Practical_Application_III_TB_1_Intro_and_EDA_Final.ipynb

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        "<a href=\"https://colab.research.google.com/github/ttb-git/Berkeley-Engr-PracticalAssignment3/blob/main/Practical_Application_III_TB_1_Intro_and_EDA_Final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
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        "id": "w_cVPvrzJ2_f"
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      "source": [
        "# Practical Application III: Comparing Classifiers\n",
        "\n",
        "**Overview**: In this practical application, your goal is to compare the performance of the classifiers we encountered in this section, namely K Nearest Neighbor, Logistic Regression, Decision Trees, and Support Vector Machines.  We will utilize a dataset related to marketing bank products over the telephone.  \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLY8yYE0J2_h"
      },
      "source": [
        "### Getting Started\n",
        "\n",
        "Our dataset comes from the UCI Machine Learning repository [link](https://archive.ics.uci.edu/ml/datasets/bank+marketing).  The data is from a Portugese banking institution and is a collection of the results of multiple marketing campaigns.  We will make use of the article accompanying the dataset [here](CRISP-DM-BANK.pdf) for more information on the data and features.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1P71AbUqJ2_i"
      },
      "source": [
        "### Problem 1: Understanding the Data\n",
        "\n",
        "To gain a better understanding of the data, please read the information provided in the UCI link above, and examine the **Materials and Methods** section of the paper.  How many marketing campaigns does this data represent?"
      ]
    },
    {
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      "execution_count": 3,
      "metadata": {
        "id": "EX3w9WKSJ2_j"
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      "source": [
        "# Importing modules and libraries\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pickle\n",
        "import sklearn.manifold\n",
        "\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-_Ub18XJ2_j"
      },
      "source": [
        "### Problem 2: Read in the Data\n",
        "\n",
        "Use pandas to read in the dataset `bank-additional-full.csv` and assign to a meaningful variable name."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-uVoplZRamK",
        "outputId": "b9886448-1dc7-4ffc-aad4-2cf26d8ed483"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "VVkJFMV5J2_k"
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      "outputs": [],
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/Berkeley-Engineering-Projects/module_17_starter/data/bank-additional-full.csv', sep = ';')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6vbNo22J2_l"
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      "source": [
        "### Problem 3: Understanding the Features\n",
        "\n",
        "\n",
        "Examine the data description below, and determine if any of the features are missing values or need to be coerced to a different data type.\n",
        "\n",
        "\n",
        "```\n",
        "Input variables:\n",
        "# bank client data:\n",
        "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')\n",
        "# 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'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.\n",
        "# other attributes:\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')\n",
        "# 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)\n",
        "\n",
        "Output variable (desired target):\n",
        "21 - y - has the client subscribed a term deposit? (binary: 'yes','no')\n",
        "```\n",
        "\n"
      ]
    },
    {
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        "outputId": "6d7299de-a78c-43a2-df9f-30035491f62b"
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      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "1     17642\n",
            "2     10570\n",
            "3      5341\n",
            "4      2651\n",
            "5      1599\n",
            "6       979\n",
            "7       629\n",
            "8       400\n",
            "9       283\n",
            "10      225\n",
            "11      177\n",
            "12      125\n",
            "13       92\n",
            "14       69\n",
            "17       58\n",
            "16       51\n",
            "15       51\n",
            "18       33\n",
            "20       30\n",
            "19       26\n",
            "21       24\n",
            "22       17\n",
            "23       16\n",
            "24       15\n",
            "27       11\n",
            "29       10\n",
            "28        8\n",
            "26        8\n",
            "25        8\n",
            "31        7\n",
            "30        7\n",
            "35        5\n",
            "32        4\n",
            "33        4\n",
            "34        3\n",
            "42        2\n",
            "40        2\n",
            "43        2\n",
            "56        1\n",
            "39        1\n",
            "41        1\n",
            "37        1\n",
            "Name: campaign, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(data[\"campaign\"].value_counts())"
      ]
    },
    {
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      "source": [
        "data.head()"
      ],
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      "outputs": [
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          "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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              "    <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",
