Decision Tree Model trained on https://archive.ics.uci.edu/ml/datasets/Bank+Marketing

Will_the_client_subscribe.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Will the client subscribe",
      "provenance": [],
      "collapsed_sections": [
        "VsmP23cCUh6r"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Will the client subscribe\n",
        "\n",
        "## Description\n",
        "\n",
        "This notebook aims to predict the success rate at which clients subscribe to bank plans. In a more broad point of view, it predicts the success of bank telemarketing.\n",
        "\n",
        "## Data\n",
        "\n",
        "The data used is gathered from this [Bank Marketing Data Set](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing). \n",
        "\n",
        "This dataset is publicly available for research. The details are described in S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, In press, http://dx.doi.org/10.1016/j.dss.2014.03.001.\n",
        "\n",
        "Available at: [pdf](http://dx.doi.org/10.1016/j.dss.2014.03.001) and [bib](http://www3.dsi.uminho.pt/pcortez/bib/2014-dss.txt)\n",
        "\n"
      ],
      "metadata": {
        "id": "VsmP23cCUh6r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing"
      ],
      "metadata": {
        "id": "Dhf0u7m2ZBKM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "T3j7zLXoT1qL"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from pandas.plotting import scatter_matrix\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from google.colab import files\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "\n",
        "import io"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Two files are targetted: bank-additional-full.csv and bank-additional.csv\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "lqWd6lz9ZG1i",
        "outputId": "ec980af7-125f-44ec-9425-6207e855987e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-67f3f08e-8796-4948-8315-46cea493ab04\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-67f3f08e-8796-4948-8315-46cea493ab04\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving bank-additional-full.csv to bank-additional-full (1).csv\n",
            "Saving bank-additional.csv to bank-additional (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "# Read CSV data using pandas \n",
        "# The data is read in a DataFrame \n",
        "\n",
        "# Train data\n",
        "train_data = pd.read_table(io.BytesIO(uploaded['bank-additional-full.csv']), sep=\";\", quotechar='\"', quoting=csv.QUOTE_ALL)\n",
        "# Test data\n",
        "test_data = pd.read_table(io.BytesIO(uploaded['bank-additional.csv']), sep=\";\", quotechar='\"', quoting=csv.QUOTE_ALL)\n",
        "\n",
        "def convertDataToPlottable(dataset):\n",
        "  # Convert the boolean y (yes | no) result into [-1 1] range, so it can be plotted\n",
        "  dataset['subscribed'] = np.where(dataset.y == 'yes', 1, 0)\n",
        "  dataset['previous'] = dataset.default.map(dict(yes=1, no=-1, unknown=0))\n",
        "\n",
        "convertDataToPlottable(train_data)\n",
        "convertDataToPlottable(test_data)"
      ],
      "metadata": {
        "id": "lt53DMySmOJw"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data is composed of **41188** objects of **21** attributes, one of which is the boolean of interest (`yes` | `no`)."
      ],
      "metadata": {
        "id": "5S_MY9TUbACJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns', None)\n",
        "train_data.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "9P52XCzZb8Rn",
        "outputId": "6ec2bc8e-2602-4204-ffd3-0322aa2ac1db"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age          job  marital            education  default housing loan  \\\n",
              "0   56    housemaid  married             basic.4y       no      no   no   \n",
              "1   57     services  married          high.school  unknown      no   no   \n",
              "2   37     services  married          high.school       no     yes   no   \n",
              "3   40       admin.  married             basic.6y       no      no   no   \n",
              "4   56     services  married          high.school       no      no  yes   \n",
              "5   45     services  married             basic.9y  unknown      no   no   \n",
              "6   59       admin.  married  professional.course       no      no   no   \n",
              "7   41  blue-collar  married              unknown  unknown      no   no   \n",
              "8   24   technician   single  professional.course       no     yes   no   \n",
              "9   25     services   single          high.school       no     yes   no   \n",
              "\n",
              "     contact month day_of_week  duration  campaign  pdays  previous  \\\n",
              "0  telephone   may         mon       261         1    999        -1   \n",
              "1  telephone   may         mon       149         1    999         0   \n",
              "2  telephone   may         mon       226         1    999        -1   \n",
              "3  telephone   may         mon       151         1    999        -1   \n",
              "4  telephone   may         mon       307         1    999        -1   \n",
              "5  telephone   may         mon       198         1    999         0   \n",
              "6  telephone   may         mon       139         1    999        -1   \n",
              "7  telephone   may         mon       217         1    999         0   \n",
              "8  telephone   may         mon       380         1    999        -1   \n",
              "9  telephone   may         mon        50         1    999        -1   \n",
              "\n",
              "      poutcome  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
              "0  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "1  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "2  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "3  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "4  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "5  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "6  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "7  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "8  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "9  nonexistent           1.1          93.994          -36.4      4.857   \n",
              "\n",
              "   nr.employed   y  subscribed  \n",
              "0       5191.0  no           0  \n",
              "1       5191.0  no           0  \n",
              "2       5191.0  no           0  \n",
              "3       5191.0  no           0  \n",
              "4       5191.0  no           0  \n",
              "5       5191.0  no           0  \n",
              "6       5191.0  no           0  \n",
              "7       5191.0  no           0  \n",
              "8       5191.0  no           0  \n",
              "9       5191.0  no           0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cad67ce5-a21c-4a57-809c-45d5c43da929\">\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>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>month</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>y</th>\n",
              "      <th>subscribed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>housemaid</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.4y</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>226</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.6y</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>307</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>45</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.9y</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>59</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>139</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>41</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>24</td>\n",
              "      <td>technician</td>\n",
              "      <td>single</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>380</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>25</td>\n",
              "      <td>services</td>\n",
              "      <td>single</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cad67ce5-a21c-4a57-809c-45d5c43da929')\"\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-cad67ce5-a21c-4a57-809c-45d5c43da929 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-cad67ce5-a21c-4a57-809c-45d5c43da929');\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": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4u79QbbBoB0f",
        "outputId": "4a429bb2-e7fe-47b2-836a-79d8d99c7592"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41188 entries, 0 to 41187\n",
            "Data columns (total 22 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             41188 non-null  int64  \n",
            " 1   job             41188 non-null  object \n",
            " 2   marital         41188 non-null  object \n",
            " 3   education       41188 non-null  object \n",
            " 4   default         41188 non-null  object \n",
            " 5   housing         41188 non-null  object \n",
            " 6   loan            41188 non-null  object \n",
            " 7   contact         41188 non-null  object \n",
            " 8   month           41188 non-null  object \n",
            " 9   day_of_week     41188 non-null  object \n",
            " 10  duration        41188 non-null  int64  \n",
            " 11  campaign        41188 non-null  int64  \n",
            " 12  pdays           41188 non-null  int64  \n",
            " 13  previous        41188 non-null  int64  \n",
            " 14  poutcome        41188 non-null  object \n",
            " 15  emp.var.rate    41188 non-null  float64\n",
            " 16  cons.price.idx  41188 non-null  float64\n",
            " 17  cons.conf.idx   41188 non-null  float64\n",
            " 18  euribor3m       41188 non-null  float64\n",
            " 19  nr.employed     41188 non-null  float64\n",
            " 20  y               41188 non-null  object \n",
            " 21  subscribed      41188 non-null  int64  \n",
            "dtypes: float64(5), int64(6), object(11)\n",
            "memory usage: 6.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualising and Analizing\n",
        "\n",
        "This step is crucial and it determines the success of the model training."
