J+4wDQFDn9TPHfKyws1N69e3XzzTerV69e8vDwUEpKipKSkiRJaWlpOnjwoOLj4x0cKQAAruepp57S9OnT9cQTT6hbt27y8PCw6w8ICHBQZKz0AgAAAAAAcKaMjAwdP35cN954o9q0aaOMjAydOHFC/v7+iomJ0aFDh7R48WJlZGSwhDqAJs/pi+IzZszQ8OHDFR0drV9++UWPPPKI3NzcNGbMGAUGBur222/XtGnTFBISooCAAE2ZMkXx8fHq27evo0MHAMDlXHvttZKkwYMH27UbhiGLxaLy8vILfo6K1VxycnLUqlUrsz0nJ0c9e/a84PMDAAAAAAA0BSdOnJB0evU9q9VaqfBdsUpfxTgAaMqcvih+6NAhjRkzRkePHlVoaKj69++vrVu3KjQ0VJL0/PPPy2q1KikpScXFxRo6dKgWLlzo4KgBAHBNGzZsqPfniI2NVXh4uFJSUswieEFBgbZt26a777673p8fAAAAAACgMfD395d0eqJBVFRUpf6cnBy7cQDQlDl9UXz58uVn7ff29taCBQu0YMGCBooIAIDG66qrrqqT8xQWFio9Pd083r9/v3bu3KmQkBBFRUXp3nvv1eOPP64OHTooNjZWs2bNUuvWrTVy5Mg6eX4AAAAAAIDGLiYmRsHBwdq4caPGjRsnq9Vq9tlsNm3cuFHBwcGKiYlxXJAA4CScvigOAAAazubNm8/af+WVV9boPNu3b9fAgQPN42nTpkmSxo8fr6VLl+q+++5TUVGRJk6cqLy8PPXv31/r1q2Tt7d37YMHAAAAAKCJmTt3rlatWqWffvpJPj4+uvzyy/WPf/xDcXFx5pgBAwZo06ZNdo+78847tXjx4oYOF3XMarUqISFBb7/9tt58800NGDBAYWFhysnJ0caNG5WWlqYxY8bYFcsBoKmiKA4AAEwDBgyo1GaxWMzva7qn+IABA2QYRrX9FotFjz76qB599NHzjhEAAAAAAJy2adMmTZo0SZdddpnKysr0wAMP6JprrtEPP/wgX19fc9wdd9xhdw3erFkzR4SLetC1a1eNGTNGa9eutbvRITg4WGPGjFHXrl0dGB0AOA+K4gAAwHT8+HG749LSUn3zzTeaNWuWnnjiCQdFBQA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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create subplots\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
        "\n",
        "# Countplot for 'international_plan'\n",
        "df_train.groupby(['international_plan', 'churn']).size().unstack().plot(kind='bar', ax=axes[0],color=['lightblue','pink'])\n",
        "axes[0].set_xlabel('International Plan')\n",
        "axes[0].set_ylabel('Count')\n",
        "axes[0].set_title('Churn by International Plan')\n",
        "\n",
        "# Countplot for 'voice_mail_plan'\n",
        "df_train.groupby(['voice_mail_plan', 'churn']).size().unstack().plot(kind='bar', ax=axes[1],color=['lightblue','pink'])\n",
        "axes[1].set_xlabel('Voice Mail Plan')\n",
        "axes[1].set_ylabel('Count')\n",
        "axes[1].set_title('Churn by Voice Mail Plan')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "_hhJzmPlJLVv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "0af0acc5-deef-4c06-b28e-82e9b65bd079"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# From the chart above, it can be concluded that when the international plan is activated,\n",
        "# the number for customer churn is higher\n",
        "# To validate this conclusion, let's calculate the proportion of customer that churned when\n",
        "# they activated the international plan\n",
        "international_plan_customers = df_train[df_train['international_plan'] ==1]\n",
        "churned_international_plan_customers = international_plan_customers[international_plan_customers['churn'] ==1]\n",
        "percentage_churned_international_plan_customers = len(churned_international_plan_customers) / len(international_plan_customers)*100\n",
        "print(\"Percentage of customers who churned when they activated the international plan: {:.3f}%\".format(percentage_churned_international_plan_customers))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JA7slrioB6Uu",
        "outputId": "fa4ca58b-e42f-44ad-c8d2-7a72f1bf64c8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of customers who churned when they activated the international plan: 42.172%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a column chart for churn percentage among states\n",
        "churn_state = df_train.groupby(['state'])['churn'].mean().round(decimals=3)\n",
        "churn_state_df = churn_state.reset_index()\n",
        "# Sort the DataFrame by churn rate in descending order\n",
        "churn_state_df_sorted = churn_state_df.sort_values(by='churn', ascending=False)\n",
        "top_3_states = churn_state_df_sorted['state'].tolist()[:3]\n",
        "print('Three states with the highest churn percentage among states are:', top_3_states)\n",
        "\n",
        "plt.figure(figsize=(15, 6))\n",
        "sns.barplot(data=churn_state_df_sorted, x='state', y='churn')\n",
        "# Add annotations for the top 3 highest churn rates\n",
        "for i, (index, row) in enumerate(churn_state_df_sorted.iloc[:3].iterrows()):\n",
        "  if row['state'] in top_3_states:\n",
        "    plt.text(i, row['churn'] + 0.01, f\"{row['churn']:.3f}%\", ha='center', va='top')\n",
        "\n",
        "# Set labels and title\n",
        "plt.xlabel('State')\n",
        "plt.ylabel('Churn Rate (%)')\n",
        "plt.title('Churn Percentage by State')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "VUUrP6MKGEk5",
        "outputId": "d3966f2b-abcf-449f-d9e4-d96dce1888ba"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Three states with the highest churn percentage among states are: ['NJ', 'CA', 'WA']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelling"
      ],
      "metadata": {
        "id": "TpSxvyi9VuBm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the numerical values\n",