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              "      <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",
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              "      <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",
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              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>housemaid</td>\n",
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              "      <td>no</td>\n",
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              "      <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>high.school</td>\n",
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              "      <td>mon</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
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              "      <td>93.994</td>\n",
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              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
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              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.6y</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
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              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
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              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
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        "data.tail()"
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      "execution_count": 8,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age          job  marital            education default housing loan  \\\n",
              "41183   73      retired  married  professional.course      no     yes   no   \n",
              "41184   46  blue-collar  married  professional.course      no      no   no   \n",
              "41185   56      retired  married    university.degree      no     yes   no   \n",
              "41186   44   technician  married  professional.course      no      no   no   \n",
              "41187   74      retired  married  professional.course      no     yes   no   \n",
              "\n",
              "        contact month day_of_week  ...  campaign  pdays  previous  \\\n",
              "41183  cellular   nov         fri  ...         1    999         0   \n",
              "41184  cellular   nov         fri  ...         1    999         0   \n",
              "41185  cellular   nov         fri  ...         2    999         0   \n",
              "41186  cellular   nov         fri  ...         1    999         0   \n",
              "41187  cellular   nov         fri  ...         3    999         1   \n",
              "\n",
              "          poutcome emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
              "41183  nonexistent         -1.1          94.767          -50.8      1.028   \n",
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              "                      0            1            2            3      \\\n",
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              "    <tr>\n",
              "      <th>job</th>\n",
              "      <td>housemaid</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>admin.</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>admin.</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>technician</td>\n",
              "      <td>services</td>\n",
              "      <td>...</td>\n",
              "      <td>retired</td>\n",
              "      <td>retired</td>\n",
              "      <td>admin.</td>\n",
              "      <td>admin.</td>\n",
              "      <td>unemployed</td>\n",
              "      <td>retired</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>retired</td>\n",
              "      <td>technician</td>\n",
              "      <td>retired</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital</th>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>single</td>\n",
              "      <td>single</td>\n",
              "      <td>...</td>\n",
              "      <td>married</td>\n",
              "      <td>divorced</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>single</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "      <td>married</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <td>basic.4y</td>\n",
              "      <td>high.school</td>\n",
              "      <td>high.school</td>\n",
              "      <td>basic.6y</td>\n",
              "      <td>high.school</td>\n",
              "      <td>basic.9y</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>unknown</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>high.school</td>\n",
              "      <td>...</td>\n",
              "      <td>university.degree</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>university.degree</td>\n",
              "      <td>university.degree</td>\n",
              "      <td>basic.4y</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>university.degree</td>\n",
              "      <td>professional.course</td>\n",
              "      <td>professional.course</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>default</th>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>...</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>...</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>...</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contact</th>\n",
              "      <td>telephone</td>\n",
              "      <td>telephone</td>\n",
              "      <td>telephone</td>\n",