      ],
      "metadata": {
        "id": "_jOpNXPRoQCD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.hist(bins=50, figsize=(25,25))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "C4rJqHv0ucVL",
        "outputId": "587d8e7b-3ff0-4594-f9e5-c814680c74ae"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1800 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histogram\n",
        "\n",
        "By plotting histograms for each field, **inbalance** can be spotted.\n",
        "\n",
        "The field `pdays` has almost all the values set to `999`, which means the client was never previously contacted. Since this field retains no meaningful data at the dataset level, it can be safely removed.\n",
        "\n",
        "All the other fields appear to be looking fine either because they match the expected behavior, or because data is somewhat evenly distributed."
      ],
      "metadata": {
        "id": "iIpv8LTavNzT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect value counts for pdays\n",
        "train_data['pdays'].value_counts().head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uis9WbNixgsK",
        "outputId": "fc7f262c-9910-48dc-d1cd-7420d0c6c8f1"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "999    39673\n",
              "3        439\n",
              "6        412\n",
              "4        118\n",
              "9         64\n",
              "2         61\n",
              "7         60\n",
              "12        58\n",
              "10        52\n",
              "5         46\n",
              "Name: pdays, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next in line is a **scatter matrix**. This will help with elimination of hurtful relations between columns which decrease the realistic prediction model."
      ],
      "metadata": {
        "id": "yLhxLYv4zMtv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "attr_to_scatter = [\"subscribed\", \"age\", \"duration\", \"campaign\", \"previous\"]\n",
        "scatter_matrix(train_data[attr_to_scatter], figsize=(12, 12))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "i03HorJKzKQB",
        "outputId": "eafe7f55-f7fa-4127-dca8-36d09122c740"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are several entries which can be optionally removed.\n",
        "\n",
        "1. Old people with long call time (duration)\n",
        "2. People with large number of contacts and long call time (duration)\n",
        "\n",
        "Removing these is not crucial, but it can be something to keep in mind.\n",
        "\n",
        "There is, however, a tiny segment of the plots between `subscribed` and `duration` which unveils a possible flawd relation that the model might catch not up on."
      ],
      "metadata": {
        "id": "Uk6n9KoU1yEB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scatter_matrix(train_data[[\"subscribed\", \"duration\"]], figsize=(12, 12))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "wy8rgJ4d7Lsz",
        "outputId": "9a8b92c1-af73-44b9-bf62-3e8f68710745"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**From previous plots, it is known that:**\n",
        "\n",
        "* There is a high density of unsubscribed people\n",
        "* There is a high density of low call durations\n",
        "\n",
        "The last scattered plot shows that **ALL** people with *low duration calls* are **NOT** subscribed in the end.\n",
        "\n",
        "**This is true:** people close the call the second they know it is not a matter of interest for them.\n",
        "\n",
        "But this can lead to unrealistic predictions. What does it mean to have a **0** second call. Maybe the time it took is unknown, or the call never happened in the first place. Thus the **duration** column will be removed.\n"
      ],
      "metadata": {
        "id": "15rr3fJM8dfT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The last item of interest is the `default` attribute and its effects on `age`, `subscribed`, `campaign`."
      ],
      "metadata": {
        "id": "SFt-UnytBv_S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.default.value_counts().head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XOJWpdZmDsgS",
        "outputId": "b87e2007-32e2-45f4-c464-9b243c14a04f"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no         32588\n",
              "unknown     8597\n",
              "yes            3\n",
              "Name: default, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scatter_matrix(train_data[[\"previous\", \"campaign\", \"age\"]], figsize=(12, 12))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "hmC7Zg67DxCf",
        "outputId": "c76879e9-21ba-4487-bd58-1e38c6d48df7"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No destructive data is found, however, the model that is trained on this data will predict if users **DO NOT** subscribe to the bank plan, rather than the other way around."
      ],
      "metadata": {
        "id": "Ict04zKDEwD_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cleaning\n",
        "\n",
        "Taking the notes written above into account, `pdays` and `duration` columns will be removed to preserve the model prediction correctness."
      ],
      "metadata": {
        "id": "2vh9aMgN_DgM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def dropColumns(dataset):\n",
        "  dataset.drop('pdays', axis=1, inplace=True)\n",
        "  dataset.drop('duration', axis=1, inplace=True)\n",
        "  dataset.drop('subscribed', axis=1, inplace=True)\n",
        "\n",
        "dropColumns(train_data)\n",
        "dropColumns(test_data)"
      ],
      "metadata": {
        "id": "kOPsMTP7_XbY"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "OyXfbNX7AY_X",
        "outputId": "00b67dd0-23da-40aa-bb96-ad3f704d0a2a"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age          job  marital            education  default housing loan  \\\n",
              "0       56    housemaid  married             basic.4y       no      no   no   \n",
              "1       57     services  married          high.school  unknown      no   no   \n",
              "2       37     services  married          high.school       no     yes   no   \n",
              "3       40       admin.  married             basic.6y       no      no   no   \n",
              "4       56     services  married          high.school       no      no  yes   \n",
              "...    ...          ...      ...                  ...      ...     ...  ...   \n",
              "41183   73      retired  married  professional.course       no     yes   no   \n",
              "41184   46  blue-collar  married  professional.course       no      no   no   \n",
              "41185   56      retired  married    university.degree       no     yes   no   \n",
              "41186   44   technician  married  professional.course       no      no   no   \n",
              "41187   74      retired  married  professional.course       no     yes   no   \n",
              "\n",
              "         contact month day_of_week  campaign  previous     poutcome  \\\n",
              "0      telephone   may         mon         1        -1  nonexistent   \n",
              "1      telephone   may         mon         1         0  nonexistent   \n",
              "2      telephone   may         mon         1        -1  nonexistent   \n",
              "3      telephone   may         mon         1        -1  nonexistent   \n",
              "4      telephone   may         mon         1        -1  nonexistent   \n",
              "...          ...   ...         ...       ...       ...          ...   \n",
              "41183   cellular   nov         fri         1        -1  nonexistent   \n",
              "41184   cellular   nov         fri         1        -1  nonexistent   \n",
              "41185   cellular   nov         fri         2        -1  nonexistent   \n",
              "41186   cellular   nov         fri         1        -1  nonexistent   \n",
              "41187   cellular   nov         fri         3        -1      failure   \n",
              "\n",
              "       emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \\\n",
              "0               1.1          93.994          -36.4      4.857       5191.0   \n",
              "1               1.1          93.994          -36.4      4.857       5191.0   \n",
              "2               1.1          93.994          -36.4      4.857       5191.0   \n",
              "3               1.1          93.994          -36.4      4.857       5191.0   \n",
              "4               1.1          93.994          -36.4      4.857       5191.0   \n",
              "...             ...             ...            ...        ...          ...   \n",
              "41183          -1.1          94.767          -50.8      1.028       4963.6   \n",
              "41184          -1.1          94.767          -50.8      1.028       4963.6   \n",
              "41185          -1.1          94.767          -50.8      1.028       4963.6   \n",
              "41186          -1.1          94.767          -50.8      1.028       4963.6   \n",
              "41187          -1.1          94.767          -50.8      1.028       4963.6   \n",
              "\n",
              "         y  \n",
              "0       no  \n",
              "1       no  \n",
              "2       no  \n",
              "3       no  \n",
              "4       no  \n",
              "...    ...  \n",
              "41183  yes  \n",
              "41184   no  \n",
              "41185   no  \n",
              "41186  yes  \n",
              "41187   no  \n",
              "\n",
              "[41188 rows x 19 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ddb7b7a5-c2ea-43f7-b247-dfac9eca3ab9\">\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>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>month</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>campaign</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>housemaid</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.4y</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.6y</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41183</th>\n",
              "      <td>73</td>\n",
              "      <td>retired</td>\n",
              "      <td>married</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>fri</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41184</th>\n",
              "      <td>46</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>fri</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41185</th>\n",
              "      <td>56</td>\n",
              "      <td>retired</td>\n",
              "      <td>married</td>\n",
              "      <td>university.degree</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>fri</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41186</th>\n",
              "      <td>44</td>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>fri</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41187</th>\n",
              "      <td>74</td>\n",
              "      <td>retired</td>\n",
              "      <td>married</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>fri</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>failure</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>94.767</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>1.028</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41188 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ddb7b7a5-c2ea-43f7-b247-dfac9eca3ab9')\"\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",
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              "  </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",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-ddb7b7a5-c2ea-43f7-b247-dfac9eca3ab9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-ddb7b7a5-c2ea-43f7-b247-dfac9eca3ab9');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fortunately, there are no missing columns, however, there are states that describe missing data such as: **unknown**, **0**, **999**, **nonexistent**. I wish to keep this as is since it refers to `employment`, `call statistics` and `previous campaing statistics`. This type of **missing data** provides meaningful semantic value to the dataset."
