Scikit_learn_Pipeline_Demo.ipynb

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      "toc_visible": true,
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/patrickabadi/python-pytorch-notebooks/blob/main/Scikit_learn_Pipeline_Demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gUGKj3qMbz9"
      },
      "source": [
        "2021-2-22"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YrDE6HnJOGxj"
      },
      "source": [
        "# Global Functions"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install xlrd -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mczeGEA9KcR0",
        "outputId": "7a5203de-95e9-4c24-f358-88ef2247e305"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: xlrd in /usr/local/lib/python3.10/dist-packages (2.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_a5CpRh3MTkX"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "from IPython.display import display\n",
        "\n",
        "# to make this notebook's output stable across runs\n",
        "np.random.seed(10086)\n",
        "\n",
        "def check_data_balance(series, style=\"seaborn-pastel\"):\n",
        "  with plt.style.context(style):\n",
        "    unique = series.value_counts()\n",
        "    display(unique) #show unique value counts of the target\n",
        "    plt.pie(unique, explode=[0.05]*len(unique), labels=unique.index, autopct='%1.1f%%'); #plot a pie chart for the target to see if data are balanced"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ptbohDmYLUQ4",
        "outputId": "396006f8-62ba-4840-f802-e4676dfb1c24"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.5.3'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-TE5Ur9JPuq"
      },
      "source": [
        "# Goal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQo_n0PRJPus"
      },
      "source": [
        "Q1. Build a classification model for the default of credit card clients dataset. More info here:\n",
        "https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients\n",
        "\n",
        "- Explore the data\n",
        "- Make sure you build a full data pipeline\n",
        "- Do you require any data pre-processing? Are all the features useful? (Use only raw features)\n",
        "- set the random seed to 123 (For splitting or any other random algorithm)\n",
        "- Split data into training (80%) and testing (20%)\n",
        "- Follow similar procedure as the one for week 2 (End-to-end Machine Learning Project). Remember apendix B\n",
        "- Study the ROC Curve, decide threshold\n",
        "- Use 2 classifiers.\n",
        "    - Random Forest\n",
        "        - tune only: n_estimators: {3, 4, 6, 7, 10, 20, 50, 100}\n",
        "    - KNN Classfier\n",
        "        - tune only: n_neighbors: {3, 4, 5, 7, 10, 20, 50}\n",
        "    - Which one performs better in the cross validation?\n",
        "    \n",
        "http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
        "\n",
        "http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\n",
        "\n",
        "- Cross-validation with 4-folds.\n",
        "\n",
        "- Other paramenters -> Use default\n",
        "\n",
        "Notes:\n",
        "  - Make your code modular, the second part of the assignmet you will have to repeat.\n",
        "  - Include documentation for your code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t8BYAScbNAhQ"
      },
      "source": [
        "# Data Information\n",
        "\n",
        "This research aimed at the case of customersâ€™ default payments in Taiwan and compares the predictive accuracy of\n",
        "probability of default among six data mining methods. From the perspective of risk management, the result of\n",
        "predictive accuracy of the estimated probability of default will be more valuable than the binary result of\n",
        "classification - credible or not credible clients. Because the real probability of default is unknown, this study\n",
        "presented the novel â€œSorting Smoothing Methodâ€ to estimate the real probability of default. With the real\n",
        "probability of default as the response variable (Y), and the predictive probability of default as the independent\n",
        "variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by\n",
        "artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to\n",
        "zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural\n",
        "network is the only one that can accurately estimate the real probability of default.\n",
        "\n",
        "\n",
        "# Attribute Information\n",
        "\n",
        "This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable.\n",
        "This study reviewed the literature and used the following 23 variables as explanatory variables:\n",
        "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her\n",
        "family (supplementary) credit.\n",
        "X2: Gender (1 = male; 2 = female).\n",
        "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).\n",
        "X4: Marital status (1 = married; 2 = single; 3 = others).\n",
        "X5: Age (year).\n",
        "X6 - X11: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n",
        "X12-X17: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005.\n",
        "X18-X23: Amount of previous payment (NT dollar). X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005.\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLW2twrdM27k"
      },
      "source": [
        "# Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "uyQhko2HMz65",
        "outputId": "118952c3-0e0d-4eb0-b8b8-e6a84eee0bdb"
      },
      "source": [
        "df = pd.read_excel(\"https://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls\", skiprows=1, header=0, index_col=0)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
              "ID                                                                            \n",
              "1          20000    2          2         1   24      2      2     -1     -1   \n",
              "2         120000    2          2         2   26     -1      2      0      0   \n",
              "3          90000    2          2         2   34      0      0      0      0   \n",
              "4          50000    2          2         1   37      0      0      0      0   \n",
              "5          50000    1          2         1   57     -1      0     -1      0   \n",
              "...          ...  ...        ...       ...  ...    ...    ...    ...    ...   \n",
              "29996     220000    1          3         1   39      0      0      0      0   \n",
              "29997     150000    1          3         2   43     -1     -1     -1     -1   \n",
              "29998      30000    1          2         2   37      4      3      2     -1   \n",
              "29999      80000    1          3         1   41      1     -1      0      0   \n",
              "30000      50000    1          2         1   46      0      0      0      0   \n",
              "\n",
              "       PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
              "ID            ...                                                        \n",
              "1         -2  ...          0          0          0         0       689   \n",
              "2          0  ...       3272       3455       3261         0      1000   \n",
              "3          0  ...      14331      14948      15549      1518      1500   \n",
              "4          0  ...      28314      28959      29547      2000      2019   \n",
              "5          0  ...      20940      19146      19131      2000     36681   \n",
              "...      ...  ...        ...        ...        ...       ...       ...   \n",
              "29996      0  ...      88004      31237      15980      8500     20000   \n",
              "29997      0  ...       8979       5190          0      1837      3526   \n",
              "29998      0  ...      20878      20582      19357         0         0   \n",
              "29999      0  ...      52774      11855      48944     85900      3409   \n",
              "30000      0  ...      36535      32428      15313      2078      1800   \n",
              "\n",
              "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
              "ID                                                                         \n",
              "1             0         0         0         0                           1  \n",
              "2          1000      1000         0      2000                           1  \n",
              "3          1000      1000      1000      5000                           0  \n",
              "4          1200      1100      1069      1000                           0  \n",
              "5         10000      9000       689       679                           0  \n",
              "...         ...       ...       ...       ...                         ...  \n",
              "29996      5003      3047      5000      1000                           0  \n",
              "29997      8998       129         0         0                           0  \n",
              "29998     22000      4200      2000      3100                           1  \n",
              "29999      1178      1926     52964      1804                           1  \n",
              "30000      1430      1000      1000      1000                           1  \n",
              "\n",
              "[30000 rows x 24 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LIMIT_BAL</th>\n",
              "      <th>SEX</th>\n",
              "      <th>EDUCATION</th>\n",
              "      <th>MARRIAGE</th>\n",
              "      <th>AGE</th>\n",
              "      <th>PAY_0</th>\n",
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              "      <th>PAY_AMT2</th>\n",
              "      <th>PAY_AMT3</th>\n",
              "      <th>PAY_AMT4</th>\n",
              "      <th>PAY_AMT5</th>\n",
              "      <th>PAY_AMT6</th>\n",
              "      <th>default payment next month</th>\n",
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              "      <th>ID</th>\n",
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              "      <td>-1</td>\n",
              "      <td>-2</td>\n",
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              "      <th>2</th>\n",
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              "      <td>3272</td>\n",
              "      <td>3455</td>\n",
              "      <td>3261</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>90000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
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              "      <td>14331</td>\n",
              "      <td>14948</td>\n",
              "      <td>15549</td>\n",
              "      <td>1518</td>\n",
              "      <td>1500</td>\n",
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              "      <td>1000</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>52774</td>\n",
              "      <td>11855</td>\n",
              "      <td>48944</td>\n",
              "      <td>85900</td>\n",
              "      <td>3409</td>\n",
              "      <td>1178</td>\n",
              "      <td>1926</td>\n",
              "      <td>52964</td>\n",
              "      <td>1804</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30000</th>\n",
              "      <td>50000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>36535</td>\n",
              "      <td>32428</td>\n",
              "      <td>15313</td>\n",
