project1_ML.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Buushra1dm/ML_Academy_BY_bamboogeeks/blob/main/project1_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-lBHHS3WCRZ"
      },
      "source": [
        "1- **Bushra Dajam**\n",
        "\n",
        "2- **Shahad hani**\n",
        "\n",
        "3- **Sahar Mal**\n",
        "\n",
        "4- **Rawaa Mal**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Tunisian Company of Electricity and Gas (STEG) is a public and a\n",
        "non-administrative company, it is responsible for delivering electricity and gas across\n",
        "Tunisia. The company suffered tremendous losses in the order of 200 million Tunisian\n",
        "Dinars due to fraudulent manipulations of meters by consumers.\n",
        "Using the client’s billing history, the aim of the challenge is to detect and recognize\n",
        "clients involved in fraudulent activities.\n",
        "The solution will enhance the company’s revenues and reduce the losses caused by\n",
        "such fraudulent activities.\n",
        "Data Problem:\n",
        "Building a model that will help classify which customer is likely to commit fraud and by\n",
        "that saving the company from making losses."
      ],
      "metadata": {
        "id": "c9RtVs-LWH-L"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "33Hx-92XLnHI"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "import lightgbm as lgb\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn import metrics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MvatLRJQLnHR"
      },
      "outputs": [],
      "source": [
        "#this function help to convert to date dat tybe\n",
        "def convert_to_date(df,col):\n",
        "    df[col] = pd.to_datetime(df[col])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OMBGdcx0LnHS"
      },
      "outputs": [],
      "source": [
        "def convert_t(df):\n",
        "  convert_to_date(df,'creation_date')\n",
        "  #df['client_catg'] = df['client_catg'].astype('object')\n",
        "  #df['disrict'] = df['disrict'].astype('object')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2H0biqv9jkzE"
      },
      "outputs": [],
      "source": [
        "def convert_invoice(df):\n",
        "  counter={\"ELEC\":0,\"GAZ\":1}\n",
        "  convert_to_date(df,'invoice_date')\n",
        "  df['counter_type']=df['counter_type'].replace(counter)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YTPCuRMXMjlS"
      },
      "outputs": [],
      "source": [
        "#The con_level dictionary specifies the columns to aggregate and the aggregation function to apply.\n",
        "#it calculates the mean for each of the columns.The invoice_train df is grouped by the client_id column, and con_level dictionary is applied to calculate the aggregations.\n",
        "#The column names of the resulting aggregated DataFrame are modified using a list comprehension. The format is '{col[0]}_{col[1]}', where col[0] represents the original column name and col[1] represents the aggregation function name.\n",
        "#The index of the aggregated DataFrame is reset, so that the client_id column becomes a regular column.\n",
        "#Another DataFrame df is created by grouping the invoice_train DataFrame by client_id and calculating the size of each group. The resulting column is named total_invoice.\n",
        "#The df DataFrame and the agg_invoice DataFrame are merged on the client_id column using a left join. This combines the total invoice count with the aggregated values for each client.\n",
        "#The final result is the agg_invoice DataFrame, which contains the total invoice count and the mean values for each of the specified columns, grouped by client_id.\n",
        "def calculate_agg_trans(invoice_train):\n",
        "    con_level = {\n",
        "        'consommation_level_1': ['mean'],\n",
        "        'consommation_level_2': ['mean'],\n",
        "        'consommation_level_3': ['mean'],\n",
        "        'consommation_level_4': ['mean']\n",
        "    }\n",
        "\n",
        "    agg_invoice = invoice_train.groupby('client_id').agg(con_level)\n",
        "    agg_invoice.columns = [f'{col[0]}_{col[1]}' for col in agg_invoice.columns]\n",
        "    agg_invoice.reset_index(inplace=True)\n",
        "\n",
        "    df = invoice_train.groupby('client_id').size().reset_index(name='total_invoice')\n",
        "\n",
        "    agg_invoice = pd.merge(df, agg_invoice, on='client_id', how='left')\n",
        "\n",
        "    return agg_invoice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "23dba06b-8b24-46d5-ca49-3c82b23a9dc5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-yy2NKBbLnHJ",
        "outputId": "478ea31f-156d-49ae-832b-ff72d5cf5483"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-57ed265c79c0>:3: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  invoice_train = pd.read_csv('/content/drive/MyDrive/train_set/invoice_train.csv')\n"
          ]
        }
      ],
      "source": [
        "#load data\n",
        "client_train = pd.read_csv('/content/drive/MyDrive/train_set/client_train.csv')\n",
        "invoice_train = pd.read_csv('/content/drive/MyDrive/train_set/invoice_train.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   disrict          client_id  client_catg  region creation_date  target\n",
              "0       60     train_Client_0           11     101    31/12/1994     0.0\n",
              "1       69     train_Client_1           11     107    29/05/2002     0.0\n",
              "2       62    train_Client_10           11     301    13/03/1986     0.0\n",
              "3       69   train_Client_100           11     105    11/07/1996     0.0\n",
              "4       62  train_Client_1000           11     303    14/10/2014     0.0"
            ],
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          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "client_train.head()"
      ]
    },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        client_id invoice_date  tarif_type  counter_number counter_statue  \\\n",
              "0  train_Client_0   2014-03-24          11         1335667              0   \n",
              "1  train_Client_0   2013-03-29          11         1335667              0   \n",
              "2  train_Client_0   2015-03-23          11         1335667              0   \n",
              "3  train_Client_0   2015-07-13          11         1335667              0   \n",
              "4  train_Client_0   2016-11-17          11         1335667              0   \n",
              "\n",
              "   counter_code  reading_remarque  counter_coefficient  consommation_level_1  \\\n",
              "0           203                 8                    1                    82   \n",
              "1           203                 6                    1                  1200   \n",
              "2           203                 8                    1                   123   \n",
              "3           207                 8                    1                   102   \n",
              "4           207                 9                    1                   572   \n",
              "\n",
              "   consommation_level_2  consommation_level_3  consommation_level_4  \\\n",
              "0                     0                     0                     0   \n",
              "1                   184                     0                     0   \n",
              "2                     0                     0                     0   \n",
              "3                     0                     0                     0   \n",
              "4                     0                     0                     0   \n",
              "\n",
              "   old_index  new_index  months_number counter_type  \n",
              "0      14302      14384              4         ELEC  \n",
              "1      12294      13678              4         ELEC  \n",
              "2      14624      14747              4         ELEC  \n",
              "3      14747      14849              4         ELEC  \n",
              "4      15066      15638             12         ELEC  "
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              "      <th></th>\n",
              "      <th>client_id</th>\n",
              "      <th>invoice_date</th>\n",
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              "      <th>counter_statue</th>\n",
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              "      <th>counter_coefficient</th>\n",
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              "      <th>consommation_level_3</th>\n",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14302</td>\n",
              "      <td>14384</td>\n",
              "      <td>4</td>\n",
              "      <td>ELEC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>train_Client_0</td>\n",
              "      <td>2013-03-29</td>\n",
              "      <td>11</td>\n",
              "      <td>1335667</td>\n",
              "      <td>0</td>\n",
              "      <td>203</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1200</td>\n",
              "      <td>184</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12294</td>\n",
              "      <td>13678</td>\n",
              "      <td>4</td>\n",
              "      <td>ELEC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>train_Client_0</td>\n",
              "      <td>2015-03-23</td>\n",
              "      <td>11</td>\n",
              "      <td>1335667</td>\n",
              "      <td>0</td>\n",
              "      <td>203</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14624</td>\n",
              "      <td>14747</td>\n",
              "      <td>4</td>\n",
              "      <td>ELEC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>train_Client_0</td>\n",
              "      <td>2015-07-13</td>\n",
              "      <td>11</td>\n",
              "      <td>1335667</td>\n",
              "      <td>0</td>\n",
              "      <td>207</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>102</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14747</td>\n",
              "      <td>14849</td>\n",
              "      <td>4</td>\n",
              "      <td>ELEC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>train_Client_0</td>\n",
              "      <td>2016-11-17</td>\n",
              "      <td>11</td>\n",
              "      <td>1335667</td>\n",
              "      <td>0</td>\n",
              "      <td>207</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>572</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15066</td>\n",
              "      <td>15638</td>\n",
              "      <td>12</td>\n",
              "      <td>ELEC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8762e70-9f2c-4a2b-b49f-b1ef7642d170')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-153e3032-088d-4350-b540-a8df22262b6a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-153e3032-088d-4350-b540-a8df22262b6a')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-153e3032-088d-4350-b540-a8df22262b6a button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8762e70-9f2c-4a2b-b49f-b1ef7642d170 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-e8762e70-9f2c-4a2b-b49f-b1ef7642d170');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "invoice_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6aWR0f8LnHK",
        "outputId": "f2c3a053-d74d-4e69-d6e5-5a216ce8bb37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['disrict', 'client_id', 'client_catg', 'region', 'creation_date',\n",
              "       'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "client_train.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBTbeh_aLnHK",
        "outputId": "8836715c-97a9-4489-ee48-2c4bc64ec039"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['client_id', 'invoice_date', 'tarif_type', 'counter_number',\n",
              "       'counter_statue', 'counter_code', 'reading_remarque',\n",
              "       'counter_coefficient', 'consommation_level_1', 'consommation_level_2',\n",
              "       'consommation_level_3', 'consommation_level_4', 'old_index',\n",
              "       'new_index', 'months_number', 'counter_type'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "invoice_train.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7O9e_n-LnHL",
        "outputId": "7173224f-1c72-4345-e884-54d020627585"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(135493, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "#n. of rows and columns\n",
        "client_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTubbbOaLnHL",
        "outputId": "70a4a150-89ca-40da-e192-c92636de390e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4476749, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "invoice_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "jagsSpHWLnHL",
        "outputId": "122e507e-cd5c-4ee5-8018-6996cd89fe65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             disrict    client_catg         region         target\n",
              "count  135493.000000  135493.000000  135493.000000  135493.000000\n",
              "mean       63.511222      11.512506     206.159809       0.055841\n",
              "std         3.354400       4.423761     104.207044       0.229614\n",
              "min        60.000000      11.000000     101.000000       0.000000\n",
              "25%        62.000000      11.000000     103.000000       0.000000\n",
              "50%        62.000000      11.000000     107.000000       0.000000\n",
              "75%        69.000000      11.000000     307.000000       0.000000\n",
              "max        69.000000      51.000000     399.000000       1.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-975e9dc2-651f-44d9-b092-798572afe8e5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>disrict</th>\n",
              "      <th>client_catg</th>\n",
              "      <th>region</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>135493.000000</td>\n",
              "      <td>135493.000000</td>\n",
              "      <td>135493.000000</td>\n",
              "      <td>135493.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>63.511222</td>\n",
              "      <td>11.512506</td>\n",
              "      <td>206.159809</td>\n",
              "      <td>0.055841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.354400</td>\n",
              "      <td>4.423761</td>\n",
              "      <td>104.207044</td>\n",
              "      <td>0.229614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>69.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>69.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>399.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-975e9dc2-651f-44d9-b092-798572afe8e5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-5c735dd6-b105-4a3e-9e2a-ea88e39bfd5a\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5c735dd6-b105-4a3e-9e2a-ea88e39bfd5a')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5c735dd6-b105-4a3e-9e2a-ea88e39bfd5a button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-975e9dc2-651f-44d9-b092-798572afe8e5 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-975e9dc2-651f-44d9-b092-798572afe8e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "client_train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "UUmvGQFYLnHM",
        "outputId": "33378b89-93ee-41f6-90cf-8dcbab13287f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         tarif_type  counter_number  counter_code  reading_remarque  \\\n",
              "count  4.476749e+06    4.476749e+06  4.476749e+06      4.476749e+06   \n",
              "mean   2.012804e+01    1.230587e+11  1.724884e+02      7.321702e+00   \n",
              "std    1.347256e+01    1.657267e+12  1.338871e+02      1.571654e+00   \n",
              "min    8.000000e+00    0.000000e+00  0.000000e+00      5.000000e+00   \n",
              "25%    1.100000e+01    1.211080e+05  5.000000e+00      6.000000e+00   \n",
              "50%    1.100000e+01    4.945610e+05  2.030000e+02      8.000000e+00   \n",
              "75%    4.000000e+01    1.115161e+06  2.070000e+02      9.000000e+00   \n",
              "max    4.500000e+01    2.798115e+13  6.000000e+02      4.130000e+02   \n",
              "\n",
              "       counter_coefficient  consommation_level_1  consommation_level_2  \\\n",
              "count         4.476749e+06          4.476749e+06          4.476749e+06   \n",
              "mean          1.003040e+00          4.109795e+02          1.093225e+02   \n",
              "std           3.083466e-01          7.573080e+02          1.220123e+03   \n",
              "min           0.000000e+00          0.000000e+00          0.000000e+00   \n",
              "25%           1.000000e+00          7.900000e+01          0.000000e+00   \n",
              "50%           1.000000e+00          2.740000e+02          0.000000e+00   \n",
              "75%           1.000000e+00          6.000000e+02          0.000000e+00   \n",
              "max           5.000000e+01          9.999100e+05          9.990730e+05   \n",
              "\n",
              "       consommation_level_3  consommation_level_4     old_index     new_index  \\\n",
              "count          4.476749e+06          4.476749e+06  4.476749e+06  4.476749e+06   \n",
              "mean           2.030620e+01          5.292588e+01  1.776700e+04  1.834970e+04   \n",
              "std            1.574239e+02          8.754725e+02  4.036693e+04  4.095321e+04   \n",
              "min            0.000000e+00          0.000000e+00  0.000000e+00  0.000000e+00   \n",
              "25%            0.000000e+00          0.000000e+00  1.791000e+03  2.056000e+03   \n",
              "50%            0.000000e+00          0.000000e+00  7.690000e+03  8.192000e+03   \n",