      ],
      "metadata": {
        "id": "ibHNXx0zFaWR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "619rWofIFKTJ",
        "outputId": "18804e24-c548-4033-c1b8-871a5dd4c8c9"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.isin(['unknown', '0', '999', 'nonexistent']).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dny34UTJG9va",
        "outputId": "307293f1-65b9-45d6-a825-68d5384a70fd"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                   0\n",
              "job                 330\n",
              "marital              80\n",
              "education          1731\n",
              "default            8597\n",
              "housing             990\n",
              "loan                990\n",
              "contact               0\n",
              "month                 0\n",
              "day_of_week           0\n",
              "campaign              0\n",
              "previous              0\n",
              "poutcome          35563\n",
              "emp.var.rate          0\n",
              "cons.price.idx        0\n",
              "cons.conf.idx         0\n",
              "euribor3m             0\n",
              "nr.employed           0\n",
              "y                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Balancing\n",
        "\n",
        "Now that the data is cleaned, it is time to balance it."
      ],
      "metadata": {
        "id": "8oXy3WZoJaNZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data['y'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkyqsMINLtOE",
        "outputId": "1506ed06-2d53-43a9-c70e-0647c86d9b3c"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     36548\n",
              "yes     4640\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After cleaning the data set looks like this:"
      ],
      "metadata": {
        "id": "TEtg-GQzLK_T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0XPudw6K_5-",
        "outputId": "8d17d723-5bf4-4444-ea9b-acd1fdc7ca21"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41188 entries, 0 to 41187\n",
            "Data columns (total 19 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             41188 non-null  int64  \n",
            " 1   job             41188 non-null  object \n",
            " 2   marital         41188 non-null  object \n",
            " 3   education       41188 non-null  object \n",
            " 4   default         41188 non-null  object \n",
            " 5   housing         41188 non-null  object \n",
            " 6   loan            41188 non-null  object \n",
            " 7   contact         41188 non-null  object \n",
            " 8   month           41188 non-null  object \n",
            " 9   day_of_week     41188 non-null  object \n",
            " 10  campaign        41188 non-null  int64  \n",
            " 11  previous        41188 non-null  int64  \n",
            " 12  poutcome        41188 non-null  object \n",
            " 13  emp.var.rate    41188 non-null  float64\n",
            " 14  cons.price.idx  41188 non-null  float64\n",
            " 15  cons.conf.idx   41188 non-null  float64\n",
            " 16  euribor3m       41188 non-null  float64\n",
            " 17  nr.employed     41188 non-null  float64\n",
            " 18  y               41188 non-null  object \n",
            "dtypes: float64(5), int64(3), object(11)\n",
            "memory usage: 6.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "r8EIwO8-JcoE"
      },
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Labelling and Encoding\n",
        "\n",
        "The first step is to assign numerical codes to the attributes which are not numers already. I choose to label everything in a **one-hot numeric way** such that models do not make relations between values (*ie: no > yes, unknown+no = yes, divorced < single, unknown > married*).\n",
        "\n",
        "As a future test, i can try using a **Label encoder for `job`, `poutcome`, `loan` and `housing`** to inspect differences in the model training."
      ],
      "metadata": {
        "id": "ubWSQyqtM1Bw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def hotEncode(data_set, column_name):\n",
        "  encoder = OneHotEncoder(sparse=False)\n",
        "  encoded_x = pd.DataFrame(encoder.fit_transform(data_set[[column_name]]))\n",
        "  encoded_x.columns = encoder.get_feature_names_out([column_name])\n",
        "  new_set = data_set.drop([column_name], axis=1, inplace=False)\n",
        "  return pd.concat([new_set, encoded_x ], axis=1)\n",
        "\n",
        "def hotEncodeMany(data_set, columns):\n",
        "  if (not columns or columns == None or len(columns) == 0):\n",
        "    return data_set\n",
        "  else:\n",
        "    return hotEncodeMany(hotEncode(data_set, columns[0]), columns[1:])\n",
        "\n",
        "def hotEncodeDataSet(data_set):\n",
        "  encoded_data_set = hotEncodeMany(data_set, ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'poutcome'])\n",
        "\n",
        "  y_le = LabelEncoder()\n",
        "  encoded_data_set['y'] = y_le.fit_transform(encoded_data_set['y'])\n",
        "\n",
        "  return encoded_data_set\n",
        "\n",
        "encoded_train_data = hotEncodeDataSet(train_data)\n",
        "encoded_test_data = hotEncodeDataSet(test_data)\n",
        "\n",
        "encoded_train_data.info()\n",
        "\n",
        "# The method below is abandoned due to not being able to keep column_names\n",
        "# ct = ColumnTransformer(\n",
        "#     [\n",
        "#      (\"job\", OneHotEncoder(),[1]),\n",
        "#      (\"marital\", OneHotEncoder(),[2]),\n",
        "#      (\"education\", OneHotEncoder(),[3]),\n",
        "#      (\"default\", OneHotEncoder(),[4]),\n",
        "#      (\"housing\", OneHotEncoder(),[5]),\n",
        "#      (\"loan\", OneHotEncoder(),[6]),\n",
        "#      (\"contact\", OneHotEncoder(),[7]),\n",
        "#      (\"month\", OneHotEncoder(),[8]),\n",
        "#      (\"day_of_week\", OneHotEncoder(),[9]),\n",
        "#      (\"poutcome\", OneHotEncoder(),[12])\n",
        "#     ],\n",
        "#     remainder=\"passthrough\"\n",
        "# )\n",
        "# encoded_train_data = pd.DataFrame(ct.fit_transform(train_data))\n",
        "# encoded_train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tOIf7U2jNL26",
        "outputId": "0bcc13d9-d376-4c7a-dec9-dfd14d9a767a"
      },
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41188 entries, 0 to 41187\n",
            "Data columns (total 62 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
            " 0   age                            41188 non-null  int64  \n",
            " 1   campaign                       41188 non-null  int64  \n",
            " 2   previous                       41188 non-null  int64  \n",
            " 3   emp.var.rate                   41188 non-null  float64\n",
            " 4   cons.price.idx                 41188 non-null  float64\n",
            " 5   cons.conf.idx                  41188 non-null  float64\n",
            " 6   euribor3m                      41188 non-null  float64\n",
            " 7   nr.employed                    41188 non-null  float64\n",
            " 8   y                              41188 non-null  int64  \n",
            " 9   job_admin.                     41188 non-null  float64\n",
            " 10  job_blue-collar                41188 non-null  float64\n",