              "      <td>2078</td>\n",
              "      <td>1800</td>\n",
              "      <td>1430</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30000 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00f73cc8-cd2a-477f-90e3-7727626bca49')\"\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",
              "  <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-00f73cc8-cd2a-477f-90e3-7727626bca49 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-00f73cc8-cd2a-477f-90e3-7727626bca49');\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": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__D_WBuBNZuW"
      },
      "source": [
        "# Data Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUrDAHFESn6d"
      },
      "source": [
        "## Check for missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kg8-AUXWSqNf",
        "outputId": "170b3257-915e-4c10-bde8-0b4a14de9a1c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 30000 entries, 1 to 30000\n",
            "Data columns (total 24 columns):\n",
            " #   Column                      Non-Null Count  Dtype\n",
            "---  ------                      --------------  -----\n",
            " 0   LIMIT_BAL                   30000 non-null  int64\n",
            " 1   SEX                         30000 non-null  int64\n",
            " 2   EDUCATION                   30000 non-null  int64\n",
            " 3   MARRIAGE                    30000 non-null  int64\n",
            " 4   AGE                         30000 non-null  int64\n",
            " 5   PAY_0                       30000 non-null  int64\n",
            " 6   PAY_2                       30000 non-null  int64\n",
            " 7   PAY_3                       30000 non-null  int64\n",
            " 8   PAY_4                       30000 non-null  int64\n",
            " 9   PAY_5                       30000 non-null  int64\n",
            " 10  PAY_6                       30000 non-null  int64\n",
            " 11  BILL_AMT1                   30000 non-null  int64\n",
            " 12  BILL_AMT2                   30000 non-null  int64\n",
            " 13  BILL_AMT3                   30000 non-null  int64\n",
            " 14  BILL_AMT4                   30000 non-null  int64\n",
            " 15  BILL_AMT5                   30000 non-null  int64\n",
            " 16  BILL_AMT6                   30000 non-null  int64\n",
            " 17  PAY_AMT1                    30000 non-null  int64\n",
            " 18  PAY_AMT2                    30000 non-null  int64\n",
            " 19  PAY_AMT3                    30000 non-null  int64\n",
            " 20  PAY_AMT4                    30000 non-null  int64\n",
            " 21  PAY_AMT5                    30000 non-null  int64\n",
            " 22  PAY_AMT6                    30000 non-null  int64\n",
            " 23  default payment next month  30000 non-null  int64\n",
            "dtypes: int64(24)\n",
            "memory usage: 5.7 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4A5uXQHOY5k"
      },
      "source": [
        "## Is it a balanced dataset?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "iJxtYOWtPBiF",
        "outputId": "57c3b426-fa70-4696-f714-55b5ac4956c2"
      },
      "source": [
        "check_data_balance(df[\"default payment next month\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0    23364\n",
              "1     6636\n",
              "Name: default payment next month, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TE4HYLM7RmMl"
      },
      "source": [
        "## Histograms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "id": "q6xse7L9Rlnn",
        "outputId": "244cff00-2673-4007-960d-9d3afdfb7619"
      },
      "source": [
        "_ = df.hist(figsize=(20,15))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 25 Axes>"
            ],
            "image/png": 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WKSkp8PPzK1cuWvXr18dff/1Vaow235LqPXz4cERFRWHBggUYOHDgS+VDRJWvcePG8PHxQWJiIvLy8kq9Y8TQz5tWVlbi1IR//vknYmJicOfOHfHulxcNGDAAoaGhyM/Px+nTp7Fo0SLk5eXBzKzsiU9yc3Oxd+9ehIaG6kxx+Oabb+KHH37Ab7/9htdee03s27R9/PO++uorPHjwADk5OSWeb/bo0UO8+IeIyFD6nu/Vr1+/2H5qwYIFCA0NBYBiZ7UBXv4cmKonDrBQlTNs2DDEx8cjIiIC7du3F78UrCrMzMzQq1cvxMXF4dq1a2jdujUEQcC3336LR48eFZvvnTt38PDhw2JHwImIjMnS0hJvvPEG3njjDbz22msYPXo0kpKSMH/+fIPKKSgowIQJE+Di4oJffvkFfn5++OSTT9CvXz/Y2dmZJnkiov9fRkYGzM3N4e7uDuDZF4UAip0yBgDy8vLEGADigqQXLlyAl5dXmc938OBBZGdnY8uWLTrT2WglJCS89ABLy5YtkZ6ejqysLDRp0qTYmPPnzwNAiee/2rtYRo0apXMFJRFVX8OGDcP48eOhVCoREBBQ7HlWeT5vmpub61xU4+/vD09PT3z00UfYuXNnkTJeeeUVMb5v375o1KgRQkND0atXrzLXIUhKSoJarUZsbCxiY2OL7E9ISEBkZCRsbW3h7Oxc7LqB2jVZyjs1JBFRWfQ93/P09MS5c+eg0Wh0pglr165dRaZL1QjXYKEqp1u3bmjSpAkOHz5c6XevlEQ7x612RPvIkSP4888/sWDBAiQlJen8rF27Fnl5edixY0clZkxEtZH2ypns7GyDj/3iiy9w9uxZfPnll7CxsUF8fDz+/vtvzJo1y9hpEhHpyMrKwpEjRyCXy8WrnbXrlly9erXYY65evaqztklAQADMzc2xefNmvZ4zISEBDg4ORc7jkpKSEBQUhO3bt5c4uKOvfv36AQD+85//FLtfpVLhxx9/hKenpzjFTnE+/PBDNG/eHJGRkVyLhagGePfdd2FmZoYTJ06U+PnXGJ83nZ2dMW3aNOzatQsnTpwoM6+PPvoIzZo1Q3h4eJl9TUJCAtq0aVNsH6q9Q0crMDAQ169fx6lTp8rMgYjImPQ93+vXrx8eP36M7du3V3bKVE3wDhaqciQSifjF3ogRIyo7nSI0Gg2Sk5NhaWmJli1bAvjndu3p06frXD2ptXTpUiQkJHBqHSIyiUOHDqFnz55F5tPeu3cvABS7kGlp/vjjD8ybNw/vvPOOOAWNl5cXPv74Y8TFxWH06NHiVYZERMZ09+5dBAUFoaCgAP/+97/F7c7OzvDy8sLmzZvx6aef6lzhnZaWhhMnTuDjjz8Wt7m6umL8+PGIj4/Hl19+icmTJ+s8T2FhIZYvX44PPvgADRs2xLZt2zBkyJBip+ZxcXHBt99+i507d+KDDz4od93ee+89LFy4EIsXL0afPn10po8oLCzEpEmTcO/ePaxevbrUcp6/i4WIqr969ephzZo1uHXrFvr3719sjLE+b06ePBlLly7F4sWLyxyQsbCwwCeffIJ//etf+PHHH0uclvCPP/7A0aNHERkZWWwfmp+fj+HDh+PkyZPw9vbGjBkzkJiYiDFjxiAlJQWOjo468Rw4JiJTePz4sd7ne5MmTcKXX36JadOmwcvLq8iaKuyn6EUcYCGT+Oabb7Bv374i2wcMGKDX8QMGDNA71tR++uknXLlyBcCzW68TExNx7do1zJo1CzY2NlCr1fjhhx/g6+tb7MkuALzzzjuIi4vDnTt34ODgAODZQE1xC03b29vjX//6l+kqREQ1zuTJk5GXl4d3330Xnp6eyM/Px/Hjx/Hdd9+hadOmGD16tBj73//+t9gruuvVqyd+cJ48eTIEQcCXX36pExMZGYmtW7di4sSJOHPmDBfrI6ISPX/+9LyuXbvi1VdfBQD89ttv2Lx5MwRBgEqlwrlz55CUlISHDx9i2bJl6NOnj86xy5Ytg7+/P7y8vDBq1Ci4uLjg8uXLWLt2LZydnTF79myd+NjYWNy4cQMff/wxtm3bhn79+qFBgwbIyspCUlISrly5gqFDh2Lnzp148OAB3nnnnWLr0qVLFzRu3BgJCQkvNcBiaWmJ77//Hr1790a3bt0wevRodOrUCbm5uUhMTMSvv/6KTz75BEOHDi2zLO1aLOnp6eXOh4iqjuDg4BL3lffzZnEaNmyI0aNHY/Xq1bh8+bJ4wWBJRo0ahXnz5mHJkiUlDrAkJiZCEIQS+9C+ffvCwsICCQkJ8Pb2RosWLZCYmIigoCB4eHhg+PDhaN++PQRBQGZmJhITE2FmZoZXXnmlSFnff/99sdNu+/r6FhmoISJ6nqHne9u3b0f//v3Rvn17DB06FG+88QakUin++OMPJCUlAUCxU77qcw5MNZBAZEQbNmwQAJT48/PPPwsAhKVLl4rHHDp0SAAgJCUllVr2W2+9JbRu3Vpnm5ubmxAYGFhs/OnTpwUAwoYNG4xWFysrK8HLy0tYs2aNUFhYKAiCIPzwww8CAGH9+vUllnX48GEBgBAXFycIgiAEBweX2EbNmjUrV75EVHv99NNPwpgxYwRPT0+hXr16gqWlpdC8eXNh8uTJQk5Ojhjn5uZWYt/j5uYmCIIgbN++XQAgfP7558U+1/fffy8AEJYtW1YRVSOiaqasc0Htednz28zMzAQ7OzuhQ4cOwpQpU4SLFy+WWP6JEyeEfv36CQ0aNBAsLCyE//u//xPGjRsn/Pnnn8XGP336VFi3bp3QvXt3wdbWVpBKpYKbm5swevRo4ezZs4IgCEL//v0FKysr4dGjRyU+76hRowSpVCr89ddf4ralS5cKAITMzMxijwEghISEFNl+584dISwsTGjevLkgk8kEOzs7wcfHR9i5c2eR2MzMzCLnzlrPt/X//ve/EnMnoqpF+949ffp0qXHaz7rl/bxZt27dYmNv3LghmJubC8HBweK2kvorQRCEiIgIAYBw6NChYvNv27at0KRJk1Lr0rNnT8HBwUHQaDTituvXrwuTJk0SmjdvLlhZWQl16tQRPD09hYkTJwrp6ek6x8+fP7/Uvy3a3IiISlKe873s7Gxh+vTpQqtWrYQ6deoIMplMePXVV4WRI0cKR48e1TlW33NgqpkkgsD7moiIiIiIiIiIiIiIiAzBRe6JiIiIiIiIiIiIiIgMxDVYqNZ5+PAhHj58WGpM48aNubYAEREREREREREREZWIAyxU63z++eeIjIwsNSYzMxNNmzatmISIiIiIiIiIiIiIqNrhGixU69y8eRM3b94sNaZbt26wsrKqoIyIiIiIiIiIiIiIqLrhAAsREREREREREREREZGBuMg9ERERERERERERERGRgWr1GiyFhYW4ffs26tevD4lEUtnpEJEJCIKABw8ewMXFBWZmHFMuDftEopqPfaL+2CcS1XzsE/XHPpGo5mOfqD/2iUQ1nyF9Yq0eYLl9+zZcXV0rOw0iqgB//PEHXnnllcpOo0pjn0hUe7BPLBv7RKLag31i2dgnEtUe7BPLxj6RqPbQp0+s1QMs9evXB/CsoWxsbIqN0Wg0SE5Ohp+fH6RSaUWm91KqY97VMWeAeVc0Q/NWqVRwdXUV3+9UMn36RK3q+voxBOtY/dX0+gHsE02JfaLpsL30x7YyDPtE02GfaDpsL/2xrQzDPtF0SusT+TotH7Zb+bHtyqesdjOkT6zVAyza2/hsbGxKHWCxtraGjY1NtXqRVse8q2POAPOuaOXNm7ftlk2fPlGrur5+DME6Vn81vX4A+0RTYp9oOmwv/bGtDMM+0XTYJ5oO20t/bCvDsE80ndL6RL5Oy4ftVn5su/LRt9306RM5qSIREREREREREREREZGBOMBCRERERERERERERERkIIMHWI4ePYr+/fvDxcUFEokEO3bsEPdpNBrMnDkTbdu2Rd26deHi4oKRI0fi9u3bOmU0bdoUEolE52fx4sU6MefPn0f37t1hZWUFV1dXxMTEFMklKSkJnp6esLKyQtu2bbF3715Dq0NERERERERERERERGQwgwdYHj16hPbt22PVqlVF9uXl5eHXX3/F3Llz8euvv2Lbtm24evUq3nnnnSKxCxYsQHZ2tvgzefJkcZ9KpYKfnx/c3NyQlpaGpUuXIiIiAmvXrhVjjh8/jqCgIIwdOxZnz57FwIEDMXDgQGRkZBhaJSIiIiIiIiIiIiIiIoMYvMh9QEAAAgICit1na2sLhUKhs23lypXo3LkzsrKy0KRJE3F7/fr14eTkVGw5CQkJyM/PxzfffANLS0u0bt0a6enpWLZsGSZMmAAAiIuLQ58+fTB9+nQAQFRUFBQKBVauXIn4+HhDq0VERERERERERERERKQ3gwdYDHX//n1IJBLY2dnpbF+8eDGioqLQpEkTDBs2DNOmTYOFxbN0UlNT0aNHD1haWorx/v7+WLJkCe7du4cGDRogNTUVYWFhOmX6+/vrTFn2IrVaDbVaLT5WqVQAnk1tptFoij1Gu72k/VVVdcy7OuYMMO+KZmje1a1+REREREREREREVD2YdIDlyZMnmDlzJoKCgmBjYyNu//jjj/H666/D3t4ex48fx+zZs5GdnY1ly5YBAJRKJdzd3XXKcnR0FPc1aNAASqVS3PZ8jFKpLDGf6OhoREZGFtmenJwMa2vrUuvy4p051UV1zLs65gww74qmb955eXkmzoSISD9NZ+0xepkycwExnY1eLNFLK+31rn3dtonYD3WBxOCyby0OfJnUiIiokpX3nEifvx/8G0G1jSk+YwB8LxEZwmQDLBqNBu+//z4EQcCaNWt09j1/50m7du1gaWmJjz76CNHR0ZDJZKZKCbNnz9Z5bpVKBVdXV/j5+ekMAD1Po9FAoVDA19cXHRYeNFluGRH+Ri3v+bylUqlRyzaV6pgzwLwrmqF5a+9UIyIiIiIiIqKqqaCgABEREdi8eTOUSiVcXFwwatQohIeHQyJ5NqAmCALmz5+Pr7/+Grm5uXjzzTexZs0atGjRQizn7t27mDx5Mnbt2gUzMzMMHjwYcXFxqFevnhhz/vx5hISE4PTp02jcuDEmT56MGTNmVHidiahmMMkAi3Zw5ffff8fBgwdLHLzQ8vb2xtOnT3Hr1i14eHjAyckJOTk5OjHax9p1W0qKKWldFwCQyWTFDuBIpdIyv6iVSqXlusJOX6b6glufulU11TFngHlXNH3zro51IyIiIiIiIqpNlixZgjVr1mDTpk1o3bo1zpw5g9GjR8PW1hYff/wxACAmJgZffPEFNm3aBHd3d8ydOxf+/v64dOkSrKysAADDhw9HdnY2FAoFNBoNRo8ejQkTJiAxMRHAs4sw/fz84OPjg/j4eFy4cAFjxoyBnZ2duO4zEZEhzIxdoHZw5dq1azhw4AAaNmxY5jHp6ekwMzODg4MDAEAul+Po0aM6aycoFAp4eHigQYMGYkxKSopOOQqFAnK53Ii1ISIiIiIiIiIiIlM6fvw4BgwYgMDAQDRt2hTvvfce/Pz8cOrUKQDP7l5ZsWIFwsPDMWDAALRr1w7/+c9/cPv2bXE95suXL2Pfvn1Yt24dvL290a1bN3z55ZfYsmULbt++DQBISEhAfn4+vvnmG7Ru3RpDhw7Fxx9/LC5bQERkKIMHWB4+fIj09HSkp6cDADIzM5Geno6srCxoNBq89957OHPmDBISElBQUAClUgmlUon8/HwAzxawX7FiBc6dO4ebN28iISEB06ZNw4cffigOngwbNgyWlpYYO3YsLl68iO+++w5xcXE603tNmTIF+/btQ2xsLK5cuYKIiAicOXMGoaGhRmgWIiIiIiIiIiIiqghdu3ZFSkoKfvvtNwDAuXPncOzYMQQEBAB49v2jUqmEj4+PeIytrS28vb2RmpoK4Nl3jnZ2dujUqZMY4+PjAzMzM5w8eVKM6dGjBywtLcUYf39/XL16Fffu3TN5PYmo5jF4irAzZ86gV69e4mPtoEdwcDAiIiKwc+dOAICXl5fOcYcOHULPnj0hk8mwZcsWREREQK1Ww93dHdOmTdMZPLG1tUVycjJCQkLQsWNHNGrUCPPmzdO5Va9r165ITExEeHg45syZgxYtWmDHjh1o06aNoVUiIiIiIiIiIiKiSjJr1iyoVCp4enrC3NwcBQUFWLhwIYYPHw4AUCqVAABHR0ed4xwdHcV9SqVSnB1Hy8LCAvb29jox7u7uRcrQ7tNe/P08tVoNtVotPtau9arRaHRm39Fue/5fU5OZCyYpt6Lyf/H5Kvp5awK2XfmU1W6GtKfBAyw9e/aEIJT85i1tHwC8/vrrOHHiRJnP065dO/z888+lxgwZMgRDhgwpsywiIiIiIiKiinT06FEsXboUaWlpyM7Oxvbt2zFw4EBx/6hRo7Bp0yadY/z9/bFv3z7xsbEWa05KSsLcuXNx69YttGjRAkuWLEHfvn1NU3EionLYunUrEhISkJiYiNatWyM9PR1Tp06Fi4sLgoODKzW36OhoREZGFtmenJwMa2vrYo9RKBSmTgsAENPZNOXu3bvXNAWXoaLarSZi25VPSe2Wl5endxkmWeSeiIiIiIiIqDZ79OgR2rdvjzFjxmDQoEHFxvTp0wcbNmwQH8tkMp39xlis+fjx4wgKCkJ0dDT69euHxMREDBw4EL/++itngCCiKmP69OmYNWsWhg4dCgBo27Ytfv/9d0RHRyM4OBhOTk4AgJycHDg7O4vH5eTkiLPoODk54c6dOzrlPn36FHfv3hWPd3JyQk5Ojk6M9rE25kWzZ8/WmXlHpVLB1dUVfn5+sLGx0YnVaDRQKBTw9fWFVCo1tBkM1iZiv0nKzYjwN0m5JanodqtJ2HblU1a7ae9U0wcHWIiIiIiIiIiMLCAgQFw7oCQymazEL/S0izWfPn1aXE/gyy+/RN++ffH555/DxcVFZ7FmS0tL8arvZcuWiQMscXFx6NOnD6ZPnw4AiIqKgkKhwMqVKxEfH2/EGhMRlV9eXh7MzHSXijY3N0dhYSEAwN3dHU5OTkhJSREHVFQqFU6ePIlJkyYBAORyOXJzc5GWloaOHTsCAA4ePIjCwkJ4e3uLMf/+97+h0WjEL1UVCgU8PDyKnR4MeNZXvzgADgBSqbTEL7RL22dM6gKJScqtrC/qK6rdaiK2XfmU1G6GtKXBi9wTERERERER0cs7fPgwHBwc4OHhgUmTJuHvv/8W9xlrsebU1FSdRaG1MdpFoYmIqoL+/ftj4cKF2LNnD27duoXt27dj2bJlePfddwEAEokEU6dOxWeffYadO3fiwoULGDlyJFxcXMTpF1u2bIk+ffpg/PjxOHXqFH755ReEhoZi6NChcHFxAQAMGzYMlpaWGDt2LC5evIjvvvsOcXFxOneoEBEZgnewEBEREREREVWwPn36YNCgQXB3d8eNGzcwZ84cBAQEIDU1Febm5kZbrFmpVJa6KHRxDFnQ+UVcbNcwtbG9yrsot8xM0Pm3OLWpHcti6Gurstvuyy+/xNy5c/Gvf/0Ld+7cgYuLCz766CPMmzdPjJkxYwYePXqECRMmIDc3F926dcO+fftgZWUlxiQkJCA0NBS9e/cW16764osvxP22trZITk5GSEgIOnbsiEaNGmHevHniXX9ERIbiAAsRERERERFRBdOuMwA8W2ugXbt2aNasGQ4fPozevXtXYmblW9D5RVxs1zC1qb1edlHuqE6FJe6rrIW5qzJ9X1uGLOhsCvXr18eKFSuwYsWKEmMkEgkWLFiABQsWlBhjb28vrlNVknbt2uHnn38ub6pERDo4wEJERERERERUyV599VU0atQI169fR+/evY22WHNJMSWt/QIYtqDzi7jYrmFqY3uVd1FumZmAqE6FmHvGDOrC4tedqOiFuasyQ19bhizoTERE/+AACxEREREREVEl+/PPP/H333/D2dkZgPEWa5bL5UhJScHUqVPF51IoFJDL5SXmUp4FnV8mlmpXe73sotzqQkmJZdSWNjSEvq8tth0RUflwkXsiIiIiIiIiI3v48CHS09ORnp4OAMjMzER6ejqysrLw8OFDTJ8+HSdOnMCtW7eQkpKCAQMGoHnz5vD3f3YFvrEWa54yZQr27duH2NhYXLlyBREREThz5gxCQ0MrvE2IiIiIahoOsBARERGRURw9ehT9+/eHi4sLJBIJduzYIe7TaDSYOXMm2rZti7p168LFxQUjR47E7du3dcpo2rQpJBKJzs/ixYt1Ys6fP4/u3bvDysoKrq6uiImJKZJLUlISPD09YWVlhbZt23JOdiKqcGfOnEGHDh3QoUMHAEBYWBg6dOiAefPmwdzcHOfPn8c777yD1157DWPHjkXHjh3x888/69w5kpCQAE9PT/Tu3Rt9+/ZFt27dsHbtWnG/drHmzMxMdOzYEZ988kmRxZq7du2KxMRErF27Fu3bt8f333+PHTt2oE2bNhXXGEREREQ1FKcIIyIiIiKjePToEdq3b48xY8Zg0KBBOvvy8vLw66+/Yu7cuWjfvj3u3buHKVOm4J133sGZM2d0YhcsWIDx48eLj+vXry/+X6VSwc/PDz4+PoiPj8eFCxcwZswY2NnZiV8oHj9+HEFBQYiOjka/fv2QmJiIgQMH4tdff+UXikRUYXr27AlBEErcv39/2etQGGux5iFDhmDIkCFlPh8RERERGYYDLERERERkFAEBAQgICCh2n62tLRQKhc62lStXonPnzsjKykKTJk3E7fXr1y9x8eWEhATk5+fjm2++gaWlJVq3bo309HQsW7ZMHGCJi4tDnz59MH36dABAVFQUFAoFVq5cifj4eGNUlYiIiIiIiIgDLERERERUOe7fvw+JRAI7Ozud7YsXL0ZUVBSaNGmCYcOGYdq0abCweHbampqaih49esDS0lKM9/f3x5IlS3Dv3j00aNAAqampOusPaGOen7LsRWq1Gmq1WnysUqkAPJvaTKPRlFoP7f6y4moTmXnJV+3LzASdfw1Vm9qZry3DGNpebFciIiIielkcYCEiIiKiCvfkyRPMnDkTQUFBsLGxEbd//PHHeP3112Fvb4/jx49j9uzZyM7OxrJlywAASqUS7u7uOmU5OjqK+xo0aAClUiluez5GqVSWmE90dDQiIyOLbE9OToa1tbVedXrxDp3aLKZz2TFRnQrLVXZtXE+Hry3D6NteeXl5Js6EiIiIiGo6DrAQERERUYXSaDR4//33IQgC1qxZo7Pv+TtP2rVrB0tLS3z00UeIjo7WWfjZ2GbPnq3z3CqVCq6urvDz89MZACqORqOBQqGAr68vpFKpyXKsTtpElLy2hMxMQFSnQsw9YwZ1ocTgsjMi/F8mtWqFry3DGNpe2jvViIiIiIjKiwMsRERERFRhtIMrv//+Ow4ePFjm4IW3tzeePn2KW7duwcPDA05OTsjJydGJ0T7WrttSUkxJ67oAgEwmK3YARyqV6v3FtiGxNZ26oOyBE3WhRK+4F9XGNuZryzD6thfblIiIiIhellllJ0BEREREtYN2cOXatWs4cOAAGjZsWOYx6enpMDMzg4ODAwBALpfj6NGjOmsnKBQKeHh4oEGDBmJMSkqKTjkKhQJyudyItSEiIiIiIqLajnewEBEREZFRPHz4ENevXxcfZ2ZmIj09Hfb29nB2dsZ7772HX3/9Fbt370ZBQYG4Joq9vT0sLS2RmpqKkydPolevXqhfvz5SU1Mxbdo0fPjhh+LgybBhwxAZGYmxY8di5syZyMjIQFxcHJYvXy4+75QpU/DWW28hNjYWgYGB2LJlC86cOYO1a9dWbIMQERERERFRjcYBFiIiIiIyijNnzqBXr17iY+2aJsHBwYiIiMDOnTsBAF5eXjrHHTp0CD179oRMJsOWLVsQEREBtVoNd3d3TJs2TWdtFFtbWyQnJyMkJAQdO3ZEo0aNMG/ePEyYMEGM6dq1KxITExEeHo45c+agRYsW2LFjB9q0aWPC2hMREREREVFtwwEWIiIiIjKKnj17QhCEEveXtg8AXn/9dZw4caLM52nXrh1+/vnnUmOGDBmCIUOGlFkWERERERERUXlxgIWI6CUcPXoUS5cuRVpaGrKzs7F9+3YMHDhQ3D9q1Chs2rRJ5xh/f3/s27dPfHz37l1MnjwZu3btgpmZGQYPHoy4uDjUq1dPjDl//jxCQkJw+vRpNG7cGJMnT8aMGTN0yk1KSsLcuXNx69YttGjRAkuWLEHfvn1NU3EiIiIiItJL01l7StwnMxcQ0xloE7Ef6gKJwWXfWhz4MqkRERHRS+Ii90REL+HRo0do3749Vq1aVWJMnz59kJ2dLf58++23OvuHDx+OixcvQqFQYPfu3Th69KjOVDcqlQp+fn5wc3NDWloali5dioiICJ21BI4fP46goCCMHTsWZ8+excCBAzFw4EBkZGQYv9JERERERERERETEO1iIiF5GQEAAAgICSo2RyWRwcnIqdt/ly5exb98+nD59Gp06dQIAfPnll+jbty8+//xzuLi4ICEhAfn5+fjmm29gaWmJ1q1bIz09HcuWLRMHYuLi4tCnTx9Mnz4dABAVFQWFQoGVK1ciPj7eiDUmIiIiIiIiIiIigHewEBGZ3OHDh+Hg4AAPDw9MmjQJf//9t7gvNTUVdnZ24uAKAPj4+MDMzAwnT54UY3r06AFLS0sxxt/fH1evXsW9e/fEGB8fH53n9ff3R2pqqimrRkREREREREREVGvxDhYiIhPq06cPBg0aBHd3d9y4cQNz5sxBQEAAUlNTYW5uDqVSCQcHB51jLCwsYG9vD6VSCQBQKpVwd3fXiXF0dBT3NWjQAEqlUtz2fIy2jOKo1Wqo1WrxsUqlAgBoNBpoNJpS66XdX1ZcdcY6Vn9VrX4y89IXeC9XmWbPytS3jlWlLYiIiIiIiIhqAoMHWMpa0FkQBMyfPx9ff/01cnNz8eabb2LNmjVo0aKFGMMFnYmothg6dKj4/7Zt26Jdu3Zo1qwZDh8+jN69e1diZkB0dDQiIyOLbE9OToa1tbVeZSgUCmOnVeWwjtVfValfTGfTla1vHfPy8kyXBBEREREREVEtY/AAi3ZB5zFjxmDQoEFF9sfExOCLL77Apk2b4O7ujrlz58Lf3x+XLl2ClZUVgGcLOmdnZ0OhUECj0WD06NGYMGECEhMTAfyzoLOPjw/i4+Nx4cIFjBkzBnZ2duJ6A9oFnaOjo9GvXz8kJiZi4MCB+PXXX9GmTZuXaRMiIpN59dVX0ahRI1y/fh29e/eGk5MT7ty5oxPz9OlT3L17V1y3xcnJCTk5OTox2sdlxZS09gsAzJ49G2FhYeJjlUoFV1dX+Pn5wcbGptR6aDQaKBQK+Pr6QiqVllHr6ol1rP6qWv3aROw3epkyMwFRnQr1rqP2TjUiIiIiIiIienkGD7CUtqCzIAhYsWIFwsPDMWDAAADAf/7zHzg6OmLHjh0YOnQoF3Qmolrtzz//xN9//w1nZ2cAgFwuR25uLtLS0tCxY0cAwMGDB1FYWAhvb28x5t///jc0Go34BapCoYCHhwcaNGggxqSkpGDq1KnicykUCsjl8hJzkclkkMlkRbZLpVK9v4w2JLa6Yh2rv6pSP3WBxGRl61vHqtAORERERERERDWFURe5z8zMhFKp1Flo2dbWFt7e3uJCy1zQmYhqkocPHyI9PR3p6ekAnvWD6enpyMrKwsOHDzF9+nScOHECt27dQkpKCgYMGIDmzZvD398fANCyZUv06dMH48ePx6lTp/DLL78gNDQUQ4cOhYuLCwBg2LBhsLS0xNixY3Hx4kV89913iIuL07n7ZMqUKdi3bx9iY2Nx5coVRERE4MyZMwgNDa3wNiEiIiIiIiIiIqoNjLrIvXYx5dIWWq5uCzo/v0CuKRanffF5jF1edVrMtjrmDDDvimZo3qau35kzZ9CrVy/xsXbQIzg4GGvWrMH58+exadMm5ObmwsXFBX5+foiKitK5cyQhIQGhoaHo3bu3uC7VF198Ie63tbVFcnIyQkJC0LFjRzRq1Ajz5s0T7+gDgK5duyIxMRHh4eGYM2cOWrRogR07dnDKRCIiIiIiIiIiIhMx6gBLVfcyCzorFAqTLk67d+9ek5RbVRb2NUR1zBlg3hWtqizo3LNnTwhCyYOv+/eXveaCvb29uAZVSdq1a4eff/651JghQ4ZgyJAhZT4fERERERERERERvTyjDrBoF1POyckR1xfQPvby8hJjqtOCzs8vkNth4cESy35ZGRH+Ri2vqi3sq4/qmDPAvCuaoXlzQWciIiIiIiIiIiIyBaMOsLi7u8PJyQkpKSnigIpKpcLJkycxadIkANV3QWepVGryxWlNVW51+vIcqJ45A8y7onFBZyIiIiIiIiIi42s6a4/Jyr61ONBkZRNVBoMXuS9tQWeJRIKpU6fis88+w86dO3HhwgWMHDkSLi4uGDhwIAAu6ExERERERERERERERNWfwXewlLag88aNGzFjxgw8evQIEyZMQG5uLrp164Z9+/bByspKPIYLOhMRERERERERERERUXVm8ABLWQs6SyQSLFiwAAsWLCgxhgs6ExERERERERERERFRdWbwFGFERERERERERERERES1HQdYiIiIiIiIiIiIiIiIDGTwFGFERERERFRxms7aY5Jyby0ONEm5REREROXx3//+FzNnzsRPP/2EvLw8NG/eHBs2bECnTp0AAIIgYP78+fj666+Rm5uLN998E2vWrEGLFi3EMu7evYvJkydj165d4rrPcXFxqFevnhhz/vx5hISE4PTp02jcuDEmT56MGTNmVHh9iahm4B0sREREREREREREVGnu3buHN998E1KpFD/99BMuXbqE2NhYNGjQQIyJiYnBF198gfj4eJw8eRJ169aFv78/njx5IsYMHz4cFy9ehEKhwO7du3H06FFMmDBB3K9SqeDn5wc3NzekpaVh6dKliIiIwNq1ayu0vkRUc/AOFiIiIiIiIiIiIqo0S5YsgaurKzZs2CBuc3d3F/8vCAJWrFiB8PBwDBgwAADwn//8B46OjtixYweGDh2Ky5cvY9++fTh9+rR418uXX36Jvn374vPPP4eLiwsSEhKQn5+Pb775BpaWlmjdujXS09OxbNkynYEYIiJ98Q4WIiIiIjKKo0ePon///nBxcYFEIsGOHTt09guCgHnz5sHZ2Rl16tSBj48Prl27phNz9+5dDB8+HDY2NrCzs8PYsWPx8OFDnZjz58+je/fusLKygqurK2JiYorkkpSUBE9PT1hZWaFt27bYu3ev0etLRERERMaxc+dOdOrUCUOGDIGDgwM6dOiAr7/+WtyfmZkJpVIJHx8fcZutrS28vb2RmpoKAEhNTYWdnZ04uAIAPj4+MDMzw8mTJ8WYHj16wNLSUozx9/fH1atXce/ePVNXk4hqIN7BQkRERERG8ejRI7Rv3x5jxozBoEGDiuzXTuuwadMmuLu7Y+7cufD398elS5dgZWUF4Nm0DtnZ2VAoFNBoNBg9ejQmTJiAxMREAP9M6+Dj44P4+HhcuHABY8aMgZ2dnXjV4fHjxxEUFITo6Gj069cPiYmJGDhwIH799Ve0adOm4hqEiIiIiPRy8+ZNrFmzBmFhYZgzZw5Onz6Njz/+GJaWlggODoZSqQQAODo66hzn6Ogo7lMqlXBwcNDZb2FhAXt7e52Y5++Meb5MpVKpMyWZllqthlqtFh+rVCoAgEajgUaj0YnVPn5xu6nIzIUKeR5jKq5tKrrdahK2XfmU1W6GtCcHWIiIiIjIKAICAhAQEFDsvoqc1iEuLg59+vTB9OnTAQBRUVFQKBRYuXIl4uPjK6AliIiIiMgQhYWF6NSpExYtWgQA6NChAzIyMhAfH4/g4OBKzS06OhqRkZFFticnJ8Pa2rrYYxQKhanTAgDEdK6QpzGq0u4sr6h2q4nYduVTUrvl5eXpXQYHWIiIiIjI5Mqa1mHo0KFlTuvw7rvvljitw5IlS3Dv3j00aNAAqampCAsL03l+f3//IlOWPc+QKxNfZOqrxkx1ZaIpr3IrLWeZmaDzr6Fq09V5vCLRMIa2F9uViKjqcHZ2RqtWrXS2tWzZEj/88AMAwMnJCQCQk5MDZ2dnMSYnJwdeXl5izJ07d3TKePr0Ke7evSse7+TkhJycHJ0Y7WNtzItmz56tc26pUqng6uoKPz8/2NjY6MRqNBooFAr4+vpCKpXqVfeX0SZiv8mfw9gyIvyLbKvodqtJ2HblU1a7aT8P6oMDLERERERkchU5rYNSqSz1eYpTnisTX2Sqq8ZMdWWiKdel0SfnqE6F5Sq7Nq6nwysSDaNvexlyZSIREZnWm2++iatXr+ps++233+Dm5gbg2YL3Tk5OSElJEQdUVCoVTp48iUmTJgEA5HI5cnNzkZaWho4dOwIADh48iMLCQnh7e4sx//73v6HRaMQvVRUKBTw8PIqdHgwAZDIZZDJZke1SqbTEL7RL22dM6gKJyZ/D2Eprl4pqt5qIbVc+JbWbIW3JARYiIiIiqvUMuTLxRaa+asxUVyYWd/WgsZSWs8xMQFSnQsw9YwZ1oeFfCpgy76qGVyQaxtD2MuTKRCIiMq1p06aha9euWLRoEd5//32cOnUKa9euxdq1awEAEokEU6dOxWeffYYWLVqI6/m5uLhg4MCBAJ7d8dKnTx+MHz8e8fHx0Gg0CA0NxdChQ+Hi4gIAGDZsGCIjIzF27FjMnDkTGRkZiIuLw/Llyyur6kRUzXGAhYiIiIhMriKndSgppqRpH4DyXZn4MrGGMNWViab8wl6fnNWFknLVrTYONPCKRMPo215sUyKiquONN97A9u3bMXv2bCxYsADu7u5YsWIFhg8fLsbMmDEDjx49woQJE5Cbm4tu3bph3759sLKyEmMSEhIQGhqK3r17w8zMDIMHD8YXX3wh7re1tUVycjJCQkLQsWNHNGrUCPPmzRPX8iMiMhQHWIiIiIjI5CpyWge5XI6UlBRMnTpVfH6FQgG5XF5BtSUiIiIiQ/Xr1w/9+vUrcb9EIsGCBQuwYMGCEmPs7e2RmJhY6vO0a9cOP//8c7nzJCJ6nlllJ0BERERENcPDhw+Rnp6O9PR0AM8Wtk9PT0dWVpbOtA47d+7EhQsXMHLkyBKndTh16hR++eWXYqd1sLS0xNixY3Hx4kV89913iIuL05nea8qUKdi3bx9iY2Nx5coVRERE4MyZMwgNDa3oJiEiIiIiIqIajHewEBEREZFRnDlzBr169RIfawc9goODsXHjxgqb1qFr165ITExEeHg45syZgxYtWmDHjh1o06ZNBbQCERERERER1RYcYCEiIiIio+jZsycEQShxf0VO6zBkyBAMGTKk9ISJiIiIiIiIXgKnCCMiIiIiIiIiIiIiIjIQB1iIiIiIiIiIiIiIiIgMxAEWIiIiIiIiIiIiIiIiA3ENFiIiKrc2EfuhLpAYvdxbiwONXiYREREREREREZEx8Q4WIiIiIiIiIiM7evQo+vfvDxcXF0gkEuzYsUNnvyAImDdvHpydnVGnTh34+Pjg2rVrOjF3797F8OHDYWNjAzs7O4wdOxYPHz7UiTl//jy6d+8OKysruLq6IiYmpkguSUlJ8PT0hJWVFdq2bYu9e/cavb5EREREtREHWIiIiIiIiIiM7NGjR2jfvj1WrVpV7P6YmBh88cUXiI+Px8mTJ1G3bl34+/vjyZMnYszw4cNx8eJFKBQK7N69G0ePHsWECRPE/SqVCn5+fnBzc0NaWhqWLl2KiIgIrF27Vow5fvw4goKCMHbsWJw9exYDBw7EwIEDkZGRYbrKExEREdUSnCKMiIiIiIiIyMgCAgIQEBBQ7D5BELBixQqEh4djwIABAID//Oc/cHR0xI4dOzB06FBcvnwZ+/btw+nTp9GpUycAwJdffom+ffvi888/h4uLCxISEpCfn49vvvkGlpaWaN26NdLT07Fs2TJxICYuLg59+vTB9OnTAQBRUVFQKBRYuXIl4uPjK6AliIiIiGouDrAQERERERERVaDMzEwolUr4+PiI22xtbeHt7Y3U1FQMHToUqampsLOzEwdXAMDHxwdmZmY4efIk3n33XaSmpqJHjx6wtLQUY/z9/bFkyRLcu3cPDRo0QGpqKsLCwnSe39/fv8iUZc9Tq9VQq9XiY5VKBQDQaDTQaDSl1k27v6y42kRmLpS8z0zQ+ddQ1bGdS2uPUo/To62qY3uYiqHvRbYdEVH5cIClimg6a49Ry5OZC4jpbNQiiYiIiIiIyAiUSiUAwNHRUWe7o6OjuE+pVMLBwUFnv4WFBezt7XVi3N3di5Sh3degQQMolcpSn6c40dHRiIyMLLI9OTkZ1tbW+lQRCoVCr7jaQJ/P5lGdCstVdnVcT+dlv6sora2qY3uYmr7vxby8PBNnQkRUMxl9gKVp06b4/fffi2z/17/+hVWrVqFnz544cuSIzr6PPvpI59bkrKwsTJo0CYcOHUK9evUQHByM6OhoWFj8k+7hw4cRFhaGixcvwtXVFeHh4Rg1apSxq0NERERERERUq8yePVvnrheVSgVXV1f4+fnBxsam1GM1Gg0UCgV8fX0hlUpNnWq10CZif4n7ZGYCojoVYu4ZM6gLJQaXnRHh/zKpVYrS2qM0+rRVdWwPUzH0vai9U42Mz9gXVRNR1WL0AZbTp0+joKBAfJyRkQFfX18MGTJE3DZ+/HgsWLBAfPz8FTAFBQUIDAyEk5MTjh8/juzsbIwcORJSqRSLFi0C8Ox26sDAQEycOBEJCQlISUnBuHHj4OzsDH9//jElIiIiIiKiqsvJyQkAkJOTA2dnZ3F7Tk4OvLy8xJg7d+7oHPf06VPcvXtXPN7JyQk5OTk6MdrHZcVo9xdHJpNBJpMV2S6VSvUeNDEktqZTF5Q9cKIulOgV96Lq2MblqafO8aW0VXVsD1PT973ItiMiKh8zYxfYuHFjODk5iT+7d+9Gs2bN8NZbb4kx1tbWOjHPXwGTnJyMS5cuYfPmzfDy8kJAQACioqKwatUq5OfnAwDi4+Ph7u6O2NhYtGzZEqGhoXjvvfewfPlyY1eHiIiIiIiIyKjc3d3h5OSElJQUcZtKpcLJkychl8sBAHK5HLm5uUhLSxNjDh48iMLCQnh7e4sxR48e1Vk7QaFQwMPDAw0aNBBjnn8ebYz2eYiIiIio/Ey6Bkt+fj42b96MsLAwSCT/XF2QkJCAzZs3w8nJCf3798fcuXPFu1hSU1PRtm1bnTli/f39MWnSJFy8eBEdOnRAamqqzmKA2pipU6eWmk95Fup7flGw8i7EVhm0i75Vp0XKqutiiMy7YnGhPiIiIiKqDh4+fIjr16+LjzMzM5Geng57e3s0adIEU6dOxWeffYYWLVrA3d0dc+fOhYuLCwYOHAgAaNmyJfr06YPx48cjPj4eGo0GoaGhGDp0KFxcXAAAw4YNQ2RkJMaOHYuZM2ciIyMDcXFxOhcfTpkyBW+99RZiY2MRGBiILVu24MyZM1i7dm2FtgcRERFRTWTSAZYdO3YgNzdXZ22UYcOGwc3NDS4uLjh//jxmzpyJq1evYtu2bQBQ4gJ82n2lxahUKjx+/Bh16tQpNp+XWahPoVBUy0Xjq+PCgtUxZ4B5VzQu1EdEREREVdmZM2fQq1cv8bF2TZPg4GBs3LgRM2bMwKNHjzBhwgTk5uaiW7du2LdvH6ysrMRjEhISEBoait69e8PMzAyDBw/GF198Ie63tbVFcnIyQkJC0LFjRzRq1Ajz5s3DhAkTxJiuXbsiMTER4eHhmDNnDlq0aIEdO3agTZs2FdAKRERERDWbSQdY1q9fj4CAAPHqGgA6J3pt27aFs7MzevfujRs3bqBZs2amTKdcC/U9vyhYh4UHTZqfMWkXf6tOCwtW18UQmXfF4kJ9RERERFQd9OzZE4JQ8iwIEokECxYs0Fmf9EX29vZITEws9XnatWuHn3/+udSYIUOG6KyLSkRERETGYbIBlt9//x0HDhwQ70wpiXbu2OvXr6NZs2ZwcnLCqVOndGL0XaTPxsamxLtXgJdbqE8qlb70QmyVoTouLFgdcwaYd0XjQn1ERERERERERERUmUw2wLJhwwY4ODggMDCw1Lj09HQAgLOzM4BnC/AtXLgQd+7cgYODA4BnUwHZ2NigVatWYszevXt1yuEifURERERERERERERVV9NZe4psk5kLiOkMtInY/1IXuN9aXPr30ESmYGaKQgsLC7FhwwYEBwfDwuKfMZwbN24gKioKaWlpuHXrFnbu3ImRI0eiR48eaNeuHQDAz88PrVq1wogRI3Du3Dns378f4eHhCAkJEe8+mThxIm7evIkZM2bgypUrWL16NbZu3Ypp06aZojpERCU6evQo+vfvDxcXF0gkEuzYsUNnvyAImDdvHpydnVGnTh34+Pjg2rVrOjF3797F8OHDYWNjAzs7O4wdOxYPHz7UiTl//jy6d+8OKysruLq6IiYmpkguSUlJ8PT0hJWVFdq2bVtkIJqIiIiIiIiIiIiMxyQDLAcOHEBWVhbGjBmjs93S0hIHDhyAn58fPD098cknn2Dw4MHYtWuXGGNubo7du3fD3NwccrkcH374IUaOHKkzL627uzv27NkDhUKB9u3bIzY2FuvWrYO/v78pqkNEVKJHjx6hffv2WLVqVbH7Y2Ji8MUXXyA+Ph4nT55E3bp14e/vjydPnogxw4cPx8WLF6FQKLB7924cPXpUZ70qlUoFPz8/uLm5IS0tDUuXLkVERATWrl0rxhw/fhxBQUEYO3Yszp49i4EDB2LgwIHIyMgwXeWJiIiIiIiIiIhqMZNMEebn51fsYn6urq44cuRImce7ubmVeeV1z549cfbs2XLnSERkDAEBAQgICCh2nyAIWLFiBcLDwzFgwAAAwH/+8x84Ojpix44dGDp0KC5fvox9+/bh9OnT6NSpEwDgyy+/RN++ffH555/DxcUFCQkJyM/PxzfffANLS0u0bt0a6enpWLZsmTgQExcXhz59+mD69OkAgKioKCgUCqxcuRLx8fEV0BJERERERERERES1i0nuYCEiIiAzMxNKpRI+Pj7iNltbW3h7eyM1NRUAkJqaCjs7O3FwBQB8fHxgZmaGkydPijE9evSApaWlGOPv74+rV6/i3r17Yszzz6ON0T4PEVFV0LRpU0gkkiI/ISEhAJ5dQPPivokTJ+qUkZWVhcDAQFhbW8PBwQHTp0/H06dPdWIOHz6M119/HTKZDM2bN8fGjRsrqopERERERERUi5hskXsiotpOqVQCABwdHXW2Ozo6ivuUSiUcHBx09ltYWMDe3l4nxt3dvUgZ2n0NGjSAUqks9XmKo1aroVarxccqlQoAoNFooNFoSq2bdr/MrOjdisZQ1vNXBG0OVSEXU6