              "75%            0.000000e+00          0.000000e+00  2.166000e+04  2.234300e+04   \n",
              "max            6.449200e+04          5.479460e+05  2.800280e+06  2.870972e+06   \n",
              "\n",
              "       months_number  \n",
              "count   4.476749e+06  \n",
              "mean    4.483095e+01  \n",
              "std     3.128335e+03  \n",
              "min     0.000000e+00  \n",
              "25%     4.000000e+00  \n",
              "50%     4.000000e+00  \n",
              "75%     4.000000e+00  \n",
              "max     6.366240e+05  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-254e0170-804a-4be2-b8ab-84fa2b7095c4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tarif_type</th>\n",
              "      <th>counter_number</th>\n",
              "      <th>counter_code</th>\n",
              "      <th>reading_remarque</th>\n",
              "      <th>counter_coefficient</th>\n",
              "      <th>consommation_level_1</th>\n",
              "      <th>consommation_level_2</th>\n",
              "      <th>consommation_level_3</th>\n",
              "      <th>consommation_level_4</th>\n",
              "      <th>old_index</th>\n",
              "      <th>new_index</th>\n",
              "      <th>months_number</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.012804e+01</td>\n",
              "      <td>1.230587e+11</td>\n",
              "      <td>1.724884e+02</td>\n",
              "      <td>7.321702e+00</td>\n",
              "      <td>1.003040e+00</td>\n",
              "      <td>4.109795e+02</td>\n",
              "      <td>1.093225e+02</td>\n",
              "      <td>2.030620e+01</td>\n",
              "      <td>5.292588e+01</td>\n",
              "      <td>1.776700e+04</td>\n",
              "      <td>1.834970e+04</td>\n",
              "      <td>4.483095e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.347256e+01</td>\n",
              "      <td>1.657267e+12</td>\n",
              "      <td>1.338871e+02</td>\n",
              "      <td>1.571654e+00</td>\n",
              "      <td>3.083466e-01</td>\n",
              "      <td>7.573080e+02</td>\n",
              "      <td>1.220123e+03</td>\n",
              "      <td>1.574239e+02</td>\n",
              "      <td>8.754725e+02</td>\n",
              "      <td>4.036693e+04</td>\n",
              "      <td>4.095321e+04</td>\n",
              "      <td>3.128335e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.100000e+01</td>\n",
              "      <td>1.211080e+05</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>7.900000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.791000e+03</td>\n",
              "      <td>2.056000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.100000e+01</td>\n",
              "      <td>4.945610e+05</td>\n",
              "      <td>2.030000e+02</td>\n",
              "      <td>8.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.740000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>7.690000e+03</td>\n",
              "      <td>8.192000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000e+01</td>\n",
              "      <td>1.115161e+06</td>\n",
              "      <td>2.070000e+02</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.000000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.166000e+04</td>\n",
              "      <td>2.234300e+04</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.500000e+01</td>\n",
              "      <td>2.798115e+13</td>\n",
              "      <td>6.000000e+02</td>\n",
              "      <td>4.130000e+02</td>\n",
              "      <td>5.000000e+01</td>\n",
              "      <td>9.999100e+05</td>\n",
              "      <td>9.990730e+05</td>\n",
              "      <td>6.449200e+04</td>\n",
              "      <td>5.479460e+05</td>\n",
              "      <td>2.800280e+06</td>\n",
              "      <td>2.870972e+06</td>\n",
              "      <td>6.366240e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-254e0170-804a-4be2-b8ab-84fa2b7095c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-7fd58270-d551-44af-b149-6127fe763035\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7fd58270-d551-44af-b149-6127fe763035')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-7fd58270-d551-44af-b149-6127fe763035 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-254e0170-804a-4be2-b8ab-84fa2b7095c4 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-254e0170-804a-4be2-b8ab-84fa2b7095c4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "invoice_train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wUJvDFALnHM",
        "outputId": "6de3167d-71e0-4db2-c711-0cb35fb1c301"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    127927\n",
              "1.0      7566\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "client_train['target'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aakEtnIdRlLr"
      },
      "source": [
        "** it seems not balneced data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "hCz5Sj2RLnHN",
        "outputId": "395ca56c-64e6-4639-d27f-eeac8bb28665"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "client_train.target.value_counts().plot(kind ='bar')\n",
        "\n",
        "plt.title('Number of target label')\n",
        "\n",
        "#show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFh7kI3J7NZY"
      },
      "source": [
        "0 ---> not fruad\n",
        "\n",
        "\n",
        "1 ---> fruad\n",
        "\n",
        "\n",
        "\n",
        "data not balnced"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmgJAG_dLnHN",
        "outputId": "8606d76f-1c72-4829-fe6b-784e2ab2d65d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disrict            int64\n",
              "client_id         object\n",
              "client_catg        int64\n",
              "region             int64\n",
              "creation_date     object\n",
              "target           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "client_train.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSHrM-pHLnHN",
        "outputId": "46e663ab-d872-4ab7-b960-fb112a5915a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "client_id               object\n",
              "invoice_date            object\n",
              "tarif_type               int64\n",
              "counter_number           int64\n",
              "counter_statue          object\n",
              "counter_code             int64\n",
              "reading_remarque         int64\n",
              "counter_coefficient      int64\n",
              "consommation_level_1     int64\n",
              "consommation_level_2     int64\n",
              "consommation_level_3     int64\n",
              "consommation_level_4     int64\n",
              "old_index                int64\n",
              "new_index                int64\n",
              "months_number            int64\n",
              "counter_type            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "invoice_train.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rU9h_nF3LnHN",
        "outputId": "bc47abf1-7f22-47d5-f6c9-0ba633401913"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 135493 entries, 0 to 135492\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   disrict        135493 non-null  int64  \n",
            " 1   client_id      135493 non-null  object \n",
            " 2   client_catg    135493 non-null  int64  \n",
            " 3   region         135493 non-null  int64  \n",
            " 4   creation_date  135493 non-null  object \n",
            " 5   target         135493 non-null  float64\n",
            "dtypes: float64(1), int64(3), object(2)\n",
            "memory usage: 6.2+ MB\n"
          ]
        }
      ],
      "source": [
        "client_train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0M-WP2lLnHN",
        "outputId": "a5a69e64-9462-4fc9-b36d-af0788b03adb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4476749 entries, 0 to 4476748\n",
            "Data columns (total 16 columns):\n",
            " #   Column                Dtype \n",
            "---  ------                ----- \n",
            " 0   client_id             object\n",
            " 1   invoice_date          object\n",
            " 2   tarif_type            int64 \n",
            " 3   counter_number        int64 \n",
            " 4   counter_statue        object\n",
            " 5   counter_code          int64 \n",
            " 6   reading_remarque      int64 \n",
            " 7   counter_coefficient   int64 \n",
            " 8   consommation_level_1  int64 \n",
            " 9   consommation_level_2  int64 \n",
            " 10  consommation_level_3  int64 \n",
            " 11  consommation_level_4  int64 \n",
            " 12  old_index             int64 \n",
            " 13  new_index             int64 \n",
            " 14  months_number         int64 \n",
            " 15  counter_type          object\n",
            "dtypes: int64(12), object(4)\n",
            "memory usage: 546.5+ MB\n"
          ]
        }
      ],
      "source": [
        "invoice_train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WvxiJ7JiLnHO",
        "outputId": "e29f8835-dcea-48e0-e651-3ad54ab3b523"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disrict          0\n",
              "client_id        0\n",
              "client_catg      0\n",
              "region           0\n",
              "creation_date    0\n",
              "target           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "client_train.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmTTlVzYLnHO",
        "outputId": "60d527f9-59ba-4079-85bd-5b285dc61aab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "client_id               0\n",
              "invoice_date            0\n",
              "tarif_type              0\n",
              "counter_number          0\n",
              "counter_statue          0\n",
              "counter_code            0\n",
              "reading_remarque        0\n",
              "counter_coefficient     0\n",
              "consommation_level_1    0\n",
              "consommation_level_2    0\n",
              "consommation_level_3    0\n",
              "consommation_level_4    0\n",
              "old_index               0\n",
              "new_index               0\n",
              "months_number           0\n",
              "counter_type            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "invoice_train.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vo6CJoNYR1tc"
      },
      "source": [
        "there is non null values or missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y7GgZvGDLnHP",
        "outputId": "7cd2a487-8831-42df-fcf3-f8f201f45823"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "client_train.target.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTErPWCiLnHP",
        "outputId": "ec54e4ef-abca-4ffa-e8bb-0966d057d16b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Frequency of Categories for varible disrict\n",
            "62    40353\n",
            "69    34231\n",
            "60    31922\n",
            "63    28987\n",
            "Name: disrict, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible client_catg\n",
            "11    131494\n",
            "12      2321\n",
            "51      1678\n",
            "Name: client_catg, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible region\n",
            "101    33770\n",
            "104    12865\n",
            "311    12406\n",
            "107     9998\n",
            "301     9544\n",
            "103     8964\n",
            "306     6044\n",
            "303     5982\n",
            "310     4851\n",
            "312     4379\n",
            "302     4311\n",
            "304     3828\n",
            "309     3669\n",
            "307     3376\n",
            "305     2716\n",
            "313     2076\n",
            "371     2065\n",
            "105     1645\n",
            "308      841\n",
            "106      706\n",
            "372      644\n",
            "379      583\n",
            "399      175\n",
            "206       53\n",
            "199        2\n",
            "Name: region, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible creation_date\n",
            "20/11/1997    214\n",
            "31/12/2004    207\n",
            "28/05/2002    191\n",
            "20/11/1996    188\n",
            "31/12/1996    186\n",
            "             ... \n",
            "27/12/2001      1\n",
            "21/01/1985      1\n",
            "15/02/1993      1\n",
            "21/04/1995      1\n",
            "25/05/1978      1\n",
            "Name: creation_date, Length: 8088, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible target\n",
            "0.0    127927\n",
            "1.0      7566\n",
            "Name: target, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "for col in client_train:\n",
        "  if col == 'client_id':\n",
        "    continue\n",
        "  print ('\\nFrequency of Categories for varible %s'%col)\n",
        "  print (client_train[col].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxMFXgaOLnHP"
      },
      "source": [
        "101,104.311 is  most region\n",
        "\n",
        "62 is most disrict\n",
        "\n",
        "client_catg [11,12,51]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "_bwl4j5lLnHQ",
        "outputId": "195541fa-7128-493f-a863-71f34d67cff7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'disrict'}>,\n",
              "        <Axes: title={'center': 'client_catg'}>],\n",
              "       [<Axes: title={'center': 'region'}>,\n",
              "        <Axes: title={'center': 'target'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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s2DEcOXKkRF92djaUSiU8PT0t2n19fZGdnS3FFC98zP3mvvJi8vLycO/ePdy8eRNFRUWlxpw7d67M3BMTE0u9Y29aWhrc3NzKXA4AdDpduf11aX63km1zQ03VWtd3331Xw2yqx5bn92H2lCtQN/nevXu31rdJRFRZVSp+fvvtN0yePBk6nc4u72o7Y8YMxMXFSY/NX5IUERFR7vf86HQ6DBgwwGa/S6F9wv/OhVI5CMwNNeHdow4wmKr+sdepBK01U6uQPcyvmT3lCtRtvuajqkREtqhKxU9GRgauXr2Krl27Sm1FRUXYu3cvli1bhh07dqCgoAC3bt2yOPqTk5MDPz8/AICfn1+Jq7LMV4MVj3n4CrGcnByo1Wq4urrC0dERjo6OpcaY11EalUoFlUpVot3Z2bnCPw6ViakrpZ3bYzApqnXOT12N0Zbn92H2lCtQN/na0/wQkfxU6Wqv/v374+TJk8jMzJR+QkNDMXr0aOl3Z2dnpKenS8ucP38ely5dgkajAQBoNBqcPHnS4qosnU4HtVqN4OBgKab4Oswx5nUolUqEhIRYxJhMJqSnp0sxRERERKWp0pGf+vXro3379hZt7u7uaNCggdQ+btw4xMXFwdvbG2q1Gm+88QY0Gg26d+8OAIiIiEBwcDDGjBmD+fPnIzs7GzNnzkR0dLR0VGbixIlYtmwZpk2bhldffRU7d+7E119/je3b/3dFRFxcHKKiohAaGopu3bph8eLFyM/Pl67+IiIiIiqN1e/ttWjRIjg4OGDYsGEwGAzQarX49NNPpX5HR0ds27YNkyZNgkajgbu7O6KiojBnzhwpJigoCNu3b8eUKVOwZMkSNGnSBCtXrpQucweAESNG4Nq1a4iPj0d2djY6d+6M1NTUEidBExERERVX4+Jn9+7dFo9dXFyQlJSEpKSkMpcJDAys8KqiPn364Pjx4+XGxMTEICYmptK5EhEREfHeXkRERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJilNdJ0BEj16z6duttq6L8yKtti4iorrAIz9EREQkKyx+iIiISFZY/BAREZGssPghIiIiWWHxQ0RERLLC4oeIiIhkhcUPERERyQq/56eS2ifsgKFIYZV18XtSiIiI6g6P/BAREZGssPghIiIiWWHxQ0S1Yu/evXjuuefg7+8PhUKBzZs3W/QLIRAfH4/GjRvD1dUV4eHhuHDhgkXMjRs3MHr0aKjVanh6emLcuHG4c+eORcyJEyfQs2dPuLi4ICAgAPPnzy+Ry8aNG9GmTRu4uLigQ4cO+O6776qcCxHZryoVP8uXL0fHjh2hVquhVquh0Wjw/fffS/33799HdHQ0GjRogHr16mHYsGHIycmxWMelS5cQGRkJNzc3+Pj4YOrUqSgsLLSI2b17N7p27QqVSoWWLVsiJSWlRC5JSUlo1qwZXFxcEBYWhsOHD1dlKERUy/Lz89GpUyckJSWV2j9//nwsXboUycnJOHToENzd3aHVanH//n0pZvTo0Th9+jR0Oh22bduGvXv3YsKECVJ/Xl4eIiIiEBgYiIyMDHz00UdISEjA559/LsUcOHAAo0aNwrhx43D8+HEMGTIEQ4YMwalTp6qUCxHZryoVP02aNMG8efOQkZGBo0ePol+/fhg8eDBOnz4NAJgyZQq2bt2KjRs3Ys+ePbh8+TKGDh0qLV9UVITIyEgUFBTgwIEDWLt2LVJSUhAfHy/FZGVlITIyEn379kVmZiZiY2Px2muvYceOHVLMV199hbi4OMyaNQvHjh1Dp06doNVqcfXq1ZrOBxE9IoMGDcJ7772HF154oUSfEAKLFy/GzJkzMXjwYHTs2BFffPEFLl++LB0hOnv2LFJTU7Fy5UqEhYWhR48e+OSTT7BhwwZcvnwZALBu3ToUFBRg9erVaNeuHUaOHIk333wTCxculLa1ZMkSDBw4EFOnTkXbtm0xd+5cdO3aFcuWLat0LkRk36p0tddzzz1n8fj999/H8uXLcfDgQTRp0gSrVq3C+vXr0a9fPwDAmjVr0LZtWxw8eBDdu3dHWloazpw5gx9++AG+vr7o3Lkz5s6di7fffhsJCQlQKpVITk5GUFAQFixYAABo27Yt9u/fj0WLFkGr1QIAFi5ciPHjx2Ps2LEAgOTkZGzfvh2rV6/G9OnTazwpRFS7srKykJ2djfDwcKnNw8MDYWFh0Ov1GDlyJPR6PTw9PREaGirFhIeHw8HBAYcOHcILL7wAvV6PXr16QalUSjFarRYffvghbt68CS8vL+j1esTFxVlsX6vVSoVNZXIpjcFggMFgkB7n5eUBAIxGI4xGo/R78X/NVI6i0nNVkYfXbW1ljcGecAy2oSZjqOm4q32pe1FRETZu3Ij8/HxoNBpkZGTAaDRa7DDatGmDpk2bQq/Xo3v37tDr9ejQoQN8fX2lGK1Wi0mTJuH06dPo0qUL9Hq9xTrMMbGxsQCAgoICZGRkYMaMGVK/g4MDwsPDodfry825Mjunh5nbVQ62u3MqvuM051ndfGv7jWRPb2B7yhWwzLe2/7hWdY6ys7MBwGLfYH5s7svOzoaPj49Fv5OTE7y9vS1igoKCSqzD3Ofl5YXs7OwKt1NRLqVJTEzE7NmzS7SnpaXBzc3Nok2n01k8nt+tzNVW2cPnLz0qD4/BHnEMtqE6Y7h7926Ntlnl4ufkyZPQaDS4f/8+6tWrh02bNiE4OBiZmZlQKpXw9PS0iH94p1LaDsXcV15MXl4e7t27h5s3b6KoqKjUmHPnzpWbe1V2Tg+bG2oqt78qrL1zKm3HWd18a2vH+TB7egPbU67Ag3xr+49rTXdM9mjGjBkWR5Ty8vIQEBCAiIgIqNVqAA+KQp1OhwEDBsDZ2VmKbZ+wo8T6qutUgtZq6ypNWWOwJxyDbajJGMwHL6qrysVP69atkZmZidzcXHzzzTeIiorCnj17apREbanMzulh5ifn3aMOMJis8yWH1t45Fd9xqhwE5oaaqp3vo95xPsye3sD2lCtgmW+X93dabb2VeY1Udcfk5+cHAMjJyUHjxo2l9pycHHTu3FmKefi8vsLCQty4cUNa3s/Pr8RFFubHFcUU768ol9KoVCqoVKoS7c7OziVeLw+3WesLVM3rrg2ljcvecAy2oTpjqOmYq1z8KJVKtGzZEgAQEhKCI0eOYMmSJRgxYgQKCgpw69Yti6M/D+9UHr4qq7I7JrVaDVdXVzg6OsLR0bHcnVdZqrJzepjBpLDaDsraL9TS8qpuvnX1JrKnN7A95Qo8yLe2/7hWdX6CgoLg5+eH9PR0qcDIy8vDoUOHMGnSJACARqPBrVu3kJGRgZCQEADAzp07YTKZEBYWJsX8/e9/h9FolHLQ6XRo3bo1vLy8pJj09HTpo3RzjEajqXQuRGTfanx7C5PJBIPBgJCQEDg7OyM9PR3Dhg0DAJw/fx6XLl2SdioajQbvv/8+rl69Kn12r9PpoFarERwcLMU8fFi9+I5JqVQiJCQE6enpGDJkiJRDeno6YmJiajoc2Ws2fbtV18dbeZDZnTt38NNPP0mPs7KykJmZCW9vbzRt2hSxsbF477330KpVKwQFBeHdd9+Fv7+/9D5v27YtBg4ciPHjxyM5ORlGoxExMTEYOXIk/P39AQAvvfQSZs+ejXHjxuHtt9/GqVOnsGTJEixatEja7uTJk9G7d28sWLAAkZGR2LBhA44ePSpdDq9QKCrMhYjsW5WKnxkzZmDQoEFo2rQpbt++jfXr12P37t3YsWMHPDw8MG7cOMTFxcHb2xtqtRpvvPEGNBoNunfvDgCIiIhAcHAwxowZg/nz5yM7OxszZ85EdHS0dERm4sSJWLZsGaZNm4ZXX30VO3fuxNdff43t2//3RzkuLg5RUVEIDQ1Ft27dsHjxYuTn50tXfxGR7Tl69Cj69u0rPTZ/BB0VFYWUlBRMmzYN+fn5mDBhAm7duoUePXogNTUVLi4u0jLr1q1DTEwM+vfvDwcHBwwbNgxLly6V+j08PJCWlobo6GiEhISgYcOGiI+Pt/guoKeffhrr16/HzJkz8c4776BVq1bYvHkz2rdvL8VUJhcisl9