2_Preprocessing_Classification.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sahel-Eskandar/Project-Notebooks/blob/main/2_Preprocessing_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "source": [
        "SaveName = '2-Preprocessing-Classification'\n",
        "Version = 'Dec05'\n",
        "\n",
        "Input = 'Interview/Data/'\n",
        "Output = '/content/Interview/Model/'"
      ],
      "metadata": {
        "id": "ApyG7mXf_HTC"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "##Import Libraries"
      ],
      "metadata": {
        "id": "WBmH97kS84Ux"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2"
      ],
      "metadata": {
        "id": "7Cf3xGDv-GYt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade pyod"
      ],
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          "base_uri": "https://localhost:8080/"
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyod\n",
            "  Downloading pyod-1.0.6.tar.gz (141 kB)\n",
            "\u001b[K     |████████████████████████████████| 141 kB 3.9 MB/s \n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->pyod) (1.4.4)\n",
            "Requirement already satisfied: pandas>=0.21 in /usr/local/lib/python3.8/dist-packages (from statsmodels->pyod) (1.3.5)\n",
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            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.21->statsmodels->pyod) (2022.6)\n",
            "Building wheels for collected packages: pyod\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-1.0.6-py3-none-any.whl size=175098 sha256=df0811e4b09fba873e87e0404721f994d770751effff4292173460e7f92eecc1\n",
            "  Stored in directory: /root/.cache/pip/wheels/98/93/e6/6d40410d9635ecde42d06041a1ba7f2ee7396e036fcf702e73\n",
            "Successfully built pyod\n",
            "Installing collected packages: pyod\n",
            "Successfully installed pyod-1.0.6\n"
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      "cell_type": "code",
      "source": [
        "import os, sys\n",
        "import os.path\n",
        "\n",
        "import math\n",
        "import time\n",
        "from datetime import datetime as dt\n",
        "from collections import OrderedDict\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import cnames\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import random\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "sns.set_style('whitegrid') # style\n",
        "# render plots inline\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "C5wTKM8f_-Bk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ensure_dir(file_path):\n",
        "    directory = os.path.dirname(file_path)\n",
        "    if not os.path.exists(directory):\n",
        "        os.makedirs(directory)"
      ],
      "metadata": {
        "id": "17KFg-AeCayN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Extraction"
      ],
      "metadata": {
        "id": "UXjhL-lZ4Rna"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = Input + 'term_deposit_data.csv'\n",
        "df = pd.read_csv(path, sep= ';')"
      ],
      "metadata": {
        "id": "F26OpePm88xl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. age (numeric)\n",
        "2. job : type of job (categorical: 'admin.','blue-collar', 'entrepreneur','housemaid','management', 'retired','self-employed','services','student','technician','unemployed','unknown')\n",
        "3. marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
        "4. education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course', 'university.degree', 'unknown')\n",
        "5. default: has credit in default? (categorical: 'no','yes','unknown')\n",
        "6. housing: has housing loan? (categorical: 'no','yes','unknown')\n",
        "7. loan: has personal loan? (categorical: 'no','yes','unknown') # related with the last contact of the current campaign:\n",
        "8. contact: contact communication type (categorical: 'cellular','telephone')\n",
        "9. month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
        "10. day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')\n",
        "11. duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no')\n",
        "12. campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
        "13. pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
        "14. previous: number of contacts performed before this campaign and for this client (numeric)\n",
        "15. poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') # social and economic context attributes\n",
        "16. emp.var.rate: employment variation rate - quarterly indicator (numeric)\n",
        "17. cons.price.idx: consumer price index - monthly indicator (numeric)\n",
        "18. cons.conf.idx: consumer confidence index - monthly indicator (numeric)\n",
        "19. euribor3m: euribor 3 month rate - daily indicator (numeric)\n",
        "20. nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target):\n",
        "21. y - has the client subscribed a term deposit? (binary: 'yes','no') "
      ],
      "metadata": {
        "id": "FfX6GrgDiaxD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
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        "outputId": "62f7085b-f019-4f87-93f2-a5833fbf5361"
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      "execution_count": null,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41188 entries, 0 to 41187\n",
            "Data columns (total 21 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",
            "dtypes: float64(5), int64(5), object(11)\n",
            "memory usage: 6.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
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          "height": 300
        },
        "id": "5nKJ5P4S-9zk",
        "outputId": "744375a2-f83f-4da2-de1d-9fbfb50505e9"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age        job  marital    education  default housing loan    contact  \\\n",
              "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
              "1   57   services  married  high.school  unknown      no   no  telephone   \n",
              "2   37   services  married  high.school       no     yes   no  telephone   \n",
              "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
              "4   56   services  married  high.school       no      no  yes  telephone   \n",
              "\n",
              "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
              "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "\n",
              "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
              "0          93.994          -36.4      4.857       5191.0  no  \n",
              "1          93.994          -36.4      4.857       5191.0  no  \n",
              "2          93.994          -36.4      4.857       5191.0  no  \n",
              "3          93.994          -36.4      4.857       5191.0  no  \n",
              "4          93.994          -36.4      4.857       5191.0  no  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th></th>\n",
              "      <th>age</th>\n",
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              "      <th>education</th>\n",
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              "      <th>y</th>\n",
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              "      <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",
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              "      <td>93.994</td>\n",
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              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
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              "      <td>no</td>\n",
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              "      <td>999</td>\n",
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              "      <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",