              "      <td>telephone</td>\n",
              "      <td>telephone</td>\n",
              "      <td>telephone</td>\n",
              "      <td>telephone</td>\n",
              "      <td>telephone</td>\n",
              "      <td>telephone</td>\n",
              "      <td>telephone</td>\n",
              "      <td>...</td>\n",
              "      <td>cellular</td>\n",
              "      <td>cellular</td>\n",
              "      <td>cellular</td>\n",
              "      <td>cellular</td>\n",
              "      <td>cellular</td>\n",
              "      <td>cellular</td>\n",
              "      <td>cellular</td>\n",
              "      <td>cellular</td>\n",
              "      <td>cellular</td>\n",
              "      <td>cellular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>may</td>\n",
              "      <td>may</td>\n",
              "      <td>may</td>\n",
              "      <td>may</td>\n",
              "      <td>may</td>\n",
              "      <td>may</td>\n",
              "      <td>may</td>\n",
              "      <td>may</td>\n",
              "      <td>may</td>\n",
              "      <td>may</td>\n",
              "      <td>...</td>\n",
              "      <td>nov</td>\n",
              "      <td>nov</td>\n",
              "      <td>nov</td>\n",
              "      <td>nov</td>\n",
              "      <td>nov</td>\n",
              "      <td>nov</td>\n",
              "      <td>nov</td>\n",
              "      <td>nov</td>\n",
              "      <td>nov</td>\n",
              "      <td>nov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day_of_week</th>\n",
              "      <td>mon</td>\n",
              "      <td>mon</td>\n",
              "      <td>mon</td>\n",
              "      <td>mon</td>\n",
              "      <td>mon</td>\n",
              "      <td>mon</td>\n",
              "      <td>mon</td>\n",
              "      <td>mon</td>\n",
              "      <td>mon</td>\n",
              "      <td>mon</td>\n",
              "      <td>...</td>\n",
              "      <td>thu</td>\n",
              "      <td>fri</td>\n",
              "      <td>fri</td>\n",
              "      <td>fri</td>\n",
              "      <td>fri</td>\n",
              "      <td>fri</td>\n",
              "      <td>fri</td>\n",
              "      <td>fri</td>\n",
              "      <td>fri</td>\n",
              "      <td>fri</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration</th>\n",
              "      <td>261</td>\n",
              "      <td>149</td>\n",
              "      <td>226</td>\n",
              "      <td>151</td>\n",
              "      <td>307</td>\n",
              "      <td>198</td>\n",
              "      <td>139</td>\n",
              "      <td>217</td>\n",
              "      <td>380</td>\n",
              "      <td>50</td>\n",
              "      <td>...</td>\n",
              "      <td>483</td>\n",
              "      <td>151</td>\n",
              "      <td>254</td>\n",
              "      <td>281</td>\n",
              "      <td>112</td>\n",
              "      <td>334</td>\n",
              "      <td>383</td>\n",
              "      <td>189</td>\n",
              "      <td>442</td>\n",
              "      <td>239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>campaign</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pdays</th>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>9</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "      <td>999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previous</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poutcome</th>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>...</td>\n",
              "      <td>success</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>success</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>failure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp.var.rate</th>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cons.price.idx</th>\n",
              "      <td>93.994</td>\n",
              "      <td>93.994</td>\n",
              "      <td>93.994</td>\n",
              "      <td>93.994</td>\n",
              "      <td>93.994</td>\n",
              "      <td>93.994</td>\n",
              "      <td>93.994</td>\n",
              "      <td>93.994</td>\n",
              "      <td>93.994</td>\n",
              "      <td>93.994</td>\n",
              "      <td>...</td>\n",
              "      <td>94.767</td>\n",
              "      <td>94.767</td>\n",
              "      <td>94.767</td>\n",
              "      <td>94.767</td>\n",
              "      <td>94.767</td>\n",
              "      <td>94.767</td>\n",
              "      <td>94.767</td>\n",
              "      <td>94.767</td>\n",
              "      <td>94.767</td>\n",
              "      <td>94.767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <td>-36.4</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>...</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>-50.8</td>\n",
              "      <td>-50.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>euribor3m</th>\n",
              "      <td>4.857</td>\n",
              "      <td>4.857</td>\n",
              "      <td>4.857</td>\n",
              "      <td>4.857</td>\n",
              "      <td>4.857</td>\n",
              "      <td>4.857</td>\n",
              "      <td>4.857</td>\n",
              "      <td>4.857</td>\n",
              "      <td>4.857</td>\n",
              "      <td>4.857</td>\n",
              "      <td>...</td>\n",
              "      <td>1.031</td>\n",
              "      <td>1.028</td>\n",
              "      <td>1.028</td>\n",