        "cols_to_scale = []\n",
        "for col in df_train:\n",
        "  if col not in cols_to_drop:\n",
        "    cols_to_scale.append(col)\n",
        "\n",
        "# Scaled the numerical values\n",
        "scaler = StandardScaler()\n",
        "scaled_cols = scaler.fit_transform(df_train[cols_to_scale])\n",
        "df_train_scaled = pd.DataFrame(scaled_cols, columns=cols_to_scale)\n",
        "df_train_scaled[cols_to_drop]=df_train[cols_to_drop]\n",
        "\n",
        "# Remove 'state' and 'area_code' columns\n",
        "df_train_scaled.drop(columns=['state', 'area_code'], inplace=True)\n",
        "print(df_train_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSeKKFSJWKQW",
        "outputId": "0f22ac54-7751-46b0-eb4a-80a3e4998d88"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      account_length  number_vmail_messages  total_day_minutes  \\\n",
            "0           0.170399               1.366857          -0.345510   \n",
            "1           0.926186              -0.567911           1.169136   \n",
            "2          -0.409038              -0.567911           2.206058   \n",
            "3          -0.635774              -0.567911          -0.251076   \n",
            "4           0.523099               1.218029           0.702522   \n",
            "...              ...                    ...                ...   \n",
            "4245       -0.434231              -0.567911           0.148880   \n",
            "4246       -0.686160              -0.567911          -0.043691   \n",
            "4247       -0.635774              -0.567911          -0.177010   \n",
            "4248       -1.265596               2.408655           1.026560   \n",
            "4249       -0.358652               1.962170          -0.941739   \n",
            "\n",
            "      total_day_calls  total_eve_minutes  total_eve_calls  \\\n",
            "0            1.163449          -0.093025         0.141841   \n",
            "1            0.710014          -1.571820         0.493490   \n",
            "2           -1.456398          -2.752070        -0.611691   \n",
            "3            0.659633          -1.032448         1.096316   \n",
            "4           -0.599910           2.952139         0.393019   \n",
            "...               ...                ...              ...   \n",
            "4245        -1.506780           0.868291        -0.611691   \n",
            "4246        -0.549528          -1.372790        -0.913104   \n",
            "4247         0.055052          -0.140792         1.297257   \n",
            "4248         1.364976           0.454308         1.297257   \n",
            "4249         0.105434           1.332032         0.192077   \n",
            "\n",
            "      total_night_minutes  total_night_calls  total_intl_minutes  \\\n",
            "0                1.070003           0.157309            1.247901   \n",
            "1               -0.753320           0.207083            0.704379   \n",
            "2               -0.072057          -0.539526           -1.324770   \n",
            "3               -0.270676           1.053239           -0.056552   \n",
            "4                0.239775           0.903917           -0.998657   \n",
            "...                   ...                ...                 ...   \n",
            "4245             0.261623          -1.037264            0.015918   \n",
            "4246            -0.284579          -0.539526            0.450735   \n",
            "4247            -1.418694           0.207083           -1.216066   \n",
            "4248             1.926052           0.804369           -0.129022   \n",
            "4249            -0.908243           0.007987           -0.346430   \n",
            "\n",
            "      total_intl_calls  number_customer_service_calls  international_plan  \\\n",
            "0            -0.579164                      -0.426346                   0   \n",
            "1             0.232927                      -1.188960                   0   \n",
            "2             1.045017                       0.336268                   1   \n",
            "3            -0.579164                       1.098882                   1   \n",
            "4             1.045017                       1.098882                   0   \n",
            "...                ...                            ...                 ...   \n",
            "4245          0.638972                      -1.188960                   0   \n",
            "4246          0.638972                       1.098882                   0   \n",
            "4247          1.045017                      -0.426346                   0   \n",
            "4248          0.232927                       0.336268                   0   \n",
            "4249          4.699425                      -1.188960                   0   \n",
            "\n",