VKn6uXr2Kl19+GVeuXIGHhwc6duyIHTt2YMCAAQCARYsWSTskg8EArVaLTz/9VFre0dER27Ztw6RJk6DRaODu7o6oqCjMmTNHigkKCsL27dsxZcoULFmyBE2aNMHKlSuly9wBYMSIEbh27Rri4+ORnZ2Nzp07IzU1tcRJ0ERkO/r06QMhyr7qTKFQYM6cORb7g4d5e3tj/fr15W6nY8eO2LdvX7kxw4cPx/Dhw2uUCxHZryoVP6tWrSq338XFBUlJSWV+gysABAYGVni1SJ8+fXD8+PFyY2JiYvgxFxEREVUZ7+1FREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJSpWKn8TERDz11FOoX78+fHx8MGTIEJw/f94i5v79+4iOjkaDBg1Qr149DBs2DDk5ORYxly5dQmRkJNzc3ODj44OpU6eisLDQImb37t3o2rUrVCoVWrZsiZSUlBL5JCUloVmzZnBxcUFYWBgOHz5cleEQERGRDFWp+NmzZw+io6Nx8OBB6HQ6GI1GREREID8/X4qZMmUKtm7dio0bN2LPnj24fPkyhg4dKvUXFRUhMjISBQUFOHDgANauXYuUlBTEx8dLMVlZWYiMjETfvn2RmZmJ2NhYvPbaa9ixY4cU89VXXyEuLg6zZs3CsWPH0KlTJ2i1Wly9erUm80FERESPOaeqBKemplo8TklJgY+PDzIyMtCrVy/k5uZi1apVWL9+Pfr16wcAWLNmDdq2bYuDBw+ie/fuSEtLw5kzZ/DDDz/A19cXnTt3xty5c/H2228jISEBSqUSycnJCAoKwoIFCwAAbdu2xf79+7Fo0SJotVoAwMKFCzF+/HiMHTsWAJCcnIzt27dj9erVmD59eo0nhoiIiB5PVSp+HpabmwsA8Pb2BgBkZGTAaDQiPDxcimnTpg2aNm0KvV6P7t27Q6/Xo0OHDvD19ZVitFotJk2ahNOnT6NLly7Q6/UW6zDHxMbGAgAKCgqQkZGBGTNmSP0ODg4IDw+HXq8vM1+DwQCDwSA9zsvLAwAYjUYYjcZSlzG3qxxEhfNRWWVtq7pUjv/LzZynNfOtiYrGau639pw8CvaUK2CZb/HXiLXWW9MYIqK6Uu3ix2QyITY2Fs888wzat28PAMjOzoZSqYSnp6dFrK+vL7Kzs6WY4oWPud/cV15MXl4e7t27h5s3b6KoqKjUmHPnzpWZc2JiImbPnl2iPS0tDW5ubuWOd26oqdz+qvjuu++sti4AmN+tZJs1862Jyo5Vp9M94kysx55yBR7kW9prpLoq85zevXvXehskIrKyahc/0dHROHXqFPbv32/NfB6pGTNmIC4uTnqcl5eHgIAAREREQK1Wl7qM0WiETqfDu0cdYDAprJLHqQStVdZj1j7hf+dCqRwE5oaarJpvTVQ0VvP8DhgwAM7OzrWUVfXYU66AZb5d3t9ptfVW5vVrPqpKRGSLqlX8xMTEYNu2bdi7dy+aNGkitfv5+aGgoAC3bt2yOPqTk5MDPz8/Kebhq7LMV4MVj3n4CrGcnByo1Wq4urrC0dERjo6OpcaY11EalUoFlUpVot3Z2bnCP2YGkwKGIusUE9b+w1laXtbMtyYqO9bKPAe2wp5yBR7ka83XQmXGbk/zQ0TyU6WrvYQQiImJwaZNm7Bz504EBQVZ9IeEhMDZ2Rnp6elS2/nz53Hp0iVoNBoAgEajwcmTJy2uytLpdFCr1QgODpZiiq/DHGNeh1KpREhIiEWMyWRCenq6FENERERUmiod+YmOjsb69evx7bffon79+tI5Oh4eHnB1dYWHhwfGjRuHuLg4eHt7Q61W44033oBGo0H37t0BABEREQgODsaYMWMwf/58ZGdnY+bMmYiOjpaOykycOBHLli3DtGnT8Oqrr2Lnzp34+uuvsX37dimXuLg4REVFITQ0FN26dcPixYuRn58vXf1FREREVJoqFT/Lly8HAPTp08eifc2aNXjllVcAAIsWLYKDgwOGDRsGg8EArVaLTz/9VIp1dHTEtm3bMGnSJGg0Gri7uyMqKgpz5syRYoKCgrB9+3ZMmTIFS5YsQZMmTbBy5UrpMncAGDFiBK5du4b4+HhkZ2ejc+fOSE1NLXESNBEREVFxVSp+hKj4clkXFxckJSUhKSmpzJjAwMAKrxjp06cPjh8/Xm5MTEwMYmJiKsyJiIiIyIz39iIiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EJHNSEhIgEKhsPhp06aN1H///n1ER0ejQYMGqFevHoYNG1biHn+XLl1CZGQk3Nzc4OPjg6lTp6KwsNAiZvfu3ejatStUKhVatmyJlJSUErkkJSWhWbNmcHFxQVhYWIl7EhKR/WLxQ0Q2pV27drhy5Yr0s3//fqlvypQp2Lp1KzZu3Ig9e/bg8uXLGDp0qNRfVFSEyMhIFBQU4MCBA1i7di1SUlIQHx8vxWRlZSEyMhJ9+/ZFZmYmYmNj8dprr2HHjh1SzFdffYW4uDjMmjULx44dQ6dOnaDVai3uSUhE9ovFDxHZFCcnJ/j5+Uk/DRs2BADk5uZi1apVWLhwIfr164eQkBCsWbMGBw4cwMGDBwEAaWlpOHPmDL788kt07twZgwYNwty5c5GUlISCggIAQHJyMoKCgrBgwQK0bdsWMTEx+POf/4xFixZJOSxcuBDjx4/H2LFjERwcjOTkZLi5uWH16tW1PyFEZHVVur0FEdGjduHCBfj7+8PFxQUajQaJiYlo2rQpMjIyYDQaER4eLsW2adMGTZs2hV6vR/fu3aHX69GhQweLe/xptVpMmjQJp0+fRpcuXaDX6y3WYY6JjY0FABQUFCAjIwMzZsyQ+h0cHBAeHg69Xl9m3gaDAQaDQXqcl5cHADAajTAajdLvxf81UzlWfOugynp43dZW1hjsCcdgG2oyhpqOm8UPEdmMsLAwpKSkoHXr1rhy5Qpmz56Nnj174tSpU8jOzoZSqYSnp6fFMr6+vsjOzgYAZGdnl7i5sflxRTF5eXm4d+8ebt68iaKiolJjzp07V2buiYmJmD17don2tLQ0uLm5WbTpdDqLx/O7lbnaKqvovonW8vAY7BHHYBuqM4a7d+/WaJssfojIZgwaNEj6vWPHjggLC0NgYCC+/vpruLq61mFmFZsxYwbi4uKkx3l5eQgICEBERATUajWAB/9b1el0GDBgAJydnaXY9gk7Sqyvuk4laK22rtKUNQZ7wjHYhpqMwXxktbpY/BCRzfL09MSTTz6Jn376CQMGDEBBQQFu3bplcfQnJycHfn5+AAA/P78SV2WZrwYrHvPwFWI5OTlQq9VwdXWFo6MjHB0dS40xr6M0KpUKKpWqRLuzs3OJHfvDbYYiRZnrrara+kNY2rjsDcdgG6ozhpqOmSc8E5HNunPnDn7++Wc0btwYISEhcHZ2Rnp6utR//vx5XLp0CRqNBgCg0Whw8uRJi6uydDod1Go1goODpZji6zDHmNehVCoREhJiEWMymZCeni7FEJF9Y/FDRDbjb3/7G/bs2YOLFy/iwIEDeOGFF+Do6IhRo0bBw8MD48aNQ1xcHHbt2oWMjAyMHTsWGo0G3bt3BwBEREQgODgYY8aMwb///W/s2LEDM2fORHR0tHRUZuLEifjll18wbdo0nDt3Dp9++im+/vprTJkyRcojLi4OK1aswNq1a3H27FlMmjQJ+fn5GDt2bJ3MCxFZFz/2IiKb8fvvv2PUqFG4fv06GjVqhB49euDgwYNo1KgRAGDRokVwcHDAsGHDYDAYoNVq8emnn0rLOzo6Ytu2bZg0aRI0Gg3c3d0RFRWFOXPmSDFBQUHYvn07pkyZgiVLlqBJkyZYuXIltNr/nSszYsQIXLt2DfHx8cjOzkbnzp2Rmppa4iRoIrJPLH6IyGZs2LCh3H4XFxckJSUhKSmpzJjAwMAKr3jq06cPjh8/Xm5MTEwMYmJiyo0hIvvEj72IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVqpc/OzduxfPPfcc/P39oVAosHnzZot+IQTi4+PRuHFjuLq6Ijw8HBcuXLCIuXHjBkaPHg21Wg1PT0+MGzcOd+7csYg5ceIEevbsCRcXFwQEBGD+/Pklctm4cSPatGkDFxcXdOjQocJvdSUiIiKqcvGTn5+PTp06lfn18vPnz8fSpUuRnJyMQ4cOwd3dHVqtFvfv35diRo8ejdOnT0On02Hbtm3Yu3cvJkyYIPXn5eUhIiICgYGByMjIwEcffYSEhAR8/vnnUsyBAwcwatQojBs3DsePH8eQIUMwZMgQnDp1qqpDIiIiIhmp8r29Bg0ahEGDBpXaJ4TA4sWLMXPmTAwePBgA8MUXX8DX1xebN2/GyJEjcfbsWaSmpuLIkSMIDQ0FAHzyySd49tln8fHHH8Pf3x/r1q1DQUEBVq9eDaVSiXbt2iEzMxMLFy6UiqQlS5Zg4MCBmDp1KgBg7ty50Ol0WLZsGZKTk0vNz2AwwGAwSI/z8vIAAEajEUajsdRlzO0qB1HVqSpTWduqLpXj/3Iz52nNfGuiorGa+609J4+CPeUKWOZb/DVirfXWNIaIqK5Y9camWVlZyM7ORnh4uNTm4eGBsLAw6PV6jBw5Enq9Hp6enlLhAwDh4eFwcHDAoUOH8MILL0Cv16NXr15QKpVSjFarxYcffoibN2/Cy8sLer0ecXFxFtvXarUlPoYrLjExEbNnzy7RnpaWBjc3t3LHNjfUVNHwK83aH8/N71ayzZr51kRlx6rT6R5xJtZjT7kCD/It7TVSXZV5Tu/evWu9DRIRWZlVi5/s7GwAgK+vr0W7r6+v1JednQ0fHx/LJJyc4O3tbRETFBRUYh3mPi8vL2RnZ5e7ndLMmDHDomDKy8tDQEAAIiIioFarS13GaDRCp9Ph3aMOMJgUZa67Kk4laK2yHrP2CTuk31UOAnNDTVbNtyYqGqt5fgcMGABnZ+dayqp67ClXwDLfLu/vtNp6K/P6NR9VJSKyRVYtfmydSqWCSqUq0e7s7FzhHzODSQFDkXWKCWv/4SwtL2vmWxOVHWtlngNbYU+5Ag/yteZroTJjt6f5ISL5seql7n5+fgCAnJwci/acnBypz8/PD1evXrXoLywsxI0bNyxiSltH8W2UFWPuJyIiIiqNVYufoKAg+Pn5IT09XWrLy8vDoUOHoNFoAAAajQa3bt1CRkaGFLNz506YTCaEhYVJMXv37rU4aVKn06F169bw8vKSYopvxxxj3g4RERFRaapc/Ny5cweZmZnIzMwE8OAk58zMTFy6dAkKhQKxsbF47733sGXLFpw8eRIvv/wy/P39MWTIEABA27ZtMXDgQIwfPx6HDx/Gjz/+iJiYGIwcORL+/v4AgJdeeglKpRLjxo3D6dOn8dVXX2HJkiUW5+tMnjwZqampWLBgAc6dO4eEhAQcPXoUMTExNZ8VIiIiemxV+Zyfo0ePom/fvtJjc0ESFRWFlJQUTJs2Dfn5+ZgwYQJu3bqFHj16IDU1FS4uLtIy69atQ0xMDPr37w8HBwcMGzYMS5culfo9PDyQlpaG6OhohISEoGHDhoiPj7f4LqCnn34a69evx8yZM/HOO++gVatW2Lx5M9q3b1+tiSAiIiJ5qHLx06dPHwhR9neGKBQKzJkzB3PmzCkzxtvbG+vXry93Ox07dsS+ffvKjRk+fDiGDx9efsJERERExfDeXkRERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkazYffGTlJSEZs2awcXFBWFhYTh8+HBdp0REjwnuX4geT051nUBNfPXVV4iLi0NycjLCwsKwePFiaLVanD9/Hj4+PnWdHhHZMe5fiCqn2fTt1VpO5SgwvxvQPmEHDEUKqf3ivEhrpVYmuy5+Fi5ciPHjx2Ps2LEAgOTkZGzfvh2rV6/G9OnTS8QbDAYYDAbpcW5uLgDgxo0bMBqNpW7DaDTi7t27cDI6oMikKDWmqq5fv26V9Zg5Feb/73eTwN27JqvmWxMVjdU8v9evX4ezs3MtZVU99pQrYJlv8ddITVXm9Xv79m0AgBDCatutbY9i/1LWa6i2n5+asLf3QWk4Buuq7uu3rL9XtbKPEXbKYDAIR0dHsWnTJov2l19+WTz//POlLjNr1iwBgD/84U8t/fz222+1sDewPu5f+MMf+/ip7j7Gbo/8/Pe//0VRURF8fX0t2n19fXHu3LlSl5kxYwbi4uKkxyaTCTdu3ECDBg2gUJR+lCQvLw8BAQH47bffoFarrTeAR4T5Pjr2lCtQt/kKIXD79m34+/vX6nat5VHtX+ztNVQajsE2yH0MNd3H2G3xUx0qlQoqlcqizdPTs1LLqtVqu3qBMd9Hx55yBeouXw8Pj1rfZl2qyv7F3l5DpeEYbIOcx1CTfYzdXu3VsGFDODo6Iicnx6I9JycHfn5+dZQVET0OuH8herzZbfGjVCoREhKC9PR0qc1kMiE9PR0ajaYOMyMie8f9C9Hjza4/9oqLi0NUVBRCQ0PRrVs3LF68GPn5+dLVGdagUqkwa9asEoezbRXzfXTsKVfA/vK1NY9i//I4PCccg23gGGpGIYQdX4sKYNmyZfjoo4+QnZ2Nzp07Y+nSpQgLC6vrtIjoMcD9C9Hjye6LHyIiIqKqsNtzfoiIiIiqg8UPERERyQqLHyIiIpIVFj9EREQkKyx+ivnjjz/wl7/8BQ0aNICrqys6dOiAo0ePSv1CCMTHx6Nx48ZwdXVFeHg4Lly4YHO5Go1GvP322+jQoQPc3d3h7++Pl19+GZcvX66TXCvK92ETJ06EQqHA4sWLazfJ/1OZXM+ePYvnn38eHh4ecHd3x1NPPYVLly7ZZL537txBTEwMmjRpAldXVwQHByM5OblOcpWLvXv34rnnnoO/vz8UCgU2b95s0W9L+5LSJCYm4qmnnkL9+vXh4+ODIUOG4Pz58xYx9+/fR3R0NBo0aIB69eph2LBhJb4Usi4tX74cHTt2lL49WKPR4Pvvv5f6bT3/0sybNw8KhQKxsbFSm62PIyEhAQqFwuKnTZs2Un9d5c/i5//cvHkTzzzzDJydnfH999/jzJkzWLBgAby8vKSY+fPnY+nSpUhOTsahQ4fg7u4OrVaL+/fv21Sud+/exbFjx/Duu+/i2LFj+Ne//oXz58/j+eefr9U8K5tvcZs2bcLBgwfr7J5Qlcn1559/Ro8ePdCmTRvs3r0bJ06cwLvvvgsXFxebzDcuLg6pqan48ssvcfbsWcTGxiImJgZbtmyp9XzlIj8/H506dUJSUlKp/bayLynLnj17EB0djYMHD0Kn08FoNCIiIgL5+f+7e/eUKVOwdetWbNy4EXv27MHly5cxdOjQOszaUpMmTTBv3jxkZGTg6NGj6NevHwYPHozTp08DsP38H3bkyBF89tln6Nixo0W7PYyjXbt2uHLlivSzf/9+qa/O8q/W7VAfQ2+//bbo0aNHmf0mk0n4+fmJjz76SGq7deuWUKlU4h//+EdtpCipKNfSHD58WAAQv/766yPKqmyVzff3338XTzzxhDh16pQIDAwUixYtevTJPaQyuY4YMUL85S9/qaWMyleZfNu1ayfmzJlj0da1a1fx97///VGmRv8HgMXd4W1pX1JZV69eFQDEnj17hBAP8nV2dhYbN26UYs6ePSsACL1eX1dpVsjLy0usXLnS7vK/ffu2aNWqldDpdKJ3795i8uTJQgj7eB5mzZolOnXqVGpfXebPIz//Z8uWLQgNDcXw4cPh4+ODLl26YMWKFVJ/VlYWsrOzER4eLrV5eHggLCwMer3epnItTW5uLhQKRaVv5GpNlcnXZDJhzJgxmDp1Ktq1a1frOZpVlKvJZML27dvx5JNPQqvVwsfHB2FhYSU+1rCVfAHg6aefxpYtW/DHH39ACIFdu3bhP//5DyIiIuokZ7mzpX1JZeXm5gIAvL29AQAZGRkwGo0WY2jTpg2aNm1qk2MoKirChg0bkJ+fD41GY3f5R0dHIzIy0iJfwH6ehwsXLsDf3x/NmzfH6NGjpVME6jJ/Fj//55dffsHy5cvRqlUr7NixA5MmTcKbb76JtWvXAgCys7MBAL6+vhbL+fr6Sn22kuvD7t+/j7fffhujRo2qk7v/VibfDz/8EE5OTnjzzTdrPb/iKsr16tWruHPnDubNm4eBAwciLS0NL7zwAoYOHYo9e/bYXL4A8MknnyA4OBhNmjSBUqnEwIEDkZSUhF69etV6vmRb+5LKMJlMiI2NxTPPPIP27dsDeDAGpVJZ4j9TtjaGkydPol69elCpVJg4cSI2bdqE4OBgu8kfADZs2IBjx44hMTGxRJ89jCMsLAwpKSlITU3F8uXLkZWVhZ49e+L27dt1mr9d39vLmkwmE0JDQ/HBBx8AALp06YJTp04hOTkZUVFRdZydparkajQa8eKLL0IIgeXLl9dFuhXmm5GRgSVLluDYsWNQKBR1kmNlczWZTACAwYMHY8qUKQCAzp0748CBA0hOTkbv3r1tKl/gQfFz8OBBbNmyBYGBgdi7dy+io6Ph7+9f4n+SRA+Ljo7GqVOnLM7TsBetW7dGZmYmcnNz8c033yAqKqpO/pNSXb/99hsmT54MnU5XJ+cUWsOgQYOk3zt27IiwsDAEBgbi66+/hqura53lxSM//6dx48YIDg62aGvbtq10eM7Pzw8ASpyFnpOTI/XVlopyNTMXPr/++it0Ol2dHPUBKs533759uHr1Kpo2bQonJyc4OTnh119/xVtvvYVmzZrZVK4NGzaEk5NTpea/NlSU77179/DOO+9g4cKFeO6559CxY0fExMRgxIgR+Pjjj2s9X7KtfUlFYmJisG3bNuzatQtNmjSR2v38/FBQUIBbt25ZxNvaGJRKJVq2bImQkBAkJiaiU6dOWLJkid3kn5GRgatXr6Jr167SvnHPnj1YunQpnJyc4OvraxfjKM7T0xNPPvkkfvrppzp9Hlj8/J9nnnmmxKWc//nPfxAYGAgACAoKgp+fH9LT06X+vLw8HDp0CBqNxqZyBf5X+Fy4cAE//PADGjRoUKs5FldRvmPGjMGJEyeQmZkp/fj7+2Pq1KnYsWOHTeWqVCrx1FNPVTj/taWifI1GI4xGIxwcLN/qjo6O0lEsql22tC8pixACMTEx2LRpE3bu3ImgoCCL/pCQEDg7O1uM4fz587h06ZLNjKE0JpMJBoPBbvLv378/Tp48abFvDA0NxejRo6Xf7WEcxd25cwc///wzGjduXLfPwyM9ndqOHD58WDg5OYn3339fXLhwQaxbt064ubmJL7/8UoqZN2+e8PT0FN9++604ceKEGDx4sAgKChL37t2zqVwLCgrE888/L5o0aSIyMzPFlStXpB+DwVCruVYm39LU1dVelcn1X//6l3B2dhaff/65uHDhgvjkk0+Eo6Oj2Ldvn03m27t3b9GuXTuxa9cu8csvv4g1a9YIFxcX8emnn9Z6vnJx+/Ztcfz4cXH8+HEBQCxcuFAcP35cutrSVvYlZZk0aZLw8PAQu3fvtth/3L17V4qZOHGiaNq0qdi5c6c4evSo0Gg0QqPR1GHWlqZPny727NkjsrKyxIkTJ8T06dOFQqEQaWlpQgjbz78sxa/2EsL2x/HWW2+J3bt3i6ysLPHjjz+K8PBw0bBhQ3H16lUhRN3lz+KnmK1bt4r27dsLlUol2rRpIz7//HOLfpPJJN59913h6+srVCqV6N+/vzh//rzN5ZqVlSUAlPqza9cum8u3NHVV/AhRuVxXrVolWrZsKVxcXESnTp3E5s2b6yDTByrK98qVK+KVV14R/v7+wsXFRbRu3VosWLBAmEymOsr48bdr165S339RUVFCCNval5SmrP3HmjVrpJh79+6Jv/71r8LLy0u4ubmJF154QVy5cqXukn7Iq6++KgIDA4VSqRSNGjUS/fv3lwofIWw//7I8XPzY+jhGjBghGjduLJRKpXjiiSfEiBEjxE8//ST111X+CiGEeLTHloiIiIhsB8/5ISIiIllh8UNERESywuKHiIiIZIXFDxEREckKix+yOxcvXoRCoUBKSkpdp0JERHaIxQ8REdmFAwcOICEhocQ3Atsie8pVjlj8kN0JDAzEvXv3MGbMmLpOhYhq0YEDBzB79my7KCjsKVc5YvFDj1R+fr7V16lQKODi4gJHR0err5uI5EMIgXv37tV1GlQHWPyQ1SQkJEChUODMmTN46aWX4OXlhR49egAAvvzyS4SEhMDV1RXe3t4YOXIkfvvttxLrSEpKQvPmzeHq6opu3bph37596NOnD/r06SPFlHXOz86dO9GzZ0+4u7vD09MTgwcPxtmzZ0vN8aeffsIrr7wCT09PeHh4YOzYsbh7967V54SIrCMhIQFTp04F8OD+aAqFAgqFAhcvXsSaNWvQr18/+Pj4QKVSITg4GMuXLy+xjmbNmuFPf/oTduzYgdDQULi6uuKzzz4DAPz66694/vnn4e7uDh8fH0yZMgU7duyAQqHA7t27LdZz6NAhDBw4EB4eHnBzc0Pv3r3x448/VipXsg1OdZ0APX6GDx+OVq1a4YMPPoAQAu+//z7effddvPjii3jttddw7do1fPLJJ+jVqxeOHz8OT09PAMDy5csRExODnj17YsqUKbh48SKGDBkCLy8viztKl+aHH37AoEGD0Lx5cyQkJODevXv45JNP8Mwzz+DYsWMl7g7/4osvIigoCImJiTh27BhWrlwJHx8ffPjhh49oVoioJoYOHYr//Oc/+Mc//oFFixahYcOGAIBGjRph+fLlaNeuHZ5//nk4OTlh69at+Otf/wqTyYTo6GiL9Zw/fx6jRo3C66+/jvHjx6N169bIz89Hv379cOXKFUyePBl+fn5Yv349du3aVSKPnTt3YtCgQQgJCcGsWbPg4OAgFV/79u1Dt27dys2VbMQjv4EGycasWbMEADFq1Cip7eLFi8LR0VG8//77FrEnT56UbsgphBAGg0E0aNBAPPXUU8JoNEpxKSkpAoDo3bu31Ga+d1nx+wx17txZ+Pj4iOvXr0tt//73v4WDg4N4+eWXS+T46quvWuTzwgsviAYNGtRo/ET0aH300UcCgMjKyrJoL37DVTOtViuaN29u0RYYGCgAiNTUVIv2BQsWCAAW9+i7d++eaNOmjcU9EU0mk2jVqpXQarUW98a7e/euCAoKEgMGDKgwV7IN/NiLrG7ixInS7//6179gMpnw4osv4r///a/04+fnh1atWkn/szp69CiuX7+O8ePHw8npfwckR48eDS8vr3K3d+XKFWRmZuKVV16Bt7e31N6xY0cMGDAA3333Xbk5AkDPnj1x/fp15OXlVWvMRFR3XF1dpd9zc3Px3//+F71798Yvv/yC3Nxci9igoCBotVqLttTUVDzxxBN4/vnnpTYXFxeMHz/eIi4zMxMXLlzASy+9hOvXr0v7s/z8fPTv3x979+6FyWR6BCMka+PHXmR1QUFB0u8XLlyAEAKtWrUqNdbZ2RnAg8/bAaBly5YW/U5OTiU+snqYednWrVuX6Gvbti127NiB/Px8uLu7S+1Nmza1iDMXWDdv3oRarS53e0RkW3788UfMmjULer2+xLl7ubm58PDwkB4X3z+Z/frrr2jRogUUCoVF+8P7owsXLgAAoqKiyswlNze3wv+wUd1j8UNWV/x/YSaTCQqFAt9//32pV2fVq1evNlOTlHWlmBCiljMhopr4+eef0b9/f7Rp0wYLFy5EQEAAlEolvvvuOyxatKjEkZji+6eqMq/ro48+QufOnUuNqat9GlUNix96pFq0aAEhBIKCgvDkk0+WGRcYGAgA+Omnn9C3b1+pvbCwEBcvXkTHjh0rXPb8+fMl+s6dO4eGDRtaHPUhIvv08JEZANi6dSsMBgO2bNlicUS3tJOVyxIYGIgzZ85ACGGxjZ9++skirkWLFgAAtVqN8PDwKudKtoPn/NAjNXToUDg6OmL27NkljqoIIXD9+nUAQGhoKBo0aIAVK1agsLBQilm3bh1u3rxZ7jYaN26Mzp07Y+3atRZfKHbq1CmkpaXh2Weftd6AiKjOmP8TU/x9bj6KW3z/kpubizVr1lR6vVqtFn/88Qe2bNkitd2/fx8rVqywiAsJCUGLFi3w8ccf486dOyXWc+3atXJzJdvBIz/0SLVo0QLvvfceZsyYIV26Xr9+fWRlZWHTpk2YMGEC/va3v0GpVCIhIQFvvPEG+vXrhxdffBEXL15ESkpKqZ/FP+yjjz7CoEGDoNFoMG7cOOlSdw8PDyQkJNTOYInokQoJCQEA/P3vf8fIkSPh7OyMXr16QalU4rnnnsPrr7+OO3fuYMWKFfDx8cGVK1cqtd7XX38dy5Ytw6hRozB58mQ0btwY69atg4uLC4D/HcVxcHDAypUrMWjQILRr1w5jx47FE088gT/++AO7du2CWq3G1q1by8z1ueee41FoW1GHV5rRY8Z8Gfm1a9dK9P3zn/8UPXr0EO7u7sLd3V20adNGREdHi/Pnz1vELV26VAQGBgqVSiW6desmfvzxRxESEiIGDhwoxZR2qbsQQvzwww/imWeeEa6urkKtVovnnntOnDlzplI5rlmzhpelEtmBuXPniieeeEI4ODhI79ktW7aIjh07ChcXF9GsWTPx4YcfitWrV5d4TwcGBorIyMhS1/vLL7+IyMhI4erqKho1aiTeeust8c9//lMAEAcPHrSIPX78uBg6dKho0KCBUKlUIjAwULz44osiPT29wlzJNiiE4BmeZLtMJhMaNWqEoUOHljgETUT0KC1evBhTpkzB77//jieeeKKu0yEr4jk/ZDPu379f4rygL774Ajdu3LC4vQURkbU9fI+v+/fv47PPPkOrVq1Y+DyGeM4P2YyDBw9iypQpGD58OBo0aIBjx45h1apVaN++PYYPH17X6RHRY2zo0KFo2rQpOnfujNzcXHz55Zc4d+4c1q1bV9ep0SPA4odsRrNmzRAQEIClS5fixo0b8Pb2xssvv4x58+ZBqVTWdXpE9BjTarVYuXIl1q1bh6KiIgQHB2PDhg0YMWJEXadGjwDP+SEiIiJZ4Tk/REREJCssfoiIiEhWZH3Oj8lkwuXLl1G/fn1+FTmRFQkhcPv2bfj7+8PBQZ7/x+L+hejRqek+RtbFz+XLlxEQEFDXaRA9tn777Tc0adKkrtOoE9y/ED161d3HyLr4qV+/PoAHk6dWq0uNMRqNSEtLQ0REBJydnWszPZvCeeAcmFVmHvLy8hAQECC9x+SoMvsX4PF4XXEMtkFOY6jpPkbWxY/5ULRarS63+HFzc4NarbbbF5M1cB44B2ZVmQc5f9xTmf0L8Hi8rjgG2yDHMVR3HyPPD+OJiIhItlj8EBERkayw+CEiIiJZYfFDREREsiLrE56ron3CDhiKrHPy5sV5kVZZDxE9Pqy1j+H+hahiPPJDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrVS5+/vjjD/zlL39BgwYN4Orqig4dOuDo0aNSvxAC8fHxaNy4MVxdXREeHo4LFy5YrOPGjRsYPXo01Go1PD09MW7cONy5c8ci5sSJE+jZsydcXFwQEBCA+fPnl8hl48aNaNOmDVxcXNChQwd89913VR0OERERyUyVip+bN2/imWeegbOzM77//nucOXMGCxYsgJeXlxQzf/58LF26FMnJyTh06BDc3d2h1Wpx//59KWb06NE4ffo0dDodtm3bhr1792LChAlSf15eHiIiIhAYGIiMjAx89NFHSEhIwOeffy7FHDhwAKNGjcK4ceNw/PhxDBkyBEOGDMGpU6dqMh9ERET0mHOqSvCHH36IgIAArFmzRmoLCgqSfhdCYPHixZg5cyYGDx4MAPjiiy/g6+uLzZs3Y+TIkTh79ixSU1Nx5MgRhIaGAgA++eQTPPvss/j444/h7++PdevWoaCgAKtXr4ZSqUS7du2QmZmJhQsXSkXSkiVLMHDgQEydOhUAMHfuXOh0OixbtgzJyck1mxUiIiJ6bFWp+NmyZQu0Wi2GDx+OPXv24IknnsBf//pXjB8/HgCQlZWF7OxshIeHS8t4eHggLCwMer0eI0eOhF6vh6enp1T4AEB4eDgcHBxw6NAhvPDCC9Dr9ejVqxeUSqUUo9Vq8eGHH+LmzZvw8vKCXq9HXFycRX5arRabN28uM3+DwQCDwSA9zsvLAwAYjUYYjcZSlzG3qxxEJWepYmVty5aZc7bH3K2Fc/BAZeZB7nNERLatSsXPL7/8guXLlyMuLg7vvPMOjhw5gjfffBNKpRJRUVHIzs4GAPj6+los5+vrK/VlZ2fDx8fHMgknJ3h7e1vEFD+iVHyd2dnZ8PLyQnZ2drnbKU1iYiJmz55doj0tLQ1ubm7ljn1uqKnc/qqw53OTdDpdXadQ5zgHD5Q3D3fv3q3FTIiIqqZKxY/JZEJoaCg++OADAECXLl1w6tQpJCcnIyoq6pEkaE0zZsywOFqUl5eHgIAAREREQK1Wl7qM0WiETqfDu0cdYDAprJLHqQStVdZTm8zzMGDAADg7O9d1OnWCc/BAZebBfFSViMgWVan4ady4MYKDgy3a2rZti3/+858AAD8/PwBATk4OGjduLMXk5OSgc+fOUszVq1ct1lFYWIgbN25Iy/v5+SEnJ8cixvy4ohhzf2lUKhVUKlWJdmdn5wr/mBlMChiKrFP82PMfzsrM1eOOc/BAefPA+SEiW1alq72eeeYZnD9/3qLtP//5DwIDAwE8OPnZz88P6enpUn9eXh4OHToEjUYDANBoNLh16xYyMjKkmJ07d8JkMiEsLEyK2bt3r8V5AzqdDq1bt5auLNNoNBbbMceYt0NERERUmioVP1OmTMHBgwfxwQcf4KeffsL69evx+eefIzo6GgCgUCgQGxuL9957D1u2bMHJkyfx8ssvw9/fH0OGDAHw4EjRwIEDMX78eBw+fBg//vgjYmJiMHLkSPj7+wMAXnrpJSiVSowbNw6nT5/GV199hSVLllh8ZDV58mSkpqZiwYIFOHfuHBISEnD06FHExMRYaWqIiIjocVSlj72eeuopbNq0CTNmzMCcOXMQFBSExYsXY/To0VLMtGnTkJ+fjwkTJuDWrVvo0aMHUlNT4eLiIsWsW7cOMTEx6N+/PxwcHDBs2DAsXbpU6vfw8EBaWhqio6MREhKChg0bIj4+3uK7gJ5++mmsX78eM2fOxDvvvINWrVph8+bNaN++fU3mg4iIiB5zVSp+AOBPf/oT/vSnP5XZr1AoMGfOHMyZM6fMGG9vb6xfv77c7XTs2BH79u0rN2b48OEYPnx4+QkTERERFcN7exEREZGssPghIiIiWWHxQ0RERLLC4oeIiIhkhcUPERERyQqLHyKqFXv37sVzzz0Hf39/KBSKEjchFkIgPj4ejRs3hqurK8LDw3HhwgWLmBs3bmD06NFQq9Xw9PTEuHHjcOfOHYuYEydOoGfPnnBxcUFAQADmz59fIpeNGzeiTZs2cHFxQYcOHUrcb68yuRCR/WLxQ0S1Ij8/H506dUJSUlKp/fPnz8fSpUuRnJyMQ4cOwd3dHVqtFvfv35diRo8ejdOnT0On02Hbtm3Yu3evxfd/5eXlISIiAoGBgcjIyMBHH32EhIQEfP7551LMgQMHMGrUKIwbNw7Hjx/HkCFDMGTIEJw6dapKuRCR/ary9/wQEVXHoEGDMGjQoFL7hBBYvHgxZs6cicGDBwMAvvjiC/j6+mLz5s0YOXIkzp49i9TUVBw5cgShoaEAgE8++QTPPvssPv74Y/j7+2PdunUoKCjA6tWroVQq0a5dO2RmZmLhwoVSkbRkyRIMHDgQU6dOBQDMnTsXOp0Oy5YtQ3JycqVyISL7xuKHiOpcVlYWsrOzER4eLrV5eHggLCwMer0eI0eOhF6vh6enp1T4AEB4eDgcHBxw6NAhvPDCC9Dr9ejVqxeUSqUUo9Vq8eGHH+LmzZvw8vKCXq+3uFWOOcb8MVxlcimNwWCAwWCQHpvvbG80Gi3uU/gwc5/KQVQ0TZVS3rYeFfM262Lb1sIx2IbKjqGmY2TxQ0R1Ljs7GwDg6+tr0e7r6yv1ZWdnw8fHx6LfyckJ3t7eFjFBQUEl1mHu8/LyQnZ2doXbqSiX0iQmJmL27Nkl2tPS0uDm5lbmcmZzQ00VxlTGw+cv1SadTldn27YWjsE2VDSGu3fv1mj9LH6IiKxgxowZFkeU8vLyEBAQgIiICKjV6jKXMxqN0Ol0ePeoAwwmRY3zOJWgrfE6qso8hgEDBsDZ2bnWt28NHINtqOwYzEdWq4vFDxHVOT8/PwBATk4OGjduLLXn5OSgc+fOUszVq1ctlissLMSNGzek5f38/JCTk2MRY35cUUzx/opyKY1KpYJKpSrR7uzsXKk/RAaTAoaimhc/dflHr7JjtWUcg22oaAw1HR+v9iKiOhcUFAQ/Pz+kp6dLbXl5eTh06BA0Gg0AQKPR4NatW8jIyJBidu7cCZPJhLCwMClm7969FucD6HQ6tG7dGl5eXlJM8e2YY8zbqUwuRGTfWPwQUa24c+cOMjMzkZmZCeDBicWZmZm4dOkSFAoFYmNj8d5772HLli04efIkXn75Zfj7+2PIkCEAgLZt22LgwIEYP348Dh8+jB9//BExMTEYOXIk/P39AQAvvfQSlEolxo0bh9OnT+Orr77CkiVLLD6Omjx5MlJTU7FgwQKcO3cOCQkJOHr0KGJiYgCgUrkQkX3jx15EVCuOHj2Kvn37So/NBUlUVBRSUlIwbdo05OfnY8KECbh16xZ69OiB1NRUuLi4SMusW7cOMTEx6N+/PxwcHDBs2DAsXbpU6vfw8EBaWhqio6MREhKChg0bIj4+3uK7gJ5++mmsX78eM2fOxDvvvINWrVph8+bNaN++vRRTmVyIyH6x+CGiWtGnTx8IUfbl3AqFAnPmzMGcOXPKjPH29sb69evL3U7Hjh2xb9++cmOGDx+O4cOH1ygXIrJf/NiLiIiIZIXFDxEREckKix8iIiKSFRY/REREJCssfoiIiEhWWPwQERGRrLD4ISIiIllh8UNERESywuKHiIiIZIXFDxEREckKix8iIiKSFRY/REREJCssfoiIiEhWWPwQERGRrLD4ISIiIllh8UNERESywuKHiIiIZIXFDxEREclKjYqfefPmQaFQIDY2Vmq7f/8+oqOj0aBBA9SrVw/Dhg1DTk6OxXKXLl1CZGQk3Nzc4OPjg6lTp6KwsNAiZvfu3ejatStUKhVatmyJlJSUEttPSkpCs2bN4OLigrCwMBw+fLgmwyEiIiIZqHbxc+TIEXz22Wfo2LGjRfuUKVOwdetWbNy4EXv27MHly5cxdOhQqb+oqAiRkZEoKCjAgQMHsHbtWqSkpCA+Pl6KycrKQmRkJPr27YvMzEzExsbitddew44dO6SYr776CnFxcZg1axaOHTuGTp06QavV4urVq9UdEhEREclAtYqfO3fuYPTo0VixYgW8vLyk9tzcXKxatQoLFy5Ev379EBISgjVr1uDAgQM4ePAgACAtLQ1nzpzBl19+ic6dO2PQoEGYO3cukpKSUFBQAABITk5GUFAQFixYgLZt2yImJgZ//vOfsWjRImlbCxcuxPjx4zF27FgEBwcjOTkZbm5uWL16dU3mg4iIiB5zTtVZKDo6GpGRkQgPD8d7770ntWdkZMBoNCI8PFxqa9OmDZo2bQq9Xo/u3btDr9ejQ4cO8PX1lWK0Wi0mTZqE06dPo0uXLtDr9RbrMMeYP14rKChARkYGZsyYIfU7ODggPDwcer2+zLwNBgMMBoP0OC8vDwBgNBphNBpLXcbcrnIQFU1LpZW1LVtmztkecy9L+4QdFQcVo3IQmBsKhMxJhcGkKNF/KkFrrdRsWmVeC4/T64SIHj9VLn42bNiAY8eO4ciRIyX6srOzoVQq4enpadHu6+uL7OxsKaZ44WPuN/eVF5OXl4d79+7h5s2bKCoqKjXm3LlzZeaemJiI2bNnl2hPS0uDm5tbmcsBwNxQU7n9VfHdd99ZbV21TafT1XUKVjO/W/WWK+u1YM/Pa3WU91q4e/