              "      <td>1.028</td>\n",
              "      <td>1.028</td>\n",
              "      <td>1.028</td>\n",
              "      <td>1.028</td>\n",
              "      <td>1.028</td>\n",
              "      <td>1.028</td>\n",
              "      <td>1.028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nr.employed</th>\n",
              "      <td>5191.0</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>4963.6</td>\n",
              "      <td>4963.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>...</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21 rows × 41188 columns</p>\n",
              "</div>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-998fb4d5-a1b2-406d-bd0c-99e1483dff88');\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",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "XQQSgOfjJ2_m",
        "outputId": "a9a35fe3-cfc9-4927-981b-955f04fa5e82"
      },
      "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-bb36d754-27dd-4ccb-9128-3be5c3931b2c\">\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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              "        vertical-align: top;\n",
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              "\n",
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              "        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>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-bb36d754-27dd-4ccb-9128-3be5c3931b2c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-bb36d754-27dd-4ccb-9128-3be5c3931b2c 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-bb36d754-27dd-4ccb-9128-3be5c3931b2c');\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": 10
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4LzN6cLJ2_m",
        "outputId": "161cb0d5-3d36-4117-d9ad-10ae5b0a33ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5LiChlIJ2_m"
      },
      "source": [
        "### Problem 4: Understanding the Task\n",
        "\n",
        "After examining the description and data, your goal now is to clearly state the *Business Objective* of the task.  State the objective below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-oI-XYZJ2_n",
        "outputId": "cd1a7eaa-b2fc-47f1-f0eb-2bb98e69a3ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "no     36548\n",
            "yes     4640\n",
            "Name: y, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(data[\"y\"].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####From the above distribution we can be sure that the data is imbalanced, as the number of \"no\"s are also 8 times the number of \"yes\""
      ],
      "metadata": {
        "id": "knie9EInkfHT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNcK6FT3J2_n",
        "outputId": "f27780dc-ea62-4be8-991e-b6c9fa4cbb8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "admin.           10422\n",
            "blue-collar       9254\n",
            "technician        6743\n",
            "services          3969\n",
            "management        2924\n",
            "retired           1720\n",
            "entrepreneur      1456\n",
            "self-employed     1421\n",
            "housemaid         1060\n",
            "unemployed        1014\n",
            "student            875\n",
            "unknown            330\n",
            "Name: job, dtype: int64\n",
            "*************************\n",
            "married     24928\n",
            "single      11568\n",
            "divorced     4612\n",
            "unknown        80\n",
            "Name: marital, dtype: int64\n",
            "*************************\n",
            "university.degree      12168\n",
            "high.school             9515\n",
            "basic.9y                6045\n",
            "professional.course     5243\n",
            "basic.4y                4176\n",
            "basic.6y                2292\n",
            "unknown                 1731\n",
            "illiterate                18\n",
            "Name: education, dtype: int64\n"
          ]
        }
      ],
      "source": [
        " print(data[\"job\"].value_counts())\n",
        " print(\"*\"*25)\n",
        " print(data[\"marital\"].value_counts())\n",
        " print(\"*\"*25)\n",
        " print(data[\"education\"].value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#EDA (Exploratory Data Analysis)\n",
        "* Next, Let's dig deeper into the data by performing EDA"
      ],
      "metadata": {
        "id": "1KDrez58ln_E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# I am going to look at the distribution of class variables\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "Y = data[\"y\"]\n",
        "total = len(Y)*1.\n",
        "ax=sns.countplot(x=\"y\", data=data)\n",
        "for p in ax.patches:\n",
        "  ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x()+0.1, p.get_height()+5))\n",
        "\n",
        "#puting 11 ticks (therefore 10 steps), from 0 to the total number of rows in the dataframe\n",
        "ax.yaxis.set_ticks(np.linspace(0, total, 11))\n",
        "\n",
        "#adjusting the ticklabel to the desired format, without changing the position of the ticks. ax.set_yticklabels(map('{:.1f}%'.format, 100*ax.yaxis.get_majorticklocs()/total)) ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
        "ax.legend(labels=[\"no\",\"yes\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "I32yh4xvmTBC",
        "outputId": "29ed3a88-53a1-465f-998b-69b371bf1a8e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Univariate Analysis\n",
        "\n",
        "def countplot(label, dataset):\n",
        "  plt.figure(figsize=(15,10))\n",
        "  Y = data[label]\n",