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              "    <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",
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              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
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              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
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              "      <td>5191.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.6y</td>\n",
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              "      <td>no</td>\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-943eebee-1030-4f01-ad1c-1bd2d3bd44fd 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-943eebee-1030-4f01-ad1c-1bd2d3bd44fd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "Lw6uwa2O_Tzu",
        "outputId": "5f99e91e-68df-4a4f-aafa-e753d1c6be12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age      duration      campaign         pdays      previous  \\\n",
              "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
              "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
              "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
              "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
              "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
              "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
              "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
              "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
              "\n",
              "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
              "count  41188.000000    41188.000000   41188.000000  41188.000000  41188.000000  \n",
              "mean       0.081886       93.575664     -40.502600      3.621291   5167.035911  \n",
              "std        1.570960        0.578840       4.628198      1.734447     72.251528  \n",
              "min       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n",
              "25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n",
              "50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n",
              "75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \n",
              "max        1.400000       94.767000     -26.900000      5.045000   5228.100000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d42307b6-e4b1-4bab-a3ce-37f8382274e6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41188.00000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.02406</td>\n",
              "      <td>258.285010</td>\n",
              "      <td>2.567593</td>\n",
              "      <td>962.475454</td>\n",
              "      <td>0.172963</td>\n",
              "      <td>0.081886</td>\n",
              "      <td>93.575664</td>\n",
              "      <td>-40.502600</td>\n",
              "      <td>3.621291</td>\n",
              "      <td>5167.035911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.42125</td>\n",
              "      <td>259.279249</td>\n",
              "      <td>2.770014</td>\n",
              "      <td>186.910907</td>\n",
              "      <td>0.494901</td>\n",
              "      <td>1.570960</td>\n",
              "      <td>0.578840</td>\n",
              "      <td>4.628198</td>\n",
              "      <td>1.734447</td>\n",
              "      <td>72.251528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.400000</td>\n",
              "      <td>92.201000</td>\n",
              "      <td>-50.800000</td>\n",
              "      <td>0.634000</td>\n",
              "      <td>4963.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.00000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.800000</td>\n",
              "      <td>93.075000</td>\n",
              "      <td>-42.700000</td>\n",
              "      <td>1.344000</td>\n",
              "      <td>5099.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.00000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>93.749000</td>\n",
              "      <td>-41.800000</td>\n",
              "      <td>4.857000</td>\n",
              "      <td>5191.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>47.00000</td>\n",
              "      <td>319.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>93.994000</td>\n",
              "      <td>-36.400000</td>\n",
              "      <td>4.961000</td>\n",
              "      <td>5228.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>98.00000</td>\n",
              "      <td>4918.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>94.767000</td>\n",
              "      <td>-26.900000</td>\n",
              "      <td>5.045000</td>\n",
              "      <td>5228.100000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d42307b6-e4b1-4bab-a3ce-37f8382274e6')\"\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",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d42307b6-e4b1-4bab-a3ce-37f8382274e6 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-d42307b6-e4b1-4bab-a3ce-37f8382274e6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Visualization"
      ],
      "metadata": {
        "id": "1NEybxe7iiMH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(bins= 30, figsize=(20, 10))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "PhJ1lLia_ZAK",
        "outputId": "25e068dc-5717-4af7-f19a-805a7a4de864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pair-plot\n",
        "\n",
        "Pair plots are a combination of scatter plots and histograms.\n",
        "\n",
        "They are done for each pair of features (e.g. age vs. job)\n",
        "\n",
        "https://seaborn.pydata.org/generated/seaborn.pairplot.html"
      ],
      "metadata": {
        "id": "NSiJ0Cus7asn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#sns.set_theme(style=\"darkgrid\")\n",
        "#sns.set(rc={'figure.figsize':(30,30)})\n",
        "#sns.pairplot(df,hue=\"y\")\n",
        "#plt.show()"
      ],
      "metadata": {
        "id": "wguv7iLy6gWf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df.drop('pdays',axis=1)"
      ],
      "metadata": {
        "id": "KqdHG-aTIDrD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation\n",
        "\n",
        "Correlations provide a metric to indicate whether two variables are strongly dependent.\n",
        "\n",
        "https://www.statisticssolutions.com/correlation-pearson-kendall-spearman/"
      ],
      "metadata": {
        "id": "16YWe-oRRkPc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr(method='pearson')\n",
        "# a graphical way to viewing the correlation matrix\n",
        "fig, ax = plt.subplots(figsize=(10, 7))\n",
        "sns.heatmap(df.corr(method='pearson'), annot=True, fmt='.2g',\n",
        "            annot_kws=None, linewidths=1, linecolor='black')\n",
        "plt.xticks(fontsize=12)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "5KiNk4HZRjUm",
        "outputId": "3fedce90-36f9-483a-806b-4afd8b597ad1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop one of the features of those highly correlated. Normally with high correlation (>0.8) and almost zero correlation there are issues.\n",
        "\n",
        "features 'emp.var.rate' and 'euribor3m' were selected based on the correlation matrix and kbest feature analysis"
      ],
      "metadata": {
        "id": "Ha4brnMFio6Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "remove_cols = ['emp.var.rate','euribor3m']\n",
        "df2 = df.drop(remove_cols, axis= 1)\n",
        "#euribor3m nr.employed emp.var.rate"
      ],
      "metadata": {
        "id": "PkCAg6HVLiJG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.corr(method='pearson')\n",
        "# a graphical way to viewing the correlation matrix\n",
        "fig, ax = plt.subplots(figsize=(10, 7))\n",
        "sns.heatmap(df2.corr(method='pearson'), annot=True, fmt='.2g',\n",
        "            annot_kws=None, linewidths=1, linecolor='black')\n",