            " 11  job_entrepreneur               41188 non-null  float64\n",
            " 12  job_housemaid                  41188 non-null  float64\n",
            " 13  job_management                 41188 non-null  float64\n",
            " 14  job_retired                    41188 non-null  float64\n",
            " 15  job_self-employed              41188 non-null  float64\n",
            " 16  job_services                   41188 non-null  float64\n",
            " 17  job_student                    41188 non-null  float64\n",
            " 18  job_technician                 41188 non-null  float64\n",
            " 19  job_unemployed                 41188 non-null  float64\n",
            " 20  job_unknown                    41188 non-null  float64\n",
            " 21  marital_divorced               41188 non-null  float64\n",
            " 22  marital_married                41188 non-null  float64\n",
            " 23  marital_single                 41188 non-null  float64\n",
            " 24  marital_unknown                41188 non-null  float64\n",
            " 25  education_basic.4y             41188 non-null  float64\n",
            " 26  education_basic.6y             41188 non-null  float64\n",
            " 27  education_basic.9y             41188 non-null  float64\n",
            " 28  education_high.school          41188 non-null  float64\n",
            " 29  education_illiterate           41188 non-null  float64\n",
            " 30  education_professional.course  41188 non-null  float64\n",
            " 31  education_university.degree    41188 non-null  float64\n",
            " 32  education_unknown              41188 non-null  float64\n",
            " 33  default_no                     41188 non-null  float64\n",
            " 34  default_unknown                41188 non-null  float64\n",
            " 35  default_yes                    41188 non-null  float64\n",
            " 36  housing_no                     41188 non-null  float64\n",
            " 37  housing_unknown                41188 non-null  float64\n",
            " 38  housing_yes                    41188 non-null  float64\n",
            " 39  loan_no                        41188 non-null  float64\n",
            " 40  loan_unknown                   41188 non-null  float64\n",
            " 41  loan_yes                       41188 non-null  float64\n",
            " 42  contact_cellular               41188 non-null  float64\n",
            " 43  contact_telephone              41188 non-null  float64\n",
            " 44  month_apr                      41188 non-null  float64\n",
            " 45  month_aug                      41188 non-null  float64\n",
            " 46  month_dec                      41188 non-null  float64\n",
            " 47  month_jul                      41188 non-null  float64\n",
            " 48  month_jun                      41188 non-null  float64\n",
            " 49  month_mar                      41188 non-null  float64\n",
            " 50  month_may                      41188 non-null  float64\n",
            " 51  month_nov                      41188 non-null  float64\n",
            " 52  month_oct                      41188 non-null  float64\n",
            " 53  month_sep                      41188 non-null  float64\n",
            " 54  day_of_week_fri                41188 non-null  float64\n",
            " 55  day_of_week_mon                41188 non-null  float64\n",
            " 56  day_of_week_thu                41188 non-null  float64\n",
            " 57  day_of_week_tue                41188 non-null  float64\n",
            " 58  day_of_week_wed                41188 non-null  float64\n",
            " 59  poutcome_failure               41188 non-null  float64\n",
            " 60  poutcome_nonexistent           41188 non-null  float64\n",
            " 61  poutcome_success               41188 non-null  float64\n",
            "dtypes: float64(58), int64(4)\n",
            "memory usage: 19.5 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analize balance\n",
        "\n",
        "Now it is time to see how bad the situation is by preparing a 2D array of thescattered `y` output."
      ],
      "metadata": {
        "id": "urDIYGIzmhNg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components='mle', svd_solver='full')\n",
        "data_2d = pd.DataFrame(pca.fit_transform(encoded_train_data.iloc[:,0:61]))\n",
        "\n",
        "data_2d= pd.concat([data_2d, encoded_train_data['y']], axis=1)\n",
        "# data_2d.columns = ['x', 'y', 'yes']\n",
        "\n",
        "data_2d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "HdWL6Yy5Kw4D",
        "outputId": "e5a4e980-19e3-42ad-d179-f1eb424220e2"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                0          1          2         3         4         5  \\\n",
              "0      -23.979764  16.291155   2.906707 -1.627112  1.065630 -0.079231   \n",
              "1      -23.980241  17.293181   2.823890 -1.628676  1.322669  1.029336   \n",
              "2      -24.028357  -2.665948   4.264779 -1.559576  0.981290 -0.059319   \n",
              "3      -24.021138   0.327465   4.064291 -1.565831  1.041911 -0.068138   \n",
              "4      -23.979676  16.275995   2.899503 -1.625875  1.057168 -0.198990   \n",
              "...           ...        ...        ...       ...       ...       ...   \n",
              "41183  203.572526  31.749811 -10.983536 -0.714774  3.664637 -2.118588   \n",
              "41184  203.499499   4.822065  -9.047238 -0.614910  3.923396 -1.226411   \n",
              "41185  203.521585  14.799405  -9.784596  0.349438  3.607595 -2.011503   \n",
              "41186  203.495954   2.827820  -8.882263 -0.606913  3.833294 -1.572523   \n",
              "41187  203.566327  32.749328 -11.129469  1.275743  3.538605 -2.052732   \n",
              "\n",
              "              6         7         8         9        10        11        12  \\\n",
              "0     -0.774977 -0.700645 -0.137029 -0.103022 -0.132096 -0.012580 -0.101388   \n",
              "1     -0.490291  0.147711  0.506189 -0.034023  0.030124  1.119605  0.369953   \n",
              "2      0.622240 -0.964057 -0.245925 -0.178292 -0.055110  1.075637  0.213769   \n",
              "3     -0.760168 -0.618707 -0.160746  0.482241 -0.126563  0.342308 -0.202858   \n",
              "4     -0.713544 -0.818122  0.026244 -0.177643  1.425143  0.769130  0.212524   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "41183  0.849554  1.097658 -1.003487 -0.219546 -0.291850 -0.136826  0.412291   \n",
              "41184 -0.404526  1.326297 -1.596746 -0.212283 -0.206100 -0.367242  0.076243   \n",
              "41185  0.865421  1.188226 -0.995619  0.678442 -0.389753 -0.211301 -0.006651   \n",
              "41186 -0.427981  1.481257 -1.542757 -0.137722 -0.265766 -0.326979  1.033393   \n",
              "41187  0.836238  1.050405 -0.993755 -0.256143 -0.270573 -0.084455  0.376155   \n",
              "\n",
              "             13        14        15        16        17        18        19  \\\n",
              "0     -0.135756 -0.575915 -0.831453 -0.060538 -0.007312  0.000294  0.150748   \n",
              "1     -0.235982 -0.496904 -0.801519  0.050687 -0.052837  0.092690  0.441805   \n",
              "2     -0.119242 -0.517543 -0.808300 -0.021201 -0.016601  0.105859  0.482076   \n",