npdaxq9ZOZG//9on0P6lvHym6L06dPo6CgQHyckZEBX19fDBkyRNw2fvx4nalhra2txf8XFBQgMDAQTk5OOH78OLKzszFy5EhIpVIsWrQIwLPB7cDAQEycOBEJCQlISUnBuHHj4OzszOlkiYiIiIiIyKg4wEJEVEtFR0cjMjKyyPbk5GSdLzRLE9Wp0NhpAUCZ00RWJIVCUdkpmFxNr2NVqV9MZ9OVrW8d8/LyTJeEHho3bqzzePHixWjWrBneeustcZu1tTWcnJyKPT45ORmXLl3CgQMH4OjoCC8vL0RFRWHmzJmIiIiApaUl4uPj4e7ujtjYWABAy5YtcezYMSxfvpwDLERERERERGRUHGAhIjIR7ReEOTk5cHZ2Frfn5OTAy8tLjLlz547OcU+fPsXdu3fF452cnJCTk6MTo31cVkxJX1ICwOzZsxEWFiY+VqlUcHV1hZ+fH2xsbEqtm0ajgUKhwNwzZlAXSkqNLY+MiMr/ElRbR19fX0il0spOxyRqeh2rWv3aROw3epkyMwFRnQr1rqP2TrWqID8/H5s3b0ZYWBgkkn/6kYSEBGzevBlOTk7o378/5s6dKw76pqamom3btjp37Pn7+2PSpEm4ePEiOnToUOKUiVOnTq2QehEREREREVHtwQEWIiITcXd3h5OTE1JSUsQBFZVKhZMnT2LSpEkAALlcjtzcXKSlpaFjx44AgIMHD6KwsBDe3t5izL///W9oNBrxC1SFQgEPDw80aNBAjElJSdH5AlGhUEAul5eYn0wmg0wmK7JdKpXq/WW0ulACdYHxB1iqwpfhWoa0R3VV0+tYVepniveKlr51rArtoLVjxw7k5uZi1KhR4rZhw4bBzc0NLi4uOH/+PGbOnImrV69i27ZtAFDidIjafaXFqFQqPH78GHXq1Ck2H2NMm2iqKdhMMb0cYNop40rLWTu1XXmnmazsqe4qUlWb6rCqM7S92K5ERERE9LI4wEJE9BIePnyI69evi48zMzORnp4Oe3t7NGnSBFOnTsVnn32GFi1awN3dHXPnzoWLiwsGDhwI4NnUNX369MH48eMRHx8PjUaD0NBQDB06FC4uLgCefeEYGRmJsWPHYubMmcjIyEBcXByWL18uPu+UKVPw1ltvITY2FoGBgdiyZQvOnDmDtWvXVmh7EBHpa/369QgICBD7OgCYMGGC+P+2bdvC2dkZvXv3xo0bN9CsWTOT5mOMaRNNNR2dqaaXM+V0jPrkXN5pJqvSNJIVpapMdVhdVJdpE4mIiIio+uMACxHRSzhz5gx69eolPtZOuRUcHIyNGzdixowZePToESZMmIDc3Fx069YN+/btg5WVlXhMQkICQkND0bt3b5iZmWHw4MH44osvxP22trZITk5GSEgIOnbsiEaNGmHevHk6X0R27doViYmJCA8Px5w5c9CiRQvs2LEDbdq0qYBWICIyzO+//44DBw6Id6aURHsn3/Xr19GsWTM4OTnh1KlTOjH6TploY2NT4t0rgHGmTTTVdHSmmF4OMO10jKXlrJ3arrzTTFaFaSQrSlWb6rCqM7S9qtK0iURERERUPXGAhYjoJfTs2ROCUPIUJxKJBAsWLMCCBQtKjLG3t0diYmKpz9OuXTv8/PPPpcYMGTIEQ4YMKT1hIqIqYMOGDXBwcEBgYGCpcenp6QAgrmMll8uxcOFC3LlzBw4ODgCeXaluY2ODVq1aiTEv3uFQ1pSJgHGmTTTVdHSmml7OlF/Y65NzeaeZrI0DDVVlqsPqojpOm0hERERE1ZNZZSdARERERLVHYWEhNmzYgODgYFhY/HOtz40bNxAVFYW0tDTcunULO3fuxMiRI9GjRw+0a9cOAODn54dWrVphxIgROHfuHPbv34/w8HCEhISIgyMTJ07EzZs3MWPGDFy5cgWrV6/G1q1bMW3atEqpLxEREREREdVcHGAhIiIiogpz4MABZGVlYcyYMTrbLS0tceDAAfj5+cHT0xOffPIJBg8ejF27dokx5ubm2L17N8zNzSGXy/Hhhx9i5MiROncJuru7Y8+ePVAoFGjfvj1iY2Oxbt06+PvXnmmliIiIiKq7xYsXQyKRYOrUqeK2J0+eICQkBA0bNkS9evUwePDgIlPDZmVlITAwENbW1nBwcMD06dPx9OlTnZjDhw/j9ddfh0wmQ/PmzbFx48YKqBER1VScIoyIiIiIKoyfn1+xUyu6urriyJEjZR7v5uZW5iLnPXv2xNmzZ8udIxERERFVntOnT+Orr74S72LWmjZtGvbs2YOkpCTY2toiNDQUgwYNwi+//AIAKCgoQGBgIJycnHD8+HFkZ2dj5MiRkEqlWLRoEQAgMzMTgYGBmDhxIhISEpCSkoJx48bB2dmZF+QQUbnwDhYiIiIiIiIiIiKqdA8fPsTw4cPx9ddfo0GDBuL2+/fvY/369Vi2bBnefvttdOzYERs2bMDx48dx4sQJAEBycjIuXbqEzZs3w8vLCwEBAYiKisKqVauQn58PAIiPj4e7uztiY2PRsmVLhIaG4r333sPy5csrpb5EVP1xgIWIiIiIiIiIiIgqXUhICAIDA+Hj46OzPS0tDRqNRme7p6cnmjRpgtTUVABAamoq2rZtC0dHRzHG398fKpUKFy9eFGNeLNvf318sg4jIUJwijIiIiIiIiIiIiCrVli1b8Ouvv+L06dNF9imVSlhaWsLOzk5nu6OjI5RKpRjz/OCKdr92X2kxKpUKjx8/Rp06dYo8t1qthlqtFh+rVCoAgEajgUaj0YnVPn5+u8y86PS4pEtmJuj8W14v/j5qg+Jec1S2strNkPbkAAsRERERERERERFVmj/++ANTpkyBQqGAlZVVZaejIzo6GpGRkUW2Jycnw9rauthjFAqF+P+YziZLrcaJ6lT4UseXtVZjTfb8a470V1K75eXl6V0GB1iIiIiIiIiIiIio0qSlpeHOnTt4/fXXxW0FBQU4evQoVq5cif379yM/Px+5ubk6d7Hk5OTAyckJAODk5IRTp07plJuTkyPu0/6r3fZ8jI2NTbF3rwDA7NmzERYWJj5WqVRwdXWFn58fbGxsdGI1Gg0UCgV8fX0hlUoBAG0i9hvSFLWSzExAVKdCzD1jBnWhpNzlZET4GzGr6qG41xyVrax2096ppg8OsBAREREREREREVGl6d27Ny5cuKCzbfTo0fD09MTMmTPh6uoKqVSKlJQUDB48GABw9epVZGVlQS6XAwDkcjkWLlyIO3fuwMHBAcCzq9NtbGzQqlUrMebFuxwUCoVYRnFkMhlkMlmR7VKptMQvtJ/fpy4o/4BBbaMulLxUe9XmAYbSXo9UspLazZC25AALERERERERERERVZr69eujTZs2Otvq1q2Lhg0bitvHjh2LsLAw2Nvbw8bGBpMnT4ZcLkeXLl0AAH5+fmjVqhVGjBiBmJgYKJVKhIeHIyQkRBwgmThxIlauXIkZM2ZgzJgxOHjwILZu3Yo9e/ZUbIWJqMbgAAsRERERERERERFVacuXL4eZmRkGDx4MtVoNf39/rF69Wtxvbm6O3bt3Y9KkSZDL5ahbty6Cg4OxYMECMcbd3R179uzBtGnTEBcXh1deeQXr1q2Dv3/tm1qKiIyDAyxERERERERERERUpRw+fFjnsZWVFVatWoVVq1aVeIybm1uZC5337NkTZ8+eNUaKREQwq+wEiIiIiIiIiIiIiIiIqhsOsBARERERERERERERERmIAyxEREREREREREREREQG4gALERERERERERERERGRgYw+wBIREQGJRKLz4+npKe5/8uQJQkJC0LBhQ9SrVw+DBw9GTk6OThlZWVkIDAyEtbU1HBwcMH36dDx9+lQn5vDhw3j99dchk8nQvHlzbNy40dhVISIiIiIiIiIiIiIiKpZJ7mBp3bo1srOzxZ9jx46J+6ZNm4Zdu3YhKSkJR44cwe3btzFo0CBxf0FBAQIDA5Gfn4/jx49j06ZN2LhxI+bNmyfGZGZmIjAwEL169UJ6ejqmTp2KcePGYf/+/aaoDhERERERERERERERkQ4LkxRqYQEnJ6ci2+/fv4/169cjMTERb7/9NgBgw4YNaNmyJU6cOIEuXbogOTkZly5dwoEDB+Do6AgvLy9ERUVh5syZiIiIgKWlJeLj4+Hu7o7Y2FgAQMuWLXHs2DEsX74c/v7+pqgSERERERERERERERGRyCR3sFy7dg0uLi549dVXMXz4cGRlZQEA0tLSoNFo4OPjI8Z6enqiSZMmSE1NBQCkpqaibdu2cHR0FGP8/f2hUqlw8eJFMeb5MrQx2jKIiIiIiIiIiIiIiIhMyeh3sHh7e2Pjxo3w8PBAdnY2IiMj0b17d2RkZECpVMLS0hJ2dnY6xzg6OkKpVAIAlEqlzuCKdr92X2kxKpUKjx8/Rp06dYrNTa1WQ61Wi49VKhUAQKPRQKPRFHuMdrtGo4HMXNCnCaoEmdmzXDsu2Ad1ocQkz5ERYdy7hZ5v6+qEeVcsQ/OubvUjIiIiIiIiIiKi6sHoAywBAQHi/9u1awdvb2+4ublh69atJQ58VJTo6GhERkYW2Z6cnAxra+tSj1UoFIjpbKrMTCeqU6HJyt67d69JylUoFCYp19SYd8XSN++8vDwTZ0JERERERERERES1kUnWYHmenZ0dXnvtNVy/fh2+vr7Iz89Hbm6uzl0sOTk54potTk5OOHXqlE4ZOTk54j7tv9ptz8fY2NiUOogze/ZshIWFiY9VKhVcXV3h5+cHGxubYo/RaDRQKBTw9fVFh4UH9a94JZOZCYjqVIi5Z8yq1R0s2raWSqVGLduUmHfFMjRv7Z1qRERERERERERERMZk8gGWhw8f4saNGxgxYgQ6duwIqVSKlJQUDB48GABw9epVZGVlQS6XAwDkcjkWLlyIO3fuwMHBAcCzK9VtbGzQqlUrMebFuycUCoVYRklkMhlkMlmR7VKptMwvaqVSKdQFphmoMCV1ocRkeZvqS3l9fh9VEfOuWPrmXR3rRkRERERERERERFWf0Re5//TTT3HkyBHcunULx48fx7vvvgtzc3MEBQXB1tYWY8eORVhYGA4dOoS0tDSMHj0acrkcXbp0AQD4+fmhVatWGDFiBM6dO4f9+/cjPDwcISEh4uDIxIkTcfPmTcyYMQNXrlzB6tWrsXXrVkybNs3Y1SEiIiIiI4mIiIBEItH58fT0FPc/efIEISEhaNiwIerVq4fBgwcXuWs5KysLgYGBsLa2hoODA6ZPn46nT5/qxBw+fBivv/46ZDIZmjdvjo0bN1ZE9YiIiIiIiKiWMfodLH/++SeCgoLw999/o3HjxujWrRtOnDiBxo0bAwCWL18OMzMzDB48GGq1Gv7+/li9erV4vLm5OXbv3o1JkyZBLpejbt26CA4OxoIFC8QYd3d37NmzB9OmTUNcXBxeeeUVrFu3Dv7+xp2yioiIiIiMq3Xr1jhw4ID42MLin9PRadOmYc+ePUhKSoKtrS1CQ0MxaNAg/PLLLwCAgoICBAYGwsnJCcePH0d2djZGjhwJqVSKRYsWAQAyMzMRGBiIiRMnIiEhASkpKRg3bhycnZ15rkhERERERERGZfQBli1btpS638rKCqtWrcKqVatKjHFzcytzAfWePXvi7Nmz5cqRiIiIiCqHhYWFuK7e8+7fv4/169cjMTERb7/9NgBgw4YNaNmyJU6cOIEuXbogOTkZly5dwoEDB+Do6AgvLy9ERUVh5syZiIiIgKWlJeLj4+Hu7o7Y2FgAQMuWLXHs2DEsX76cAyxERERERERkVCZfg4WIiIiISOvatWtwcXGBlZUV5HI5oqOj0aRJE6SlpUGj0cDHx0eM9fT0RJMmTZCamoouXbogNTUVbdu2haOjoxjj7++PSZMm4eLFi+jQoQNSU1N1ytDGTJ06tdS81Go11Gq1+FilUgEANBoNNBpNqcdq95cVV14yc8Ek5ZoqX6D0nGVmgs6/hjJl3lWNqV9bNY2h7cV2JSIiIqKXxQEWIiIiIqoQ3t7e2LhxIzw8PJCdnY3IyEh0794dGRkZUCqVsLS0hJ2dnc4xjo6OUCqVAAClUqkzuKLdr91XWoxKpcLjx49Rp06dYnOLjo5GZGRkke3JycmwtrbWq34KhUKvOEPFdDZJsWXeMf4y9Mk5qlNhuco2Zd5VlaleWzWVvu2Vl5dn4kyIiIiIqKbjAAsRERERVYiAgADx/+3atYO3tzfc3NywdevWEgc+Ksrs2bMRFhYmPlapVHB1dYWfnx9sbGxKPVaj0UChUMDX1xdSqdToubWJ2G/0MgEgI8J0U6aVlrPMTEBUp0LMPWMGdaHE4LIrK++XUd6cTf3aqmkMbS/tnWpEREREROXFARYiIiIiqhR2dnZ47bXXcP36dfj6+iI/Px+5ubk6d7Hk5OSIa7Y4OTnh1KlTOmXk5OSI+7T/arc9H2NjY1PqII5MJoNMJiuyXSqV6v3FtiGxhlAXGD4IoQ9TfmGvT87qQkm56lbZeZfHy+ZsqtdWTaVve7FNiYiIiOhlmVV2AkRERERUOz18+BA3btyAs7MzOnbsCKlUipSUFHH/1atXkZWVBblcDgCQy+W4cOEC7ty5I8YoFArY2NigVatWYszzZWhjtGUQERERERERGQsHWIiIiIioQnz66ac4cuQIbt26hePHj+Pdd9+Fubk5goKCYGtri7FjxyIsLAyHDh1CWloaRo8eDblcji5dugAA/Pz80KpVK4wYMQLnzp3D/v37ER4ejpCQEPHuk4kTJ+LmzZuYMWMGrly5gtWrV2Pr1q2YNm1aZVadiKiIiIgISCQSnR9PT09x/5MnTxASEoKGDRuiXr16GDx4cJE79LKyshAYGAhra2s4ODhg+vTpePr0qU7M4cOH8frrr0Mmk6F58+bYuHFjRVSPiIiIqFbgFGFEREREVCH+/PNPBAUF4e+//0bjxo3RrVs3nDhxAo0bNwYALF++HGZmZhg8eDDUajX8/f2xevVq8Xhzc3Ps3r0bkyZNglwuR926dREcHIwFCxaIMe7u7tizZw+mTZuGuLg4vPLKK1i3bh38/U23bgcRUXm1bt0aBw4cEB9bWPzzEX3atGnYs2cPkpKSYGtri9DQUAwaNAi//PILAKCgoACBgYFwcnLC8ePHkZ2djZEjR0IqlWLRokUAgMzMTAQGBmLixIlISEhASkoKxo0bB2dnZ/aLREREREbAARYiIiIiqhBbtmwpdb+VlRVWrVqFVatWlRjj5uaGvXv3llpOz549cfbs2XLlSERUkSwsLMQ1pJ53//59rF+/HomJiXj77bcBABs2bEDLli1x4sQJdOnSBcnJybh06RIOHDgAR0dHeHl5ISoqCjNnzkRERAQsLS0RHx8Pd3d3xMbGAgBatmyJY8eOYfny5RxgISIiIjICDrAQERERERERVYJr167BxcUFVlZWkMvliI6ORpMmTZCWlgaNRgMfHx8x1tPTE02aNEFqaiq6dOmC1NRUtG3bFo6OjmKMv78/Jk2ahIsXL6JDhw5ITU3VKUMbM3Xq1FLzUqvVUKvV4mOVSgUA0Gg00Gg0pR6r3V9WXG0iMxdK3mcm6PxrqOrYzqW1R6nH6dFW1bE9TMXQ9yLbjoiofDjAQkRERERERFTBvL29sXHjRnh4eCA7OxuRkZHo3r07MjIyoFQqYWlpCTs7O51jHB0doVQqAQBKpVJncEW7X7uvtBiVSoXHjx+jTp06xeYWHR2NyMjIItuTk5NhbW2tV/0UCoVecbVBTOeyY6I6FZar7LLu6qyK9GmP0pTWVtWxPUxN3/diXl6eiTMhMr2ms/aYrOxbiwNNVjZVbxxgISIiIiIiIqpgAQEB4v/btWsHb29vuLm5YevWrSUOfFSU2bNnIywsTHysUqng6uoKPz8/2NjYlHqsRqOBQqGAr68vpFKpqVOtFtpE7C9xn8xMQFSnQsw9YwZ1ocTgsjMiqt9Ub6W1R2n0aavq2B6mYuh7UXunGhERGYYDLEREJhQREVHk6j8PDw9cuXIFAPDkyRN88skn2LJli86Czs9faZiVlYVJkybh0KFDqFevHoKDgxEdHa2zCOrhw4cRFhaGixcvwtXVFeHh4Rg1alSF1JGIiIiIXp6dnR1ee+01XL9+Hb6+vsjPz0dubq7OXSw5OTnimi1OTk44deqUThk5OTniPu2/2m3Px9jY2JQ6iCOTySCTyYpsl0qleg+aGBJb06kLyh44URdK9Ip7UXVs4/LUU+f4UtqqOraHqen7XmTbERGVj1llJ0BEVNO1bt0a2dnZ4s+xY8fEfdOmTcOuXbuQlJSEI0eO4Pbt2xg0aJC4v6CgAIGBgcjPz8fx48exadMmbNy4EfPmzRNjMjMzERgYiF69eiE9PR1Tp07FuHHjsH9/+a4MIyIiIqKK9/DhQ9y4cQPOzs7o2LEjpFIpUlJSxP1Xr15FVlYW5HI5AEAul+PChQu4c+eOGKNQKGBjY4NWrVqJMc+XoY3RlkFEREREL4d3sBARmZiFhYV4FeHz7t+/j/Xr1yMxMRFvv/02AGDDhg1o2bIlTpw4gS5duiA5ORmXLl3CgQMH4OjoCC8vL0RFRWHmzJmIiIiApaUl4uPj4e7ujtjYWABAy5YtcezYMSxfvhz+/rxFnoiIiKgq+vTTT9G/f3+4ubnh9u3bmD9/PszNzREUFARbW1uMHTsWYWFhsLe3h42NDSZPngy5XI4uXboAAPz8/NCqVSuMGDECMTExUCqVCA8PR0hIiHj3ycSJE7Fy5UrMmDEDY8aMwcGDB7F161bs2WO6OeqJiIiIahPewUJEZGLXrl2Di4sLXn31VQwfPhxZWVkAgLS0NGg0Gvj4+Iixnp6eaNKkCVJTUwEAqampaNu2rc6UYf7+/lCpVLh48aIY83wZ2hhtGURERERU9fz5558ICgqCh4cH3n//fTRs2BAnTpxA48aNAQDLly9Hv379MHjwYPTo0QNOTk7Ytm2beLy5uTl2794Nc3NzyOVyfPjhhxg5ciQWLFggxri7u2PPnj1QKBRo3749YmNjsW7dOl6EQ0RERGQkvIOFiMiEvL29sXHjRnh4eCA7OxuRkZHo3r07MjIyoFQqYWlpqTOvNgA4OjpCqVQCAJRKpc7gina/dl9pMSqVCo8fPy5xfm21Wg21Wi0+1i5qqNFooNFoSq2Xdr/MTCg1rrzKev6KoM2hKuRiKjW9jlWtfjJz479ftO9BfetYVdqCiIiALVu2lLrfysoKq1atwqpVq0qMcXNzw969e0stp2fPnjh79my5ciQiIiKi0nGAhYjIhAICAsT/t2vXDt7e3nBzc8PWrVtLXVi0IkRHRyMyMrLI9uTkZFhbW+tVRlSnQmOnBQBlflFQkRQKRWWnYHI1vY5VpX4xnU1Xtr51zMvLM10SRERERERERLUMB1iIiCqQnZ0dXnvtNVy/fh2+vr7Iz89Hbm6uzl0sOTk54potTk5OOHXqlE4ZOTk54j7tv9ptz8fY2NiUOogze/ZshIWFiY9VKhVcXV3h5+cHGxubUuuh0WigUCgw94wZ1IWSsituoIyIyp+2QltHX19fSKXSyk7HJGp6Hata/dpE7Dd6mTIzAVGdCvWuo/ZONSIiIiKiqiQ6Ohrbtm3DlStXUKdOHXTt2hVLliyBh4eHGPPkyRN88skn2LJlC9RqNfz9/bF69WqdGR2ysrIwadIkHDp0CPXq1UNwcDCio6NhYfHPV6CHDx9GWFgYLl68CFdXV4SHh2PUqFEVWV0iqkE4wEJEVIEePnyIGzduYMSIEejYsSOkUilSUlIwePBgAMDVq1eRlZUFuVwOAJDL5Vi4cCHu3LkDBwcHAM+uVLexsUGrVq3EmBfv+FAoFGIZJZHJZOICqM+TSqV6fxmtLpRAXWD8AZaq8GW4liHtUV3V9DpWlfqZ4r2ipW8dq0I7EBERERG96MiRIwgJCcEbb7yBp0+fYs6cOfDz88OlS5dQt25dAMC0adOwZ88eJCUlwdbWFqGhoRg0aBB++eUXAEBBQQECAwPh5OSE48ePIzs7GyNHjoRUKsWiRYsAAJmZmQgMDMTEiRORkJCAlJQUjBs3Ds7OzlyfiojKhQMsREQm9Omnn6J///5wc3PD7du3MX/+fJibmyMoKAi2trYYO3YswsLCYG9vDxsbG0yePBlyuRxdunQBAPj5+aFVq1YYMWIEYmJioFQqER4ejpCQEHFwZOLEiVi5ciVmzJiBMWPG4ODBg9i6dSv27NlTmVUnIiIiIiIi0su+fft0Hm/cuBEODg5IS0tDjx49cP/+faxfvx6JiYl4++23AQAbNmxAy5YtceLECXTp0gXJycm4dOkSDhw4AEdHR3h5eSEqKgozZ85EREQELC0tER8fD3d3d8TGxgIAWrZsiWPHjmH58uUcYCGicjGr7ASIiGqyP//8E0FBQfDw8MD777+Phg0b4sSJE2jcuDEAYPny5ejXrx8GDx6MHj16wMnJCdu2bROPNzc3x+7du2Fubg65XI4PP/wQI0eOxIIFC8QYd3d37NmzBwqFAu3bt0dsbCzWrVvHk0MiIiIiIiKqlu7fvw8AsLe3BwCkpaVBo9HAx8dHjPH09ESTJk2QmpoKAEhNTUXbtm11pgzz9/eHSqXCxYsXxZjny9DGaMsgIjIU72AhIjKhLVu2lLrfysoKq1atwqpVq0qMcXNzK3PR9549e+Ls2bPlypGIiIiIiIioqigsLMTUqVPx5ptvok2bNgAApVIJS0tLnfVLAcDR0RFKpVKMeX5wRbtfu6+0GJVKhcePHxe7jqlarYZarRYfa9c11Gg00Gg0OrHax89vl5kL+lW8FpOZCTr/VkUv/q6riuJec1S2strNkPbkAAsRERERERERERFVCSEhIcjIyMCxY8cqOxUAQHR0NCIjI4tsT05OhrW1dbHHKBQK8f8xnU2WWo0T1amwslMoUVkXvla2519zpL+S2i0vL0/vMjjAQkRERERERERERJUuNDQUu3fvxtGjR/HKK6+I252cnJCfn4/c3Fydu1hycnLg5OQkxpw6dUqnvJycHHGf9l/ttudjbGxsir17BQBmz56NsLAw8bFKpYKrqyv8/PxgY2OjE6vRaKBQKODr6wupVAoAaBOx35AmqJVkZgKiOhVi7hkzqAsllZ1OsTIiquY07MW95qhsZbWb9k41fXCAhYiIiIiIiIiIiCqNIAiYPHkytm/fjsOHD8Pd3V1nf8eOHSGVSpGSkoLBgwcDAK5evYqsrCzI5XIAgFwux8KFC3Hnzh04ODgAeHZ1uo2NDVq1aiXGvHgngkKhEMsojkwmg0wmK7JdKpWW+IX28/vUBVVzwKAqUhdKqmx7VfXBi9Jej1SyktrNkLY0+gBLdHQ0tm3bhitXrqBOnTro2rUrlixZAg8PDzGmZ8+eOHLkiM5xH330EeLj48XHWVlZmDRpEg4dOoR69eohODgY0dHRsLD4J+XDhw8jLCwMFy9ehKurK8LDwzFq1ChjV4lK0HTWHqOWJzMXENP52cj+1YX9jFo2ERERERERERFVTSEhIUhMTMSPP/6I+vXri2um2Nraok6dOrC1tcXYsWMRFhYGe3t72NjYYPLkyZDL5ejSpQsAwM/PD61atcKIESMQExMDpVKJ8PBwhISEiAMkEydOxMqVKzFjxgyMGTMGBw8exNatW7Fnj3G/46Kax9jfg2rdWhxoknKp4pgZu8AjR44gJCQEJ06cgEKhgEajgZ+fHx49eqQTN378eGRnZ4s/MTEx4r6CggIEBgYiPz8fx48fx6ZNm7Bx40bMmzdPjMnMzERgYCB69eqF9PR0TJ06FePGjcP+/bztjoiIiIiIiIiIqLpYs2YN7t+/j549e8LZ2Vn8+e6778SY5cuXo1+/fhg8eDB69OgBJycnbNu2Tdxvbm6O3bt3w9zcHHK5HB9++CFGjhyJBQsWiDHu7u7Ys2cPFAoF2rdvj9jYWKxbtw7+/lVz+iciqvqMfgfLvn37dB5v3LgRDg4OSEtLQ48ePcTt1tbW4vyHL0pOTsalS5dw4MABODo6wsvLC1FRUZg5cyYiIiJgaWmJ+Ph4uLu7IzY2FgDQsmVLHDt2DMuXL2enSEREREREREREVE0IglBmjJWVFVatWoVVq1aVGOPm5lbmYuQ9e/bE2bNnDc6RiKg4Jl+D5f79+wAAe3t7ne0JCQnYvHkznJyc0L9/f8ydOxfW1tYAgNTUVLRt2xaOjo5ivL+/PyZNmoSLFy+iQ4cOSE1NhY+Pj06Z/v7+mDp1aom5qNVqqNVq8bF2sRqNRgONRlPsMdrtGo0GMvOyO/uqQmYm6PxbHTyfc0m/j6ro+ddIdVJb8q5u9SMiIiIiIiIiIqLqwaQDLIWFhZg6dSrefPNNtGnTRtw+bNgwuLm5wcXFBefPn8fMmTNx9epV8bY+pVKpM7gCQHysnYOxpBiVSoXHjx+jTp06RfKJjo5GZGRkke3Jycni4E5JFAoFYjrrUekqJqpTYWWnYLCoToVlXm1QFSkUispOoVxqet55eXkmzoSIiPTFtfqIiIiIiIioJjHpAEtISAgyMjJw7Ngxne0TJkwQ/9+2bVs4Ozujd+/euHHjBpo1a2ayfGbPno2wsDDxsUqlgqurK/z8/GBjY1PsMRqNBgqFAr6+vuiw8KDJcjM2mZmAqE6FmHvGDOpCSWWno5fnc06b16ey09Hb868RqVRa2enorbbkrb1TjYiIKp92rb433ngDT58+xZw5c+Dn54dLly6hbt26Ytz48eN15sp+/kIY7Vp9Tk5OOH78OLKzszFy5EhIpVIsWrQIwD9r9U2cOBEJCQlISUnBuHHj4OzszKlkiYiIiIiIyGhMNsASGhqK3bt34+jRo3jllVdKjfX29gYAXL9+Hc2aNYOTkxNOnTqlE5OTkwMA4rotTk5O4rbnY2xsbIq9ewUAZDIZZDJZke1SqbTML2qlUinUBdVjoOJ56kJJtctbXSipVl/4a+nzOqqKanre1bFuREQ1FdfqIyIiIiIioprE6AMsgiBg8uTJ2L59Ow4fPgx3d/cyj0lPTwcAODs7AwDkcjkWLlyIO3fuwMHBAcCz6YBsbGzQqlUrMebFaaQUCgXkcrkRa0NEREREplLd1+rTMvW6ZqZaB9CU65SVlvPLrhVYWXm/jPLmXF3XzKssXKuPiIiIiCqa0QdYQkJCkJiYiB9//BH169cX10yxtbVFnTp1cOPGDSQmJqJv375o2LAhzp8/j2nTpqFHjx5o164dAMDPzw+tWrXCiBEjEBMTA6VSifDwcISEhIh3oEycOBErV67EjBkzMGbMGBw8eBBbt27Fnj17jF0lIiIiIjKymrRWn5ap1jUz1TqAplzzTp+cy7tWYGXnXR4vm3N1XTOvsnCtPiIiIiKqKEYfYFmzZg2AZwuUPm/Dhg0YNWoULC0tceDAAaxYsQKPHj2Cq6srBg8ejPDwcDHW3Nwcu3fvxqRJkyCXy1G3bl0EBwfrzMXt7u6OPXv2YNq0aYiLi8Mrr7yCdevWcdoHIiIiomqgJqzVp2Xqdc3aROw3epkAkBFhuvPm0nJ+2bUCKyvvl1HenKvrmnmVhWv1EREREVFFM8kUYaVxdXXFkSNHyizHzc2tzCu9evbsibNnzxqUHxERERFVrpq2Vl95Yg1hqvX0TPmFvT45l3etwMrOuzxeNufqumZeZeFafURERERUUcwqOwEiIiIiqh0EQUBoaCi2b9+OgwcPlnutvgsXLuDOnTtiTHFr9aWkpOiUw7X6iIiIiIiIyNg4wEJEREREFSIkJASbN29GYmKiuFafUqnE48ePAQA3btxAVFQU0tLScOvWLezcuRMjR44sca2+c+fOYf/+/cWu1Xfz5k3MmDEDV65cwerVq7F161ZMmzat0upORERERERENQ8HWIiIiIioQqxZswb3799Hz5494ezsLP589913ACCu1efn5wdPT0988sknGDx4MHbt2iWWoV2rz9zcHHK5HB9++CFGjhxZ7Fp9CoUC7du3R2xsLNfqIyIiIiIiIqMz+hosRERERETF4Vp9REREREREVJPwDhYiIiIiIiIiIiIiIiIDcYCFiIiIiIiIiIiIiIjIQJwijKqkprP2mKzsW4sDTVY2EREREREREREREdUOvIOFiIiIiIiIiIiIiIjIQBxgISIiIiIiIiIiIiIiMhAHWIiIiIiIiIiIiIiIiAzEARYiIiIiIiIiIiIiIiIDcYCFiIiIiIiIiIiIiIjIQBxgISIiIiIiIiIiIiIiMhAHWIiIiIiIiIiIiIiIiAzEARYiIiIiIiIiIiIiIiIDWVR2AkREREREREREREREtU3TWXte6niZuYCYzkCbiP1QF0h09t1aHPhSZZN+eAcLERERERERERERERGRgTjAQkREREREREREREREZCAOsBARERERERERERERERmIAyxEREREREREREREREQG4gALERERERERERERERGRgTjAQkREREREREREREREZCAOsBARERERERERERERERmIAyxEREREREREREREREQGqvYDLKtWrULTpk1hZWUFb29vnDp1qrJTIiKqNOwTiYj+wT6RiOgf7BOJiP7BPpGIjMWishN4Gd999x3CwsIQHx8Pb29vrFixAv7+/rh69SocHBwqOz0iogrFPpGI6B/sE4leXtNZe0xW9q3FgSYrm4pin0hUtZmiv5WZC4jpbPRiawT2iVRb8FyuYlTrO1iWLVuG8ePHY/To0WjVqhXi4+NhbW2Nb775prJTIyKqcOwTiYj+wT6RiOgf7BOJiP7BPpGIjKna3sGSn5+PtLQ0zJ49W9xmZmYGHx8fpKamVmJmVNUZe/SWV4VQVcA+kYjoH+wTqaop7/mn9jyzTcR+qAskxcbw6kEqC/tEIqJ/sE8kImOrtgMsf/31FwoKCuDo6Kiz3dHREVeuXCn2GLVaDbVaLT6+f/8+AODu3bvQaDTFHqPRaJCXl4e///4bFk8fGSl707MoFJCXVwgLjRkKCov/MFbVVMecgX/y9vr3NqirUd4yMwHhHQrx999/QyqVVnY6env+PalP3g8ePAAACIJg6tQqVUX1iVra34Op3q9///230cs0lKGvteqoptexqtXPFOcR2r9B7BN1VVafaKpzAVOdsJuyry3t9f6y53yVlXdl0Ketqmt7NP90q9HLNPT8ln1i9ewTT87ubfQyTa269ommUt6+pbL7RFPieWLFMXWfWNxnkKp2flEVVdfvBKuCymo7U5zLmdrz5xBlfV9gSJ9YbQdYyiM6OhqRkZFFtru7u1dCNqY3rLITKIfqmDPAvKuDBw8ewNbWtrLTqFKqcp/YKLayMyCqPsrTl7NPLKoq94mmUpl97cucg9S2vxFltVVta4+ysE80jqrcJ9bE1zz7RP2xTzQM+0TjqMp9Yk1Sm76jMja2nX7K8zdCnz6x2g6wNGrUCObm5sjJydHZnpOTAycnp2KPmT17NsLCwsTHhYWFuHv3Lho2bAiJpPgRPpVKBVdXV/zxxx+wsbExXgVMrDrmXR1zBph3RTM0b0EQ8ODBA7i4uFRAdpWnovpErer6+jEE61j91fT6AewTS8I+sWpje+mPbWUY9onFY59YtbG99Me2Mgz7xOKZuk/k67R82G7lx7Yrn7LazZA+sdoOsFhaWqJjx45ISUnBwIEDATzr4FJSUhAaGlrsMTKZDDKZTGebnZ2dXs9nY2NTLV+k1THv6pgzwLwrmiF514arbyq6T9Sqrq8fQ7CO1V9Nrx/APvFF7BOrB7aX/thWhmGfqIt9YvXA9tIf28ow7BN1VVSfyNdp+bDdyo9tVz6ltZu+fWK1HWABgLCwMAQHB6NTp07o3LkzVqxYgUePHmH06NGVnRoRUYVjn0hE9A/2iURE/2CfSET0D/aJRGRM1XqA5YMPPsD//vc/zJs3D0qlEl5eXti3b1+RhaqIiGoD9olERP9gn0hE9A/2iURE/2CfSETGVK0HWAAgNDS0xFv4jEEmk2H+/PlFbgWs6qpj3tUxZ4B5V7TqmndFMXWfqFUbfg+sY/VX0+sH1I46vgz2iVUT20t/bCvDsL1Kxz6xamJ76Y9tZRi2V+lM1Sey3cuH7VZ+bLvyMWa7SQRBEIyQExERERERERERERERUa1hVtkJEBERERERERERERERVTccYCEiIiIiIiIiIiIiIjIQB1iIiIiIiIiIiIiIiIgMxAEWAKtWrULTpk1hZWUFb29vnDp1qtT4pKQkeHp6wsrKCm3btsXevXsrKFNdhuT99ddfo3v37mjQoAEaNGgAHx+fMutpCoa2tdaWLVsgkUgwcOBA0yZYAkPzzs3NRUhICJydnSGTyfDaa69VyuvE0LxXrFgBDw8P1KlTB66urpg2bRqePHlSQdk+c/ToUfTv3x8uLi6QSCTYsWNHmcccPnwYr7/+OmQyGZo3b46NGzeaPM+axFRtXt73uykYWsdt27bB19cXjRs3ho2NDeRyOfbv368TExERAYlEovPj6elpwlqUztA6Hj58uEj+EokESqVSJ646/x5HjRpVbB1bt24txlSl32N0dDTeeOMN1K9fHw4ODhg4cCCuXr1a5nFlnZcIgoB58+bB2dkZderUgY+PD65du2aqatQ6Vek9UpWV9/VNzyxevBgSiQRTp06t7FSqrP/+97/48MMP0bBhQ9SpUwdt27bFmTNnKjutWod9ov7Kcw5eW/FviGHWrFmDdu3awcbGRvws89NPP1V2WjWKPq/JJ0+eICQkBA0bNkS9evUwePBg5OTkVFLGVVNx5zdst5KVda7Dz31FFRQUYO7cuXB3d0edOnXQrFkzREVF4fkl6Y3RbrV+gOW7775DWFgY5s+fj19//RXt27eHv78/7ty5U2z88ePHERQUhLFjx+Ls2bMYOHAgBg4ciIyMjCqd9+HDhxEUFIRDhw4hNTUVrq6u8PPzw3//+98qm7PWrVu38Omnn6J79+4VlKkuQ/POz8+Hr68vbt26he+//x5Xr17F119/jf/7v/+r0nknJiZi1qxZmD9/Pi5fvoz169fju+++w5w5cyo070ePHqF9+/ZYtWqVXvGZmZkIDAxEr169kJ6ejqlTp2LcuHFFvgynkpmizcv7fjcVQ+t49OhR+Pr6Yu/evUhLS0OvXr3Qv39/nD17VieudevWyM7OFn+OHTtmivT1Ymgdta5evapTBwcHB3Ffdf89xsXF6dTtjz/+gL29PYYMGaITV1V+j0eOHEFISAhOnDgBhUIBjUYDPz8/PHr0qMRj9DkviYmJwRdffIH4+HicPHkSdevWhb+/f4UPoNdEVe09UpWV5/VNz5w+fRpfffUV2rVrV9mpVFn37t3Dm2++CalUip9++gmXLl1CbGwsGjRoUNmp1SrsEw1T3nO32oh/QwzzyiuvYPHixUhLS8OZM2fw9ttvY8CAAbh48WJlp1Zj6POanDZtGnbt2oWkpCQcOXIEt2/fxqBBgyox66qlpPMbtlvx9DnX4ee+opYsWYI1a9Zg5cqVuHz5MpYsWYKYmBh8+eWXYoxR2k2o5Tp37iyEhISIjwsKCgQXFxchOjq62Pj3339fCAwM1Nnm7e0tfPTRRybN80WG5v2ip0+fCvXr1xc2bdpkqhSLKE/OT58+Fbp27SqsW7dOCA4OFgYMGFABmeoyNO81a9YIr776qpCfn19RKRbL0LxDQkKEt99+W2dbWFiY8Oabb5o0z9IAELZv315qzIwZM4TWrVvrbPvggw8Ef39/E2ZWcxmrzV+2jzIlfepYnFatWgmRkZHi4/nz5wvt27c3XmJGpE8dDx06JAAQ7t27V2JMTfs9bt++XZBIJMKtW7fEbVX593jnzh0BgHDkyJESY8o6LyksLBScnJyEpUuXivtzc3MFmUwmfPvtt6ZJvBapyu+Rqk6f1zcJwoMHD4QWLVoICoVCeOutt4QpU6ZUdkpV0syZM4Vu3bpVdhq1HvvE8ivv+Wltxb8hhmvQoIGwbt26yk6jxnrxNZmbmytIpVIhKSlJjLl8+bIAQEhNTa2sNKuMks5v2G4lK+tch5/7ihcYGCiMGTNGZ9ugQYOE4cOHC4JgvHar1Xew5OfnIy0tDT4+PuI2MzMz+Pj4IDU1tdhjUlNTdeIBwN/fv8R4UyhP3i/Ky8uDRqOBvb29qdLUUd6cFyxYAAcHB4wdO7Yi0iyiPHnv3LkTcrkcISEhcHR0RJs2bbBo0SIUFBRUVNrlyrtr165IS0sTb+O/efMm9u7di759+1ZIzuVVFd6TtU1ZbW6MPqqqKSwsxIMHD4r0mdeuXYOLiwteffVVDB8+HFlZWZWUYfl5eXnB2dkZvr6++OWXX8TtNfH3uH79evj4+MDNzU1ne1X9Pd6/fx8ASv1bXdb7MTMzE0qlUifG1tYW3t7e1fb3WFXUxPdIRdLn9U1ASEgIAgMDi7zPSdfOnTvRqVMnDBkyBA4ODujQoQO+/vrryk6rVmGfSBWJf0P0V1BQgC1btuDRo0eQy+WVnU6N9eJrMi0tDRqNRqdP9PT0RJMmTdgnouTzG7Zbyco61+HnvuJ17doVKSkp+O233wAA586dw7FjxxAQEADAeO1mYdy0q5e//voLBQUFcHR01Nnu6OiIK1euFHuMUqksNv7FOetNqTx5v2jmzJlwcXGpsA9r5cn52LFjWL9+PdLT0ysgw+KVJ++bN2/i4MGDGD58OPbu3Yvr16/jX//6FzQaDebPn18RaZcr72HDhuGvv/5Ct27dIAgCnj59iokTJ1b4FGGGKuk9qVKp8PjxY9SpU6eSMqu5ymrze/fuvXQfVdV8/vnnePjwId5//31xm7e3NzZu3AgPDw9kZ2cjMjIS3bt3R0ZGBurXr1+J2erH2dkZ8fHx6NSpE9RqNdatW4eePXvi5MmTeP31143yt6YquX37Nn766SckJibqbK+qv8fCwkJMnToVb775Jtq0aVNiXFnnJdp/K/vcpSaqae+RiqTv67u227JlC3799VecPn26slOp8m7evIk1a9YgLCwMc+bMwenTp/Hxxx/D0tISwcHBlZ1ercA+kSoK/4bo58KFC5DL5Xjy5Anq1auH7du3o1WrVpWdVo1U3GtSqVTC0tISdnZ2OrE8By/9/IbtVrKyznX4ua94s2bNgkqlgqenJ8zNzVFQUICFCxdi+PDhAIz3eblWD7DUVosXL8aWLVtw+PBhWFlZVXY6xXrw4AFGjBiBr7/+Go0aNarsdAxSWFgIBwcHrF27Fubm5ujYsSP++9//YunSpRU2wFIehw8fxqJFi7B69Wp4e3vj+vXrmDJlCqKiojB37tzKTo+o0iQmJiIyMhI//vijzvok2iseAKBdu3bw9vaGm5sbtm7dWml33RnCw8MDHh4e4uOuXbvixo0bWL58Of7f//t/lZiZaWzatAl2dnYYOHCgzvaq+nsMCQlBRkZGpa7rQ2QqfH2X7Y8//sCUKVOgUCiq7Pl6VVJYWIhOnTph0aJFAIAOHTogIyMD8fHxHGAhqmH4N0Q/Hh4eSE9Px/379/H9998jODgYR44c4SCLCfA1qT+e35Qfz3XKZ+vWrUhISEBiYiJat24triPs4uJi1Har1QMsjRo1grm5OXJycnS25+TkwMnJqdhjnJycDIo3hfLkrfX5559j8eLFOHDgQIUulGlozjdu3MCtW7fQv39/cVthYSEAwMLCAlevXkWzZs1MmzTK19bOzs6QSqUwNzcXt7Vs2RJKpRL5+fmwtLQ0ac5A+fKeO3cuRowYgXHjxgEA2rZti0ePHmHChAn497//DTOzqjmjYEnvSRsbG969YiJltbm5uXm5+6iqZsuWLRg3bhySkpLKvOPPzs4Or732Gq5fv15B2Rlf586dxQ8GL/O3pqoRBAHffPMNRowYUWYfXBV+j6Ghodi9ezeOHj2KV155pdTYss5LtP/m5OTA2dlZJ8bLy8u4idcyNek9UpEMeX3XZmlpabhz5w5ef/11cVtBQQGOHj2KlStXQq1W65xr1nbOzs5FvjRs2bIlfvjhh0rKqPZhn0gVgX9D9GdpaYnmzZsDADp27IjTp08jLi4OX331VSVnVrOU9Jp0cnJCfn4+cnNzde7GqO19YlnnN/v372e7laCscx1+7ive9OnTMWvWLAwdOhTAs+86f//9d0RHRyM4ONho7VY1vzGtIJaWlujYsSNSUlLEbYWFhUhJSSlxbkq5XK4TDwAKhaJC57IsT94AEBMTg6ioKOzbtw+dOnWqiFRFhubs6emJCxcuID09Xfx555130KtXL6Snp8PV1bVK5g0Ab775Jq5fvy4OCAHAb7/9Bmdn5woZXAHKl3deXl6RQRTtB3dBEEyX7EuqCu/J2qasNi9vH1XVfPvttxg9ejS+/fZbBAYGlhn/8OFD3LhxQ+ePcnWTnp4u5l9Tfo8AcOTIEVy/fl2vO1Iq8/coCAJCQ0Oxfft2HDx4EO7u7mUeU9b70d3dHU5OTjoxKpUKJ0+erHa/x6qmJr1HKkJ5Xt+1We/evYucC3fq1AnDhw9Heno6B1de8Oabb+Lq1as623777bcia26R6bBPJFPi35CXV1hYCLVaXdlp1BhlvSY7duwIqVSq0ydevXoVWVlZtbpPLOv8plOnTmy3EpR1rsPPfcUr6btO7Xe2Rmu3Yha+r1W2bNkiyGQyYePGjcKlS5eECRMmCHZ2doJSqRQEQRB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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40tB5_h2OjiJ"
      },
      "source": [
        "## Pair plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeN-y5UfM4dC"
      },
      "source": [
        "# sns.pairplot(df.sample(1000, random_state=10086), hue=\"default payment next month\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sTQtRpScTolq"
      },
      "source": [
        "# Machine Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxKbkHtoURwI"
      },
      "source": [
        "## Choose X and y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIXWuBrJUp4F",
        "outputId": "c18f0545-8b5f-4255-9610-8b21976f5f64"
      },
      "source": [
        "TARGET = \"default payment next month\"\n",
        "columns_predictors = [col for col in df.columns if col not in [TARGET]]\n",
        "columns_categorical = ['SEX', 'EDUCATION', 'MARRIAGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n",
        "columns_numerical = [col for col in columns_predictors if col not in columns_categorical]\n",
        "print(f\"TARGET: {TARGET}\")\n",
        "print(f\"columns_predictors: {columns_predictors}\")\n",
        "print(f\"columns_categorical: {columns_categorical}\")\n",
        "print(f\"columns_numerical: {columns_numerical}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TARGET: default payment next month\n",
            "columns_predictors: ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n",
            "columns_categorical: ['SEX', 'EDUCATION', 'MARRIAGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n",
            "columns_numerical: ['LIMIT_BAL', 'AGE', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "fm3QF8IFVtzo",
        "outputId": "5c4c0e1d-29ed-41bc-96e7-5705f2f9ca83"
      },
      "source": [
        "X = df[columns_predictors]\n",
        "y = df[TARGET]\n",
        "display(X)\n",
        "display(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
              "ID                                                                            \n",
              "1          20000    2          2         1   24      2      2     -1     -1   \n",
              "2         120000    2          2         2   26     -1      2      0      0   \n",
              "3          90000    2          2         2   34      0      0      0      0   \n",
              "4          50000    2          2         1   37      0      0      0      0   \n",
              "5          50000    1          2         1   57     -1      0     -1      0   \n",
              "...          ...  ...        ...       ...  ...    ...    ...    ...    ...   \n",