        "plt.xticks(fontsize=12)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "CCjUrcNqMGAJ",
        "outputId": "9c5441ff-1d92-4b46-9730-9d32c0108044"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "marital_percent = pd.crosstab(df['marital'], df['y']).apply(lambda x: x/x.sum() * 100)\n",
        "marital_percent.sort_values(by='yes', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JyMSEolStB7z",
        "outputId": "56a1145c-1d4d-430c-b121-c8583992e3ba"
      },
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              "y                no        yes\n",
              "marital                       \n",
              "married   61.278319  54.568966\n",
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        "contact_percent = pd.crosstab(df['contact'], df['y']).apply(lambda x: x/x.sum() * 100)\n",
        "contact_percent.sort_values(by='yes', ascending=False)"
      ],
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            "text/plain": [
              "y                 no        yes\n",
              "contact                        \n",
              "cellular   60.991026  83.038793\n",
              "telephone  39.008974  16.961207"
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    {
      "cell_type": "code",
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        "df.contact.value_counts()"
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        "id": "QrsFd5ceRfCN",
        "outputId": "f862f4a9-1d6f-4127-90be-86b4bdd1b49e"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cellular     26144\n",
              "telephone    15044\n",
              "Name: contact, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "month_percent = pd.crosstab(df['month'], df['y']).apply(lambda x: x/x.sum() * 100)\n",
        "month_percent.sort_values(by='yes', ascending=False)"
      ],
      "metadata": {
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          "height": 394
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        {
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              "y             no        yes\n",
              "month                      \n",
              "may    35.249535  19.094828\n",
              "aug    15.111634  14.116379\n",
              "jul    17.853234  13.987069\n",
              "jun    13.021232  12.047414\n",
              "apr     5.726716  11.616379\n",
              "nov    10.082631   8.965517\n",
              "oct     1.102660   6.788793\n",
              "mar     0.738755   5.948276\n",
              "sep     0.859144   5.517241\n",
              "dec     0.254460   1.918103"
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "job_percent = pd.crosstab(df['job'], df['y']).apply(lambda x: x/x.sum() * 100)\n",
        "job_percent.sort_values(by='yes', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "KuzQNGXrtBxR",
        "outputId": "8d244a54-f8f0-4b83-8120-9ddb024f28b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "y                     no        yes\n",
              "job                                \n",
              "admin.         24.816679  29.137931\n",
              "technician     16.452337  15.732759\n",
              "blue-collar    23.574477  13.750000\n",
              "retired         3.518660   9.353448\n",
              "management      7.102988   7.068966\n",
              "services        9.975922   6.961207\n",
              "student         1.641677   5.926724\n",
              "self-employed   3.480355   3.211207\n",
              "unemployed      2.380431   3.103448\n",
              "entrepreneur    3.644522   2.672414\n",
              "housemaid       2.610266   2.284483\n",
              "unknown         0.801685   0.797414"
            ],
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              "      <th>self-employed</th>\n",
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              "      <th>unemployed</th>\n",
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              "      <td>3.103448</td>\n",
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              "      <th>entrepreneur</th>\n",
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              "      <td>2.672414</td>\n",
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              "      <th>housemaid</th>\n",
              "      <td>2.610266</td>\n",
              "      <td>2.284483</td>\n",
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              "    <tr>\n",
              "      <th>unknown</th>\n",
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
      "source": [
        "df['job'].replace({'entrepreneur': 'self-employed', 'technician': 'blue-collar',\n",
        "                   'admin.': 'white-collar', 'management': 'white-collar',\n",
        "                  'services': 'pink-collar', 'housemaid': 'pink-collar'}, inplace=True)"
      ],
      "metadata": {
        "id": "939a1XI6KgDe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['job'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLPTzq7vKxes",
        "outputId": "8bd929bf-82e2-4554-a36b-0523235c51d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "blue-collar      15997\n",
              "white-collar     13346\n",
              "pink-collar       5029\n",
              "self-employed     2877\n",
              "retired           1720\n",
              "unemployed        1014\n",
              "student            875\n",
              "unknown            330\n",
              "Name: job, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "education_percent = pd.crosstab(df['education'], df['y']).apply(lambda x: x/x.sum() * 100)\n",
        "education_percent.sort_values(by='yes', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "bcMAJksJtCCe",
        "outputId": "03d1687f-c9ef-4da5-c69b-27b529990586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "y                           no        yes\n",
              "education                                \n",
              "university.degree    28.723870  35.991379\n",
              "high.school          23.213309  22.219828\n",
              "professional.course  12.717522  12.823276\n",
              "basic.9y             15.245704  10.193966\n",
              "basic.4y             10.255007   9.224138\n",
              "unknown               4.049469   5.409483\n",
              "basic.6y              5.756813   4.051724\n",
              "illiterate            0.038306   0.086207"
            ],
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "source": [
        "df['education'].replace({'basic.9y': 'basic','basic.4y': 'basic','basic.6y':'basic'},inplace=True)"
      ],
      "metadata": {
        "id": "YxvBYwPXKrJZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['education'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4x0uXLwKwWz",
        "outputId": "92ff02eb-bd5b-4b6c-be3e-3126326fe1c3"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "basic                  12513\n",
              "university.degree      12168\n",
              "high.school             9515\n",
              "professional.course     5243\n",
              "unknown                 1731\n",
              "illiterate                18\n",
              "Name: education, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.y.value_counts()"
      ],
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        "id": "qGihvUFxDty2",
        "colab": {
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        },
        "outputId": "7eadaa79-9a38-4a77-eb69-69765f964a93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     36548\n",
              "yes     4640\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.y.value_counts()\n",
        "f,ax=plt.subplots(1,1, figsize=(6,4))\n",
        "X.plot.pie(explode=[0,0.1],autopct='%1.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "ZIGVQKL-0HVj",
        "outputId": "37d01e63-4df1-44d7-dec5-086a2a03b128"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Shows impalance data."