              "3     -0.163875 -0.558416 -0.835913 -0.073436  0.033472 -0.035835 -0.396067   \n",
              "4     -0.167396 -0.474700 -0.851869 -0.000553 -0.055954  0.071407  0.430418   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "41183 -0.294150 -0.009944  0.492094  0.140206  0.909292 -0.346988  0.058639   \n",
              "41184 -0.168665 -0.080326  0.402589  0.067675  0.973121 -0.241909 -0.072372   \n",
              "41185 -0.279473 -0.054408  0.407296  0.076242  0.910390 -0.147262  0.586378   \n",
              "41186 -0.558373 -0.084917  0.523481  0.171077  0.870696 -0.304575  0.000062   \n",
              "41187 -0.448650 -0.041531  0.537476  0.157179  0.986730  1.006483 -0.301462   \n",
              "\n",
              "             20        21        22        23        24        25        26  \\\n",
              "0     -0.339192 -0.112169 -0.575396  0.053707 -0.416298  0.359000  0.563423   \n",
              "1     -0.292591 -0.020848  0.334952 -0.005318 -0.052453  0.083946 -0.172999   \n",
              "2     -0.226412  0.151205  0.133216 -0.049236 -0.155822  0.167712 -0.105923   \n",
              "3      0.142238 -0.406953 -0.272106  0.047962 -0.087560  0.127729 -0.490955   \n",
              "4     -0.387827  0.042616  0.271444 -0.021831 -0.074534  0.094671 -0.173426   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "41183 -0.073187 -0.346340  0.236959  1.454690  0.073356  0.581180 -0.442168   \n",
              "41184  0.138623 -0.072659 -0.106554  1.682695 -0.261762 -0.404657 -0.631006   \n",
              "41185 -0.182782  0.154887  0.304304  1.416235 -0.261692 -0.399141 -0.027161   \n",
              "41186  0.273998 -0.284799  0.140171  1.486632  0.338408  0.338250 -0.054414   \n",
              "41187 -0.298156 -0.309098  0.158488  1.162960 -0.517483 -0.119203 -0.459109   \n",
              "\n",
              "             27        28        29        30        31        32        33  \\\n",
              "0      0.046147 -0.065306  0.065565  0.258295  0.029001 -0.168384 -0.103732   \n",
              "1     -0.108756  0.030270 -0.360822 -0.283456  0.132661  0.025561 -0.029282   \n",
              "2     -0.093286  0.031178 -0.321676 -0.183422  0.120649  0.026117 -0.049116   \n",
              "3     -0.115718  0.060521 -0.127067  0.754394  0.131023 -0.505603 -0.095448   \n",
              "4     -0.195247 -0.083983 -0.377250 -0.235386  0.143535  0.035124 -0.045415   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "41183  0.014070 -0.019129  0.089627 -0.242426 -0.482452 -0.055483  0.463462   \n",
              "41184  0.023203 -0.065936 -0.012356 -0.558720 -0.237089  0.122898  0.494305   \n",
              "41185  0.099346 -0.100177 -0.254662  0.006312 -0.480660 -0.033412  0.460670   \n",
              "41186  0.013150 -0.028857 -0.139028 -0.132609 -0.022705 -0.072478  0.483292   \n",
              "41187  0.026400 -0.055699  0.108189 -0.226637 -0.497609 -0.058091  0.442090   \n",
              "\n",
              "             34        35        36        37        38        39        40  \\\n",
              "0     -0.011273 -0.361521  0.476173 -0.593601  0.017824 -0.091406  0.016520   \n",
              "1      0.019569 -0.087577  0.023537  0.000766  0.000457  0.079570  0.006531   \n",
              "2     -0.008341  0.035796 -0.001652 -0.006815  0.002094  0.016656  0.003890   \n",
              "3     -0.046776 -0.048145 -0.077707  0.085473 -0.007235  0.058800  0.001517   \n",
              "4      0.015254 -0.101809  0.029578  0.009989  0.001556  0.087131  0.006037   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "41183 -0.063516  0.551731 -0.041581 -0.144326 -0.173098 -0.098793 -0.305447   \n",
              "41184  0.066303 -0.075806  0.050602 -0.100730 -0.142487 -0.029120 -0.293012   \n",
              "41185 -0.102923  0.741375 -0.034351 -0.158278 -0.169412 -0.145603 -0.298900   \n",
              "41186  0.042024 -0.122827 -0.038123 -0.025845 -0.156735 -0.037370 -0.303960   \n",
              "41187 -0.071700  0.575508 -0.033202 -0.142196 -0.133550 -0.077115 -0.276388   \n",
              "\n",
              "             41        42        43        44        45        46        47  \\\n",
              "0     -0.000113 -0.006479  0.023574 -0.044340 -0.014808 -0.002175 -0.002880   \n",
              "1      0.008123 -0.004607  0.033475 -0.028622 -0.009868  0.000541 -0.004124   \n",
              "2      0.012667 -0.002264  0.034848 -0.015839 -0.006606 -0.000269 -0.000089   \n",
              "3      0.009444 -0.007166  0.030835 -0.024656 -0.003487 -0.001038 -0.002317   \n",
              "4      0.008021 -0.005608  0.034996 -0.022695 -0.011935 -0.000641 -0.004150   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "41183 -0.132782 -0.299530  0.164789 -0.029924  0.010681  0.003785 -0.010946   \n",
              "41184 -0.056767 -0.245152  0.195024 -0.021500  0.013945  0.004191  0.007152   \n",
              "41185 -0.062010 -0.238084  0.186586 -0.035352  0.011658  0.001221  0.002482   \n",
              "41186 -0.123893 -0.306846  0.163701 -0.021449  0.016172  0.004141  0.001601   \n",
              "41187 -0.093773 -0.276498  0.212645 -0.045038  0.007579  0.002402 -0.007218   \n",
              "\n",
              "             48        49  y  \n",
              "0     -0.000040  0.000534  0  \n",
              "1     -0.000080 -0.000454  0  \n",
              "2      0.000688  0.000308  0  \n",
              "3      0.000287  0.000361  0  \n",
              "4      0.000220  0.000373  0  \n",
              "...         ...       ... ..  \n",
              "41183 -0.002196 -0.000209  1  \n",
              "41184 -0.000832 -0.000446  0  \n",
              "41185 -0.000883  0.000209  0  \n",
              "41186 -0.000066 -0.000701  1  \n",
              "41187 -0.001321 -0.000658  0  \n",
              "\n",
              "[41188 rows x 51 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6389ad78-25b7-4603-a8de-631d3ac17191\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>y</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-23.979764</td>\n",
              "      <td>16.291155</td>\n",
              "      <td>2.906707</td>\n",
              "      <td>-1.627112</td>\n",
              "      <td>1.065630</td>\n",
              "      <td>-0.079231</td>\n",
              "      <td>-0.774977</td>\n",
              "      <td>-0.700645</td>\n",
              "      <td>-0.137029</td>\n",
              "      <td>-0.103022</td>\n",
              "      <td>-0.132096</td>\n",
              "      <td>-0.012580</td>\n",
              "      <td>-0.101388</td>\n",
              "      <td>-0.135756</td>\n",
              "      <td>-0.575915</td>\n",
              "      <td>-0.831453</td>\n",
              "      <td>-0.060538</td>\n",
              "      <td>-0.007312</td>\n",
              "      <td>0.000294</td>\n",
              "      <td>0.150748</td>\n",
              "      <td>-0.339192</td>\n",
              "      <td>-0.112169</td>\n",
              "      <td>-0.575396</td>\n",
              "      <td>0.053707</td>\n",
              "      <td>-0.416298</td>\n",
              "      <td>0.359000</td>\n",
              "      <td>0.563423</td>\n",
              "      <td>0.046147</td>\n",
              "      <td>-0.065306</td>\n",
              "      <td>0.065565</td>\n",
              "      <td>0.258295</td>\n",