duLWZCRFQ1VSp+fvvtN0yePBk6nQ4uLi6PKqdHZsaMGYiLi5Me5+XlISAgABEREVCr1aUuYzQaodPp8O5Rh1L/t18d9niEwDwPAwYMgLOzc12nYxXVO/JjKvO1YI/Pa3VU5rVgPqpKRGSLqlT8ZGRk4OrVq+jatavUVlRUhL1792LZsmXYsWMHCgoKcOvWLYujPzk5OfDz8wMA+Pn5lbgqy3w1WPGYh68Qy8nJgVqthqurKxwdHeHo6FhqjHkdpVGpVFCpVCXanZ2dK/yDbjApYCiyTvFjz8VDZebKXlT3+SzrtfC4zEtllfdakNtcEJF9qdIJz/3798fJkyeRmZkp/YSGhmL06NHS787OzkhPT5eWOX/+PC5dugSNRgMA0Gg0OHnypMVVWTqdDmq1GsHBwVJM8XWYY8zrUCqVCAkJsYgxmUxIT0+XYoiIiIhKU6UjP/Xr10f79u0t2tzd3dGgQQOpfdy4cYiLi4O3tzfUajXeeOMNaDQadO/eHQAQERGB4OBgjBkzBvPnz0d2djZmzpyJ6Oho6ajMxIkTsWzZMkybNg2vvvoqdu7cia+//hrbt2+XthsXF4eoqCiEhoaiW7duWLx4MfLz8zF27NgaTQgRERE93qp1tVd5Fi1aBAcHBwwbNgwGgwFarRaffvqp1O/o6Iht27Zh0qRJ0Gg0cHd3R1RUFObMmSPFBAUFYfv27ZgyZQqWLFmCJk2aYOXKldBq/3dOxYgRI3Dt2jXEx8cjOzsbnTt3RmpqaomToImIiIiKq3Hxs3v3bovHLi4uSEpKQlJSUpnLBAYGVnhlTJ8+fXD8+PFyY2JiYhATE1PpXImIiIh4by8iIiKSFRY/REREJCssfoiIiEhWWPwQERGRrLD4ISIiIllh8UNERESywuKHiIiIZIXFDxEREckKix8iIiKSFRY/REREJCssfoiIiEhWWPwQERGRrLD4ISIiIllh8UNERESywuKHiIiIZIXFDxEREckKix8iIiKSFRY/REREJCssfoiIiEhWWPwQERGRrLD4ISKbkZCQAIVCYfHTpk0bqf/+/fuIjo5GgwYNUK9ePQwbNgw5OTkW67h06RIiIyPh5uYGHx8fTJ06FYWFhRYxu3fvRteuXaFSqdCyZUukpKSUyCUpKQnNmjWDi4sLwsLCcPjw4UcyZiKqfSx+iMimtGvXDleuXJF+9u/fL/VNmTIFW7duxcaNG7Fnzx5cvnwZQ4cOlfqLiooQGRmJgoICHDhwAGvXrkVKSgri4+OlmKysLERGRqJv377IzMxEbGwsXnvtNezYsUOK+eqrrxAXF4dZs2bh2LFj6NSpE7RaLa5evVo7k0BEj5RTXSdARFSck5MT/Pz8SrTn5uZi1apVWL9+Pfr16wcAWLNmDdq2bYuDBw+ie/fuSEtLw5kzZ/DDDz/A19cXnTt3xty5c/H2228jISEBSqUSycnJCAoKwoIFCwAAbdu2xf79+7Fo0SJotVoAwMKFCzF+/HiMHTsWAJCcnIzt27dj9erVmD59eql5GwwGGAwG6XFeXh4AwGg0wmg0ljlec5/KQVR1qspdX20yb7Mutm0tHINtqOwYajpGFj9EZFMuXLgAf39/uLi4QKPRIDExEU2bNkVGRgaMRiPCw8Ol2DZt2qBp06bQ6/Xo3r079Ho9OnToAF9fXylGq9Vi0qRJOH36NLp06QK9Xm+xDnNMbGwsAKCgoAAZGRmYMWOG1O/g4IDw8HDo9foy805MTMTs2bNLtKelpcHNza3Ccc8NNVUYUxnfffedVdZTHTqdrs62bS0cg22oaAx3796t0fpZ/BCRzQgLC0NKSgpat26NK1euYPbs2ejZsydOnTqF7OxsKJVKeHp6Wizj6+uL7OxsAEB2drZF4WPuN/eVF5OXl4d79+7h5s2bKCoqKjXm3LlzZeY+Y8YMxMXFSY/z8vIQEBCAiIgIqNXqMpczGo3Q6XR496gDDCZFmXGVdSpBW+N1VJV5DAMGDICzs3Otb98aOAbbUNkxmI+sVheLHyKyGYMGDZJ+79ixI8LCwhAYGIivv/4arq6udZhZxVQqFVQqVYl2Z2fnSv0hMpgUMBTVvPipyz96lR2rLeMYbENFY6jp+HjCMxHZLE9PTzz55JP46aef4Ofnh4KCAty6dcsiJicnRzpHyM/Pr8TVX+bHFcWo1Wq4urqiYcOGcHR0LDWmtHORiMj+sPghIpt1584d/Pzzz2jcuDFCQkLg7OyM9PR0qf/8+fO4dOkSNBoNAECj0eDkyZMWV2XpdDqo1WoEBwdLMcXXYY4xr0OpVCIkJMQixmQyIT09XYohIvvG4oeIbMbf/vY37NmzBxcvXsSBAwfwwgsvwNHREaNGjYKHhwfGjRuHuLg47Nq1CxkZGRg7diw0Gg26d+8OAIiIiEBwcDDGjBmDf//739ixYwdmzpyJ6Oho6SOpiRMn4pdffsG0adNw7tw5fPrpp/j6668xZcoUKY+4uDisWLECa9euxdmzZzFp0iTk5+dLV38RkX3jOT9EZDN+//13jBo1CtevX0ejRo3Qo0cPHDx4EI0aNQIALFq0CA4ODhg2bBgMBgO0Wi0+/fRTaXlHR0ds27YNkyZNgkajgbu7O6KiojBnzhwpJigoCNu3b8eUKVOwZMkSNGnSBCtXrpQucweAESNG4Nq1a4iPj0d2djY6d+6M1NTUEidBE5F9YvFDZCXNpm+32rouzou02rrsyYYNG8rtd3FxQVJSEpKSksqMCQwMrPBy7z59+uD48ePlxsTExCAmJqbcGCKyT/zYi4iIiGSFxQ8RERHJCosfIiIikpUqFT+JiYl46qmnUL9+ffj4+GDIkCE4f/68RQzvukxERES2rErFz549exAdHY2DBw9Cp9PBaDQiIiIC+fn5UgzvukxERES2rEpXe6Wmplo8TklJgY+PDzIyMtCrVy+bv+syERERUY0udc/NzQUAeHt7A4DN33XZYDDAYDBIj803RjMajTAajaUuY25XOYgK56OyytqWLTPnbI+5l0XlWLXn1PwasOZroSy2PM+VeS3Ycv5ERNUufkwmE2JjY/HMM8+gffv2AGDzd11OTEzE7NmzS7SnpaXBzc2t3PHODTWV218VFX0HiS3T6XR1nYLVzO9WveWs+Vooiz28Rsp7Ldy9e7cWMyEiqppqFz/R0dE4deoU9u/fb818HqkZM2YgLi5OepyXl4eAgABERERArVaXuozRaIROp8O7Rx1gMNX8jssAcCpBW3GQjTHPw4ABA+z+bsFm7RN2VBxUjMpBYG6oyaqvhbLY8mukMq8F81FVIiJbVK3iJyYmBtu2bcPevXvRpEkTqb34XZeLH/15+K7LD1+VVdW7Ljs6OlbrrssqlUq6v09xzs7OFf5BN5gUMBRZ5w+ePRcPlZkre1Hd59Oar4Wy2MMcl/dasIf8iUi+qnS1lxACMTEx2LRpE3bu3ImgoCCLft51mYiIiGxdlY78REdHY/369fj2229Rv3596RwdDw8PuLq6Wtx12dvbG2q1Gm+88UaZd12eP38+srOzS73r8rJlyzBt2jS8+uqr2LlzJ77++mts3/6/eyfFxcUhKioKoaGh6NatGxYvXsy7LhMREVGFqlT8LF++HMCDmwIWt2bNGrzyyisAeNdlIiIism1VKn6EqPgSX951mYiIiGwZ7+1FREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrLH6IiIhIVlj8EBERkayw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJCosfIiIikhUWP0RERCQrTnWdABEREdmvZtO3W21dKkeB+d2stroysfghkgFr7pwuzI2w2rqIiOoCP/YiIiIiWWHxQ0RERLLC4oeIiIhkxe6Ln6SkJDRr1gwuLi4ICwvD4cOH6zolInpMcP9C9Hiy6+Lnq6++QlxcHGbNmoVjx46hU6dO0Gq1uHr1al2nRkR2jvsXoseXXRc/CxcuxPjx4zF27FgEBwcjOTkZbm5uWL16dV2nRkR2jvsXoseX3V7qXlBQgIyMDMyYMUNqc3BwQHh4OPR6fanLGAwGGAwG6XFubi4A4MaNGzAajaUuYzQacffuXTgZHVBkUlgl9+vXr1tlPbXJPA/Xr1+Hs7NzXadjFU6F+VWLNwncvWuy6muhLNZ+jVR1rOW5fv16ha+F27dvAwCEEFbbbm2qrf0LYP19TF3sXx6H/QPHUH3W3L+Y97MVjaGm+xi7LX7++9//oqioCL6+vhbtvr6+OHfuXKnLJCYmYvbs2SXag4KCHkmOZWm4oFY3R1b0Ui1tx5ZfI42rkNvt27fh4eHx6JJ5RLh/Iao7VdnPVncfY7fFT3XMmDEDcXFx0mOTyYQbN26gQYMGUChK/x9XXl4eAgIC8Ntvv0GtVtdWqjaH88A5MKvMPAghcPv2bfj7+9dydnWnOvsX4PF4XXEMtkFOY6jpPsZui5+GDRvC0dEROTk5Fu05OTnw8/MrdRmVSgWVSmXR5unpWantqdVqu30xWRPngXNgVtE82OMRH7Pa3r8Aj8frimOwDXIZQ032MXZ7wrNSqURISAjS09OlNpPJhPT0dGg0mjrMjIjsHfcvRI83uz3yAwBxcXGIiopCaGgounXrhsWLFyM/Px9jx46t69SIyM5x/0L0+LLr4mfEiBG4du0a4uPjkZ2djc6dOyM1NbXESYo1oVKpMGvWrBKHs+WG88A5MJPLPNTG/gV4POaTY7ANHEPlKYS9XotKREREVA12e84PERERUXWw+CEiIiJZYfFDREREssLih4iIiGSFxQ8RERHJiiyLn7179+K5556Dv78/FAoFNm/ebNEvhEB8fDwaN24MV1dXhIeH48KFCxYxN27cwOjRo6FWq+Hp6Ylx48bhzp07tTiKmklMTMRTTz2F+vXrw8fHB0OGDMH58+ctYu7fv4/o6Gg0aNAA9erVw7Bhw0p84+2lS5cQGRkJNzc3+Pj4YOrUqSgsLKzNodTI8uXL0bFjR+nbRDUaDb7//nupXw5zUJp58+ZBoVAgNjZWapPrXFRVUlISmjVrBhcXF4SFheHw4cPlxm/cuBFt2rSBi4sLOnTogO+++86ivzL7I2uryhhWrFiBnj17wsvLC15eXggPDy8R/8orr0ChUFj8DBw40GbGkJKSUiI/FxcXixhbfx769OlTYgwKhQKRkZFSTG0/DxX9rS3N7t270bVrV6hUKrRs2RIpKSklYqr6HiuVkKHvvvtO/P3vfxf/+te/BACxadMmi/558+YJDw8PsXnzZvHvf/9bPP/88yIoKEjcu3dPihk4cKDo1KmTOHjwoNi3b59o2bKlGDVqVC2PpPq0Wq1Ys2aNOHXqlMjMzBTPPvusaNq0qbhz544UM3HiRBEQECDS09PF0aNHRffu3cXTTz8t9RcWFor27duL8PBwcfz4cfHdd9+Jhg0bihkzZtTFkKply5YtYvv27eI///mPOH/+vHjnnXeEs7OzOHXqlBBCHnPwsMOHD4tmzZqJjh07ismTJ0vtcpyLqtqwYYNQKpVi9erV4vTp02L8+PHC09NT5OTklBr/448/CkdHRzF//nxx5swZMXPmTOHs7CxOnjwpxVRmf1SXY3jppZdEUlKSOH78uDh79qx45ZVXhIeHh/j999+lmKioKDFw4EBx5coV6efGjRuPJP/qjGHNmjVCrVZb5JednW0RY+vPw/Xr1y3yP3XqlHB0dBRr1qyRYmr7eajob+3DfvnlF+Hm5ibi4uLEmTNnxCeffCIcHR1FamqqFFPVeSmLLIuf4h5+Qkwmk/Dz8xMfffSR1Hbr1i2hUqnEP/7xDyGEEGfOnBEAxJEjR6SY77//XigUCvHHH3/UWu7WdPXqVQFA7NmzRwjxYMzOzs5i48aNUszZs2cFAKHX64UQD17YDg4OFjuJ5cuXC7VaLQwGQ+0OwIq8vLzEypUrZTkHt2/fFq1atRI6nU707t1bKn7kOBfV0a1bNxEdHS09LioqEv7+/iIxMbHU+BdffFFERkZatIWFhYnXX39dCFG5/ZG1VXUMDyssLBT169cXa9euldqioqLE4MGDrZ1qmao6hjVr1ggPD48y12ePz8OiRYtE/fr1Lf5DW9vPQ3GVKX6mTZsm2rVrZ9E2YsQIodVqpcc1nRczWX7sVZ6srCxkZ2cjPDxcavPw8EBYWBj0ej0AQK/Xw9PTE6GhoVJMeHg4HBwccOjQoVrP2Rpyc3MBAN7e3gCAjIwMGI1Gi3lo06YNmjZtajEPHTp0sPjGW61Wi7y8PJw+fboWs7eOoqIibNiwAfn5+dBoNLKcg+joaERGRlqMGZDn66GqCgoKkJGRYTFHDg4OCA8Pl+boYXq9vsRca7VaKb4y+6O6HsPD7t69C6PRKO1LzHbv3g0fHx+0bt0akyZNwvXr162au1l1x3Dnzh0EBgYiICAAgwcPtnjN2uPzsGrVKowcORLu7u4W7bX1PFRHRe8Ha8yLtFzN0328ZGdnA0CJr7D39fWV+rKzs+Hj42PR7+TkBG9vbynGnphMJsTGxuKZZ55B+/btATwYo1KpLHFX6ofnobR5MvfZi5MnT6JevXpQqVSYOHEiNm3ahODgYFnNAQBs2LABx44dQ2JiYok+uc1Fdfz3v/9FUVFRufuOh5U1Z8Xn1NxW2XXWRHXG8LC3334b/v7+Fn+gBg4ciC+++ALp6en48MMPsWfPHgwaNAhFRUVWzR+o3hhat26N1atX49tvv8WXX34Jk8mEp59+Gr///jsA+3seDh8+jFOnTuG1116zaK/N56E6yno/5OXl4d69e1Z5fZrZ9b29yDqio6Nx6tQp7N+/v65TqROtW7dGZmYmcnNz8c033yAqKgp79uyp67Rq1W+//YbJkydDp9OVONGTqLLmzZuHDRs2YPfu3Ravo5EjR0q/d+jQAR07dkSLFi2we/du9O/fvy5StaDRaKDRaKTHTz/9NNq2bYvPPvsMc+fOrcPMqmfVqlXo0KEDunXrZtFu689DbeKRn4f4+fkBQImrWHJycqQ+Pz8/XL161aK/sLAQN27ckGLsRUxMDLZt24Zdu3ahSZMmUrufnx8KCgpw69Yti/iH56G0eTL32QulUomWLVsiJCQEiYmJ6NSpE5YsWSKrOcjIyMDVq1fRtWtXODk5wcnJCXv27MHSpUvh5OQEX19f2cxFdTVs2BCOjo7l7jseVtacFZ9Tc1tl11kT1RmD2ccff4x58+YhLS0NHTt2LDe2efPmaNiwIX766aca5/ywmozBzNnZGV26dJHys6fnIT8/Hxs2bMC4ceMq3M6jfB6qo6z3g1qthqurq1WeWzMWPw8JCgqCn58f0tPTpba8vDwcOnRI+p+BRqPBrVu3kJGRIcXs3LkTJpMJYWFhtZ5zdQghEBMTg02bNmHnzp0ICgqy6A8JCYGzs7PFPJw/fx6XLl2ymIeTJ09aFII6nQ5qtRrBwcG1M5BHwGQywWAwyGoO+vfvj5MnTyIzM1P6CQ0NxejRo6Xf5TIX1aVUKhESEmIxRyaTCenp6RZHFYrTaDQW8cCDOTPHV2Z/VNdjAID58+dj7ty5SE1NtTgXsiy///47rl+/jsaNG1sl7+KqO4biioqKcPLkSSk/e3kegAdfnWAwGPCXv/ylwu08yuehOip6P1jjuZVU6fTox8Tt27fF8ePHxfHjxwUAsXDhQnH8+HHx66+/CiEeXNLo6ekpvv32W3HixAkxePDgUi9179Klizh06JDYv3+/aNWqlV1d6j5p0iTh4eEhdu/ebXHZ4927d6WYiRMniqZNm4qdO3eKo0ePCo1GIzQajdRvvrQ5IiJCZGZmitTUVNGoUSO7urR5+vTpYs+ePSIrK0ucOHFCTJ8+XSgUCpGWliaEkMcclKX41V5CyHsuKmvDhg1CpVKJlJQUcebMGTFhwgTh6ekpXQE3ZswYMX36dCn+xx9/FE5OTuLjjz8WZ8+eFbNmzSr1UveK9kd1OYZ58+YJpVIpvvnmG4t9ye3bt4UQD/a3f/vb34RerxdZWVnihx9+EF27dhWtWrUS9+/ft4kxzJ49W+zYsUP8/PPPIiMjQ4wcOVK4uLiI06dPW4zTlp8Hsx49eogRI0aUaK+L56Giv7XTp08XY8aMkeLNl7pPnTpVnD17ViQlJZV6qXt581JZsix+du3aJQCU+ImKihJCPLis8d133xW+vr5CpVKJ/v37i/Pnz1us4/r162LUqFGiXr16Qq1Wi7Fjx0pvdntQ2vgBWHwnxL1798Rf//pX4eXlJdzc3MQLL7wgrly5YrGeixcvikGDBglXV1fRsGFD8dZbbwmj0VjLo6m+V199VQQGBgqlUikaNWok+vfvLxU+QshjDsrycPEj57moik8++UQ0bdpUKJVK0a1bN3Hw4EGpr3fv3tJ+xuzrr78WTz75pFAqlaJdu3Zi+/btFv2V2R/V5RgCAwNL3ZfMmjVLCCHE3bt3RUREhGjUqJFwdnYWgYGBYvz48VX+Y/UoxxAbGyvF+vr6imeffVYcO3bMYn22/jwIIcS5c+cEAIt9mFldPA8V/a2NiooSvXv3LrFM586dhVKpFM2bN7f4m2RW3rxUlkIIIap2rIiIiIjIfvGcHyIiIpIVFj9EREQkKyx+iIiISFZY/BAREZGssPghIiIiWWHxQ0RERLLC4oeIiIhkhcUPERERyQqLHyIiIpIVFj9EREQkKyx+iIiISFb+P5DYu6/7aSkYAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "client_train.hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "x0ah0gBRLnHQ",
        "outputId": "e763f70b-0b83-498b-933a-8c7133da7872"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1000 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "invoice_train.hist(figsize=(14,10));"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_yc_47cLnHQ",
        "outputId": "8c92d65f-b058-4963-df3b-4099b6d8ee12"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [disrict, client_id, client_catg, region, creation_date, target]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "duplicated_rows = client_train[client_train.duplicated()]\n",
        "print(duplicated_rows)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XiJuDuoaSqNr"
      },
      "source": [
        "there is no dublicated rows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lUi5B_BLnHQ",
        "outputId": "d3b90c04-c840-4f25-ca40-d8646250f736"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   client_id invoice_date  tarif_type  counter_number  \\\n",
            "951893   train_Client_125864   2011-12-12          11          101545   \n",
            "1755798   train_Client_25894   2010-07-13          40         6986740   \n",
            "2137999    train_Client_3636   2012-01-18          40          312650   \n",
            "2163374   train_Client_37069   2010-10-11          11            5284   \n",
            "2433919   train_Client_44407   2008-02-28          11           66921   \n",
            "2805729   train_Client_54609   2010-04-06          11          200912   \n",
            "3260191   train_Client_66981   2006-07-17          11          132701   \n",
            "3260198   train_Client_66981   2006-07-17          11          132701   \n",
            "3394719    train_Client_7066   2014-10-28          40         4463323   \n",
            "3462929   train_Client_72519   2013-01-21          11          246464   \n",
            "3628821   train_Client_77029   2012-12-28          11          540417   \n",
            "\n",
            "        counter_statue  counter_code  reading_remarque  counter_coefficient  \\\n",
            "951893               5           413                 6                    1   \n",
            "1755798              5             5                 6                    1   \n",
            "2137999              5             5                 6                    1   \n",
            "2163374              5           413                 6                    1   \n",
            "2433919              5           203                 6                    1   \n",
            "2805729              5           207                 6                    1   \n",
            "3260191              5           420                 6                    1   \n",
            "3260198              5           420                 6                    1   \n",
            "3394719              5             5                 6                    1   \n",
            "3462929              5           203                 6                    1   \n",
            "3628821              5           420                 6                    1   \n",
            "\n",
            "         consommation_level_1  consommation_level_2  consommation_level_3  \\\n",
            "951893                      0                     0                     0   \n",
            "1755798                     0                     0                     0   \n",
            "2137999                     0                     0                     0   \n",
            "2163374                     0                     0                     0   \n",
            "2433919                     0                     0                     0   \n",
            "2805729                     0                     0                     0   \n",
            "3260191                     0                     0                     0   \n",
            "3260198                     0                     0                     0   \n",
            "3394719                     0                     0                     0   \n",
            "3462929                     0                     0                     0   \n",
            "3628821                     0                     0                     0   \n",
            "\n",
            "         consommation_level_4  old_index  new_index  months_number  \\\n",
            "951893                      0      13680      13680              2   \n",
            "1755798                     0          0          0              2   \n",
            "2137999                     0        623        623              2   \n",
            "2163374                     0      99429      99429              2   \n",
            "2433919                     0          0          0              2   \n",
            "2805729                     0          0          0              2   \n",
            "3260191                     0       5946       5946              2   \n",
            "3260198                     0       5946       5946              2   \n",
            "3394719                     0         80         80              2   \n",
            "3462929                     0       2954       2954              2   \n",
            "3628821                     0      26429      26429              2   \n",
            "\n",
            "        counter_type  \n",
            "951893          ELEC  \n",
            "1755798          GAZ  \n",
            "2137999          GAZ  \n",
            "2163374         ELEC  \n",
            "2433919         ELEC  \n",
            "2805729         ELEC  \n",
            "3260191         ELEC  \n",
            "3260198         ELEC  \n",
            "3394719          GAZ  \n",
            "3462929         ELEC  \n",
            "3628821         ELEC  \n"
          ]
        }
      ],
      "source": [
        "duplicated_rows = invoice_train[invoice_train.duplicated()]\n",
        "print(duplicated_rows)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBnrNK64VwGi"
      },
      "source": [
        "it seems the each clinet has more then one invoice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVv00e01LnHR",
        "outputId": "6ab6e89a-d65f-4fea-f11c-e31b0f60171e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Frequency of Categories for varible invoice_date\n",
            "2019-03-09    4127\n",
            "2017-03-10    3435\n",
            "2017-05-09    3411\n",
            "2017-02-06    3212\n",
            "2017-03-05    3195\n",
            "              ... \n",
            "2000-10-07       1\n",
            "1980-10-11       1\n",
            "1980-09-09       1\n",
            "1980-08-07       1\n",
            "1987-10-14       1\n",
            "Name: invoice_date, Length: 8275, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible tarif_type\n",
            "11    2679872\n",
            "40    1379755\n",
            "10     276210\n",
            "15      72422\n",
            "45      17552\n",
            "13      11656\n",
            "14      11611\n",
            "12      11345\n",
            "29      10090\n",
            "9        6039\n",
            "21        104\n",
            "8          43\n",
            "30         35\n",
            "24          9\n",
            "18          4\n",
            "42          1\n",
            "27          1\n",
            "Name: tarif_type, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible counter_number\n",
            "0                43161\n",
            "2786               251\n",
            "630317             230\n",
            "162709             226\n",
            "963                220\n",
            "                 ...  \n",
            "4920158              1\n",
            "431951               1\n",
            "2188701271500        1\n",
            "508843               1\n",
            "375249               1\n",
            "Name: counter_number, Length: 201893, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible counter_statue\n",
            "0         4346960\n",
            "1           73496\n",
            "0           32048\n",
            "5           20495\n",
            "4            2706\n",
            "1             540\n",
            "3             258\n",
            "5             144\n",
            "2              32\n",
            "4              23\n",
            "46             14\n",
            "A              13\n",
            "618            12\n",
            "769             6\n",
            "269375          1\n",
            "420             1\n",
            "Name: counter_statue, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible counter_code\n",
            "203    1516836\n",
            "5      1352035\n",
            "207     555628\n",
            "413     378917\n",
            "202     343251\n",
            "420      98273\n",
            "410      69080\n",
            "433      34447\n",
            "10       27744\n",
            "442      17050\n",
            "25       14934\n",
            "407      13768\n",
            "204      12427\n",
            "453       8290\n",
            "201       7672\n",