      ],
      "metadata": {
        "id": "VRe4vvEv3qWd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cd '/content/Interview/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M1MtYwp3zdjF",
        "outputId": "7f50937d-4577-4775-bba1-c082a2a2c2fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Interview\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git add '/content/Interview/Data'\n",
        "!git commit -m 'Aviva'  # commit in Colab\n",
        "!git push origin main"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPj72VO-6WDq",
        "outputId": "d572ce25-dde5-4ece-a20c-af24fda8da9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "On branch main\n",
            "Your branch is up to date with 'origin/main'.\n",
            "\n",
            "nothing to commit, working tree clean\n",
            "Everything up-to-date\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Prepration"
      ],
      "metadata": {
        "id": "SPUyd7cQ3zXv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder,LabelEncoder, StandardScaler, MinMaxScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import RandomForestClassifier, IsolationForest\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV,cross_val_score\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.decomposition import PCA\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from pyod.models.knn import KNN\n",
        "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "import xgboost as xgb\n",
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "from statsmodels.tsa.vector_ar.var_model import VAR"
      ],
      "metadata": {
        "id": "wxuEG9MM-MhI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_enc = df.copy()"
      ],
      "metadata": {
        "id": "eUziE_mYmcm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_cols = df_enc.select_dtypes(include=['float64', 'int64']).columns"
      ],
      "metadata": {
        "id": "oN1BbAJqBKNy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_enc['y'] = np.where(df_enc.y == 'yes', 1, 0)"
      ],
      "metadata": {
        "id": "kRAAJy6_7wst"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corr = df_enc.corr()['y'].abs().sort_values(ascending=False)\n",
        "cols = corr[corr < 1].index.tolist()\n",
        "sns.set_style('darkgrid')\n",
        "fig , ax = plt.subplots(5,2,figsize = (12,20))\n",
        "ax = ax.ravel()\n",
        "for i,col in enumerate(cols):\n",
        "    sns.boxplot(df_enc[col], ax = ax[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jJf8Mw4-86pH",
        "outputId": "efa576c3-2c76-48c5-ae71-46b47b48a8eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x1440 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_enc.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "LeFmLeZkkPWa",
        "outputId": "553e1ceb-e9fb-4d51-c034-f479e87c5986"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age           job  marital    education  default housing loan    contact  \\\n",
              "0   56   pink-collar  married        basic       no      no   no  telephone   \n",
              "1   57   pink-collar  married  high.school  unknown      no   no  telephone   \n",
              "2   37   pink-collar  married  high.school       no     yes   no  telephone   \n",
              "3   40  white-collar  married        basic       no      no   no  telephone   \n",
              "4   56   pink-collar  married  high.school       no      no  yes  telephone   \n",
              "\n",
              "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
              "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "\n",
              "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
              "0          93.994          -36.4      4.857       5191.0  0  \n",
              "1          93.994          -36.4      4.857       5191.0  0  \n",
              "2          93.994          -36.4      4.857       5191.0  0  \n",
              "3          93.994          -36.4      4.857       5191.0  0  \n",
              "4          93.994          -36.4      4.857       5191.0  0  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d68191d3-07ce-4158-b1c6-3fe9e4207f34\">\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>...</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>pink-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>basic</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>...</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>pink-collar</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>...</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>pink-collar</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>...</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>white-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>basic</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>...</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>pink-collar</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>...</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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d68191d3-07ce-4158-b1c6-3fe9e4207f34')\"\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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              "    .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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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-d68191d3-07ce-4158-b1c6-3fe9e4207f34 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-d68191d3-07ce-4158-b1c6-3fe9e4207f34');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "col_list = df_enc.select_dtypes(include = \"object\").columns\n",
        "print(col_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-O1WtWRbGx1s",
        "outputId": "0a8ef9e5-2eef-40a3-eed1-ee16fb9e9e40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact',\n",
            "       'month', 'day_of_week', 'poutcome'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Encoding"
      ],
      "metadata": {
        "id": "r_e8gNkEJYnQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using OneHotEncoding: pd.get_dummies"
      ],
      "metadata": {
        "id": "yQ4fh-Ce5PF6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_enc= pd.get_dummies(df_enc, columns=col_list)\n",
        "df_enc.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "r15BXDPj4efS",
        "outputId": "5341e90b-eaaa-4a29-d7b5-32e2b08f42b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  duration  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
              "0   56       261         1    999         0           1.1          93.994   \n",
              "1   57       149         1    999         0           1.1          93.994   \n",
              "2   37       226         1    999         0           1.1          93.994   \n",
              "3   40       151         1    999         0           1.1          93.994   \n",
              "4   56       307         1    999         0           1.1          93.994   \n",
              "\n",
              "   cons.conf.idx  euribor3m  nr.employed  ...  month_oct  month_sep  \\\n",
              "0          -36.4      4.857       5191.0  ...          0          0   \n",
              "1          -36.4      4.857       5191.0  ...          0          0   \n",
              "2          -36.4      4.857       5191.0  ...          0          0   \n",
              "3          -36.4      4.857       5191.0  ...          0          0   \n",
              "4          -36.4      4.857       5191.0  ...          0          0   \n",
              "\n",
              "   day_of_week_fri  day_of_week_mon  day_of_week_thu  day_of_week_tue  \\\n",
              "0                0                1                0                0   \n",
              "1                0                1                0                0   \n",
              "2                0                1                0                0   \n",