            "      voice_mail_plan  churn  \n",
            "0                   1      0  \n",
            "1                   0      0  \n",
            "2                   0      0  \n",
            "3                   0      0  \n",
            "4                   1      0  \n",
            "...               ...    ...  \n",
            "4245                0      0  \n",
            "4246                0      0  \n",
            "4247                0      0  \n",
            "4248                1      0  \n",
            "4249                1      0  \n",
            "\n",
            "[4250 rows x 14 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create x and y variable\n",
        "X = df_train_scaled.drop('churn', axis=1)\n",
        "y = df_train_scaled['churn']"
      ],
      "metadata": {
        "id": "UYxC34Zdb-2O"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split df_train_scaled to train and test randomly\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJsNffg-cijv",
        "outputId": "f8642abc-9eea-422e-ea9f-e22b86d3db3e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3400, 13) (850, 13) (3400,) (850,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize classifiers\n",
        "knn_classifier = KNeighborsClassifier()\n",
        "logistic_regression = LogisticRegression()\n",
        "random_forest = RandomForestClassifier()\n",
        "svm_classifier = SVC()\n",
        "decision_tree = DecisionTreeClassifier()\n",
        "xgboost_classifier = XGBClassifier()\n",
        "\n",
        "# Train the classifiers\n",
        "knn_classifier.fit(X_train, y_train)\n",
        "logistic_regression.fit(X_train, y_train)\n",
        "random_forest.fit(X_train, y_train)\n",
        "svm_classifier.fit(X_train, y_train)\n",
        "decision_tree.fit(X_train, y_train)\n",
        "xgboost_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "knn_predictions = knn_classifier.predict(X_test)\n",
        "logistic_regression_predictions = logistic_regression.predict(X_test)\n",
        "random_forest_predictions = random_forest.predict(X_test)\n",
        "svm_predictions = svm_classifier.predict(X_test)\n",
        "decision_tree_predictions = decision_tree.predict(X_test)\n",
        "xgboost_predictions = xgboost_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the models\n",
        "models_pred = {\n",
        "    'KNN': knn_predictions,\n",
        "    'Logistic Regression': logistic_regression_predictions,\n",
        "    'Random Forest': random_forest_predictions,\n",
        "    'SVM': svm_predictions,\n",
        "    'Decision Tree': decision_tree_predictions,\n",
        "    'XGBoost': xgboost_predictions\n",
        "}\n",
        "\n",
        "# Store the results in a list of dictionaries\n",
        "train_test_split_results = []\n",
        "\n",
        "for name, predictions in models_pred.items():\n",
        "    result = {'Model': name}\n",
        "    accuracy_train_test_split = accuracy_score(y_test, predictions)\n",
        "    result['TrainTestSplit Accuracy'] = accuracy_train_test_split\n",
        "    train_test_split_results.append(result)\n",
        "\n",
        "train_test_split_results_df = pd.DataFrame(train_test_split_results)\n",
        "print(train_test_split_results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3i6pYj2adHe0",
        "outputId": "26048b20-eeb0-4860-aa19-0afb459ad264"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Model  TrainTestSplit Accuracy\n",
            "0                  KNN                 0.876471\n",
            "1  Logistic Regression                 0.863529\n",
            "2        Random Forest                 0.951765\n",
            "3                  SVM                 0.907059\n",
            "4        Decision Tree                 0.910588\n",
            "5              XGBoost                 0.965882\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Leave-One-Out Cross Validation\n",
        "cv = LeaveOneOut()\n",
        "\n",
        "knn_model = KNeighborsClassifier()\n",
        "logistic_regression_model = LogisticRegression()\n",
        "random_forest_model = RandomForestClassifier()\n",
        "svm_model = SVC()\n",
        "decision_tree_model = DecisionTreeClassifier()\n",
        "xgboost_model = XGBClassifier()\n",
        "\n",
        "models = {\n",
        "    'KNN': knn_model,\n",
        "    'Logistic Regression': logistic_regression_model,\n",
        "    'Random Forest': random_forest_model,\n",
        "    'SVM': svm_model,\n",
        "    'Decision Tree': decision_tree_model,\n",
        "    'XGBoost': xgboost_model\n",
        "}\n",
        "\n",
        "loo_results = []\n",
        "\n",
        "# Evaluate each model\n",
        "for name, model in models.items():\n",
        "  scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=2)\n",
        "  mean_accuracy = np.mean(scores)\n",