            "467       7302\n",
            "506       3389\n",
            "483       2830\n",
            "214       2643\n",
            "40        2482\n",
            "532       1982\n",
            "565       1082\n",
            "403       1070\n",
            "450       1059\n",
            "600        807\n",
            "210        789\n",
            "102        313\n",
            "227        178\n",
            "65          74\n",
            "101         74\n",
            "16          73\n",
            "310         51\n",
            "307         50\n",
            "305         44\n",
            "222         42\n",
            "0           33\n",
            "317         16\n",
            "303          5\n",
            "333          4\n",
            "367          3\n",
            "325          1\n",
            "1            1\n",
            "Name: counter_code, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible reading_remarque\n",
            "6      2230939\n",
            "9      1416992\n",
            "8       828123\n",
            "7          661\n",
            "203         15\n",
            "413         12\n",
            "207          6\n",
            "5            1\n",
            "Name: reading_remarque, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible counter_coefficient\n",
            "1     4475102\n",
            "2         886\n",
            "3         321\n",
            "40        197\n",
            "30        137\n",
            "0          46\n",
            "6          30\n",
            "4          12\n",
            "10          6\n",
            "20          3\n",
            "9           3\n",
            "50          2\n",
            "33          1\n",
            "5           1\n",
            "11          1\n",
            "8           1\n",
            "Name: counter_coefficient, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible consommation_level_1\n",
            "0       467553\n",
            "800     247953\n",
            "200     151245\n",
            "1200    139628\n",
            "300      40313\n",
            "         ...  \n",
            "6874         1\n",
            "5375         1\n",
            "7168         1\n",
            "8421         1\n",
            "8314         1\n",
            "Name: consommation_level_1, Length: 8295, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible consommation_level_2\n",
            "0        3816179\n",
            "400       118414\n",
            "100        34472\n",
            "200        13213\n",
            "800        12230\n",
            "          ...   \n",
            "16954          1\n",
            "30134          1\n",
            "20552          1\n",
            "21832          1\n",
            "32001          1\n",
            "Name: consommation_level_2, Length: 12576, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible consommation_level_3\n",
            "0       4293391\n",
            "800       45522\n",
            "200       31423\n",
            "400        7908\n",
            "1600       4522\n",
            "         ...   \n",
            "1169          1\n",
            "2403          1\n",
            "1799          1\n",
            "2184          1\n",
            "1390          1\n",
            "Name: consommation_level_3, Length: 2253, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible consommation_level_4\n",
            "0        4383791\n",
            "200          142\n",
            "120           84\n",
            "132           81\n",
            "125           78\n",
            "          ...   \n",
            "24846          1\n",
            "8874           1\n",
            "29143          1\n",
            "11714          1\n",
            "12645          1\n",
            "Name: consommation_level_4, Length: 12075, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible old_index\n",
            "0         287906\n",
            "1           6261\n",
            "2           4391\n",
            "3           3430\n",
            "4           2634\n",
            "           ...  \n",
            "401407         1\n",
            "133882         1\n",
            "104634         1\n",
            "109681         1\n",
            "86004          1\n",
            "Name: old_index, Length: 155648, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible new_index\n",
            "0         195931\n",
            "1           6189\n",
            "2           4178\n",
            "3           3377\n",
            "4           2609\n",
            "           ...  \n",
            "75308          1\n",
            "85975          1\n",
            "116005         1\n",
            "106164         1\n",
            "83410          1\n",
            "Name: new_index, Length: 157980, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible months_number\n",
            "4         3680478\n",
            "8          278772\n",
            "2          270909\n",
            "1          113266\n",
            "12          54328\n",
            "           ...   \n",
            "430834          1\n",
            "179002          1\n",
            "186166          1\n",
            "81499           1\n",
            "463554          1\n",
            "Name: months_number, Length: 1370, dtype: int64\n",
            "\n",
            "Frequency of Categories for varible counter_type\n",
            "ELEC    3079406\n",
            "GAZ     1397343\n",
            "Name: counter_type, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "for col in invoice_train:\n",
        "  if col == 'client_id':\n",
        "    continue\n",
        "  print ('\\nFrequency of Categories for varible %s'%col)\n",
        "  print (invoice_train[col].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V7HiL3UlLnHR"
      },
      "source": [
        "11, 140, 10 are the most Tariff Types\n",
        "\n",
        "4,8,2 most months_number\n",
        "\n",
        "ELEC is  majorty class\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MJPVx_zJsb1O",
        "outputId": "520f0a6c-69e8-40a6-c367-f97e559cbbcb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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ydfS3334LMzMzxMXFQaVSSeM3btxYobYVsrOzg7m5+TPts9J+V5o2bYrvvvvOoKa4P2iKY29vDzMzM1y9erXItKfHubu7QwgBNzc3tGjRolLtrS14BlwHzZkzB5aWlpg0aRLS09OLTL927RoiIiJKXYaxsTH69++P77//3uAr2vT0dERHR6NHjx7S191//vmnwbxWVlZo1qwZ8vLyAAAPHjzAw4cPDWrc3d1Rr149qcbDwwN+fn7S4OXlJdVaWloWG6rGxsZFzh5WrFhR4tnq2rVr8ejRI+nz6tWr8fjxY4NrWO7u7jh06FCR+co6Ay48u3myPUKIYvdzYZiV9IfCk8ss7zF4Fk5OTvD09MSmTZsM2nTu3Dns3bvX4A+Uihg0aBB+/vlnHD9+XBqXmZmJzZs3lznv0z9TSqUSHh4eEEIYHMPyCggIgJGRERYuXFjkjO/JY1bSz1pxjI2NoVAoDH42rl+/Xule24yNjaHRaLBz506kpaVJ4y9evIi4uLhyLcPS0rLYr5SL+/k8duwYEhMTy902Pz8/7Ny5Ezdv3pTGX716FXv27DGoHTp0KIyNjbFgwYIiv59CCINjW1J7awueAddB7u7uiI6OxogRI9C6dWuMGzcObdu2hV6vx9GjR7F9+3aMHz++zOV89NFHiI+PR48ePfDWW2/BxMQEX375JfLy8rB48WKpzsPDA76+vvDy8oKtrS1OnjyJ//73vwgODgYAXL58Gf369cMbb7wBDw8PmJiYYMeOHUhPT8fIkSPLbIeXlxf27duHzz//HM7OznBzc4O3tzdeffVVfP3117C2toaHhwcSExOxb98+6ZGOp+n1eqkdKSkpWLVqFXr06IEhQ4ZINZMmTcLUqVMxbNgwvPLKK/jll18QFxeHhg0bltrGVq1awd3dHe+++y7++OMPqNVqfPvtt8Veey384+Ltt9+GRqOBsbFxifuhvMfgWS1ZsgQDBw6Ej48PJk6cKD2GZG1tXeluM+fMmYOvv/4aAwYMwIwZM6THkJo0aWJwj0Bx+vfvD0dHR3Tv3h0ODg64ePEiVq5cCX9//wrfYAj8dZ3y/fffx4cffoiePXti6NChUKlUOHHiBJydnbFo0SIAfx2b1atX46OPPkKzZs1gb2+Pvn37FrtMf39/fP755xgwYABGjx6NjIwMREZGolmzZmVuX0kWLFiA2NhY9OzZE2+99RYeP36MFStWoE2bNuVappeXF7Zu3YqQkBB06dIFVlZWGDx4MF599VV89913+Nvf/gZ/f3+kpqZizZo18PDwwP3798vVtvDwcOzduxfdu3fHtGnTkJ+fj5UrV6Jt27YG/YG7u7vjo48+QmhoKK5fv46AgADUq1cPqamp2LFjB6ZMmYJ333231PbWGnLcek01w+XLl8XkyZOFq6urUCqVol69eqJ79+5ixYoV4uHDh1Id/q+TgOKcOnVKaDQaYWVlJSwsLESfPn3E0aNHDWo++ugj0bVrV2FjYyPMzc1Fq1atxMcffyw98nP79m0RFBQkWrVqJSwtLYW1tbXw9vYW27ZtK9d2XLp0SfTq1UuYm5sbdMRx9+5dMWHCBNGwYUNhZWUlNBqNuHTpUpFHhp7uiKN+/frCyspKjBkzxuCxGyGEyM/PF3PnzpU6stBoNOLq1avlegzpwoULws/PT1hZWYmGDRuKyZMnS4+abNy4Uap7/PixmD59urCzsxMKhaJcHXGUdQxK6m6zIl1m7tu3T3Tv3l2Ym5sLtVotBg8ebNARx5PLK89jSEIIcebMGdG7d+8Kd8Tx5Zdfil69eokGDRoIlUol3N3dxezZs0V2drZUU/ioUWZmZpH1ltQ5xIYNG0THjh2FSqUS9evXF7179xbx8fHSdK1WK/z9/UW9evXK1RHH+vXrRfPmzYVKpRKtWrUSGzduLHbdJf2OPf1zJYQQBw8eFF5eXkKpVFaoIw4hhLh//74YPXq0sLGxMejYoqCgQHzyySeiSZMmQqVSiY4dO4pdu3aJwMBAg8eAnuyIozgJCQmiY8eOQqlUCnd3d7Fu3TrxzjvvCDMzsyK13377rejRo4ewtLQUlpaWolWrViIoKEikpKSU2d7aQiHEC3b3BRERvTACAgJKfQyxLuM1YCIiqhJPv0XtypUr2L17d5W+SrI24RkwERFVCScnJ4wfPx5NmzbFjRs3sHr1auTl5eH06dPFPr9c1/EmLCIiqhIDBgzAf/7zH2i1WqhUKvj4+OCTTz5h+JaAZ8BEREQy4DVgIiIiGdSYAP7000+hUCgwc+ZMadzDhw8RFBSEBg0awMrKCsOGDSvScURaWhr8/f1hYWEBe3t7zJ49G48fPzaoOXDgADp16gSVSoVmzZpJ3f89KTIyEq6urjAzM4O3t7dB5wBERERVrUZcAz5x4gS+/PJLtG/f3mD8rFmzEBMTg+3bt8Pa2hrBwcEYOnSo1Ol6fn4+/P394ejoiKNHj+LWrVsYN24cTE1N8cknnwAAUlNT4e/vj6lTp2Lz5s1ISEjApEmT4OTkJL1BpPBB7zVr1sDb2xvLly+HRqNBSkoK7O3ty7UNBQUFuHnzJurVq1fru08jIqKSCSFw7949ODs7F3m71tOFsrp3755o3ry5iI+PF7179xYzZswQQgiRlZUlTE1NDR7ov3jxogAgEhMThRBC7N69WxgZGQmtVivVrF69WqjVaundlnPmzCny3tARI0YIjUYjfe7atavBQ/D5+fnC2dlZLFq0qMR2P3z4UGRnZ0vDhQsXBAAOHDhw4MBBABC//fZbqfkn+xlwUFAQ/P394efnh48++kgan5SUhEePHsHPz08a16pVKzRu3BiJiYno1q0bEhMT0a5dOzg4OEg1Go0G06ZNw/nz59GxY0ckJiYaLKOwpvCrbr1ej6SkJISGhkrTjYyM4OfnV2o/qIsWLcKCBQuKjP/tt9+qpP9dIiJ6Mel0Ori4uJTZLaqsAbxlyxacOnWq2Jdqa7VaKJVK2NjYGIx3cHCQ3hOr1WoNwrdweuG00mp0Oh1yc3Nx9+5d5OfnF1tz6dKlEtseGhqKkJAQ6XPhDler1QxgIiIq83KkbAH822+/YcaMGYiPj6/wq8NqApVKZfCKMSIiooqQ7S7opKQkZGRkoFOnTjAxMYGJiQkOHjyIL774AiYmJnBwcIBery/y6q/09HQ4OjoCABwdHYvcFV34uawatVoNc3NzNGzYEMbGxsXWFC6DiIioqskWwP369cPZs2eRnJwsDZ07d8aYMWOkf5uamiIhIUGaJyUlBWlpadJL5H18fHD27FlkZGRINfHx8VCr1fDw8JBqnlxGYU3hMpRKJby8vAxqCgoKkJCQUOmX1RMREZVFtq+g69Wrh7Zt2xqMs7S0RIMGDaTxEydOREhICGxtbaFWqzF9+nT4+PigW7duAP56J6iHhwfGjh2LxYsXQ6vVYt68eQgKCpK+Hp46dSpWrlyJOXPm4M0338T+/fuxbds2xMTESOsNCQlBYGAgOnfujK5du2L58uXIycnBhAkTntPeICKqmfLz8/Ho0SO5m1GjGBsbw8TE5JkfOZX9LujSLFu2DEZGRhg2bBjy8vKg0WiwatUqabqxsTF27dqFadOmwcfHB5aWlggMDMTChQulGjc3N8TExGDWrFmIiIhAo0aNsG7dOukZYAAYMWIEMjMzERYWBq1WC09PT8TGxha5MYuIqC65f/8+fv/9dwj2WFyEhYUFnJycoFQqK70M9gVdRXQ6HaytrZGdnc27oInohZefn48rV67AwsICdnZ27GDo/wghoNfrkZmZifz8fDRv3rxIZxvlzYMafQZMRETyePToEYQQsLOzg7m5udzNqVHMzc1hamqKGzduQK/XV/pJnhrTFzQREdU8PPMtXqldTJZ3GVXQDiIiIqogBjAREZEMGMBEREQyYAATEVG18PX1NXjHu9xqWnsYwEREVGPp9Xq5m1BtGMBERFTlxo8fj4MHDyIiIgIKhQIKhQLXrl3DxIkT4ebmBnNzc7Rs2RIRERFF5gsICMDHH38MZ2dntGzZEgBw9OhReHp6wszMDJ07d8bOnTuhUCiQnJwszXvu3DkMHDgQVlZWcHBwwNixY3H79u0S23P9+vXntTuKxeeAiYioykVERODy5cto27at1Dth/fr10ahRI2zfvh0NGjTA0aNHMWXKFDg5OeGNN96Q5k1ISIBarUZ8fDyAvzq2GDx4MAYNGoTo6GjcuHGjyFfJWVlZ6Nu3LyZNmoRly5YhNzcXc+fOxRtvvIH9+/cX2x47O7vnszNKwAAmqgDX92LKLnoG1z/1r9blEz0v1tbWUCqVsLCwMHiz3IIFC6R/u7m5ITExEdu2bTMIYEtLS6xbt07q5nHNmjVQKBT46quvYGZmBg8PD/zxxx+YPHmyNM/KlSvRsWNHfPLJJ9K4DRs2wMXFBZcvX0aLFi2KbY+cGMBERPTcREZGYsOGDUhLS0Nubi70ej08PT0Natq1a2fQx3JKSgrat29v0ONU165dDeb55Zdf8OOPP8LKyqrIOq9du4YWLVpU7YZUAQYwERE9F1u2bMG7776LpUuXwsfHB/Xq1cOSJUtw7NgxgzpLS8sKL/v+/fsYPHgw/vWvfxWZ5uTkVOk2VycGMBERVQulUon8/Hzp85EjR/Dyyy/jrbfeksZdu3atzOW0bNkS33zzDfLy8qRXzZ44ccKgplOnTvj222/h6uoKE5Pio+3p9siNd0ETEVG1cHV1xbFjx3D9+nXcvn0bzZs3x8mTJxEXF4fLly/jgw8+KBKkxRk9ejQKCgowZcoUXLx4EXFxcfjss88A/P++qoOCgnDnzh2MGjUKJ06cwLVr1xAXF4cJEyZIoft0ewoKCqpv48uBAUxERNXi3XffhbGxMTw8PGBnZweNRoOhQ4dixIgR8Pb2xp9//mlwNlwStVqNH374AcnJyfD09MT777+PsLAwAJCuCzs7O+PIkSPIz89H//790a5dO8ycORM2NjbSixOebk9aWlr1bXw58H3AVYTvA64beBc01RUPHz5Eamoq3NzcKv26veq0efNmTJgwAdnZ2bK8LrG0/cP3ARMRUa3x73//G02bNsVLL72EX375RXrG90V+VzEDmIiIajytVouwsDBotVo4OTlh+PDh+Pjjj+Vu1jNhABMRUY03Z84czJkzR+5mVCnehEVERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDPgYEhERlVt19wb3tNrcOxzPgImIqNaJjIyEq6srzMzM4O3tjePHj5dav337drRq1QpmZmZo164ddu/eXe1tZAATEVGtsnXrVoSEhGD+/Pk4deoUOnToAI1Gg4yMjGLrjx49ilGjRmHixIk4ffo0AgICEBAQgHPnzlVrOxnARERUq3z++eeYPHkyJkyYAA8PD6xZswYWFhbYsGFDsfUREREYMGAAZs+ejdatW+PDDz9Ep06dsHLlymptJwOYiIhqDb1ej6SkJPj5+UnjjIyM4Ofnh8TExGLnSUxMNKgHAI1GU2J9VWEAExFRrXH79m3k5+fDwcHBYLyDgwO0Wm2x82i12grVVxUGMBERkQwYwEREVGs0bNgQxsbGSE9PNxifnp4OR0fHYudxdHSsUH1VYQATEVGtoVQq4eXlhYSEBGlcQUEBEhIS4OPjU+w8Pj4+BvUAEB8fX2J9VWFHHEREVKuEhIQgMDAQnTt3RteuXbF8+XLk5ORgwoQJAIBx48bhpZdewqJFiwAAM2bMQO/evbF06VL4+/tjy5YtOHnyJNauXVut7WQAExFRub0IPVONGDECmZmZCAsLg1arhaenJ2JjY6UbrdLS0mBk9P+/AH755ZcRHR2NefPm4Z///CeaN2+OnTt3om3bttXaTlm/gl69ejXat28PtVoNtVoNHx8f7NmzR5ru6+sLhUJhMEydOtVgGWlpafD394eFhQXs7e0xe/ZsPH782KDmwIED6NSpE1QqFZo1a4aoqKgibalorylERFRzBQcH48aNG8jLy8OxY8fg7e0tTTtw4ECRHBg+fDhSUlKQl5eHc+fOYdCgQdXeRlkDuFGjRvj000+RlJSEkydPom/fvnjttddw/vx5qWby5Mm4deuWNCxevFialp+fD39/f+j1ehw9ehSbNm1CVFQUwsLCpJrU1FT4+/ujT58+SE5OxsyZMzFp0iTExcVJNRXtNYWIiOhZKYQQQu5GPMnW1hZLlizBxIkT4evrC09PTyxfvrzY2j179uDVV1/FzZs3pa8W1qxZg7lz5yIzMxNKpRJz585FTEyMQZdiI0eORFZWFmJjYwEA3t7e6NKli9TrSUFBAVxcXDB9+nS899575Wq3TqeDtbU1srOzoVarn2EPUE1W3R3Rvwhf71Hd8PDhQ6SmpsLNzQ1mZmZyN6fGKW3/lDcPasxd0Pn5+diyZQtycnIM7jzbvHkzGjZsiLZt2yI0NBQPHjyQpiUmJqJdu3YGD1BrNBrodDrpLLqsHk4q02sKAOTl5UGn0xkMRERE5SX7TVhnz56Fj48PHj58CCsrK+zYsQMeHh4AgNGjR6NJkyZwdnbGmTNnMHfuXKSkpOC7774DUHLvJYXTSqvR6XTIzc3F3bt3S+w15dKlSyW2e9GiRViwYMGzbTwREdVZsgdwy5YtkZycjOzsbPz3v/9FYGAgDh48CA8PD0yZMkWqa9euHZycnNCvXz9cu3YN7u7uMrYaCA0NRUhIiPRZp9PBxcVFxhYREdGLRPYAViqVaNasGQDAy8sLJ06cQEREBL788ssitYV3sV29ehXu7u5wdHQscrdyYW8mhT2YlNTDiVqthrm5OYyNjSvcawoAqFQqqFSqCm4tERHRX2rMNeBCBQUFyMvLK3ZacnIyAMDJyQnAX72XnD171uBu5fj4eKjVaulr7LJ6OKlMrylERETPStYz4NDQUAwcOBCNGzfGvXv3EB0djQMHDiAuLg7Xrl1DdHQ0Bg0ahAYNGuDMmTOYNWsWevXqhfbt2wMA+vfvDw8PD4wdOxaLFy+GVqvFvHnzEBQUJJ2dTp06FStXrsScOXPw5ptvYv/+/di2bRtiYv7/3axl9ZpCRERU1WQN4IyMDIwbNw63bt2CtbU12rdvj7i4OLzyyiv47bffsG/fPikMXVxcMGzYMMybN0+a39jYGLt27cK0adPg4+MDS0tLBAYGYuHChVKNm5sbYmJiMGvWLERERKBRo0ZYt24dNBqNVFNWrylERERVrcY9B/yi4nPAdQOfA6a6osTnXMOtn29DwrOf7/rKqVY9B0xERFQVDh06hMGDB8PZ2RkKhQI7d+4sc57ydFlc1RjARERUq+Tk5KBDhw6IjIwsV315uiyuDrI/hkRERFSVBg4ciIEDB5a7fs2aNXBzc8PSpUsBAK1bt8bhw4exbNkyg/uFqhrPgImIqE4rq8vi6sIAJiKiOq2sLourCwOYiIhIBgxgIiKq08rqsri6MICJiKhOK6vL4urCACYiolrl/v37SE5Olt4fkJqaiuTkZKSlpQH4qxvkcePGSfVTp07Fr7/+ijlz5uDSpUtYtWoVtm3bhlmzZlVrO/kYEhERlV8N7ZnqSSdPnkSfPn2kz4Wvjg0MDERUVBRu3bolhTFQvi6LqwMDmIiIahVfX1+U1stycb1c+fr64vTp09XYqqL4FTQREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGViK+ML15V7BcGMBERFWFsbAwA0Ov1MrekZnrw4AEAwNTUtNLL4GNIRERUhImJCSwsLJCZmQlTU1MYGfF8DfjrzPfBgwfIyMiAjY2N9IdKZTCAiYioCIVCAScnJ6SmpuLGjRtyN6fGsbGxgaOj4zMtgwFMRETFUiqVaN68Ob+GfoqpqekznfkWYgATEVGJjIyMYGZmJnczaiV+qU9ERCQDBjAREZEMGMBEREQy4DVgIiKqENf3Yqp1+dc/9a/W5dcUPAMmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBe8J6gVR37zNA3emBhohIbjwDJiIikgEDmIiISAayBvDq1avRvn17qNVqqNVq+Pj4YM+ePdL0hw8fIigoCA0aNICVlRWGDRuG9PR0g2WkpaXB398fFhYWsLe3x+zZs/H48WODmgMHDqBTp05QqVRo1qwZoqKiirQlMjISrq6uMDMzg7e3N44fP14t20xERATIHMCNGjXCp59+iqSkJJw8eRJ9+/bFa6+9hvPnzwMAZs2ahR9++AHbt2/HwYMHcfPmTQwdOlSaPz8/H/7+/tDr9Th69Cg2bdqEqKgohIWFSTWpqanw9/dHnz59kJycjJkzZ2LSpEmIi4uTarZu3YqQkBDMnz8fp06dQocOHaDRaJCRkfH8dgYREdUpCiGEkLsRT7K1tcWSJUvw+uuvw87ODtHR0Xj99dcBAJcuXULr1q2RmJiIbt26Yc+ePXj11Vdx8+ZNODg4AADWrFmDuXPnIjMzE0qlEnPnzkVMTAzOnTsnrWPkyJHIyspCbGwsAMDb2xtdunTBypUrAQAFBQVwcXHB9OnT8d5775Wr3TqdDtbW1sjOzoZara7KXSLhTVjy42vYiPh7UJby5kGNuQacn5+PLVu2ICcnBz4+PkhKSsKjR4/g5+cn1bRq1QqNGzdGYmIiACAxMRHt2rWTwhcANBoNdDqddBadmJhosIzCmsJl6PV6JCUlGdQYGRnBz89PqilOXl4edDqdwUBERFResgfw2bNnYWVlBZVKhalTp2LHjh3w8PCAVquFUqmEjY2NQb2DgwO0Wi0AQKvVGoRv4fTCaaXV6HQ65Obm4vbt28jPzy+2pnAZxVm0aBGsra2lwcXFpVLbT0REdZPsAdyyZUskJyfj2LFjmDZtGgIDA3HhwgW5m1Wm0NBQZGdnS8Nvv/0md5OIiOgFIntHHEqlEs2aNQMAeHl54cSJE4iIiMCIESOg1+uRlZVlcBacnp4OR0dHAICjo2ORu5UL75J+subpO6fT09OhVqthbm4OY2NjGBsbF1tTuIziqFQqqFSqym00ERHVebKfAT+toKAAeXl58PLygqmpKRISEqRpKSkpSEtLg4+PDwDAx8cHZ8+eNbhbOT4+Hmq1Gh4eHlLNk8sorClchlKphJeXl0FNQUEBEhISpBoiIqKqJusZcGhoKAYOHIjGjRvj3r17iI6OxoEDBxAXFwdra2tMnDgRISEhsLW1hVqtxvTp0+Hj44Nu3boBAPr37w8PDw+MHTsWixcvhlarxbx58xAUFCSdnU6dOhUrV67EnDlz8Oabb2L//v3Ytm0bYmL+/118ISEhCAwMROfOndG1a1csX74cOTk5mDBhgiz7hYiIaj9ZAzgjIwPjxo3DrVu3YG1tjfbt2yMuLg6vvPIKAGDZsmUwMjLCsGHDkJeXB41Gg1WrVknzGxsbY9euXZg2bRp8fHxgaWmJwMBALFy4UKpxc3NDTEwMZs2ahYiICDRq1Ajr1q2DRqORakaMGIHMzEyEhYVBq9XC09MTsbGxRW7MIiIiqio17jngFxWfA64b+PwjEX8PyvLCPQdMRERUlzCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZmMjdACKiinB9L6Zal3/9U/9qXT5RIZ4BExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQxkDeBFixahS5cuqFevHuzt7REQEICUlBSDGl9fXygUCoNh6tSpBjVpaWnw9/eHhYUF7O3tMXv2bDx+/Nig5sCBA+jUqRNUKhWaNWuGqKioIu2JjIyEq6srzMzM4O3tjePHj1f5NhMREQEyB/DBgwcRFBSEn3/+GfHx8Xj06BH69++PnJwcg7rJkyfj1q1b0rB48WJpWn5+Pvz9/aHX63H06FFs2rQJUVFRCAsLk2pSU1Ph7++PPn36IDk5GTNnzsSkSZMQFxcn1WzduhUhISGYP38+Tp06hQ4dOkCj0SAjI6P6dwQREdU5JnKuPDY21uBzVFQU7O3tkZSUhF69eknjLSws4OjoWOwy9u7diwsXLmDfvn1wcHCAp6cnPvzwQ8ydOxfh4eFQKpVYs2YN3NzcsHTpUgBA69atcfjwYSxbtgwajQYA8Pnnn2Py5MmYMGECAGDNmjWIiYnBhg0b8N5771XH5hMRUR1Wo64BZ2dnAwBsbW0Nxm/evBkNGzZE27ZtERoaigcPHkjTEhMT0a5dOzg4OEjjNBoNdDodzp8/L9X4+fkZLFOj0SAxMREAoNfrkZSUZFBjZGQEPz8/qeZpeXl50Ol0BgMREVF5yXoG/KSCggLMnDkT3bt3R9u2baXxo0ePRpMmTeDs7IwzZ85g7ty5SElJwXfffQcA0Gq1BuELQPqs1WpLrdHpdMjNzcXdu3eRn59fbM2lS5eKbe+iRYuwYMGCZ9toIiKqs2pMAAcFBeHcuXM4fPiwwfgpU6ZI/27Xrh2cnJzQr18/XLt2De7u7s+7mZLQ0FCEhIRIn3U6HVxcXGRrDxERvVhqRAAHBwdj165dOHToEBo1alRqrbe3NwDg6tWrcHd3h6OjY5G7ldPT0wFAum7s6OgojXuyRq1Ww9zcHMbGxjA2Ni62pqRrzyqVCiqVqvwbSURE9ARZrwELIRAcHIwdO3Zg//79cHNzK3Oe5ORkAICTkxMAwMfHB2fPnjW4Wzk+Ph5qtRoeHh5STUJCgsFy4uPj4ePjAwBQKpXw8vIyqCkoKEBCQoJUQ0REVJVkPQMOCgpCdHQ0vv/+e9SrV0+6ZmttbQ1zc3Ncu3YN0dHRGDRoEBo0aIAzZ85g1qxZ6NWrF9q3bw8A6N+/Pzw8PDB27FgsXrwYWq0W8+bNQ1BQkHSGOnXqVKxcuRJz5szBm2++if3792Pbtm2IiYmR2hISEoLAwEB07twZXbt2xfLly5GTkyPdFU1ERFSVZA3g1atXA/irs40nbdy4EePHj4dSqcS+ffukMHRxccGwYcMwb948qdbY2Bi7du3CtGnT4OPjA0tLSwQGBmLhwoVSjZubG2JiYjBr1ixERESgUaNGWLdunfQIEgCMGDECmZmZCAsLg1arhaenJ2JjY4vcmEVERFQVZA1gIUSp011cXHDw4MEyl9OkSRPs3r271BpfX1+cPn261Jrg4GAEBweXuT4iIqJnVaOeAyYiIqorGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDJgABMREcmAAUxERCQDBjAREZEMGMBEREQyYAATERHJgAFMREQkAwYwERGRDBjAREREMmAAExERyYABTEREJAMGMBERkQwYwERERDKoVAA3bdoUf/75Z5HxWVlZaNq06TM3ioiIqLarVABfv34d+fn5Rcbn5eXhjz/+eOZGERER1XYmFSn+3//+J/07Li4O1tbW0uf8/HwkJCTA1dW1yhpHRERUW1UogAMCAgAACoUCgYGBBtNMTU3h6uqKpUuXVlnjiIiIaqsKBXBBQQEAwM3NDSdOnEDDhg2rpVFERES1XYUCuFBqampVt4OIiKhOqVQAA0BCQgISEhKQkZEhnRkX2rBhwzM3jIiIqDarVAAvWLAACxcuROfOneHk5ASFQlHV7SIiIqrVKhXAa9asQVRUFMaOHVvV7SEiIqoTKvUcsF6vx8svv1zVbSEiIqozKhXAkyZNQnR0dFW3hYiIqM6o1FfQDx8+xNq1a7Fv3z60b98epqamBtM///zzKmkcERFRbVWpAD5z5gw8PT0BAOfOnTOYxhuyiIiIylapAP7xxx+ruh1ERER1Cl9HSEREJINKBXCfPn3Qt2/fEofyWrRoEbp06YJ69erB3t4eAQEBSElJMah5+PAhgoKC0KBBA1hZWWHYsGFIT083qElLS4O/vz8sLCxgb2+P2bNn4/HjxwY1Bw4cQKdOnaBSqdCsWTNERUUVaU9kZCRcXV1hZmYGb29vHD9+vPw7hYiIqAIqFcCenp7o0KGDNHh4eECv1+PUqVNo165duZdz8OBBBAUF4eeff0Z8fDwePXqE/v37IycnR6qZNWsWfvjhB2zfvh0HDx7EzZs3MXToUGl6fn4+/P39odfrcfToUWzatAlRUVEICwuTalJTU+Hv748+ffogOTkZM2fOxKRJkxAXFyfVbN26FSEhIZg/fz5OnTqFDh06QKPRICMjozK7iIiIqFQKIYSoqoWFh4fj/v37+Oyzzyo1f2ZmJuzt7XHw4EH06tUL2dnZsLOzQ3R0NF5//XUAwKVLl9C6dWskJiaiW7du2LNnD1599VXcvHkTDg4OAP7qKGTu3LnIzMyEUqnE3LlzERMTY3DD2MiRI5GVlYXY2FgAgLe3N7p06YKVK1cC+OvFEy4uLpg+fTree++9Mtuu0+lgbW2N7OxsqNXqSm1/WVzfi6mW5T7p+qf+1b6OF1l1HwPu/7LxGMiPx6B05c2DKr0G/Pe///2Z+oHOzs4GANja2gIAkpKS8OjRI/j5+Uk1rVq1QuPGjZGYmAgASExMRLt27aTwBQCNRgOdTofz589LNU8uo7CmcBl6vR5JSUkGNUZGRvDz85NqnpaXlwedTmcwEBERlVeVBnBiYiLMzMwqNW9BQQFmzpyJ7t27o23btgAArVYLpVIJGxsbg1oHBwdotVqp5snwLZxeOK20Gp1Oh9zcXNy+fRv5+fnF1hQu42mLFi2CtbW1NLi4uFRqu4mIqG6q1GNIT16DBQAhBG7duoWTJ0/igw8+qFRDgoKCcO7cORw+fLhS8z9voaGhCAkJkT7rdDqGMBERlVulAtja2trgs5GREVq2bImFCxeif//+FV5ecHAwdu3ahUOHDqFRo0bSeEdHR+j1emRlZRmcBaenp8PR0VGqefpu5cK7pJ+sefrO6fT0dKjVapibm8PY2BjGxsbF1hQu42kqlQoqlarC20pERARUMoA3btxYJSsXQmD69OnYsWMHDhw4ADc3N4PpXl5eMDU1RUJCAoYNGwYASElJQVpaGnx8fAAAPj4++Pjjj5GRkQF7e3sAQHx8PNRqNTw8PKSa3bt3Gyw7Pj5eWoZSqYSXlxcSEhIQEBAA4K+vxBMSEhAcHFwl20pERPSkSgVwoaSkJFy8eBEA0KZNG3Ts2LFC8wcFBSE6Ohrff/896tWrJ11vtba2hrm5OaytrTFx4kSEhITA1tYWarUa06dPh4+PD7p16wYA6N+/Pzw8PDB27FgsXrwYWq0W8+bNQ1BQkHSGOnXqVKxcuRJz5szBm2++if3792Pbtm2Iifn/d/KFhIQgMDAQnTt3RteuXbF8+XLk5ORgwoQJz7KLiIiIilWpAM7IyMDIkSNx4MAB6avhrKws9OnTB1u2bIGdnV25lrN69WoAgK+vr8H4jRs3Yvz48QCAZcuWwcjICMOGDUNeXh40Gg1WrVol1RobG2PXrl2YNm0afHx8YGlpicDAQCxcuFCqcXNzQ0xMDGbNmoWIiAg0atQI69atg0ajkWpGjBiBzMxMhIWFQavVwtPTE7GxsUVuzCIiIqoKlQrg6dOn4969ezh//jxat24NALhw4QICAwPx9ttv4z//+U+5llOeR5DNzMwQGRmJyMjIEmuaNGlS5Cvmp/n6+uL06dOl1gQHB/MrZyIiei4qFcCxsbHYt2+fFL4A4OHhgcjIyErdhEVERFTXVOo54IKCgiLvAAYAU1NTFBQUPHOjiIiIartKBXDfvn0xY8YM3Lx5Uxr3xx9/YNasWejXr1+VNY6IiKi2qlQAr1y5EjqdDq6urnB3d4e7uzvc3Nyg0+mwYsWKqm4jERFRrVOpa8AuLi44deoU9u3bh0uXLgEAWrduXaS/ZSIiIipehc6A9+/fDw8PD+h0OigUCrzyyiuYPn06pk+fji5duqBNmzb46aefqqutREREtUaFAnj58uWYPHlysa9Xsra2xj/+8Q98/vnnVdY4IiKi2qpCAfzLL79gwIABJU7v378/kpKSnrlRREREtV2FAjg9Pb3Yx48KmZiYIDMz85kbRUREVNtVKIBfeuklnDt3rsTpZ86cgZOT0zM3ioiIqLarUAAPGjQIH3zwAR4+fFhkWm5uLubPn49XX321yhpHRERUW1XoMaR58+bhu+++Q4sWLRAcHIyWLVsCAC5duoTIyEjk5+fj/fffr5aGEhER1SYVCmAHBwccPXoU06ZNQ2hoqPQyBYVCAY1Gg8jISL49iIiIqBwq3BFH4ZuH7t69i6tXr0IIgebNm6N+/frV0T4iIqJaqVI9YQFA/fr10aVLl6psCxERUZ1Rqb6giYiI6NkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZCBrAB86dAiDBw+Gs7MzFAoFdu7caTB9/PjxUCgUBsOAAQMMau7cuYMxY8ZArVbDxsYGEydOxP379w1qzpw5g549e8LMzAwuLi5YvHhxkbZs374drVq1gpmZGdq1a4fdu3dX+fYSEREVkjWAc3Jy0KFDB0RGRpZYM2DAANy6dUsa/vOf/xhMHzNmDM6fP4/4+Hjs2rULhw4dwpQpU6TpOp0O/fv3R5MmTZCUlIQlS5YgPDwca9eulWqOHj2KUaNGYeLEiTh9+jQCAgIQEBCAc+fOVf1GExERATCRc+UDBw7EwIEDS61RqVRwdHQsdtrFixcRGxuLEydOoHPnzgCAFStWYNCgQfjss8/g7OyMzZs3Q6/XY8OGDVAqlWjTpg2Sk5Px+eefS0EdERGBAQMGYPbs2QCADz/8EPHx8Vi5ciXWrFlThVtMRET0lxp/DfjAgQOwt7dHy5YtMW3aNPz555/StMTERNjY2EjhCwB+fn4wMjLCsWPHpJpevXpBqVRKNRqNBikpKbh7965U4+fnZ7BejUaDxMTEEtuVl5cHnU5nMBAREZVXjQ7gAQMG4N///jcSEhLwr3/9CwcPHsTAgQORn58PANBqtbC3tzeYx8TEBLa2ttBqtVKNg4ODQU3h57JqCqcXZ9GiRbC2tpYGFxeXZ9tYIiKqU2T9CrosI0eOlP7drl07tG/fHu7u7jhw4AD69esnY8uA0NBQhISESJ91Oh1DmIiIyq1GnwE/rWnTpmjYsCGuXr0KAHB0dERGRoZBzePHj3Hnzh3purGjoyPS09MNago/l1VT0rVn4K9r02q12mAgIiIqrxcqgH///Xf8+eefcHJyAgD4+PggKysLSUlJUs3+/ftRUFAAb29vqebQoUN49OiRVBMfH4+WLVuifv36Uk1CQoLBuuLj4+Hj41Pdm0RERHWUrAF8//59JCcnIzk5GQCQmpqK5ORkpKWl4f79+5g9ezZ+/vlnXL9+HQkJCXjttdfQrFkzaDQaAEDr1q0xYMAATJ48GcePH8eRI0cQHByMkSNHwtnZGQAwevRoKJVKTJw4EefPn8fWrVsRERFh8PXxjBkzEBsbi6VLl+LSpUsIDw/HyZMnERwc/Nz3CRER1Q2yBvDJkyfRsWNHdOzYEQAQEhKCjh07IiwsDMbGxjhz5gyGDBmCFi1aYOLEifDy8sJPP/0ElUolLWPz5s1o1aoV+vXrh0GDBqFHjx4Gz/haW1tj7969SE1NhZeXF9555x2EhYUZPCv88ssvIzo6GmvXrkWHDh3w3//+Fzt37kTbtm2f384gIqI6RdabsHx9fSGEKHF6XFxcmcuwtbVFdHR0qTXt27fHTz/9VGrN8OHDMXz48DLXR0REVBVeqGvAREREtQUDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikoGsAXzo0CEMHjwYzs7OUCgU2Llzp8F0IQTCwsLg5OQEc3Nz+Pn54cqVKwY1d+7cwZgxY6BWq2FjY4OJEyfi/v37BjVnzpxBz549YWZmBhcXFyxevLhIW7Zv345WrVrBzMwM7dq1w+7du6t8e4mIiArJGsA5OTno0KEDIiMji52+ePFifPHFF1izZg2OHTsGS0tLaDQaPHz4UKoZM2YMzp8/j/j4eOzatQuHDh3ClClTpOk6nQ79+/dHkyZNkJSUhCVLliA8PBxr166Vao4ePYpRo0Zh4sSJOH36NAICAhAQEIBz585V38YTEVGdZiLnygcOHIiBAwcWO00IgeXLl2PevHl47bXXAAD//ve/4eDggJ07d2LkyJG4ePEiYmNjceLECXTu3BkAsGLFCgwaNAifffYZnJ2dsXnzZuj1emzYsAFKpRJt2rRBcnIyPv/8cymoIyIiMGDAAMyePRsA8OGHHyI+Ph4rV67EmjVrnsOeICKiuqbGXgNOTU2FVquFn5+fNM7a2hre3t5ITEwEACQmJsLGxkYKXwDw8/ODkZERjh07JtX06tULSqVSqtFoNEhJScHdu3elmifXU1hTuJ7i5OXlQafTGQxERETlVWMDWKvVAgAcHBwMxjs4OEjTtFot7O3tDaabmJjA1tbWoKa4ZTy5jpJqCqcXZ9GiRbC2tpYGFxeXim4iERHVYTU2gGu60NBQZGdnS8Nvv/0md5OIiOgFUmMD2NHREQCQnp5uMD49PV2a5ujoiIyMDIPpjx8/xp07dwxqilvGk+soqaZwenFUKhXUarXBQEREVF41NoDd3Nzg6OiIhIQEaZxOp8OxY8fg4+MDAPDx8UFWVhaSkpKkmv3796OgoADe3t5SzaFDh/Do0SOpJj4+Hi1btkT9+vWlmifXU1hTuB4iIqKqJmsA379/H8nJyUhOTgbw141XycnJSEtLg0KhwMyZM/HRRx/hf//7H86ePYtx48bB2dkZAQEBAIDWrVtjwIABmDx5Mo4fP44jR44gODgYI0eOhLOzMwBg9OjRUCqVmDhxIs6fP4+tW7ciIiICISEhUjtmzJiB2NhYLF26FJcuXUJ4eDhOnjyJ4ODg571LiIiojpD1MaSTJ0+iT58+0ufCUAwMDERUVBTmzJmDnJwcTJkyBVlZWejRowdiY2NhZmYmzbN582YEBwejX79+MDIywrBhw/DFF19I062trbF3714EBQXBy8sLDRs2RFhYmMGzwi+//DKio6Mxb948/POf/0Tz5s2xc+dOtG3b9jnsBSIiqotkDWBfX18IIUqcrlAosHDhQixcuLDEGltbW0RHR5e6nvbt2+Onn34qtWb48OEYPnx46Q0mIiKqIjX2GjAREVFtxgAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkUKMDODw8HAqFwmBo1aqVNP3hw4cICgpCgwYNYGVlhWHDhiE9Pd1gGWlpafD394eFhQXs7e0xe/ZsPH782KDmwIED6NSpE1QqFZo1a4aoqKjnsXlERFSH1egABoA2bdrg1q1b0nD48GFp2qxZs/DDDz9g+/btOHjwIG7evImhQ4dK0/Pz8+Hv7w+9Xo+jR49i06ZNiIqKQlhYmFSTmpoKf39/9OnTB8nJyZg5cyYmTZqEuLi457qdRERUt5jI3YCymJiYwNHRscj47OxsrF+/HtHR0ejbty8AYOPGjWjdujV+/vlndOvWDXv37sWFCxewb98+ODg4wNPTEx9++CHmzp2L8PBwKJVKrFmzBm5ubli6dCkAoHXr1jh8+DCWLVsGjUbzXLeViIjqjhp/BnzlyhU4OzujadOmGDNmDNLS0gAASUlJePToEfz8/KTaVq1aoXHjxkhMTAQAJCYmol27dnBwcJBqNBoNdDodzp8/L9U8uYzCmsJllCQvLw86nc5gICIiKq8aHcDe3t6IiopCbGwsVq9ejdTUVPTs2RP37t2DVquFUqmEjY2NwTwODg7QarUAAK1WaxC+hdMLp5VWo9PpkJubW2LbFi1aBGtra2lwcXF51s0lIqI6pEZ/BT1w4EDp3+3bt4e3tzeaNGmCbdu2wdzcXMaWAaGhoQgJCZE+63Q6hjAREZVbjT4DfpqNjQ1atGiBq1evwtHREXq9HllZWQY16enp0jVjR0fHIndFF34uq0atVpca8iqVCmq12mAgIiIqrxcqgO/fv49r167ByckJXl5eMDU1RUJCgjQ9JSUFaWlp8PHxAQD4+Pjg7NmzyMjIkGri4+OhVqvh4eEh1Ty5jMKawmUQERFVhxodwO+++y4OHjyI69ev4+jRo/jb3/4GY2NjjBo1CtbW1pg4cSJCQkLw448/IikpCRMmTICPjw+6desGAOjfvz88PDwwduxY/PLLL4iLi8O8efMQFBQElUoFAJg6dSp+/fVXzJkzB5cuXcKqVauwbds2zJo1S85NJyKiWq5GXwP+/fffMWrUKPz555+ws7NDjx498PPPP8POzg4AsGzZMhgZGWHYsGHIy8uDRqPBqlWrpPmNjY2xa9cuTJs2DT4+PrC0tERgYCAWLlwo1bi5uSEmJgazZs1CREQEGjVqhHXr1vERJCIiqlY1OoC3bNlS6nQzMzNERkYiMjKyxJomTZpg9+7dpS7H19cXp0+frlQbiYiIKqNGfwVNRERUWzGAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhmYyN0AIiIiA+HW1bz87OpdfjnxDJiIiEgGDGAiIiIZMICJiIhkwGvARDVJdV/7AmrM9S+iuo5nwERERDLgGTAR0ZP4LQQ9JzwDJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGfAyJDNWRTtCJiOTGM2AiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAfkpkZCRcXV1hZmYGb29vHD9+XO4mERFRLcQAfsLWrVsREhKC+fPn49SpU+jQoQM0Gg0yMjLkbhoREdUyDOAnfP7555g8eTImTJgADw8PrFmzBhYWFtiwYYPcTSMiolqGPWH9H71ej6SkJISGhkrjjIyM4Ofnh8TExCL1eXl5yMvLkz5nZ//Vw5NOp6u2NhbkPai2ZRfSKUQ1r6D69s/zUN3HoNr3P8BjUAYeg7K98Megmvd/YQ4IUfp2MID/z+3bt5Gfnw8HBweD8Q4ODrh06VKR+kWLFmHBggVFxru4uFRbG5+Hau6IEvi02tfwQnsue4fHoFQ8BvKrLf8P3bt3D9bWJa+LAVxJoaGhCAkJkT4XFBTgzp07aNCgARQKhYwtqzydTgcXFxf89ttvUKvVcjenzuH+lx+PgfxqwzEQQuDevXtwdnYutY4B/H8aNmwIY2NjpKenG4xPT0+Ho6NjkXqVSgWVSmUwzsbGpjqb+Nyo1eoX9ge/NuD+lx+Pgfxe9GNQ2plvId6E9X+USiW8vLyQkJAgjSsoKEBCQgJ8fHxkbBkREdVGPAN+QkhICAIDA9G5c2d07doVy5cvR05ODiZMmCB304iIqJZhAD9hxIgRyMzMRFhYGLRaLTw9PREbG1vkxqzaSqVSYf78+UW+Wqfng/tffjwG8qtLx0AhyrpPmoiIiKocrwETERHJgAFMREQkAwYwERGRDBjAREREMmAA10F//PEH/v73v6NBgwYwNzdHu3btcPLkSWm6EAJhYWFwcnKCubk5/Pz8cOXKFRlbXPuUdgwePXqEuXPnol27drC0tISzszPGjRuHmzdvytzq2qWs34Pw8HC0atUKlpaWqF+/Pvz8/HDs2DEZW1y7lLX/09PTMX78eDg7O8PCwgIDBgyodf8PMYDrmLt376J79+4wNTXFnj17cOHCBSxduhT169eXahYvXowvvvgCa9aswbFjx2BpaQmNRoOHDx/K2PLao6xj8ODBA5w6dQoffPABTp06he+++w4pKSkYMmSIzC2vPcrze9CiRQusXLkSZ8+exeHDh+Hq6or+/fsjMzNTxpbXDmXtfyEEAgIC8Ouvv+L777/H6dOn0aRJE/j5+SEnJ0fm1lchQXXK3LlzRY8ePUqcXlBQIBwdHcWSJUukcVlZWUKlUon//Oc/z6OJtV5Zx6A4x48fFwDEjRs3qqlVdUtljkF2drYAIPbt21dNrao7ytr/KSkpAoA4d+6cNC4/P1/Y2dmJr7766nk08bngGXAd87///Q+dO3fG8OHDYW9vj44dO+Krr76SpqempkKr1cLPz08aZ21tDW9v72Jfy0gVV9YxKE52djYUCkWt6W9cbhU9Bnq9HmvXroW1tTU6dOjwHFtaO5W1/wtf9WpmZiaNMzIygkqlwuHDh597e6uN3H8B0POlUqmESqUSoaGh4tSpU+LLL78UZmZmIioqSgghxJEjRwQAcfPmTYP5hg8fLt544w05mlzrlHUMnpabmys6deokRo8e/ZxbWnuV9xj88MMPwtLSUigUCuHs7CyOHz8uU4trl7L2v16vF40bNxbDhw8Xd+7cEXl5eeLTTz8VAET//v1lbn3VYQDXMaampsLHx8dg3PTp00W3bt2EEAzg56GsY/AkvV4vBg8eLDp27Ciys7OfVxNrvfIeg/v374srV66IxMRE8eabbwpXV1eRnp7+PJtaK5Vn/588eVJ06NBBABDGxsZCo9GIgQMHigEDBjzv5lYbfgVdxzg5OcHDw8NgXOvWrZGWlgYA0qsXy/taRqq4so5BoUePHuGNN97AjRs3EB8f/0K/mq2mKe8xsLS0RLNmzdCtWzesX78eJiYmWL9+/fNsaq1Unv3v5eWF5ORkZGVl4datW4iNjcWff/6Jpk2bPu/mVhsGcB3TvXt3pKSkGIy7fPkymjRpAgBwc3ODo6OjwWsZdTodjh07xtcyVpGyjgHw/8P3ypUr2LdvHxo0aPC8m1mrlecYFKegoEC6PkmVV5H9b21tDTs7O1y5cgUnT57Ea6+99ryaWf3kPgWn5+v48ePCxMREfPzxx+LKlSti8+bNwsLCQnzzzTdSzaeffipsbGzE999/L86cOSNee+014ebmJnJzc2Vsee1R1jHQ6/ViyJAholGjRiI5OVncunVLGvLy8mRufe1Q1jG4f/++CA0NFYmJieL69evi5MmTYsKECUKlUhncmUuVU57/h7Zt2yZ+/PFHce3aNbFz507RpEkTMXToUBlbXfUYwHXQDz/8INq2bStUKpVo1aqVWLt2rcH0goIC8cEHHwgHBwehUqlEv379REpKikytrZ1KOwapqakCQLHDjz/+KF+ja5nSjkFubq7429/+JpydnYVSqRROTk5iyJAhvAmrCpX1/1BERIRo1KiRMDU1FY0bNxbz5s2rdX+A8nWEREREMuA1YCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZMICJiIhkwAAmIiKSAQOYiIhIBgxgIirC19cXM2fOBAC4urpi+fLllV7W9evXoVAokJycXCVtI6otTORuABHVbCdOnIClpWWl53dxccGtW7fQsGHDctWPHz8eWVlZ2LlzZ6XXSfQiYAATUans7OwqPa9er4dSqeSrLImKwa+gieq4nJwcjBs3DlZWVnBycsLSpUsNpj/5FbQQAuHh4WjcuDFUKhWcnZ3x9ttvG9R++OGHGDduHNRqNaZMmVLsV9Dnz5/Hq6++CrVajXr16qFnz564du0awsPDsWnTJnz//fdQKBRQKBQ4cODAc9gLRM8fz4CJ6rjZs2fj4MGD+P7772Fvb49//vOfOHXqFDw9PYvUfvvtt1i2bBm2bNmCNm3aQKvV4pdffjGo+eyzzxAWFob58+cXu74//vgDvXr1gq+vL/bv3w+1Wo0jR47g8ePHePfdd3Hx4kXodDps3LgRAGBra1vl20xUEzCAieqw+/fvY/369fjmm2/Qr18/AMCmTZvQqFGjYuvT0tLg6OgIPz8/mJqaonHjxujatatBTd++ffHOO+9In69fv24wPTIyEtbW1tiyZQtMTU0BAC1atJCmm5ubIy8vj19bU63Hr6CJ6rBr165Br9fD29tbGmdra4uWLVsWWz98+HDk5uaiadOmmDx5Mnbs2IHHjx8b1HTu3LnUdSYnJ6Nnz55S+BLVVQxgIio3FxcXpKSkYNWqVTA3N8dbb72FXr164dGjR1JNWXdMm5ubV3cziV4IDGCiOszd3R2mpqY4duyYNO7u3bu4fPlyifOYm5tj8ODB+OKLL3DgwAEkJibi7Nmz5V5n+/bt8dNPPxmE9pOUSiXy8/PLvxFELygGMFEdZmVlhYkTJ2L27NnYv38/zp07h/Hjx8PIqPj/GqKiorB+/XqcO3cOv/76K7755huYm5ujSZMm5V5ncHAwdDodRo4ciZMnT+LKlSv4+uuvkZKSAuCvO6nPnDmDlJQU3L59u8SgJnrRMYCJ6rglS5agZ8+eGDx4MPz8/NCjRw94eXkVW2tjY4OvvvoK3bt3R/v27bFv3z788MMPaNCgQbnX16BBA+zfvx/3799H79694eXlha+++kq6Jjx58mS0bNkSnTt3hp2dHY4cOVIl20lU0yiEEELuRhAREdU1PAMmIiKSAQOYiIhIBgxgIiIiGTCAiYiIZMAAJiIikgEDmIiISAYMYCIiIhkwgImIiGTAACYiIpIBA5iIiEgGDGAiIiIZ/D8qBbdsd50fBgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "other_columns = ['disrict', 'client_catg','region']\n",
        "\n",
        " #Iterate over each column and plot the cross-tabulation\n",
        "for col in other_columns:\n",
        "    CrosstabResult = pd.crosstab(index=client_train[col], columns=client_train['target'])\n",
        "    CrosstabResult.plot.bar(figsize=(5, 5),rot=0)\n",
        "    plt.xlabel(col)\n",
        "    plt.ylabel('Count')\n",
        "    plt.title(f'Cross-tabulation of {col} and target')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3j2r-QnWU3au"
      },
      "source": [
        "relation with target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etzytRTVLnHR"
      },
      "source": [
        "**pre-prossing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L7lk92a9LnHS"
      },
      "outputs": [],
      "source": [
        "client_train['target'] = client_train['target'].astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xLvSNoWIuNjk",
        "outputId": "ac351ce5-7728-4304-b6c4-4058071febce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-82683d6a1877>:3: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
            "  df[col] = pd.to_datetime(df[col])\n"
          ]
        }
      ],
      "source": [
        "convert_t(client_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29sqEiiRLnHS",
        "outputId": "43655268-42d2-4187-87e6-efd48e5e9ea1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disrict                   int64\n",
              "client_id                object\n",
              "client_catg               int64\n",
              "region                    int64\n",
              "creation_date    datetime64[ns]\n",
              "target                    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "client_train.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ln9oMKNKMd59",
        "outputId": "37a13a7e-8cb5-4f97-dd0e-94485d884087"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   disrict          client_id  client_catg  region creation_date  target\n",
              "0       60     train_Client_0           11     101    1994-12-31       0\n",
              "1       69     train_Client_1           11     107    2002-05-29       0\n",
              "2       62    train_Client_10           11     301    1986-03-13       0\n",
              "3       69   train_Client_100           11     105    1996-11-07       0\n",
              "4       62  train_Client_1000           11     303    2014-10-14       0"
            ],
            "text/html": [
              "\n",
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>disrict</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_catg</th>\n",
              "      <th>region</th>\n",
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              "      <th>target</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>train_Client_0</td>\n",
              "      <td>11</td>\n",
              "      <td>101</td>\n",
              "      <td>1994-12-31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>69</td>\n",
              "      <td>train_Client_1</td>\n",
              "      <td>11</td>\n",
              "      <td>107</td>\n",
              "      <td>2002-05-29</td>\n",
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              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>train_Client_10</td>\n",
              "      <td>11</td>\n",
              "      <td>301</td>\n",
              "      <td>1986-03-13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>69</td>\n",
              "      <td>train_Client_100</td>\n",
              "      <td>11</td>\n",
              "      <td>105</td>\n",
              "      <td>1996-11-07</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>train_Client_1000</td>\n",
              "      <td>11</td>\n",
              "      <td>303</td>\n",
              "      <td>2014-10-14</td>\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
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              "    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",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
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              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
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              "          const element = document.querySelector('#df-6c8e4186-3bad-4f72-838d-0dc6da49e43f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<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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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "client_train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p8zTh6XWLnHS"
      },
      "source": [
        "invoice_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ob-iH8fP31_a"
      },
      "outputs": [],
      "source": [
        "convert_invoice(invoice_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cT82yh-wLnHT",
        "outputId": "be2d4650-ae81-4555-f9c1-f84188614335"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "client_id                       object\n",
              "invoice_date            datetime64[ns]\n",
              "tarif_type                       int64\n",
              "counter_number                   int64\n",
              "counter_statue                  object\n",
              "counter_code                     int64\n",
              "reading_remarque                 int64\n",
              "counter_coefficient              int64\n",
              "consommation_level_1             int64\n",
              "consommation_level_2             int64\n",
              "consommation_level_3             int64\n",
              "consommation_level_4             int64\n",
              "old_index                        int64\n",
              "new_index                        int64\n",
              "months_number                    int64\n",
              "counter_type                     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "invoice_train.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "WZJu74EqMaZ6",
        "outputId": "da5a4cf4-0c3c-4ace-b360-bf52fe09fb25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        client_id invoice_date  tarif_type  counter_number counter_statue  \\\n",
              "0  train_Client_0   2014-03-24          11         1335667              0   \n",
              "1  train_Client_0   2013-03-29          11         1335667              0   \n",
              "2  train_Client_0   2015-03-23          11         1335667              0   \n",
              "3  train_Client_0   2015-07-13          11         1335667              0   \n",
              "4  train_Client_0   2016-11-17          11         1335667              0   \n",
              "\n",
              "   counter_code  reading_remarque  counter_coefficient  consommation_level_1  \\\n",
              "0           203                 8                    1                    82   \n",
              "1           203                 6                    1                  1200   \n",
              "2           203                 8                    1                   123   \n",
              "3           207                 8                    1                   102   \n",