        "  total = len(Y)*1.\n",
        "  ax=sns.countplot(x=label, data=dataset)\n",
        "  for p in ax.patches:\n",
        "    ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x()+0.1, p.get_height()+5))\n",
        "\n",
        "#I am going to put 11 ticks (therefore 10 steps), from 0 to the total number of rows in the dataframe\n",
        "ax.yaxis.set_ticks(np.linspace(0, total, 11))\n",
        "\n",
        "#I am going to adjust the ticklabel to the desired format, without changing the position of the ticks. ax.set_yticklabels(map('{:.1f}%'.format, 100*ax.yaxis.get_majorticklocs()/total)) ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
        "ax.legend(labels=[\"no\",\"yes\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "K85uS28inHxp"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "def countplot_withY(label, dataset):\n",
        "  plt.figure(figsize=(22,15))\n",
        "  Y = data[label]\n",
        "  total = len(Y)*1.\n",
        "  ax=sns.countplot(x=label, data=dataset, hue=\"y\")\n",
        "  for p in ax.patches:\n",
        "    ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x()+0.1, p.get_height()+5))\n",
        "\n",
        "#puting 11 ticks (therefore 10 steps), from 0 to the total number of rows in the dataframe\n",
        "ax.yaxis.set_ticks(np.linspace(0, total, 11))\n",
        "\n",
        "#adjusting the ticklabel to the desired format, without changing the position of the ticks. ax.set_yticklabels(map('{:.1f}%'.format, 100*ax.yaxis.get_majorticklocs()/total)) ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
        "ax.legend(labels=[\"no\",\"yes\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "rgknthQQnH3h"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = Job (Categorical variable)"
      ],
      "metadata": {
        "id": "CLJdFvuApRrW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"job\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "8ewmwbrmnH9n",
        "outputId": "1683ef46-c22b-4dda-d962-e5cce063f089"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####From the above distribution graph we can see that most of the customers have jobs as \"admin\", \"blue-collar\" or \"technician\". One interesting thing to find out would be to see the distribution for each classes as well. For example, how many people who work as an admin have subscribed a term deposit."
      ],
      "metadata": {
        "id": "2PxUyJbipyNQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"job\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "zo1dLYOfnIDV",
        "outputId": "a2f7f6c2-60c5-4265-cb84-505f10b6e6d5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####From the above plot, we can see that the customers whoes job function is admin are the ones with the highest rate of subscribing a term deposit, but they are also the highest when it comes to not subscribing. This is simply because we have more customers working as admin than any other profession.\n",
        "We can find out the odds or ratio of subscribing and not subscribing based on the profession, to find out which profession has the highest odds of subscribing given the data. At this point we are not sure if there is any correlation between job and target variable.\n",
        "Idea: If we find that odds of one profession subscribing is greater than other job, we can use the odds or log(odds) as a feature by replacing jobs field with the odds, instead of doing one hot encoding."
      ],
      "metadata": {
        "id": "_gAaivvDqlMf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"marital\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "DNj0eVPFnIIS",
        "outputId": "f35cd1ad-2219-4292-9082-ff1e9f93b63c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"marital\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "vbe7Z6SfnIM_",
        "outputId": "404d0ce9-68eb-4f05-e7f3-9038e6cb478d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = default (categorical)\n",
        "* This is a categorical feature which means \"has credit in default\", with the values \"yes\" and \"no\" and \"unknown\"."
      ],
      "metadata": {
        "id": "i0RXgRzUrq-d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"default\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "EsmgmjZtnISI",
        "outputId": "59fb3a02-318c-450f-f474-39bde6dfa675"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"default\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "iyDHphwsnIXZ",
        "outputId": "be856f22-4535-42c6-95ca-8035b2a79c51"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is no customer with who has credit in default. Majority of the customers don't have, and the for the rest of the customers this field is unknown."
      ],
      "metadata": {
        "id": "NYlacd-FsXiD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = Education"
      ],
      "metadata": {
        "id": "gcWH3teksrmN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"education\",data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "zokJR8gynIci",
        "outputId": "717daa17-c1f8-442b-ca3a-8315d3bc1b60"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"education\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "SgwsqExatBU2",
        "outputId": "90687d1b-a8a1-4777-de69-ddda4398ecfa"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = housing (Categorical)"
      ],
      "metadata": {
        "id": "bboX1gX3tA0V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"housing\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "O_QlehxPtZhZ",
        "outputId": "ab4aa1d0-6771-4e2f-dfd3-d49c300cf971"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Majority of the customers have a housing loan."