        "  result = {'Model': name, 'LeaveOneOut Accuracy': mean_accuracy}\n",
        "  loo_results.append(result)\n",
        "\n",
        "loo_results_df = pd.DataFrame(loo_results)\n",
        "print(loo_results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-n96Q0rgbTP",
        "outputId": "9b78fe1d-d538-4174-fe15-5b5c9125344a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Model  LeaveOneOut Accuracy\n",
            "0                  KNN              0.887765\n",
            "1  Logistic Regression              0.865882\n",
            "2        Random Forest              0.956235\n",
            "3                  SVM              0.920000\n",
            "4        Decision Tree              0.916000\n",
            "5              XGBoost              0.959529\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# K-Fold Cross Validation\n",
        "n_splits_values = [3, 5, 10]\n",
        "\n",
        "cross_val_results = []\n",
        "\n",
        "# Iterate over each model\n",
        "for name, model in models.items():\n",
        "    # Store the mean accuracy for each n_splits\n",
        "    accuracies_n = []\n",
        "    # Iterate over each value of n_splits\n",
        "    for n_splits in n_splits_values:\n",
        "        kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
        "        scores = cross_val_score(model, X, y, scoring='accuracy', cv=kf, n_jobs=2)\n",
        "        mean_accuracy = np.mean(scores)\n",
        "        accuracies_n.append(mean_accuracy)\n",
        "    result = {'Model': name}\n",
        "    for i, n_splits in enumerate(n_splits_values):\n",
        "        result[f'cross_valn_splits_{n_splits}'] = accuracies_n[i]\n",
        "    cross_val_results.append(result)\n",
        "\n",
        "cross_val_results_df = pd.DataFrame(cross_val_results)\n",
        "print(cross_val_results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "is3LG06aAQsn",
        "outputId": "674afe36-cf45-40e3-9e5c-96171a74316a"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Model  cross_valn_splits_3  cross_valn_splits_5  \\\n",
            "0                  KNN             0.883062             0.883765   \n",
            "1  Logistic Regression             0.867532             0.864941   \n",
            "2        Random Forest             0.954352             0.955059   \n",
            "3                  SVM             0.911767             0.913412   \n",
            "4        Decision Tree             0.917884             0.917882   \n",
            "5              XGBoost             0.958353             0.961882   \n",
            "\n",
            "   cross_valn_splits_10  \n",
            "0              0.885412  \n",
            "1              0.865176  \n",
            "2              0.956000  \n",
            "3              0.915765  \n",
            "4              0.918353  \n",
            "5              0.960000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the number of splits for stratified k-fold cross-validation\n",
        "n_splits_values = [3, 5, 10]\n",
        "stratified_results = []\n",
        "# Iterate over each model\n",
        "for name, model in models.items():\n",
        "    # Store the mean accuracy for each n_splits\n",
        "    strat_accuracies_n = []\n",
        "    # Iterate over each value of n_splits\n",
        "    for n_splits in n_splits_values:\n",
        "        skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
        "        scores = cross_val_score(model, X, y, scoring='accuracy', cv=skf, n_jobs=-1)\n",
        "        mean_accuracy = np.mean(scores)\n",
        "        strat_accuracies_n.append(mean_accuracy)\n",
        "    result = {'Model': name}\n",
        "    for i, n_splits in enumerate(n_splits_values):\n",
        "        result[f'stratified_n_splits_{n_splits}'] = strat_accuracies_n[i]\n",
        "    stratified_results.append(result)\n",
        "\n",
        "stratified_results_df = pd.DataFrame(stratified_results)\n",
        "print(stratified_results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dO5VpUZ8X69U",
        "outputId": "63c17503-ee3b-49bd-ad4e-1877692ce0bb"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Model  stratified_n_splits_3  stratified_n_splits_5  \\\n",
            "0                  KNN               0.887293               0.885882   \n",
            "1  Logistic Regression               0.864942               0.866118   \n",
            "2        Random Forest               0.950116               0.953412   \n",
            "3                  SVM               0.912941               0.915529   \n",
            "4        Decision Tree               0.915059               0.913882   \n",
            "5              XGBoost               0.956941               0.957176   \n",
            "\n",
            "   stratified_n_splits_10  \n",
            "0                0.888706  \n",