              "4           207                 9                    1                   572   \n",
              "\n",
              "   consommation_level_2  consommation_level_3  consommation_level_4  \\\n",
              "0                     0                     0                     0   \n",
              "1                   184                     0                     0   \n",
              "2                     0                     0                     0   \n",
              "3                     0                     0                     0   \n",
              "4                     0                     0                     0   \n",
              "\n",
              "   old_index  new_index  months_number  counter_type  \n",
              "0      14302      14384              4             0  \n",
              "1      12294      13678              4             0  \n",
              "2      14624      14747              4             0  \n",
              "3      14747      14849              4             0  \n",
              "4      15066      15638             12             0  "
            ],
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              "                                                     [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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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "invoice_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "xUSPY-GqQHh2",
        "outputId": "62dab4a3-f181-4966-fb45-bda8f2c8cb95"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           client_id  total_invoice  consommation_level_1_mean  \\\n",
              "0     train_Client_0             35                 352.400000   \n",
              "1     train_Client_1             37                 557.540541   \n",
              "2    train_Client_10             18                 798.611111   \n",
              "3   train_Client_100             20                   1.200000   \n",
              "4  train_Client_1000             14                 663.714286   \n",
              "\n",
              "   consommation_level_2_mean  consommation_level_3_mean  \\\n",
              "0                  10.571429                   0.000000   \n",
              "1                   0.000000                   0.000000   \n",
              "2                  37.888889                   0.000000   \n",
              "3                   0.000000                   0.000000   \n",
              "4                 104.857143                 117.357143   \n",
              "\n",
              "   consommation_level_4_mean  \n",
              "0                   0.000000  \n",
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              "                                                     [key], {});\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "agg_invoice=calculate_agg_trans(invoice_train)\n",
        "agg_invoice.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "135dKK2ANOOi",
        "outputId": "ec8dde97-96fe-4482-d5ef-8dc902558b1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   disrict          client_id  client_catg  region creation_date  target  \\\n",
              "0       60     train_Client_0           11     101    1994-12-31       0   \n",
              "1       69     train_Client_1           11     107    2002-05-29       0   \n",
              "2       62    train_Client_10           11     301    1986-03-13       0   \n",
              "3       69   train_Client_100           11     105    1996-11-07       0   \n",
              "4       62  train_Client_1000           11     303    2014-10-14       0   \n",
              "\n",
              "   total_invoice  consommation_level_1_mean  consommation_level_2_mean  \\\n",
              "0             35                 352.400000                  10.571429   \n",
              "1             37                 557.540541                   0.000000   \n",
              "2             18                 798.611111                  37.888889   \n",
              "3             20                   1.200000                   0.000000   \n",
              "4             14                 663.714286                 104.857143   \n",
              "\n",
              "   consommation_level_3_mean  consommation_level_4_mean  \n",
              "0                   0.000000                   0.000000  \n",
              "1                   0.000000                   0.000000  \n",
              "2                   0.000000                   0.000000  \n",
              "3                   0.000000                   0.000000  \n",
              "4                 117.357143                  36.714286  "
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
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              "          const element = document.querySelector('#df-3fa2a716-bf22-4ce8-bba7-508132adc08d');\n",
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              "                                                     [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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "train_data = pd.merge(client_train, agg_invoice, on=\"client_id\",how='left')\n",
        "train_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6h6-JtzMPCbh",
        "outputId": "4e3043d0-a9a1-4c90-a1b3-d5d1ddf6b4a6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(135493, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "train_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EFEKDWv-bSgk"
      },
      "outputs": [],
      "source": [
        "df_copy = train_data.copy(deep=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-jn-TUDOQKG6"
      },
      "outputs": [],
      "source": [
        "columns_to_delete = ['client_id', 'creation_date']\n",
        "train_data.drop(columns_to_delete, axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 270
        },
        "id": "R1jZSpmLSI58",
        "outputId": "c437b441-3190-4003-fb89-56afc53f9253"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   disrict  client_catg  region  target  total_invoice  \\\n",
              "0       60           11     101       0             35   \n",
              "1       69           11     107       0             37   \n",
              "2       62           11     301       0             18   \n",
              "3       69           11     105       0             20   \n",
              "4       62           11     303       0             14   \n",
              "\n",
              "   consommation_level_1_mean  consommation_level_2_mean  \\\n",
              "0                 352.400000                  10.571429   \n",
              "1                 557.540541                   0.000000   \n",
              "2                 798.611111                  37.888889   \n",
              "3                   1.200000                   0.000000   \n",
              "4                 663.714286                 104.857143   \n",
              "\n",
              "   consommation_level_3_mean  consommation_level_4_mean  \n",
              "0                   0.000000                   0.000000  \n",
              "1                   0.000000                   0.000000  \n",
              "2                   0.000000                   0.000000  \n",
              "3                   0.000000                   0.000000  \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-6d3ee29d-14f8-4fbd-87c0-2d04918e10e1 button.colab-df-convert');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<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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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "train_data.head() #after deleted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "yuNxGRUASURb",
        "outputId": "a554dcc4-8810-409a-ac49-0fff22b7b6c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-59c34252aadb>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(df_copy.corr(),cmap='Blues')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(df_copy.corr(),cmap='Blues')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YCeLVvPxTjDj"
      },
      "outputs": [],
      "source": [
        "X = train_data.drop(['target'], axis=1)\n",
        "y = train_data['target']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-2We-RuaD1M"
      },
      "source": [
        "**RandomOverSampler**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fvT38l4zTryU",
        "outputId": "7d6b2a77-f8d0-4e28-ff8d-0e69fba5be7e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    127927\n",
              "1    127927\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "ros = RandomOverSampler(sampling_strategy=\"not majority\") # String\n",
        "X, y = ros.fit_resample(X, y)\n",
        "\n",
        "y.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p02BEENpWr7U"
      },
      "source": [
        "now its balnced data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aROFC1VBWQtx"
      },
      "source": [
        "**train model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3NJ-uTrka3y_"
      },
      "source": [
        "LightGBM is a gradient-boosting framework based on decision trees to increase the efficiency of the model and reduces memory usage.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8Ru7iWLWQFZ",
        "outputId": "ecb54747-3559-4e68-ce58-cf7ac60c028b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((204683, 8), (51171, 8), (204683,), (51171,))"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "X_train, X_vaild, y_train, y_vaild = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
        "X_train.shape, X_vaild.shape, y_train.shape, y_vaild.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "x-o_KEcXXtMM",
        "outputId": "adf03519-5610-4006-8f22-7068af2f9dfe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(num_iteration=500, random_state=0, silent=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(num_iteration=500, random_state=0, silent=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(num_iteration=500, random_state=0, silent=True)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "clf = LGBMClassifier(boosting_type='gbdt', num_iteration=500, silent=True,random_state=0)\n",
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vv67d6oMXuPN"
      },
      "outputs": [],
      "source": [
        "y_pred=clf.predict(X_vaild)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPu8dHOCXyxv",
        "outputId": "73f7296f-7178-4087-b632-f926c9e6b714"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LightGBM accuracy is: 0.80\n"
          ]
        }
      ],
      "source": [
        "print('LightGBM accuracy is: {0:0.2f}'.format(accuracy_score(y_vaild, y_pred)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxKceB_Gbxyh"
      },
      "source": [
        "# **Hyperparamter tuning using GridSearchCV**\n",
        "\n",
        "> Indented block\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SDzdD4IFb3Z0"
      },
      "outputs": [],
      "source": [
        "param_grid = {\n",
        "    'num_leaves': [20, 30, 40],\n",
        "    'learning_rate': [0.1, 0.01],\n",
        "    'n_estimators': [100, 200],\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pub-hkWqyIdp",
        "outputId": "27a105f1-edbd-4911-f83c-7b134128d3ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] num_iterations is set=500, num_iteration=500 will be ignored. Current value: num_iterations=500\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5,\n",
              "             estimator=LGBMClassifier(num_iteration=500, random_state=0,\n",
              "                                      silent=True),\n",
              "             param_grid={'learning_rate': [0.1, 0.01],\n",
              "                         'n_estimators': [100, 200],\n",
              "                         'num_leaves': [20, 30, 40]})"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=LGBMClassifier(num_iteration=500, random_state=0,\n",
              "                                      silent=True),\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.01],\n",
              "                         &#x27;n_estimators&#x27;: [100, 200],\n",
              "                         &#x27;num_leaves&#x27;: [20, 30, 40]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
              "             estimator=LGBMClassifier(num_iteration=500, random_state=0,\n",
              "                                      silent=True),\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.1, 0.01],\n",
              "                         &#x27;n_estimators&#x27;: [100, 200],\n",
              "                         &#x27;num_leaves&#x27;: [20, 30, 40]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(num_iteration=500, random_state=0, silent=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(num_iteration=500, random_state=0, silent=True)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjKfF2fYySKT",
        "outputId": "2396418e-ce16-40b4-d08c-312b17e94184"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters: {'learning_rate': 0.1, 'n_estimators': 100, 'num_leaves': 40}\n"
          ]
        }
      ],
      "source": [
        "best_params = grid_search.best_params_\n",
        "print(\"Best hyperparameters:\", best_params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8lmigqfNYl-x"
      },
      "source": [
        "Best hyperparameters: {'learning_rate': 0.1, 'n_estimators': 100, 'num_leaves': 40}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AW72_8P0b4-F",
        "outputId": "d3801914-dfff-4e22-c71d-465799569a49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightgbm/sklearn.py:598: UserWarning: 'silent' argument is deprecated and will be removed in a future release of LightGBM. Pass 'verbose' parameter via keyword arguments instead.\n",
            "  _log_warning(\"'silent' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
            "/usr/local/lib/python3.10/dist-packages/lightgbm/engine.py:177: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
            "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
          ]
        }
      ],
      "source": [
        "# Fit the model with the best hyperparameters\n",
        "best_model = LGBMClassifier(boosting_type='gbdt', num_iteration=500, silent=True, **best_params,random_state=0)\n",
        "best_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test data\n",
        "y_pred_best = best_model.predict(X_vaild)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mTsrqJQ0zMUa",
        "outputId": "3c94833a-2538-4e14-d9ce-05a9c2111281"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LightGBM accuracy with best paramters is: 0.82\n"
          ]
        }
      ],
      "source": [
        "print('LightGBM accuracy with best paramters is: {0:0.2f}'.format(accuracy_score(y_vaild, y_pred_best)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K65-C4bVD6Mc",
        "outputId": "cb9a6d64-4f9e-499b-dad4-f97360f9765d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.82361251, 0.45712524, 0.94557882, ..., 0.59425932, 0.79438776,\n",
              "       0.45158327])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "y_pred_proba = best_model.predict_proba(X_vaild)[:, 1]\n",
        "y_pred_proba"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tf3uYj7Xrf3Z"
      },
      "source": [
        "**Evalution**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBO-Qyh3on7H",
        "outputId": "e58982a8-391a-41f8-a19a-b782eca0b937"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score: 0.819\n",
            "\n",
            "Confusion matrix\n",
            " [[19820  5742]\n",
            " [ 3506 22103]]\n",
            "\n",
            "Classification report\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.78      0.81     25562\n",
            "           1       0.79      0.86      0.83     25609\n",
            "\n",
            "    accuracy                           0.82     51171\n",
            "   macro avg       0.82      0.82      0.82     51171\n",
            "weighted avg       0.82      0.82      0.82     51171\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Evaluate performance using accuracy score and confusion matrix\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score\n",
        "\n",
        "print(f\"Accuracy score: {round(accuracy_score(y_vaild, y_pred_best), 3)}\\n\")\n",
        "print(f\"Confusion matrix\\n {confusion_matrix(y_vaild, y_pred_best)}\\n\")\n",
        "print(f\"Classification report\\n {classification_report(y_vaild, y_pred_best)}\\n\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6LCv3uvepzso",
        "outputId": "2ecee9b2-2b91-4607-f6bc-2b611e26eb73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC Score: 0.8192\n"
          ]
        }
      ],
      "source": [
        "score = roc_auc_score(y_vaild, y_pred_best)\n",
        "print(f\"ROC AUC Score: {score:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "5AJizSXHozuj",
        "outputId": "8bd9ab4a-3a73-4615-aa0c-9aad80c19672"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "lgb_fpr, lgb_tpr, threshold = roc_curve(y_vaild, y_pred_best)\n",
        "auc_lgb = auc(lgb_fpr, lgb_tpr)\n",
        "plt.figure(figsize=(6, 6), dpi=100)\n",
        "plt.plot(lgb_fpr, lgb_tpr, linestyle='-', label='LGP model (auc = %0.2f)' % auc_lgb)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate ')\n",
        "\n",
        "plt.legend(fontsize=\"10\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5IxHaA9NeybR"
      },
      "source": [
        "AUC under ROC when it area under curve large and go to top-left which is better"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8kXOaLFzPrzc",
        "outputId": "ae37a14a-9f09-4a38-d8b1-e4f66ba45bb9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix\n",
            "\n",