              "3                0                1                0                0   \n",
              "4                0                1                0                0   \n",
              "\n",
              "   day_of_week_wed  poutcome_failure  poutcome_nonexistent  poutcome_success  \n",
              "0                0                 0                     1                 0  \n",
              "1                0                 0                     1                 0  \n",
              "2                0                 0                     1                 0  \n",
              "3                0                 0                     1                 0  \n",
              "4                0                 0                     1                 0  \n",
              "\n",
              "[5 rows x 58 columns]"
            ],
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              "      <th>day_of_week_wed</th>\n",
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              "          document.querySelector('#df-dd00134a-62f3-466b-8858-31a383af7bb7 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-dd00134a-62f3-466b-8858-31a383af7bb7');\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": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Label Encoding for featur selection\n",
        "The goal is keeping the features with higher correlation with the 'y' feature. Also removing those of highly correlated that were detected in matrix correlation "
      ],
      "metadata": {
        "id": "-exRqkmq5HgP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "for colsn in col_list:\n",
        "    df[colsn] = le.fit_transform(df[colsn])"
      ],
      "metadata": {
        "id": "t5KudClvlEMt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "candidate_features = df.columns.drop(['y'])\n",
        "candidate_features.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4grgDzzcveE",
        "outputId": "68c5973e-b276-40e9-8244-b19ab61a466a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
              "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
              "       'cons.conf.idx', 'euribor3m', 'nr.employed'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kbest = SelectKBest(k=16, score_func=f_classif)\n",
        "kbest.fit(df.loc[:, candidate_features], df.y)\n",
        "kbest.scores_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dw3lbnkvYbYR",
        "outputId": "981f63a7-5776-47f2-a1f3-f1ecf369c39c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.80946593e+01, 1.34624937e+02, 8.81070467e+01, 1.64283651e+02,\n",
              "       4.10590785e+02, 5.49665339e+00, 9.92371254e-01, 8.81707070e+02,\n",
              "       1.51493970e+00, 1.05030959e+01, 8.09410163e+03, 1.82156673e+02,\n",
              "       4.86117327e+03, 2.30425709e+03, 7.05678164e+02, 4.02382993e+03,\n",
              "       7.78589795e+02, 1.24409975e+02, 4.30947905e+03, 5.92661060e+03])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kbestfeatures = pd.DataFrame({'feature':candidate_features.values,'score':kbest.scores_})\n",
        "kbestfeatures.set_index('feature',inplace=True)\n",
        "kbestfeatures.sort_values(by='score', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "b3kEcXHmo-rU",
        "outputId": "533993a5-e934-41b8-f930-93cb7c15759b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      score\n",
              "feature                    \n",
              "duration        8094.101634\n",
              "nr.employed     5926.610601\n",
              "pdays           4861.173272\n",
              "euribor3m       4309.479048\n",
              "emp.var.rate    4023.829925\n",
              "previous        2304.257088\n",
              "contact          881.707070\n",
              "cons.price.idx   778.589795\n",
              "poutcome         705.678164\n",
              "default          410.590785\n",
              "campaign         182.156673\n",
              "education        164.283651\n",
              "job              134.624937\n",
              "cons.conf.idx    124.409975\n",
              "marital           88.107047\n",
              "age               38.094659\n",
              "day_of_week       10.503096\n",
              "housing            5.496653\n",
              "month              1.514940\n",
              "loan               0.992371"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-62389f88-693d-4b6b-873f-e6fe7243e548\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>feature</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>duration</th>\n",
              "      <td>8094.101634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nr.employed</th>\n",
              "      <td>5926.610601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pdays</th>\n",
              "      <td>4861.173272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>euribor3m</th>\n",
              "      <td>4309.479048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp.var.rate</th>\n",
              "      <td>4023.829925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previous</th>\n",
              "      <td>2304.257088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contact</th>\n",
              "      <td>881.707070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cons.price.idx</th>\n",
              "      <td>778.589795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poutcome</th>\n",
              "      <td>705.678164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default</th>\n",
              "      <td>410.590785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>campaign</th>\n",
              "      <td>182.156673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>164.283651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job</th>\n",
              "      <td>134.624937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <td>124.409975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital</th>\n",
              "      <td>88.107047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>38.094659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day_of_week</th>\n",
              "      <td>10.503096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing</th>\n",
              "      <td>5.496653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>1.514940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan</th>\n",
              "      <td>0.992371</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62389f88-693d-4b6b-873f-e6fe7243e548')\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-62389f88-693d-4b6b-873f-e6fe7243e548');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selected features base on the kbest method are:"
      ],
      "metadata": {
        "id": "Uc0MLdrQ60qp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selected_features = candidate_features[kbest.get_support()]\n",
        "selected_features = selected_features.insert(15,'y')\n",
        "selected_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFXRxxLb1MEY",
        "outputId": "93f03f0e-435a-4941-a322-c32a2733275c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'contact', 'duration',\n",
              "       'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate',\n",
              "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'y', 'nr.employed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "candidate_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtAIgxGZLS7J",
        "outputId": "3293cbbb-3ab5-4af9-dd52-70945d2a9078"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
              "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
              "       'cons.conf.idx', 'euribor3m', 'nr.employed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Kc7FIrBBi0D",
        "outputId": "6fdd3c68-70e0-414c-b119-43d286e7a778"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n",
              "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_1 = list(set(candidate_features).difference(selected_features))\n",