            "1                0.865412  \n",
            "2                0.955765  \n",
            "3                0.915765  \n",
            "4                0.918588  \n",
            "5                0.956706  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenate the 'Model' columns separately\n",
        "model_column = pd.concat([train_test_split_results_df['Model'],\n",
        "                          loo_results_df['Model'],\n",
        "                          cross_val_results_df['Model'],\n",
        "                          stratified_results_df['Model']], axis=0, ignore_index=True)\n",
        "\n",
        "# Concatenate the rest of the dataframes\n",
        "combined_results_df = pd.concat([model_column,\n",
        "                                 train_test_split_results_df.drop(columns='Model'),\n",
        "                                 loo_results_df.drop(columns='Model'),\n",
        "                                 cross_val_results_df.drop(columns='Model'),\n",
        "                                 stratified_results_df.drop(columns='Model')],\n",
        "                                axis=1)\n",
        "combined_results_df = combined_results_df.iloc[:6,:]\n",
        "# Print the combined DataFrame\n",
        "print(combined_results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRVoH0ijwAwm",
        "outputId": "990d9a72-547f-4518-931a-3d198e99edc9"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Model  TrainTestSplit Accuracy  LeaveOneOut Accuracy  \\\n",
            "0                  KNN                 0.876471              0.887765   \n",
            "1  Logistic Regression                 0.863529              0.865882   \n",
            "2        Random Forest                 0.951765              0.956235   \n",
            "3                  SVM                 0.907059              0.920000   \n",
            "4        Decision Tree                 0.910588              0.916000   \n",
            "5              XGBoost                 0.965882              0.959529   \n",
            "\n",
            "   cross_valn_splits_3  cross_valn_splits_5  cross_valn_splits_10  \\\n",
            "0             0.883062             0.883765              0.885412   \n",
            "1             0.867532             0.864941              0.865176   \n",
            "2             0.954352             0.955059              0.956000   \n",
            "3             0.911767             0.913412              0.915765   \n",
            "4             0.917884             0.917882              0.918353   \n",
            "5             0.958353             0.961882              0.960000   \n",
            "\n",
            "   stratified_n_splits_3  stratified_n_splits_5  stratified_n_splits_10  \n",
            "0               0.887293               0.885882                0.888706  \n",
            "1               0.864942               0.866118                0.865412  \n",
            "2               0.950116               0.953412                0.955765  \n",
            "3               0.912941               0.915529                0.915765  \n",
            "4               0.915059               0.913882                0.918588  \n",
            "5               0.956941               0.957176                0.956706  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the best accuracy across several models and training methods\n",
        "numerical_columns = combined_results_df.select_dtypes(include=np.number)\n",
        "# Get the maximum accuracy score\n",
        "best_accuracy = numerical_columns.max().max()\n",
        "\n",
        "# Find the column (training method) with the maximum accuracy\n",
        "for col in combined_results_df.columns:\n",
        "    if combined_results_df[col].max() == best_accuracy:\n",
        "        best_training_method = col\n",
        "        break\n",
        "\n",
        "# Get the model corresponding to the maximum accuracy\n",
        "best_model_name = combined_results_df.loc[combined_results_df[best_training_method].idxmax(), 'Model']\n",
        "print('The best accuracy score is:', best_accuracy)\n",
        "print('It can be obtained using the following model:', best_model_name, \"with\", best_training_method)\n",
        "print('Confusion Matrix')\n",
        "print(confusion_matrix(y_test, xgboost_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h3ZRfDB0_SMZ",
        "outputId": "9e562ceb-83ab-416b-d441-535497785d5a"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best accuracy score is: 0.9658823529411765\n",
            "It can be obtained using the following model: XGBoost with TrainTestSplit Accuracy\n",
            "Confusion Matrix\n",
            "[[716   5]\n",
            " [ 24 105]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check on the feature importance by best model\n",