              "29996     220000    1          3         1   39      0      0      0      0   \n",
              "29997     150000    1          3         2   43     -1     -1     -1     -1   \n",
              "29998      30000    1          2         2   37      4      3      2     -1   \n",
              "29999      80000    1          3         1   41      1     -1      0      0   \n",
              "30000      50000    1          2         1   46      0      0      0      0   \n",
              "\n",
              "       PAY_5  ...  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
              "ID            ...                                                         \n",
              "1         -2  ...        689          0          0          0         0   \n",
              "2          0  ...       2682       3272       3455       3261         0   \n",
              "3          0  ...      13559      14331      14948      15549      1518   \n",
              "4          0  ...      49291      28314      28959      29547      2000   \n",
              "5          0  ...      35835      20940      19146      19131      2000   \n",
              "...      ...  ...        ...        ...        ...        ...       ...   \n",
              "29996      0  ...     208365      88004      31237      15980      8500   \n",
              "29997      0  ...       3502       8979       5190          0      1837   \n",
              "29998      0  ...       2758      20878      20582      19357         0   \n",
              "29999      0  ...      76304      52774      11855      48944     85900   \n",
              "30000      0  ...      49764      36535      32428      15313      2078   \n",
              "\n",
              "       PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \n",
              "ID                                                       \n",
              "1           689         0         0         0         0  \n",
              "2          1000      1000      1000         0      2000  \n",
              "3          1500      1000      1000      1000      5000  \n",
              "4          2019      1200      1100      1069      1000  \n",
              "5         36681     10000      9000       689       679  \n",
              "...         ...       ...       ...       ...       ...  \n",
              "29996     20000      5003      3047      5000      1000  \n",
              "29997      3526      8998       129         0         0  \n",
              "29998         0     22000      4200      2000      3100  \n",
              "29999      3409      1178      1926     52964      1804  \n",
              "30000      1800      1430      1000      1000      1000  \n",
              "\n",
              "[30000 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8f4851b0-56b7-4d7c-a9ae-afb0eb8775fa\">\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>LIMIT_BAL</th>\n",
              "      <th>SEX</th>\n",
              "      <th>EDUCATION</th>\n",
              "      <th>MARRIAGE</th>\n",
              "      <th>AGE</th>\n",
              "      <th>PAY_0</th>\n",
              "      <th>PAY_2</th>\n",
              "      <th>PAY_3</th>\n",
              "      <th>PAY_4</th>\n",
              "      <th>PAY_5</th>\n",
              "      <th>...</th>\n",
              "      <th>BILL_AMT3</th>\n",
              "      <th>BILL_AMT4</th>\n",
              "      <th>BILL_AMT5</th>\n",
              "      <th>BILL_AMT6</th>\n",
              "      <th>PAY_AMT1</th>\n",
              "      <th>PAY_AMT2</th>\n",
              "      <th>PAY_AMT3</th>\n",
              "      <th>PAY_AMT4</th>\n",
              "      <th>PAY_AMT5</th>\n",
              "      <th>PAY_AMT6</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-2</td>\n",
              "      <td>...</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2682</td>\n",
              "      <td>3272</td>\n",
              "      <td>3455</td>\n",
              "      <td>3261</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>90000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>13559</td>\n",
              "      <td>14331</td>\n",
              "      <td>14948</td>\n",
              "      <td>15549</td>\n",
              "      <td>1518</td>\n",
              "      <td>1500</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>49291</td>\n",
              "      <td>28314</td>\n",
              "      <td>28959</td>\n",
              "      <td>29547</td>\n",
              "      <td>2000</td>\n",
              "      <td>2019</td>\n",
              "      <td>1200</td>\n",
              "      <td>1100</td>\n",
              "      <td>1069</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>50000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>57</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>35835</td>\n",
              "      <td>20940</td>\n",
              "      <td>19146</td>\n",
              "      <td>19131</td>\n",
              "      <td>2000</td>\n",
              "      <td>36681</td>\n",
              "      <td>10000</td>\n",
              "      <td>9000</td>\n",
              "      <td>689</td>\n",
              "      <td>679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29996</th>\n",
              "      <td>220000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>208365</td>\n",
              "      <td>88004</td>\n",
              "      <td>31237</td>\n",
              "      <td>15980</td>\n",
              "      <td>8500</td>\n",
              "      <td>20000</td>\n",
              "      <td>5003</td>\n",
              "      <td>3047</td>\n",
              "      <td>5000</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29997</th>\n",
              "      <td>150000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>43</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3502</td>\n",
              "      <td>8979</td>\n",
              "      <td>5190</td>\n",
              "      <td>0</td>\n",
              "      <td>1837</td>\n",
              "      <td>3526</td>\n",
              "      <td>8998</td>\n",
              "      <td>129</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29998</th>\n",
              "      <td>30000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>37</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2758</td>\n",
              "      <td>20878</td>\n",
              "      <td>20582</td>\n",
              "      <td>19357</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22000</td>\n",
              "      <td>4200</td>\n",
              "      <td>2000</td>\n",
              "      <td>3100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29999</th>\n",
              "      <td>80000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>76304</td>\n",
              "      <td>52774</td>\n",
              "      <td>11855</td>\n",
              "      <td>48944</td>\n",
              "      <td>85900</td>\n",
              "      <td>3409</td>\n",
              "      <td>1178</td>\n",
              "      <td>1926</td>\n",
              "      <td>52964</td>\n",
              "      <td>1804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30000</th>\n",
              "      <td>50000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>49764</td>\n",
              "      <td>36535</td>\n",
              "      <td>32428</td>\n",
              "      <td>15313</td>\n",
              "      <td>2078</td>\n",
              "      <td>1800</td>\n",
              "      <td>1430</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>30000 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f4851b0-56b7-4d7c-a9ae-afb0eb8775fa')\"\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",
              "  <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-8f4851b0-56b7-4d7c-a9ae-afb0eb8775fa 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-8f4851b0-56b7-4d7c-a9ae-afb0eb8775fa');\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": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ID\n",
              "1        1\n",
              "2        1\n",
              "3        0\n",
              "4        0\n",
              "5        0\n",
              "        ..\n",
              "29996    0\n",
              "29997    0\n",
              "29998    1\n",
              "29999    1\n",
              "30000    1\n",
              "Name: default payment next month, Length: 30000, dtype: int64"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWAF-3swTqVZ"
      },
      "source": [
        "## Train-test split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hCRUhoGpNcGm",
        "outputId": "247f7d71-60a7-412d-ab0b-b1b2a2f8e0c2"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=10086, stratify=y) #stratified sampling based on the target\n",
        "print(f\"X_train.shape: {X_train.shape}\")\n",
        "print(f\"X_test.shape: {X_test.shape}\")\n",
        "print(f\"y_train.shape: {y_train.shape}\")\n",
        "print(f\"y_test.shape: {y_test.shape}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train.shape: (22500, 23)\n",
            "X_test.shape: (7500, 23)\n",
            "y_train.shape: (22500,)\n",
            "y_test.shape: (7500,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "4rV5M8BJWKAv",
        "outputId": "89604317-4d17-4fc4-fd93-949169acda6e"
      },
      "source": [
        "display(X_train)\n",
        "display(y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
              "ID                                                                            \n",
              "17778      70000    2          3         2   55      0      0      0      0   \n",
              "11181     190000    2          2         1   45      2      0      0      0   \n",
              "18834      70000    2          2         2   24      0      0      0      0   \n",
              "19424     150000    2          1         2   31      0      0      0     -1   \n",
              "15760     130000    2          2         2   22      2      2      2      2   \n",
              "...          ...  ...        ...       ...  ...    ...    ...    ...    ...   \n",
              "14526     180000    1          2         2   43      0      0      0      0   \n",
              "21159     180000    1          2         2   39      0      0      0      0   \n",
              "15009      30000    1          3         1   43      1      3      2      2   \n",
              "26564      50000    2          3         2   56      0      0      0      0   \n",
              "21191     350000    1          2         2   36      0      0      0      0   \n",
              "\n",
              "       PAY_5  ...  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
              "ID            ...                                                         \n",
              "17778      0  ...       6703       7853       7316       7350      1100   \n",
              "11181      0  ...     131132      96083      97453      99426      6800   \n",
              "18834      0  ...      26194      27454      28159      25775      1800   \n",
              "19424      0  ...       6221       4711       6711       6711      6000   \n",
              "15760      0  ...      21213      15443      25371       4264      3412   \n",
              "...      ...  ...        ...        ...        ...        ...       ...   \n",
              "14526      0  ...     171820     160018     164717     151235      7500   \n",