        "print(result_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPwA_AgBL3R5",
        "outputId": "0f2f1dbe-3c56-4b0f-9040-6f33a558131c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['loan', 'day_of_week', 'housing', 'month']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##KNN sample selection"
      ],
      "metadata": {
        "id": "BjgnYYCXdW3s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df_enc[cols].values\n",
        "model = KNN(contamination=.1)\n",
        "model.fit(x)\n",
        "predicted = model.predict(x)\n",
        "\n",
        "outliers = df_enc.loc[(predicted == 1),:]\n",
        "inliers = df_enc.loc[(predicted == 0),:]"
      ],
      "metadata": {
        "id": "Je90LfJ19b6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_enc = df_enc.drop(index = df_enc.loc[(predicted == 1),:].index )\n",
        "df_enc.info()"
      ],
      "metadata": {
        "id": "E4pnhEj9_QcV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee4985c0-c708-4700-ecb7-86c9f64e9b88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 37600 entries, 0 to 41180\n",
            "Data columns (total 58 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
            " 0   age                            37600 non-null  int64  \n",
            " 1   duration                       37600 non-null  int64  \n",
            " 2   campaign                       37600 non-null  int64  \n",
            " 3   pdays                          37600 non-null  int64  \n",
            " 4   previous                       37600 non-null  int64  \n",
            " 5   emp.var.rate                   37600 non-null  float64\n",
            " 6   cons.price.idx                 37600 non-null  float64\n",
            " 7   cons.conf.idx                  37600 non-null  float64\n",
            " 8   euribor3m                      37600 non-null  float64\n",
            " 9   nr.employed                    37600 non-null  float64\n",
            " 10  y                              37600 non-null  int64  \n",
            " 11  job_blue-collar                37600 non-null  uint8  \n",
            " 12  job_pink-collar                37600 non-null  uint8  \n",
            " 13  job_retired                    37600 non-null  uint8  \n",
            " 14  job_self-employed              37600 non-null  uint8  \n",
            " 15  job_student                    37600 non-null  uint8  \n",
            " 16  job_unemployed                 37600 non-null  uint8  \n",
            " 17  job_unknown                    37600 non-null  uint8  \n",
            " 18  job_white-collar               37600 non-null  uint8  \n",
            " 19  marital_divorced               37600 non-null  uint8  \n",
            " 20  marital_married                37600 non-null  uint8  \n",
            " 21  marital_single                 37600 non-null  uint8  \n",
            " 22  marital_unknown                37600 non-null  uint8  \n",
            " 23  education_basic                37600 non-null  uint8  \n",
            " 24  education_high.school          37600 non-null  uint8  \n",
            " 25  education_illiterate           37600 non-null  uint8  \n",
            " 26  education_professional.course  37600 non-null  uint8  \n",
            " 27  education_university.degree    37600 non-null  uint8  \n",
            " 28  education_unknown              37600 non-null  uint8  \n",
            " 29  default_no                     37600 non-null  uint8  \n",
            " 30  default_unknown                37600 non-null  uint8  \n",
            " 31  default_yes                    37600 non-null  uint8  \n",
            " 32  housing_no                     37600 non-null  uint8  \n",
            " 33  housing_unknown                37600 non-null  uint8  \n",
            " 34  housing_yes                    37600 non-null  uint8  \n",
            " 35  loan_no                        37600 non-null  uint8  \n",
            " 36  loan_unknown                   37600 non-null  uint8  \n",
            " 37  loan_yes                       37600 non-null  uint8  \n",
            " 38  contact_cellular               37600 non-null  uint8  \n",
            " 39  contact_telephone              37600 non-null  uint8  \n",
            " 40  month_apr                      37600 non-null  uint8  \n",
            " 41  month_aug                      37600 non-null  uint8  \n",
            " 42  month_dec                      37600 non-null  uint8  \n",
            " 43  month_jul                      37600 non-null  uint8  \n",
            " 44  month_jun                      37600 non-null  uint8  \n",
            " 45  month_mar                      37600 non-null  uint8  \n",
            " 46  month_may                      37600 non-null  uint8  \n",
            " 47  month_nov                      37600 non-null  uint8  \n",
            " 48  month_oct                      37600 non-null  uint8  \n",
            " 49  month_sep                      37600 non-null  uint8  \n",
            " 50  day_of_week_fri                37600 non-null  uint8  \n",
            " 51  day_of_week_mon                37600 non-null  uint8  \n",
            " 52  day_of_week_thu                37600 non-null  uint8  \n",
            " 53  day_of_week_tue                37600 non-null  uint8  \n",
            " 54  day_of_week_wed                37600 non-null  uint8  \n",
            " 55  poutcome_failure               37600 non-null  uint8  \n",
            " 56  poutcome_nonexistent           37600 non-null  uint8  \n",
            " 57  poutcome_success               37600 non-null  uint8  \n",
            "dtypes: float64(5), int64(6), uint8(47)\n",
            "memory usage: 5.1 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#standard Scaler for Numerical Variables\n",
        "# normalize data\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler,Normalizer\n",
        "rr = RobustScaler()\n",
        "ss = StandardScaler()\n",
        "mm = MinMaxScaler()\n",
        "nn = Normalizer()\n",
        "\n",
        "scaler = ss\n",
        "\n",
        "\n",
        "df_enc[numerical_cols] = scaler.fit_transform(df_enc[numerical_cols])"
      ],
      "metadata": {
        "id": "G97HjR1z3MAA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_enc.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "8Vin6j2I53F9",
        "outputId": "8e276cb3-8293-4342-960c-817799326e68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  duration  campaign     pdays  previous  emp.var.rate  \\\n",
              "0  1.734565  0.165454 -0.602919  0.076012 -0.315927      0.579505   \n",
              "1  1.839917 -0.407251 -0.602919  0.076012 -0.315927      0.579505   \n",
              "2 -0.267123 -0.013516 -0.602919  0.076012 -0.315927      0.579505   \n",
              "3  0.048933 -0.397024 -0.602919  0.076012 -0.315927      0.579505   \n",
              "4  1.734565  0.400672 -0.602919  0.076012 -0.315927      0.579505   \n",
              "\n",
              "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  ...  month_oct  \\\n",
              "0        0.728214       0.966856   0.642695     0.224755  ...          0   \n",
              "1        0.728214       0.966856   0.642695     0.224755  ...          0   \n",
              "2        0.728214       0.966856   0.642695     0.224755  ...          0   \n",
              "3        0.728214       0.966856   0.642695     0.224755  ...          0   \n",
              "4        0.728214       0.966856   0.642695     0.224755  ...          0   \n",
              "\n",
              "   month_sep  day_of_week_fri  day_of_week_mon  day_of_week_thu  \\\n",
              "0          0                0                1                0   \n",
              "1          0                0                1                0   \n",
              "2          0                0                1                0   \n",