        "feature_importances = xgboost_classifier.feature_importances_\n",
        "feature_importances_df = pd.DataFrame({'features': list(X_train), 'feature_importances': feature_importances})\n",
        "feature_importances_df = feature_importances_df.sort_values('feature_importances', ascending=False)\n",
        "print(feature_importances_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jStyp_1NAfSb",
        "outputId": "0fc4aa9d-363a-4001-9e7b-6dc1d243bb3a"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         features  feature_importances\n",
            "11             international_plan             0.195019\n",
            "10  number_customer_service_calls             0.175613\n",
            "1           number_vmail_messages             0.171816\n",
            "2               total_day_minutes             0.108484\n",
            "9                total_intl_calls             0.079890\n",
            "4               total_eve_minutes             0.061149\n",
            "8              total_intl_minutes             0.059959\n",
            "6             total_night_minutes             0.032281\n",
            "7               total_night_calls             0.025961\n",
            "3                 total_day_calls             0.025405\n",
            "12                voice_mail_plan             0.025334\n",
            "5                 total_eve_calls             0.019808\n",
            "0                  account_length             0.019282\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementing the Model Chosen to Test Dataset"
      ],
      "metadata": {
        "id": "McCo1ddUMKLc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "replaced_columns_test = ['international_plan', 'voice_mail_plan']\n",
        "df_test[replaced_columns_test] = df_test[replaced_columns_test].replace({'yes':1,'no':0})\n",
        "print(df_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpxbjjjoIwe0",
        "outputId": "a4492bb1-7cbd-429a-9b42-580da618d97b"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      id state  account_length      area_code  international_plan  \\\n",
            "0      1    KS             128  area_code_415                   0   \n",
            "1      2    AL             118  area_code_510                   1   \n",
            "2      3    IA              62  area_code_415                   0   \n",
            "3      4    VT              93  area_code_510                   0   \n",
            "4      5    NE             174  area_code_415                   0   \n",
            "..   ...   ...             ...            ...                 ...   \n",
            "745  746    GA             130  area_code_415                   0   \n",
            "746  747    WA              73  area_code_408                   0   \n",
            "747  748    WV             152  area_code_415                   0   \n",
            "748  749    DC              61  area_code_415                   0   \n",
            "749  750    DC             109  area_code_510                   0   \n",
            "\n",
            "     voice_mail_plan  number_vmail_messages  total_day_minutes  \\\n",
            "0                  1                     25              265.1   \n",
            "1                  0                      0              223.4   \n",
            "2                  0                      0              120.7   \n",
            "3                  0                      0              190.7   \n",
            "4                  0                      0              124.3   \n",
            "..               ...                    ...                ...   \n",
            "745                0                      0              119.4   \n",
            "746                0                      0              177.2   \n",
            "747                0                      0              184.2   \n",
            "748                0                      0              140.6   \n",
            "749                0                      0              188.8   \n",
            "\n",
            "     total_day_calls  total_day_charge  total_eve_minutes  total_eve_calls  \\\n",
            "0                110             45.07              197.4               99   \n",
            "1                 98             37.98              220.6              101   \n",
            "2                 70             20.52              307.2               76   \n",
            "3                114             32.42              218.2              111   \n",
            "4                 76             21.13              277.1              112   \n",
            "..               ...               ...                ...              ...   \n",
            "745               99             20.30              226.3               97   \n",
            "746              118             30.12              270.5               84   \n",