              "      <td>0.029001</td>\n",
              "      <td>-0.168384</td>\n",
              "      <td>-0.103732</td>\n",
              "      <td>-0.011273</td>\n",
              "      <td>-0.361521</td>\n",
              "      <td>0.476173</td>\n",
              "      <td>-0.593601</td>\n",
              "      <td>0.017824</td>\n",
              "      <td>-0.091406</td>\n",
              "      <td>0.016520</td>\n",
              "      <td>-0.000113</td>\n",
              "      <td>-0.006479</td>\n",
              "      <td>0.023574</td>\n",
              "      <td>-0.044340</td>\n",
              "      <td>-0.014808</td>\n",
              "      <td>-0.002175</td>\n",
              "      <td>-0.002880</td>\n",
              "      <td>-0.000040</td>\n",
              "      <td>0.000534</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-23.980241</td>\n",
              "      <td>17.293181</td>\n",
              "      <td>2.823890</td>\n",
              "      <td>-1.628676</td>\n",
              "      <td>1.322669</td>\n",
              "      <td>1.029336</td>\n",
              "      <td>-0.490291</td>\n",
              "      <td>0.147711</td>\n",
              "      <td>0.506189</td>\n",
              "      <td>-0.034023</td>\n",
              "      <td>0.030124</td>\n",
              "      <td>1.119605</td>\n",
              "      <td>0.369953</td>\n",
              "      <td>-0.235982</td>\n",
              "      <td>-0.496904</td>\n",
              "      <td>-0.801519</td>\n",
              "      <td>0.050687</td>\n",
              "      <td>-0.052837</td>\n",
              "      <td>0.092690</td>\n",
              "      <td>0.441805</td>\n",
              "      <td>-0.292591</td>\n",
              "      <td>-0.020848</td>\n",
              "      <td>0.334952</td>\n",
              "      <td>-0.005318</td>\n",
              "      <td>-0.052453</td>\n",
              "      <td>0.083946</td>\n",
              "      <td>-0.172999</td>\n",
              "      <td>-0.108756</td>\n",
              "      <td>0.030270</td>\n",
              "      <td>-0.360822</td>\n",
              "      <td>-0.283456</td>\n",
              "      <td>0.132661</td>\n",
              "      <td>0.025561</td>\n",
              "      <td>-0.029282</td>\n",
              "      <td>0.019569</td>\n",
              "      <td>-0.087577</td>\n",
              "      <td>0.023537</td>\n",
              "      <td>0.000766</td>\n",
              "      <td>0.000457</td>\n",
              "      <td>0.079570</td>\n",
              "      <td>0.006531</td>\n",
              "      <td>0.008123</td>\n",
              "      <td>-0.004607</td>\n",
              "      <td>0.033475</td>\n",
              "      <td>-0.028622</td>\n",
              "      <td>-0.009868</td>\n",
              "      <td>0.000541</td>\n",
              "      <td>-0.004124</td>\n",
              "      <td>-0.000080</td>\n",
              "      <td>-0.000454</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-24.028357</td>\n",
              "      <td>-2.665948</td>\n",
              "      <td>4.264779</td>\n",
              "      <td>-1.559576</td>\n",
              "      <td>0.981290</td>\n",
              "      <td>-0.059319</td>\n",
              "      <td>0.622240</td>\n",
              "      <td>-0.964057</td>\n",
              "      <td>-0.245925</td>\n",
              "      <td>-0.178292</td>\n",
              "      <td>-0.055110</td>\n",
              "      <td>1.075637</td>\n",
              "      <td>0.213769</td>\n",
              "      <td>-0.119242</td>\n",
              "      <td>-0.517543</td>\n",
              "      <td>-0.808300</td>\n",
              "      <td>-0.021201</td>\n",
              "      <td>-0.016601</td>\n",
              "      <td>0.105859</td>\n",
              "      <td>0.482076</td>\n",
              "      <td>-0.226412</td>\n",
              "      <td>0.151205</td>\n",
              "      <td>0.133216</td>\n",
              "      <td>-0.049236</td>\n",
              "      <td>-0.155822</td>\n",
              "      <td>0.167712</td>\n",
              "      <td>-0.105923</td>\n",
              "      <td>-0.093286</td>\n",
              "      <td>0.031178</td>\n",
              "      <td>-0.321676</td>\n",
              "      <td>-0.183422</td>\n",
              "      <td>0.120649</td>\n",
              "      <td>0.026117</td>\n",
              "      <td>-0.049116</td>\n",
              "      <td>-0.008341</td>\n",
              "      <td>0.035796</td>\n",
              "      <td>-0.001652</td>\n",
              "      <td>-0.006815</td>\n",
              "      <td>0.002094</td>\n",
              "      <td>0.016656</td>\n",
              "      <td>0.003890</td>\n",
              "      <td>0.012667</td>\n",
              "      <td>-0.002264</td>\n",
              "      <td>0.034848</td>\n",
              "      <td>-0.015839</td>\n",
              "      <td>-0.006606</td>\n",
              "      <td>-0.000269</td>\n",
              "      <td>-0.000089</td>\n",
              "      <td>0.000688</td>\n",
              "      <td>0.000308</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-24.021138</td>\n",
              "      <td>0.327465</td>\n",
              "      <td>4.064291</td>\n",
              "      <td>-1.565831</td>\n",
              "      <td>1.041911</td>\n",
              "      <td>-0.068138</td>\n",
              "      <td>-0.760168</td>\n",
              "      <td>-0.618707</td>\n",
              "      <td>-0.160746</td>\n",
              "      <td>0.482241</td>\n",
              "      <td>-0.126563</td>\n",
              "      <td>0.342308</td>\n",
              "      <td>-0.202858</td>\n",
              "      <td>-0.163875</td>\n",
              "      <td>-0.558416</td>\n",
              "      <td>-0.835913</td>\n",
              "      <td>-0.073436</td>\n",
              "      <td>0.033472</td>\n",
              "      <td>-0.035835</td>\n",
              "      <td>-0.396067</td>\n",
              "      <td>0.142238</td>\n",
              "      <td>-0.406953</td>\n",
              "      <td>-0.272106</td>\n",
              "      <td>0.047962</td>\n",
              "      <td>-0.087560</td>\n",
              "      <td>0.127729</td>\n",
              "      <td>-0.490955</td>\n",
              "      <td>-0.115718</td>\n",
              "      <td>0.060521</td>\n",
              "      <td>-0.127067</td>\n",
              "      <td>0.754394</td>\n",
              "      <td>0.131023</td>\n",
              "      <td>-0.505603</td>\n",
              "      <td>-0.095448</td>\n",
              "      <td>-0.046776</td>\n",
              "      <td>-0.048145</td>\n",
              "      <td>-0.077707</td>\n",
              "      <td>0.085473</td>\n",
              "      <td>-0.007235</td>\n",
              "      <td>0.058800</td>\n",
              "      <td>0.001517</td>\n",
              "      <td>0.009444</td>\n",
              "      <td>-0.007166</td>\n",
              "      <td>0.030835</td>\n",
              "      <td>-0.024656</td>\n",
              "      <td>-0.003487</td>\n",
              "      <td>-0.001038</td>\n",
              "      <td>-0.002317</td>\n",
              "      <td>0.000287</td>\n",