              "3          0                0                1                0   \n",
              "4          0                0                1                0   \n",
              "\n",
              "   day_of_week_tue  day_of_week_wed  poutcome_failure  poutcome_nonexistent  \\\n",
              "0                0                0                 0                     1   \n",
              "1                0                0                 0                     1   \n",
              "2                0                0                 0                     1   \n",
              "3                0                0                 0                     1   \n",
              "4                0                0                 0                     1   \n",
              "\n",
              "   poutcome_success  \n",
              "0                 0  \n",
              "1                 0  \n",
              "2                 0  \n",
              "3                 0  \n",
              "4                 0  \n",
              "\n",
              "[5 rows x 58 columns]"
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              "        buttonEl.style.display =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_enc.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tpK8TDCsAxo4",
        "outputId": "a7b96698-167e-454a-9fb2-a71c3272e36f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n",
              "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y',\n",
              "       'job_blue-collar', 'job_pink-collar', 'job_retired',\n",
              "       'job_self-employed', 'job_student', 'job_unemployed', 'job_unknown',\n",
              "       'job_white-collar', 'marital_divorced', 'marital_married',\n",
              "       'marital_single', 'marital_unknown', 'education_basic',\n",
              "       'education_high.school', 'education_illiterate',\n",
              "       'education_professional.course', 'education_university.degree',\n",
              "       'education_unknown', 'default_no', 'default_unknown', 'default_yes',\n",
              "       'housing_no', 'housing_unknown', 'housing_yes', 'loan_no',\n",
              "       'loan_unknown', 'loan_yes', 'contact_cellular', 'contact_telephone',\n",
              "       'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun',\n",
              "       'month_mar', 'month_may', 'month_nov', 'month_oct', 'month_sep',\n",
              "       'day_of_week_fri', 'day_of_week_mon', 'day_of_week_thu',\n",
              "       'day_of_week_tue', 'day_of_week_wed', 'poutcome_failure',\n",
              "       'poutcome_nonexistent', 'poutcome_success'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##DataSet"
      ],
      "metadata": {
        "id": "meqrjwQnTd5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_enc.drop(columns=['y'])\n",
        "y = df_enc['y']\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RF8IOrgFTQZS",
        "outputId": "4751b524-7447-4b55-e37e-02c679d0dc1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(37600, 57)\n",
            "(37600,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XtA_JfttTIPI",
        "outputId": "814914fc-b2d4-4c43-be9d-43c9c8feba55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    34895\n",
              "1     2705\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Resampling"
      ],
      "metadata": {
        "id": "1uaONZ-WVKua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sampler = RandomOverSampler(random_state=42)\n",
        "X_sampled, y_sampled = sampler.fit_resample(X, y)\n",
        "pd.Series(y_sampled).value_counts().to_frame()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "vVIfCD8aTpvU",
        "outputId": "71acd9a3-f091-41be-a907-3dfb20c61cf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       y\n",
              "0  34895\n",
              "1  34895"
            ],
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              "              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-8400d9ba-26b8-4586-a174-8a296e831004 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-8400d9ba-26b8-4586-a174-8a296e831004');\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": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_samp_p = Output + 'X_sampled.csv'\n",
        "y_samp_p= Output + 'y_sampled.csv'\n",
        "X_sampled.to_csv(X_samp_p, index=False)\n",
        "y_sampled.to_csv(y_samp_p, index=False)"
      ],
      "metadata": {
        "id": "1WqQY3I0YH0d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Principal Component Analysis"
      ],
      "metadata": {
        "id": "NwimVmQzVBW6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Highly correlated with other features\n",
        "remove_cols1 = ['emp.var.rate','euribor3m']\n",
        "#Low correlation with the 'y' label\n",
        "remove_cols2 =['day_of_week', 'month', 'loan', 'housing']\n",
        "\n",
        "rm_cols =['emp.var.rate','euribor3m','housing_no', 'housing_unknown', 'housing_yes', \n",
        "          'loan_no','loan_unknown', 'loan_yes','month_apr', 'month_aug', 'month_dec',\n",
        "          'month_jul', 'month_jun','month_mar', 'month_may', 'month_nov', 'month_oct', \n",
        "          'month_sep','day_of_week_fri', 'day_of_week_mon', 'day_of_week_thu',\n",
        "          'day_of_week_tue', 'day_of_week_wed']"
      ],
      "metadata": {
        "id": "oqpJ6SsCv8TZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_rm = df_enc.drop(rm_cols,axis=1)\n",
        "df_rm.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18z_rOR3wXTR",
        "outputId": "e156e751-6d4a-4b06-9da1-9d559fde4d28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 37600 entries, 0 to 41180\n",
            "Data columns (total 35 columns):\n",
            " #   Column                         Non-Null Count  Dtype  \n",
            "---  ------                         --------------  -----  \n",
            " 0   age                            37600 non-null  float64\n",
            " 1   duration                       37600 non-null  float64\n",
            " 2   campaign                       37600 non-null  float64\n",
            " 3   pdays                          37600 non-null  float64\n",
            " 4   previous                       37600 non-null  float64\n",
            " 5   cons.price.idx                 37600 non-null  float64\n",
            " 6   cons.conf.idx                  37600 non-null  float64\n",
            " 7   nr.employed                    37600 non-null  float64\n",
            " 8   y                              37600 non-null  int64  \n",
            " 9   job_blue-collar                37600 non-null  uint8  \n",
            " 10  job_pink-collar                37600 non-null  uint8  \n",
            " 11  job_retired                    37600 non-null  uint8  \n",
            " 12  job_self-employed              37600 non-null  uint8  \n",
            " 13  job_student                    37600 non-null  uint8  \n",
            " 14  job_unemployed                 37600 non-null  uint8  \n",
            " 15  job_unknown                    37600 non-null  uint8  \n",
            " 16  job_white-collar               37600 non-null  uint8  \n",
            " 17  marital_divorced               37600 non-null  uint8  \n",
            " 18  marital_married                37600 non-null  uint8  \n",
            " 19  marital_single                 37600 non-null  uint8  \n",
            " 20  marital_unknown                37600 non-null  uint8  \n",
            " 21  education_basic                37600 non-null  uint8  \n",
            " 22  education_high.school          37600 non-null  uint8  \n",
            " 23  education_illiterate           37600 non-null  uint8  \n",
            " 24  education_professional.course  37600 non-null  uint8  \n",
            " 25  education_university.degree    37600 non-null  uint8  \n",
            " 26  education_unknown              37600 non-null  uint8  \n",