            "747               90             31.31              256.8               73   \n",
            "748               89             23.90              172.8              128   \n",
            "749               67             32.10              171.7               92   \n",
            "\n",
            "     total_eve_charge  total_night_minutes  total_night_calls  \\\n",
            "0               16.78                244.7                 91   \n",
            "1               18.75                203.9                118   \n",
            "2               26.11                203.0                 99   \n",
            "3               18.55                129.6                121   \n",
            "4               23.55                250.7                115   \n",
            "..                ...                  ...                ...   \n",
            "745             19.24                202.7                111   \n",
            "746             22.99                241.8                112   \n",
            "747             21.83                213.6                113   \n",
            "748             14.69                212.4                 97   \n",
            "749             14.59                224.4                 89   \n",
            "\n",
            "     total_night_charge  total_intl_minutes  total_intl_calls  \\\n",
            "0                 11.01                10.0                 3   \n",
            "1                  9.18                 6.3                 6   \n",
            "2                  9.14                13.1                 6   \n",
            "3                  5.83                 8.1                 3   \n",
            "4                 11.28                15.5                 5   \n",
            "..                  ...                 ...               ...   \n",
            "745                9.12                11.3                 7   \n",
            "746               10.88                12.3                 2   \n",
            "747                9.61                14.7                 2   \n",
            "748                9.56                13.6                 4   \n",
            "749               10.10                 8.5                 6   \n",
            "\n",
            "     total_intl_charge  number_customer_service_calls  \n",
            "0                 2.70                              1  \n",
            "1                 1.70                              0  \n",
            "2                 3.54                              4  \n",
            "3                 2.19                              3  \n",
            "4                 4.19                              3  \n",
            "..                 ...                            ...  \n",
            "745               3.05                              0  \n",
            "746               3.32                              3  \n",
            "747               3.97                              3  \n",
            "748               3.67                              1  \n",
            "749               2.30                              0  \n",
            "\n",
            "[750 rows x 20 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols_to_drop_test = ['state','area_code','international_plan','voice_mail_plan']\n",
        "scaler_test = scaler.transform(df_test[cols_to_scale])\n",
        "df_test_scaled = pd.DataFrame(scaler_test, columns=cols_to_scale)\n",
        "df_test_scaled[cols_to_drop_test]=df_test[cols_to_drop_test]\n",
        "df_test_scaled.drop(columns=['state', 'area_code'], inplace=True)\n",
        "print(df_test_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SgT8b07EQ4L",
        "outputId": "b5522bf1-d4da-444c-a978-5825f9168bbb"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     account_length  number_vmail_messages  total_day_minutes  \\\n",
            "0          0.699450               1.292443           1.570943   \n",
            "1          0.447521              -0.567911           0.798807   \n",
            "2         -0.963281              -0.567911          -1.102833   \n",
            "3         -0.182302              -0.567911           0.193319   \n",
            "4          1.858323              -0.567911          -1.036173   \n",
            "..              ...                    ...                ...   \n",
            "745        0.749835              -0.567911          -1.126904   \n",
            "746       -0.686160              -0.567911          -0.056653   \n",
            "747        1.304079              -0.567911           0.072962   \n",
            "748       -0.988474              -0.567911          -0.734355   \n",
            "749        0.220785              -0.567911           0.158138   \n",
            "\n",
            "     total_day_calls  total_eve_minutes  total_eve_calls  total_night_minutes  \\\n",