            " [[19820  5742]\n",
            " [ 3506 22103]]\n",
            "\n",
            "True Positives(TP) =  19820\n",
            "\n",
            "True Negatives(TN) =  22103\n",
            "\n",
            "False Positives(FP) =  5742\n",
            "\n",
            "False Negatives(FN) =  3506\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_vaild, y_pred_best)\n",
        "print('Confusion matrix\\n\\n', cm)\n",
        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "WNICshlePhQ-",
        "outputId": "e0aa13e9-a033-428f-8045-6eba98075a6e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "confusion_matrix = metrics.confusion_matrix(y_vaild, y_pred_best)\n",
        "\n",
        "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
        "\n",
        "cm_display.plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yn4V8xooXHB0"
      },
      "source": [
        "**test set**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WaiPp6H6W8AL"
      },
      "outputs": [],
      "source": [
        "client_test=pd.read_csv('/content/drive/MyDrive/test_set/client_test.csv')\n",
        "invoice_test=pd.read_csv('/content/drive/MyDrive/test_set/invoice_test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSQasH6fpbfr",
        "outputId": "a33cca04-7179-4216-8fd8-868877d97ffe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-82683d6a1877>:3: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
            "  df[col] = pd.to_datetime(df[col])\n"
          ]
        }
      ],
      "source": [
        "convert_t(client_test)\n",
        "convert_invoice(invoice_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tn7I4QuBXABD",
        "outputId": "fdda8873-082f-4e54-9485-7500baabdb99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disrict          0\n",
              "client_id        0\n",
              "client_catg      0\n",
              "region           0\n",
              "creation_date    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "client_test.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40X1VtJR7Av-",
        "outputId": "64ecc896-9e86-4d7b-cf85-1b9c74215e98"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "client_id               0\n",
              "invoice_date            0\n",
              "tarif_type              0\n",
              "counter_number          0\n",
              "counter_statue          0\n",
              "counter_code            0\n",
              "reading_remarque        0\n",
              "counter_coefficient     0\n",
              "consommation_level_1    0\n",
              "consommation_level_2    0\n",
              "consommation_level_3    0\n",
              "consommation_level_4    0\n",
              "old_index               0\n",
              "new_index               0\n",
              "months_number           0\n",
              "counter_type            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "invoice_test.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTvmxiaw7dMn"
      },
      "source": [
        "no null or missing valuse on test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "4EWkPTjbW-Ki",
        "outputId": "bdeef859-f387-48c6-9fd1-7e489fca5a4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          client_id  total_invoice  consommation_level_1_mean  \\\n",
              "0     test_Client_0             37                 488.135135   \n",
              "1     test_Client_1             22                1091.409091   \n",
              "2    test_Client_10             74                 554.040541   \n",
              "3   test_Client_100             40                 244.350000   \n",
              "4  test_Client_1000             53                 568.188679   \n",
              "\n",
              "   consommation_level_2_mean  consommation_level_3_mean  \\\n",
              "0                   3.243243                   0.000000   \n",
              "1                 843.136364                 182.318182   \n",
              "2                  37.364865                  15.743243   \n",
              "3                   0.000000                   0.000000   \n",
              "4                 145.056604                  33.679245   \n",
              "\n",
              "   consommation_level_4_mean  \n",
              "0                   0.000000  \n",
              "1                 586.318182  \n",
              "2                   0.162162  \n",
              "3                   0.000000  \n",
              "4                   0.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-4b58e9cb-0f0b-4f05-864f-e4005f31312d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>client_id</th>\n",
              "      <th>total_invoice</th>\n",
              "      <th>consommation_level_1_mean</th>\n",
              "      <th>consommation_level_2_mean</th>\n",
              "      <th>consommation_level_3_mean</th>\n",
              "      <th>consommation_level_4_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>test_Client_0</td>\n",
              "      <td>37</td>\n",
              "      <td>488.135135</td>\n",
              "      <td>3.243243</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>test_Client_1</td>\n",
              "      <td>22</td>\n",
              "      <td>1091.409091</td>\n",
              "      <td>843.136364</td>\n",
              "      <td>182.318182</td>\n",
              "      <td>586.318182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>test_Client_10</td>\n",
              "      <td>74</td>\n",
              "      <td>554.040541</td>\n",
              "      <td>37.364865</td>\n",
              "      <td>15.743243</td>\n",
              "      <td>0.162162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>test_Client_100</td>\n",
              "      <td>40</td>\n",
              "      <td>244.350000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>4</th>\n",
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              "      <td>33.679245</td>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<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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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "agg_invoice_test=calculate_agg_trans(invoice_test)\n",
        "agg_invoice_test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "MWK5eE3_pAUH",
        "outputId": "8fa63ea9-aac7-4cf0-94af-7f7c4c899c36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   disrict         client_id  client_catg  region creation_date  \\\n",
              "0       62     test_Client_0           11     307    2002-05-28   \n",
              "1       69     test_Client_1           11     103    2009-06-08   \n",
              "2       62    test_Client_10           11     310    2004-07-04   \n",
              "3       60   test_Client_100           11     101    1992-08-10   \n",
              "4       62  test_Client_1000           11     301    1977-07-21   \n",
              "\n",
              "   total_invoice  consommation_level_1_mean  consommation_level_2_mean  \\\n",
              "0             37                 488.135135                   3.243243   \n",
              "1             22                1091.409091                 843.136364   \n",
              "2             74                 554.040541                  37.364865   \n",
              "3             40                 244.350000                   0.000000   \n",
              "4             53                 568.188679                 145.056604   \n",
              "\n",
              "   consommation_level_3_mean  consommation_level_4_mean  \n",
              "0                   0.000000                   0.000000  \n",
              "1                 182.318182                 586.318182  \n",
              "2                  15.743243                   0.162162  \n",
              "3                   0.000000                   0.000000  \n",
              "4                  33.679245                   0.000000  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>disrict</th>\n",
              "      <th>client_id</th>\n",
              "      <th>client_catg</th>\n",
              "      <th>region</th>\n",
              "      <th>creation_date</th>\n",
              "      <th>total_invoice</th>\n",
              "      <th>consommation_level_1_mean</th>\n",
              "      <th>consommation_level_2_mean</th>\n",
              "      <th>consommation_level_3_mean</th>\n",
              "      <th>consommation_level_4_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>62</td>\n",
              "      <td>test_Client_0</td>\n",
              "      <td>11</td>\n",
              "      <td>307</td>\n",
              "      <td>2002-05-28</td>\n",
              "      <td>37</td>\n",
              "      <td>488.135135</td>\n",
              "      <td>3.243243</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>69</td>\n",
              "      <td>test_Client_1</td>\n",
              "      <td>11</td>\n",
              "      <td>103</td>\n",
              "      <td>2009-06-08</td>\n",
              "      <td>22</td>\n",
              "      <td>1091.409091</td>\n",
              "      <td>843.136364</td>\n",
              "      <td>182.318182</td>\n",
              "      <td>586.318182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>test_Client_10</td>\n",
              "      <td>11</td>\n",
              "      <td>310</td>\n",
              "      <td>2004-07-04</td>\n",
              "      <td>74</td>\n",
              "      <td>554.040541</td>\n",
              "      <td>37.364865</td>\n",
              "      <td>15.743243</td>\n",
              "      <td>0.162162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60</td>\n",
              "      <td>test_Client_100</td>\n",
              "      <td>11</td>\n",
              "      <td>101</td>\n",
              "      <td>1992-08-10</td>\n",
              "      <td>40</td>\n",
              "      <td>244.350000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>test_Client_1000</td>\n",
              "      <td>11</td>\n",
              "      <td>301</td>\n",
              "      <td>1977-07-21</td>\n",
              "      <td>53</td>\n",
              "      <td>568.188679</td>\n",
              "      <td>145.056604</td>\n",
              "      <td>33.679245</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "      </button>\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
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              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d3ee1b49-a744-43f2-9d60-f4e837e4358f button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                     [key], {});\n",
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              "          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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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "test_data = pd.merge(client_test, agg_invoice_test, on=\"client_id\")\n",
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2p4gp0XRLRbG",
        "outputId": "4d232077-2d9b-4477-a741-9655861adc45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(58069, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "test_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11Wcn_1jHOPt",
        "outputId": "66708595-e8ef-402e-d24c-c9e6f5622b71"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           test_Client_0\n",
              "1           test_Client_1\n",
              "2          test_Client_10\n",
              "3         test_Client_100\n",
              "4        test_Client_1000\n",
              "               ...       \n",
              "58064    test_Client_9995\n",
              "58065    test_Client_9996\n",
              "58066    test_Client_9997\n",
              "58067    test_Client_9998\n",
              "58068    test_Client_9999\n",
              "Name: client_id, Length: 58069, dtype: object"
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          },
          "metadata": {},
          "execution_count": 70
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      ],
      "source": [
        "c_id = test_data['client_id'].copy()\n",
        "c_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "CcDEOs5JpGTP",
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              "   disrict  client_catg  region  total_invoice  consommation_level_1_mean  \\\n",
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              "1       69           11     103             22                1091.409091   \n",
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              "3       60           11     101             40                 244.350000   \n",
              "4       62           11     301             53                 568.188679   \n",
              "\n",
              "   consommation_level_2_mean  consommation_level_3_mean  \\\n",
              "0                   3.243243                   0.000000   \n",
              "1                 843.136364                 182.318182   \n",
              "2                  37.364865                  15.743243   \n",
              "3                   0.000000                   0.000000   \n",
              "4                 145.056604                  33.679245   \n",
              "\n",
              "   consommation_level_4_mean  \n",
              "0                   0.000000  \n",
              "1                 586.318182  \n",
              "2                   0.162162  \n",
              "3                   0.000000  \n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "col_del=['creation_date','client_id']\n",
        "\n",
        "test_data.drop(col_del, axis=1, inplace=True)\n",
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vyxGMDBEc_a",
        "outputId": "188e4994-9048-4adf-ae02-0a25f3a4975b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(58069, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "test_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RuhEmMoq70Hy"
      },
      "outputs": [],
      "source": [
        "y_proba = best_model.predict_proba(test_data) #model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-8xB6oq2I7z4",
        "outputId": "300ff432-904a-4db8-b19b-940c039713ab"
      },
      "outputs": [
        {
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              "          0         1\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0dacc484-1fff-4432-acb5-b8bf9ff8a5c6 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-0dacc484-1fff-4432-acb5-b8bf9ff8a5c6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "y_proba = pd.DataFrame(y_proba)\n",
        "y_proba.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p6gQ-BxJJYsG"
      },
      "outputs": [],
      "source": [
        "submission = pd.DataFrame({\n",
        "        \"client_id\": c_id,\n",
        "        \"target\": y_proba[1]\n",
        "    })\n",
        "\n",
        "submission.to_csv('Sample_submission.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CclwiXKtHLzd",
        "outputId": "f5c9cc60-5c0b-4f90-f74c-19a9086e2150"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              client_id    target\n",
              "0         test_Client_0  0.369525\n",
              "1         test_Client_1  0.670986\n",
              "2        test_Client_10  0.495811\n",
              "3       test_Client_100  0.387748\n",
              "4      test_Client_1000  0.169398\n",
              "...                 ...       ...\n",
              "58064  test_Client_9995  0.000840\n",
              "58065  test_Client_9996  0.510953\n",
              "58066  test_Client_9997  0.467075\n",
              "58067  test_Client_9998  0.002983\n",
              "58068  test_Client_9999  0.500884\n",
              "\n",
              "[58069 rows x 2 columns]"
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              "\n",
              "  <div id=\"df-96dc7e79-abda-4ff0-88fe-669f547ddfb1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>0.670986</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>test_Client_10</td>\n",
              "      <td>0.495811</td>\n",
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              "      <th>3</th>\n",
              "      <td>test_Client_100</td>\n",
              "      <td>0.387748</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "    <tr>\n",
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              "      <td>test_Client_9996</td>\n",
              "      <td>0.510953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58066</th>\n",
              "      <td>test_Client_9997</td>\n",
              "      <td>0.467075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58067</th>\n",
              "      <td>test_Client_9998</td>\n",
              "      <td>0.002983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58068</th>\n",
              "      <td>test_Client_9999</td>\n",
              "      <td>0.500884</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>58069 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96dc7e79-abda-4ff0-88fe-669f547ddfb1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
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              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-96dc7e79-abda-4ff0-88fe-669f547ddfb1 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-96dc7e79-abda-4ff0-88fe-669f547ddfb1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "submission"
      ]
    }
  ],
  "metadata": {
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      "include_colab_link": true
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    "kernelspec": {
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      "name": "python3"
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