              "      <td>0.000361</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-23.979676</td>\n",
              "      <td>16.275995</td>\n",
              "      <td>2.899503</td>\n",
              "      <td>-1.625875</td>\n",
              "      <td>1.057168</td>\n",
              "      <td>-0.198990</td>\n",
              "      <td>-0.713544</td>\n",
              "      <td>-0.818122</td>\n",
              "      <td>0.026244</td>\n",
              "      <td>-0.177643</td>\n",
              "      <td>1.425143</td>\n",
              "      <td>0.769130</td>\n",
              "      <td>0.212524</td>\n",
              "      <td>-0.167396</td>\n",
              "      <td>-0.474700</td>\n",
              "      <td>-0.851869</td>\n",
              "      <td>-0.000553</td>\n",
              "      <td>-0.055954</td>\n",
              "      <td>0.071407</td>\n",
              "      <td>0.430418</td>\n",
              "      <td>-0.387827</td>\n",
              "      <td>0.042616</td>\n",
              "      <td>0.271444</td>\n",
              "      <td>-0.021831</td>\n",
              "      <td>-0.074534</td>\n",
              "      <td>0.094671</td>\n",
              "      <td>-0.173426</td>\n",
              "      <td>-0.195247</td>\n",
              "      <td>-0.083983</td>\n",
              "      <td>-0.377250</td>\n",
              "      <td>-0.235386</td>\n",
              "      <td>0.143535</td>\n",
              "      <td>0.035124</td>\n",
              "      <td>-0.045415</td>\n",
              "      <td>0.015254</td>\n",
              "      <td>-0.101809</td>\n",
              "      <td>0.029578</td>\n",
              "      <td>0.009989</td>\n",
              "      <td>0.001556</td>\n",
              "      <td>0.087131</td>\n",
              "      <td>0.006037</td>\n",
              "      <td>0.008021</td>\n",
              "      <td>-0.005608</td>\n",
              "      <td>0.034996</td>\n",
              "      <td>-0.022695</td>\n",
              "      <td>-0.011935</td>\n",
              "      <td>-0.000641</td>\n",
              "      <td>-0.004150</td>\n",
              "      <td>0.000220</td>\n",
              "      <td>0.000373</td>\n",
              "      <td>0</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",
              "      <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",
              "      <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>41183</th>\n",
              "      <td>203.572526</td>\n",
              "      <td>31.749811</td>\n",
              "      <td>-10.983536</td>\n",
              "      <td>-0.714774</td>\n",
              "      <td>3.664637</td>\n",
              "      <td>-2.118588</td>\n",
              "      <td>0.849554</td>\n",
              "      <td>1.097658</td>\n",
              "      <td>-1.003487</td>\n",
              "      <td>-0.219546</td>\n",
              "      <td>-0.291850</td>\n",
              "      <td>-0.136826</td>\n",
              "      <td>0.412291</td>\n",
              "      <td>-0.294150</td>\n",
              "      <td>-0.009944</td>\n",
              "      <td>0.492094</td>\n",
              "      <td>0.140206</td>\n",
              "      <td>0.909292</td>\n",
              "      <td>-0.346988</td>\n",
              "      <td>0.058639</td>\n",
              "      <td>-0.073187</td>\n",
              "      <td>-0.346340</td>\n",
              "      <td>0.236959</td>\n",
              "      <td>1.454690</td>\n",
              "      <td>0.073356</td>\n",
              "      <td>0.581180</td>\n",
              "      <td>-0.442168</td>\n",
              "      <td>0.014070</td>\n",
              "      <td>-0.019129</td>\n",
              "      <td>0.089627</td>\n",
              "      <td>-0.242426</td>\n",
              "      <td>-0.482452</td>\n",
              "      <td>-0.055483</td>\n",
              "      <td>0.463462</td>\n",
              "      <td>-0.063516</td>\n",
              "      <td>0.551731</td>\n",
              "      <td>-0.041581</td>\n",
              "      <td>-0.144326</td>\n",
              "      <td>-0.173098</td>\n",
              "      <td>-0.098793</td>\n",
              "      <td>-0.305447</td>\n",
              "      <td>-0.132782</td>\n",
              "      <td>-0.299530</td>\n",
              "      <td>0.164789</td>\n",
              "      <td>-0.029924</td>\n",
              "      <td>0.010681</td>\n",
              "      <td>0.003785</td>\n",
              "      <td>-0.010946</td>\n",
              "      <td>-0.002196</td>\n",
              "      <td>-0.000209</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41184</th>\n",
              "      <td>203.499499</td>\n",
              "      <td>4.822065</td>\n",
              "      <td>-9.047238</td>\n",
              "      <td>-0.614910</td>\n",
              "      <td>3.923396</td>\n",
              "      <td>-1.226411</td>\n",
              "      <td>-0.404526</td>\n",
              "      <td>1.326297</td>\n",
              "      <td>-1.596746</td>\n",
              "      <td>-0.212283</td>\n",
              "      <td>-0.206100</td>\n",
              "      <td>-0.367242</td>\n",
              "      <td>0.076243</td>\n",
              "      <td>-0.168665</td>\n",
              "      <td>-0.080326</td>\n",
              "      <td>0.402589</td>\n",
              "      <td>0.067675</td>\n",
              "      <td>0.973121</td>\n",
              "      <td>-0.241909</td>\n",
              "      <td>-0.072372</td>\n",
              "      <td>0.138623</td>\n",
              "      <td>-0.072659</td>\n",
              "      <td>-0.106554</td>\n",
              "      <td>1.682695</td>\n",
              "      <td>-0.261762</td>\n",
              "      <td>-0.404657</td>\n",
              "      <td>-0.631006</td>\n",
              "      <td>0.023203</td>\n",
              "      <td>-0.065936</td>\n",
              "      <td>-0.012356</td>\n",
              "      <td>-0.558720</td>\n",
              "      <td>-0.237089</td>\n",
              "      <td>0.122898</td>\n",
              "      <td>0.494305</td>\n",
              "      <td>0.066303</td>\n",
              "      <td>-0.075806</td>\n",
              "      <td>0.050602</td>\n",
              "      <td>-0.100730</td>\n",
              "      <td>-0.142487</td>\n",
              "      <td>-0.029120</td>\n",
              "      <td>-0.293012</td>\n",
              "      <td>-0.056767</td>\n",
              "      <td>-0.245152</td>\n",
              "      <td>0.195024</td>\n",
              "      <td>-0.021500</td>\n",
              "      <td>0.013945</td>\n",
              "      <td>0.004191</td>\n",
              "      <td>0.007152</td>\n",
              "      <td>-0.000832</td>\n",
              "      <td>-0.000446</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41185</th>\n",
              "      <td>203.521585</td>\n",
              "      <td>14.799405</td>\n",
              "      <td>-9.784596</td>\n",
              "      <td>0.349438</td>\n",
              "      <td>3.607595</td>\n",
              "      <td>-2.011503</td>\n",
              "      <td>0.865421</td>\n",
              "      <td>1.188226</td>\n",
              "      <td>-0.995619</td>\n",
              "      <td>0.678442</td>\n",
              "      <td>-0.389753</td>\n",
              "      <td>-0.211301</td>\n",