            "0           0.508488          -0.055209        -0.059101             0.877343   \n",
            "1          -0.096093           0.406541         0.041370             0.066977   \n",
            "2          -1.506780           2.130144        -1.214517             0.049101   \n",
            "3           0.710014           0.358774         0.543725            -1.408763   \n",
            "4          -1.204490           1.531062         0.593961             0.996514   \n",
            "..               ...                ...              ...                  ...   \n",
            "745        -0.045711           0.519988        -0.159572             0.043142   \n",
            "746         0.911541           1.399702        -0.812633             0.819743   \n",
            "747        -0.499146           1.127031        -1.365223             0.259637   \n",
            "748        -0.549528          -0.544824         1.397728             0.235803   \n",
            "749        -1.657925          -0.566717        -0.410749             0.474146   \n",
            "\n",
            "     total_night_calls  total_intl_minutes  total_intl_calls  \\\n",
            "0            -0.439978           -0.092787         -0.579164   \n",
            "1             0.903917           -1.433475          0.638972   \n",
            "2            -0.041787            1.030492          0.638972   \n",
            "3             1.053239           -0.781248         -0.579164   \n",
            "4             0.754596            1.900128          0.232927   \n",
            "..                 ...                 ...               ...   \n",
            "745           0.555500            0.378266          1.045017   \n",
            "746           0.605274            0.740614         -0.985209   \n",
            "747           0.655048            1.610249         -0.985209   \n",
            "748          -0.141334            1.211667         -0.173119   \n",
            "749          -0.539526           -0.636309          0.638972   \n",
            "\n",
            "     number_customer_service_calls  international_plan  voice_mail_plan  \n",
            "0                        -0.426346                   0                1  \n",
            "1                        -1.188960                   1                0  \n",
            "2                         1.861496                   0                0  \n",
            "3                         1.098882                   0                0  \n",
            "4                         1.098882                   0                0  \n",
            "..                             ...                 ...              ...  \n",
            "745                      -1.188960                   0                0  \n",
            "746                       1.098882                   0                0  \n",
            "747                       1.098882                   0                0  \n",
            "748                      -0.426346                   0                0  \n",
            "749                      -1.188960                   0                0  \n",
            "\n",
            "[750 rows x 13 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained model to make predictions on the test dataset\n",
        "y_pred = xgboost_classifier.predict(df_test_scaled)\n",
        "churn_test = pd.DataFrame({'churn':y_pred})\n",
        "# Generate an array of IDs starting from 1\n",
        "ids = np.arange(1, len(churn_test) + 1)\n",
        "churn_test['id'] = ids\n",
        "# Reorder the columns\n",
        "churn_test = churn_test[['id', 'churn']]\n",
        "churn_test['churn'] = churn_test['churn'].replace({1:'yes',0:'no'})\n",
        "print(churn_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EsdvS_ODJPbJ",
        "outputId": "ead4b952-e3fb-4c29-a5a6-b9e27f26d19a"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      id churn\n",
            "0      1    no\n",
            "1      2    no\n",
            "2      3   yes\n",
            "3      4    no\n",
            "4      5    no\n",
            "..   ...   ...\n",
            "745  746    no\n",
            "746  747    no\n",
            "747  748    no\n",
            "748  749    no\n",
            "749  750    no\n",
            "\n",
            "[750 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export the churn_test DataFrame to an Excel file\n",
        "churn_test.to_csv('churn_predictions.csv', index=False)\n",
        "# Download the CSV file\n",
        "files.download('churn_predictions.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "GiiCcU-cN2Ss",
        "outputId": "6ffa536c-02f5-4597-9265-f47194e26d1c"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_0f09e788-57e1-46c5-9edb-7e719532292e\", \"churn_predictions.csv\", 5245)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}