              "21159      0  ...     124553     127096     128992     131660      4500   \n",
              "15009      2  ...      21207      23491      22818      24321         0   \n",
              "26564      0  ...      50417      48371      15417      16194      1798   \n",
              "21191      0  ...      36582      40452      15203      20596     10000   \n",
              "\n",
              "       PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \n",
              "ID                                                       \n",
              "17778      1123      1273       407       300       300  \n",
              "11181      7000      3585      3700      3754      7900  \n",
              "18834      2000      2000      1500      1500         0  \n",
              "19424      3039      9050      2000         0      4000  \n",
              "15760      1700         0     15571      4264      1000  \n",
              "...         ...       ...       ...       ...       ...  \n",
              "14526      5900      6000      7000      7000      5000  \n",
              "21159      6000      6100      5500      4900      5500  \n",
              "15009         0      2645         0      2029      1027  \n",
              "26564      2058      1683       600      1017     20698  \n",
              "21191     16000      6000       314     10000      7000  \n",
              "\n",
              "[22500 rows x 23 columns]"
            ],
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              "<Figure size 640x480 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "wx3e8KQzW8hb",
        "outputId": "f4335f35-b40b-4fca-d2ab-0478377b6904"
      },
      "source": [
        "check_data_balance(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0    5841\n",
              "1    1659\n",
              "Name: default payment next month, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AqrD1W6WgFT"
      },
      "source": [
        "## Preprocessing with pipelines 🚀"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQExjMzheqj6"
      },
      "source": [
        "### Define the pipeline(s)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6Ly7p7bWbnp"
      },
      "source": [
        "from sklearn.pipeline import Pipeline #each pipeline can have multiple steps\n",
        "from sklearn.compose import ColumnTransformer #for selecting specific columns and transforming them with individual pipelines\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder #specific transformations we want to use in our pipelines\n",
        "\n",
        "pipeline_categorical = Pipeline([\n",
        "  ('onehot', OneHotEncoder(handle_unknown=\"ignore\")),\n",
        "])\n",
        "\n",
        "pipeline_numerical = Pipeline([\n",
        "  ('scaler', MinMaxScaler(feature_range=(0,1))),\n",
        "])\n",
        "\n",
        "pipeline_full = ColumnTransformer([\n",
        "  (\"categorical\", pipeline_categorical, columns_categorical),\n",
        "  (\"numerical\", pipeline_numerical, columns_numerical),\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_full"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "SoeoFIjLftPR",
        "outputId": "629b41c8-8f6e-44cf-d561-1a82571cda1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(transformers=[('categorical',\n",
              "                                 Pipeline(steps=[('onehot',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                 ['SEX', 'EDUCATION', 'MARRIAGE', 'PAY_0',\n",
              "                                  'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n",
              "                                  'PAY_6']),\n",
              "                                ('numerical',\n",
              "                                 Pipeline(steps=[('scaler', MinMaxScaler())]),\n",
              "                                 ['LIMIT_BAL', 'AGE', 'BILL_AMT1', 'BILL_AMT2',\n",
              "                                  'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5',\n",
              "                                  'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2',\n",
              "                                  'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5',\n",
              "                                  'PAY_AMT6'])])"
            ],
            "text/html": [
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              "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;, &#x27;PAY_0&#x27;,\n",
              "                                  &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;, &#x27;PAY_5&#x27;,\n",
              "                                  &#x27;PAY_6&#x27;]),\n",
              "                                (&#x27;numerical&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler())]),\n",
              "                                 [&#x27;LIMIT_BAL&#x27;, &#x27;AGE&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;,\n",
              "                                  &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;,\n",
              "                                  &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;,\n",
              "                                  &#x27;PAY_AMT3&#x27;, &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;,\n",
              "                                  &#x27;PAY_AMT6&#x27;])])</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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;, &#x27;PAY_0&#x27;,\n",
              "                                  &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;, &#x27;PAY_5&#x27;,\n",
              "                                  &#x27;PAY_6&#x27;]),\n",
              "                                (&#x27;numerical&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler())]),\n",
              "                                 [&#x27;LIMIT_BAL&#x27;, &#x27;AGE&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;,\n",
              "                                  &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;,\n",
              "                                  &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;,\n",
              "                                  &#x27;PAY_AMT3&#x27;, &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;,\n",
              "                                  &#x27;PAY_AMT6&#x27;])])</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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">categorical</label><div class=\"sk-toggleable__content\"><pre>[&#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;, &#x27;PAY_0&#x27;, &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;, &#x27;PAY_5&#x27;, &#x27;PAY_6&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><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-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerical</label><div class=\"sk-toggleable__content\"><pre>[&#x27;LIMIT_BAL&#x27;, &#x27;AGE&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;, &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;, &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;, &#x27;PAY_AMT3&#x27;, &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;, &#x27;PAY_AMT6&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
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        "id": "jyw8uY9S_OY4",
        "outputId": "38356bc1-2b18-4a2b-98ad-cc6ce0efd0c5"
      },
      "source": [
        "X_train[columns_categorical]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SEX  EDUCATION  MARRIAGE  PAY_0  PAY_2  PAY_3  PAY_4  PAY_5  PAY_6\n",
              "ID                                                                       \n",
              "17778    2          3         2      0      0      0      0      0      0\n",
              "11181    2          2         1      2      0      0      0      0      0\n",
              "18834    2          2         2      0      0      0      0      0      0\n",
              "19424    2          1         2      0      0      0     -1      0      0\n",
              "15760    2          2         2      2      2      2      2      0      0\n",
              "...    ...        ...       ...    ...    ...    ...    ...    ...    ...\n",
              "14526    1          2         2      0      0      0      0      0      0\n",
              "21159    1          2         2      0      0      0      0      0      0\n",
              "15009    1          3         1      1      3      2      2      2      2\n",
              "26564    2          3         2      0      0      0      0      0      0\n",
              "21191    1          2         2      0      0      0      0      0      0\n",
              "\n",
              "[22500 rows x 9 columns]"
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              "      <th>18834</th>\n",
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              "      <th>15760</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>14526</th>\n",
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              "      <td>0</td>\n",
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              "      <th>21159</th>\n",
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              "<p>22500 rows × 9 columns</p>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-788b3f46-0191-4ce7-8c72-9e09a2d9862a 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-788b3f46-0191-4ce7-8c72-9e09a2d9862a');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeAfNASqdKhN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77daae7f-2ed2-483f-b923-56fbc3156389"
      },
      "source": [
        "# testing\n",
        "# pipeline_numerical.fit_transform(X_train[columns_numerical]).shape\n",
        "pipeline_categorical.fit_transform(X_train[columns_categorical]).toarray().shape\n",
        "# pipeline_full.fit_transform(X_train).toarray().shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22500, 77)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdAFWt7Zev-v"
      },
      "source": [
        "### Apply the pipeline\n",
        "\n",
        "NOTE: <font color=\"red\">Do not fit the pipeline on X_test or X</font>\n",
        "\n",
        "The correct way to do it is to fit the pipeline on X_train once, and then use the fitted pipeline to transform both X_train and X_test. The pipeline should remember the information from X_train (i.e. the one hot encoder will remember the categories from X_train and the scaler will remember the feature ranges from X_train), and then we will use that information to transform the X_test, just as if we are dealing with the real unseen data in production. X_test transformed in this way will help reflect the model's true performance when it encounters unseen data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9z1TN8_dU83",
        "outputId": "ff209f3c-d0d0-4e54-9266-bd4d7e965576"
      },
      "source": [
        "pipeline_full.fit(X_train)\n",
        "X_train_transformed = pipeline_full.transform(X_train)\n",
        "X_test_transformed = pipeline_full.transform(X_test)\n",
        "print(f\"X_train_transformed.shape: {X_train_transformed.shape}\")\n",
        "print(f\"X_test_transformed.shape: {X_test_transformed.shape}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_transformed.shape: (22500, 91)\n",
            "X_test_transformed.shape: (7500, 91)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XJnD7gXkYKH"
      },
      "source": [