            " 27  default_no                     37600 non-null  uint8  \n",
            " 28  default_unknown                37600 non-null  uint8  \n",
            " 29  default_yes                    37600 non-null  uint8  \n",
            " 30  contact_cellular               37600 non-null  uint8  \n",
            " 31  contact_telephone              37600 non-null  uint8  \n",
            " 32  poutcome_failure               37600 non-null  uint8  \n",
            " 33  poutcome_nonexistent           37600 non-null  uint8  \n",
            " 34  poutcome_success               37600 non-null  uint8  \n",
            "dtypes: float64(8), int64(1), uint8(26)\n",
            "memory usage: 3.8 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components = 10)\n",
        "pca.fit(X_sampled)\n",
        "X = pca.transform(X_sampled)\n",
        "print(X_sampled.shape, y_sampled.shape,X.shape)"
      ],
      "metadata": {
        "id": "zjxqxFDDU5xQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38cc0312-95c5-4d66-9f4a-b2c523f46ec6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(69790, 57) (69790,) (69790, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_y = pd.DataFrame(data = y_sampled, columns = ['y'])\n",
        "df_X = pd.DataFrame(data = X, columns = ['PC_1', 'PC_2','PC_3', 'PC_4','PC_5','PC_6', 'PC_7','PC_8', 'PC_9','PC_10'])"
      ],
      "metadata": {
        "id": "lTNn2j6ZVSF3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WOQAA1VZZNpb",
        "outputId": "c3e6b333-c794-473f-ef90-8daabb7c872e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PC_1      PC_2      PC_3      PC_4      PC_5      PC_6      PC_7  \\\n",
              "0 -1.585724  1.206304 -1.281867  1.243291  1.052113 -0.130384 -0.911600   \n",
              "1 -1.562223  1.226528 -1.831567  1.083381  1.139825 -0.130978 -0.934458   \n",
              "2 -1.452580  1.103869 -1.062771  0.242538 -0.596542  0.176359 -0.838436   \n",
              "3 -1.429701  1.122022 -1.487277  0.305936 -0.313612  0.151828 -0.885753   \n",
              "4 -1.597920  1.207887 -1.046916  1.285781  0.910081 -0.120395 -0.837654   \n",
              "\n",
              "       PC_8      PC_9     PC_10  \n",
              "0  0.967854  0.090649 -0.568793  \n",
              "1  0.947986  0.240112 -0.550584  \n",
              "2  0.581073  0.059275  0.846881  \n",
              "3  0.761757  0.246368 -0.600256  \n",
              "4  0.869661  0.488248 -0.454807  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6867cc60-72b3-4992-9664-ff58899a8505\">\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",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PC_1</th>\n",
              "      <th>PC_2</th>\n",
              "      <th>PC_3</th>\n",
              "      <th>PC_4</th>\n",
              "      <th>PC_5</th>\n",
              "      <th>PC_6</th>\n",
              "      <th>PC_7</th>\n",
              "      <th>PC_8</th>\n",
              "      <th>PC_9</th>\n",
              "      <th>PC_10</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.585724</td>\n",
              "      <td>1.206304</td>\n",
              "      <td>-1.281867</td>\n",
              "      <td>1.243291</td>\n",
              "      <td>1.052113</td>\n",
              "      <td>-0.130384</td>\n",
              "      <td>-0.911600</td>\n",
              "      <td>0.967854</td>\n",
              "      <td>0.090649</td>\n",
              "      <td>-0.568793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.562223</td>\n",
              "      <td>1.226528</td>\n",
              "      <td>-1.831567</td>\n",
              "      <td>1.083381</td>\n",
              "      <td>1.139825</td>\n",
              "      <td>-0.130978</td>\n",
              "      <td>-0.934458</td>\n",
              "      <td>0.947986</td>\n",
              "      <td>0.240112</td>\n",
              "      <td>-0.550584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.452580</td>\n",
              "      <td>1.103869</td>\n",
              "      <td>-1.062771</td>\n",
              "      <td>0.242538</td>\n",
              "      <td>-0.596542</td>\n",
              "      <td>0.176359</td>\n",
              "      <td>-0.838436</td>\n",
              "      <td>0.581073</td>\n",
              "      <td>0.059275</td>\n",
              "      <td>0.846881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.429701</td>\n",
              "      <td>1.122022</td>\n",
              "      <td>-1.487277</td>\n",
              "      <td>0.305936</td>\n",
              "      <td>-0.313612</td>\n",
              "      <td>0.151828</td>\n",
              "      <td>-0.885753</td>\n",
              "      <td>0.761757</td>\n",
              "      <td>0.246368</td>\n",
              "      <td>-0.600256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.597920</td>\n",
              "      <td>1.207887</td>\n",
              "      <td>-1.046916</td>\n",
              "      <td>1.285781</td>\n",
              "      <td>0.910081</td>\n",
              "      <td>-0.120395</td>\n",
              "      <td>-0.837654</td>\n",
              "      <td>0.869661</td>\n",
              "      <td>0.488248</td>\n",
              "      <td>-0.454807</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6867cc60-72b3-4992-9664-ff58899a8505')\"\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-6867cc60-72b3-4992-9664-ff58899a8505 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-6867cc60-72b3-4992-9664-ff58899a8505');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lh7EhkpLZSOI",
        "outputId": "5d9551b1-2e3b-4e62-ed9c-2564295cb235"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "y\n",
              "0    34895\n",
              "1    34895\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!git pull '/content/Interview/'\n",
        "!git add '/content/Interview/Model'"
      ],
      "metadata": {
        "id": "t9eOcVaIY_Pp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path_y = Output + 'df_y.csv'\n",
        "path_X = Output + 'df_X.csv'\n",
        "df_y.to_csv(path_y, index=False)\n",
        "df_X.to_csv(path_X, index=False)"
      ],
      "metadata": {
        "id": "xnkufzUHVkuN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_y.to_csv('/content/df_y.csv', index=False)\n",
        "df_X.to_csv('/content/df_X.csv', index=False)"
      ],
      "metadata": {
        "id": "yuyxyTsKaYDy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cd '/content/Interview/'"
      ],
      "metadata": {
        "id": "PApjUwZIXiig"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git commit -m 'Aviva'  # commit in Colab\n",
        "!git push '/content/Interview/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQvathnlWDXn",
        "outputId": "487d264a-8f57-4a47-d813-8a12b1e630f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[main 96f2904] Aviva\n",
            " 2 files changed, 139582 insertions(+), 139582 deletions(-)\n",
            " rewrite Model/X_sampled.csv (67%)\n",
            " rewrite Model/df_X.csv (68%)\n",
            "Everything up-to-date\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git push"
      ],
      "metadata": {
        "id": "qONisf7dXrlv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e99738e-696b-4aad-9b72-7ece64a18209"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counting objects: 5, done.\n",
            "Delta compression using up to 2 threads.\n",
            "Compressing objects: 100% (5/5), done.\n",
            "Writing objects: 100% (5/5), 7.06 MiB | 1.90 MiB/s, done.\n",
            "Total 5 (delta 3), reused 0 (delta 0)\n",
            "remote: Resolving deltas: 100% (3/3), completed with 3 local objects.\u001b[K\n",
            "To https://github.com/Sahel-Eskandar/Interview.git\n",
            "   e4d9892..96f2904  main -> main\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BURAUCs-Wy5G"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}