              "      <td>-0.006651</td>\n",
              "      <td>-0.279473</td>\n",
              "      <td>-0.054408</td>\n",
              "      <td>0.407296</td>\n",
              "      <td>0.076242</td>\n",
              "      <td>0.910390</td>\n",
              "      <td>-0.147262</td>\n",
              "      <td>0.586378</td>\n",
              "      <td>-0.182782</td>\n",
              "      <td>0.154887</td>\n",
              "      <td>0.304304</td>\n",
              "      <td>1.416235</td>\n",
              "      <td>-0.261692</td>\n",
              "      <td>-0.399141</td>\n",
              "      <td>-0.027161</td>\n",
              "      <td>0.099346</td>\n",
              "      <td>-0.100177</td>\n",
              "      <td>-0.254662</td>\n",
              "      <td>0.006312</td>\n",
              "      <td>-0.480660</td>\n",
              "      <td>-0.033412</td>\n",
              "      <td>0.460670</td>\n",
              "      <td>-0.102923</td>\n",
              "      <td>0.741375</td>\n",
              "      <td>-0.034351</td>\n",
              "      <td>-0.158278</td>\n",
              "      <td>-0.169412</td>\n",
              "      <td>-0.145603</td>\n",
              "      <td>-0.298900</td>\n",
              "      <td>-0.062010</td>\n",
              "      <td>-0.238084</td>\n",
              "      <td>0.186586</td>\n",
              "      <td>-0.035352</td>\n",
              "      <td>0.011658</td>\n",
              "      <td>0.001221</td>\n",
              "      <td>0.002482</td>\n",
              "      <td>-0.000883</td>\n",
              "      <td>0.000209</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41186</th>\n",
              "      <td>203.495954</td>\n",
              "      <td>2.827820</td>\n",
              "      <td>-8.882263</td>\n",
              "      <td>-0.606913</td>\n",
              "      <td>3.833294</td>\n",
              "      <td>-1.572523</td>\n",
              "      <td>-0.427981</td>\n",
              "      <td>1.481257</td>\n",
              "      <td>-1.542757</td>\n",
              "      <td>-0.137722</td>\n",
              "      <td>-0.265766</td>\n",
              "      <td>-0.326979</td>\n",
              "      <td>1.033393</td>\n",
              "      <td>-0.558373</td>\n",
              "      <td>-0.084917</td>\n",
              "      <td>0.523481</td>\n",
              "      <td>0.171077</td>\n",
              "      <td>0.870696</td>\n",
              "      <td>-0.304575</td>\n",
              "      <td>0.000062</td>\n",
              "      <td>0.273998</td>\n",
              "      <td>-0.284799</td>\n",
              "      <td>0.140171</td>\n",
              "      <td>1.486632</td>\n",
              "      <td>0.338408</td>\n",
              "      <td>0.338250</td>\n",
              "      <td>-0.054414</td>\n",
              "      <td>0.013150</td>\n",
              "      <td>-0.028857</td>\n",
              "      <td>-0.139028</td>\n",
              "      <td>-0.132609</td>\n",
              "      <td>-0.022705</td>\n",
              "      <td>-0.072478</td>\n",
              "      <td>0.483292</td>\n",
              "      <td>0.042024</td>\n",
              "      <td>-0.122827</td>\n",
              "      <td>-0.038123</td>\n",
              "      <td>-0.025845</td>\n",
              "      <td>-0.156735</td>\n",
              "      <td>-0.037370</td>\n",
              "      <td>-0.303960</td>\n",
              "      <td>-0.123893</td>\n",
              "      <td>-0.306846</td>\n",
              "      <td>0.163701</td>\n",
              "      <td>-0.021449</td>\n",
              "      <td>0.016172</td>\n",
              "      <td>0.004141</td>\n",
              "      <td>0.001601</td>\n",
              "      <td>-0.000066</td>\n",
              "      <td>-0.000701</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41187</th>\n",
              "      <td>203.566327</td>\n",
              "      <td>32.749328</td>\n",
              "      <td>-11.129469</td>\n",
              "      <td>1.275743</td>\n",
              "      <td>3.538605</td>\n",
              "      <td>-2.052732</td>\n",
              "      <td>0.836238</td>\n",
              "      <td>1.050405</td>\n",
              "      <td>-0.993755</td>\n",
              "      <td>-0.256143</td>\n",
              "      <td>-0.270573</td>\n",
              "      <td>-0.084455</td>\n",
              "      <td>0.376155</td>\n",
              "      <td>-0.448650</td>\n",
              "      <td>-0.041531</td>\n",
              "      <td>0.537476</td>\n",
              "      <td>0.157179</td>\n",
              "      <td>0.986730</td>\n",
              "      <td>1.006483</td>\n",
              "      <td>-0.301462</td>\n",
              "      <td>-0.298156</td>\n",
              "      <td>-0.309098</td>\n",
              "      <td>0.158488</td>\n",
              "      <td>1.162960</td>\n",
              "      <td>-0.517483</td>\n",
              "      <td>-0.119203</td>\n",
              "      <td>-0.459109</td>\n",
              "      <td>0.026400</td>\n",
              "      <td>-0.055699</td>\n",
              "      <td>0.108189</td>\n",
              "      <td>-0.226637</td>\n",
              "      <td>-0.497609</td>\n",
              "      <td>-0.058091</td>\n",
              "      <td>0.442090</td>\n",
              "      <td>-0.071700</td>\n",
              "      <td>0.575508</td>\n",
              "      <td>-0.033202</td>\n",
              "      <td>-0.142196</td>\n",
              "      <td>-0.133550</td>\n",
              "      <td>-0.077115</td>\n",
              "      <td>-0.276388</td>\n",
              "      <td>-0.093773</td>\n",
              "      <td>-0.276498</td>\n",
              "      <td>0.212645</td>\n",
              "      <td>-0.045038</td>\n",
              "      <td>0.007579</td>\n",
              "      <td>0.002402</td>\n",
              "      <td>-0.007218</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_2d['y'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTimOwVqq_8y",
        "outputId": "a6b0aafd-0b9f-4e94-e125-ca47b8fc2a95"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    36548\n",
              "1     4640\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The plot determines an overall *ambiguous* output distribution"
      ],
      "metadata": {
        "id": "e-tjjYlgqjUj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "# sns.lmplot(x=\"x\", y=\"y\", data=data_2d, fit_reg=False, hue='yes')\n",
        "\n",
        "components = pca.fit_transform(data_2d)\n",
        "labels = {\n",
        "    str(i): f\"PC {i+1} ({var:.1f}%)\"\n",
        "    for i, var in enumerate(pca.explained_variance_ratio_ * 100)\n",
        "}\n",
        "fig = px.scatter_matrix(\n",
        "    components,\n",
        "    labels=labels,\n",
        "    dimensions=range(4),\n",
        "    color=data_2d[\"y\"]\n",
        ")\n",
        "fig.update_traces(diagonal_visible=False)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "GT0Vwe4cqiZR",
        "outputId": "1761fb33-285b-4b1d-92b7-fe9103fbac5e"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:1692: FutureWarning:\n",
            "\n",
            "Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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