        "#### FAQ: How to access attributes within the pipelines?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_full.named_transformers_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clf2RdvKLzNf",
        "outputId": "7e6b11bb-b379-43a6-e011-f0902ae7d022"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'categorical': Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]),\n",
              " 'numerical': Pipeline(steps=[('scaler', MinMaxScaler())])}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_full.named_transformers_[\"categorical\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "pqvfRYGqMRMO",
        "outputId": "bb39ff08-5dfe-4d87-8948-e04898cf8d90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))])"
            ],
            "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>Pipeline(steps=[(&#x27;onehot&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))])</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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;onehot&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))])</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_full.named_transformers_[\"numerical\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "7fEIyhj7LtPJ",
        "outputId": "bc8984f5-5242-4284-f3a7-4ea5b8b94dfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scaler', MinMaxScaler())])"
            ],
            "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>Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler())])</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler())])</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\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "0w_oDdzvj2Tb",
        "outputId": "368b20ed-b427-4235-a441-8919d6c8fa29"
      },
      "source": [
        "pipeline_full.named_transformers_[\"numerical\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('scaler', MinMaxScaler())])"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler())])</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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler())])</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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QdBeFWJkW42",
        "outputId": "4542eca1-2d92-4d2f-f398-380377882095"
      },
      "source": [
        "pipeline_full.named_transformers_[\"numerical\"].named_steps"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'scaler': MinMaxScaler()}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpD0FQGsnHJ0",
        "outputId": "07ecb7aa-cdc7-4455-d335-aad3131e8bfa"
      },
      "source": [
        "pipeline_full.named_transformers_[\"numerical\"].named_steps[\"scaler\"].feature_range"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0z5sC2Cjkwya",
        "outputId": "d0234227-f2e0-4010-d5ae-489ba3f90add"
      },
      "source": [
        "pipeline_full.named_transformers_[\"categorical\"].named_steps"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'onehot': OneHotEncoder(handle_unknown='ignore')}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QgygeqpkzHv",
        "outputId": "07428113-dffc-424e-871f-3cb3f4a712ff"
      },
      "source": [
        "pipeline_full.named_transformers_[\"categorical\"].named_steps[\"onehot\"].get_feature_names_out() #this is the column names after one-hot encoding"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['SEX_1', 'SEX_2', 'EDUCATION_0', 'EDUCATION_1', 'EDUCATION_2',\n",
              "       'EDUCATION_3', 'EDUCATION_4', 'EDUCATION_5', 'EDUCATION_6',\n",
              "       'MARRIAGE_0', 'MARRIAGE_1', 'MARRIAGE_2', 'MARRIAGE_3', 'PAY_0_-2',\n",
              "       'PAY_0_-1', 'PAY_0_0', 'PAY_0_1', 'PAY_0_2', 'PAY_0_3', 'PAY_0_4',\n",
              "       'PAY_0_5', 'PAY_0_6', 'PAY_0_7', 'PAY_0_8', 'PAY_2_-2', 'PAY_2_-1',\n",
              "       'PAY_2_0', 'PAY_2_1', 'PAY_2_2', 'PAY_2_3', 'PAY_2_4', 'PAY_2_5',\n",
              "       'PAY_2_6', 'PAY_2_7', 'PAY_2_8', 'PAY_3_-2', 'PAY_3_-1', 'PAY_3_0',\n",
              "       'PAY_3_1', 'PAY_3_2', 'PAY_3_3', 'PAY_3_4', 'PAY_3_5', 'PAY_3_6',\n",
              "       'PAY_3_7', 'PAY_3_8', 'PAY_4_-2', 'PAY_4_-1', 'PAY_4_0', 'PAY_4_1',\n",
              "       'PAY_4_2', 'PAY_4_3', 'PAY_4_4', 'PAY_4_5', 'PAY_4_6', 'PAY_4_7',\n",
              "       'PAY_4_8', 'PAY_5_-2', 'PAY_5_-1', 'PAY_5_0', 'PAY_5_2', 'PAY_5_3',\n",
              "       'PAY_5_4', 'PAY_5_5', 'PAY_5_6', 'PAY_5_7', 'PAY_5_8', 'PAY_6_-2',\n",
              "       'PAY_6_-1', 'PAY_6_0', 'PAY_6_2', 'PAY_6_3', 'PAY_6_4', 'PAY_6_5',\n",
              "       'PAY_6_6', 'PAY_6_7', 'PAY_6_8'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rWfVJVoegfPq"
      },
      "source": [
        "## Train an ML model on the transformed training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "-HQ9yGKdfnzQ",
        "outputId": "99a1e0c8-4197-4740-8951-cf3288296252"
      },
      "source": [
        "%%time\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model = LogisticRegression(n_jobs=-1, random_state=10086)\n",
        "model.fit(X_train_transformed, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 60.5 ms, sys: 66.7 ms, total: 127 ms\n",
            "Wall time: 1.67 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(n_jobs=-1, random_state=10086)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(n_jobs=-1, random_state=10086)</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-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(n_jobs=-1, random_state=10086)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYkc5rVoh_Zz"
      },
      "source": [
        "## Evaluate the model on the transformed testing set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WNt9--KhhTVn",
        "outputId": "575ac693-eb98-4a61-bec1-a04f9108ac7f"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
        "\n",
        "y_pred = model.predict(X_test_transformed)\n",
        "y_pred_proba = model.predict_proba(X_test_transformed) #to calculate ROC_AUC, we need model.predict_proba() instead of model.pred()\n",
        "\n",
        "print(f\"accuracy_score: {accuracy_score(y_test, y_pred)}\")\n",
        "print(f\"precision_score: {precision_score(y_test, y_pred)}\")\n",
        "print(f\"recall_score: {recall_score(y_test, y_pred)}\")\n",
        "print(f\"f1_score: {f1_score(y_test, y_pred)}\")\n",
        "print(f\"roc_auc_score: {roc_auc_score(y_test, y_pred_proba[:,1])}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy_score: 0.8162666666666667\n",
            "precision_score: 0.6707168894289186\n",
            "recall_score: 0.33273056057866185\n",
            "f1_score: 0.44480257856567285\n",
            "roc_auc_score: 0.7630505048441114\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving and loading"
      ],
      "metadata": {
        "id": "YMdDMwcmlH1b"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkcGoSyxit1j"
      },
      "source": [
        "import pickle\n",
        "with open(\"saved_pipeline.pkl\", \"wb\") as f:\n",
        "  pickle.dump(pipeline_full, f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"saved_pipeline.pkl\", \"rb\") as f:\n",
        "  loaded_pipeline = pickle.load(f)\n",
        "\n",
        "loaded_pipeline"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "9TDjD_zjlHa_",
        "outputId": "c6848296-b387-4be3-cfe5-7ad2a63be56e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(transformers=[('categorical',\n",
              "                                 Pipeline(steps=[('onehot',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                 ['SEX', 'EDUCATION', 'MARRIAGE', 'PAY_0',\n",
              "                                  'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5',\n",
              "                                  'PAY_6']),\n",
              "                                ('numerical',\n",
              "                                 Pipeline(steps=[('scaler', MinMaxScaler())]),\n",
              "                                 ['LIMIT_BAL', 'AGE', 'BILL_AMT1', 'BILL_AMT2',\n",
              "                                  'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5',\n",
              "                                  'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2',\n",
              "                                  'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5',\n",
              "                                  'PAY_AMT6'])])"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;, &#x27;PAY_0&#x27;,\n",
              "                                  &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;, &#x27;PAY_5&#x27;,\n",
              "                                  &#x27;PAY_6&#x27;]),\n",
              "                                (&#x27;numerical&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler())]),\n",
              "                                 [&#x27;LIMIT_BAL&#x27;, &#x27;AGE&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;,\n",
              "                                  &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;,\n",
              "                                  &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;,\n",
              "                                  &#x27;PAY_AMT3&#x27;, &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;,\n",
              "                                  &#x27;PAY_AMT6&#x27;])])</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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;categorical&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;, &#x27;PAY_0&#x27;,\n",
              "                                  &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;, &#x27;PAY_5&#x27;,\n",
              "                                  &#x27;PAY_6&#x27;]),\n",
              "                                (&#x27;numerical&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler())]),\n",
              "                                 [&#x27;LIMIT_BAL&#x27;, &#x27;AGE&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;,\n",
              "                                  &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;,\n",
              "                                  &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;,\n",
              "                                  &#x27;PAY_AMT3&#x27;, &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;,\n",
              "                                  &#x27;PAY_AMT6&#x27;])])</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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">categorical</label><div class=\"sk-toggleable__content\"><pre>[&#x27;SEX&#x27;, &#x27;EDUCATION&#x27;, &#x27;MARRIAGE&#x27;, &#x27;PAY_0&#x27;, &#x27;PAY_2&#x27;, &#x27;PAY_3&#x27;, &#x27;PAY_4&#x27;, &#x27;PAY_5&#x27;, &#x27;PAY_6&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><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-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerical</label><div class=\"sk-toggleable__content\"><pre>[&#x27;LIMIT_BAL&#x27;, &#x27;AGE&#x27;, &#x27;BILL_AMT1&#x27;, &#x27;BILL_AMT2&#x27;, &#x27;BILL_AMT3&#x27;, &#x27;BILL_AMT4&#x27;, &#x27;BILL_AMT5&#x27;, &#x27;BILL_AMT6&#x27;, &#x27;PAY_AMT1&#x27;, &#x27;PAY_AMT2&#x27;, &#x27;PAY_AMT3&#x27;, &#x27;PAY_AMT4&#x27;, &#x27;PAY_AMT5&#x27;, &#x27;PAY_AMT6&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><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-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uDCHPnT0lQFI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}