      ],
      "metadata": {
        "id": "pr9T-JL3tmES"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"housing\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "INQopbVjtmYo",
        "outputId": "bb8867a9-70ac-491d-c7e6-2712c458df2d"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = loan (Categorical)"
      ],
      "metadata": {
        "id": "PlhgWk6w29lA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"loan\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "kk4rw4GOtZnJ",
        "outputId": "04e34221-af84-4e5a-d13d-9cbb6a7232a5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"loan\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "Pwrqih6dtZrD",
        "outputId": "d842c1e9-127e-43f4-9aa9-64ecaff712a6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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ycGvtqWeNT6iqTq+qVVW1auPGjdvqOQAAAADgRWuqw+GFSfZPckiS+5P86VTctLX26dbaktbaknnz5k3FLQEAAADgBW3nqbxZa+2BZ7ar6jNJrhm+3pdkv3GHzh/G8hzjm5LMqaqdh7cOxx8PAAAAAGylKX3jsKpePu7rcUmeWXH56iTHV9UuVbUwyaIkX09yS5JFwwrKszK2gMrVrbWW5MYkbx/OPyXJyql4BgAAAADYEYzsjcOq+kKSNyTZu6o2JDknyRuq6pAkLck9SX4zSVprd1XV5UnWJHkqyRmttaeH67wvyXVJZiS5uLV213CLDyZZUVUfS3Jbks+O6lkAAAAAYEczsnDYWjthguHnjHuttWVJlk0wfm2SaycYX5+xVZcBAAAAgG1sOlZVBgAAAAC2c8IhAAAAANARDgEAAACAjnAIAAAAAHSEQwAAAACgIxwCAAAAAB3hEAAAAADoCIcAAAAAQEc4BAAAAAA6wiEAAAAA0BEOAQAAAICOcAgAAAAAdIRDAAAAAKAjHAIAAAAAHeEQAAAAAOgIhwAAAABARzgEAAAAADrCIQAAAADQEQ4BAAAAgI5wCAAAAAB0hEMAAAAAoCMcAgAAAAAd4RAAAAAA6AiHAAAAAEBHOAQAAAAAOsIhAAAAANARDgEAAACAjnAIAAAAAHSEQwAAAACgIxwCAAAAAB3hEAAAAADoCIcAAAAAQEc4BAAAAAA6wiEAAAAA0BEOAQAAAICOcAgAAAAAdIRDAAAAAKAjHAIAAAAAHeEQAAAAAOgIhwAAAABARzgEAAAAADrCIQAAAADQEQ4BAAAAgI5wCAAAAAB0hEMAAAAAoCMcAgAAAAAd4RAAAAAA6AiHAAAAAEBHOAQAAAAAOsIhAAAAANARDgEAAACAjnAIAAAAAHSEQwAAAACgIxwCAAAAAB3hEAAAAADoCIcAAAAAQEc4BAAAAAA6wiEAAAAA0BEOAQAAAICOcAgAAAAAdIRDAAAAAKAjHAIAAAAAHeEQAAAAAOgIhwAAAABARzgEAAAAADrCIQAAAADQEQ4BAAAAgI5wCAAAAAB0hEMAAAAAoCMcAgAAAAAd4RAAAAAA6AiHAAAAAEBHOAQAAAAAOnXJsVsAAB5HSURBVMIhAAAAANARDgEAAACAjnAIAAAAAHSEQwAAAACgIxwCAAAAAB3hEAAAAADoCIcAAAAAQEc4BAAAAAA6wiEAAAAA0BEOAQAAAICOcAgAAAAAdIRDAAAAAKAjHAIAAAAAHeEQAAAAAOgIhwAAAABARzgEAAAAADrCIQAAAADQEQ4BAAAAgI5wCAAAAAB0hEMAAAAAoCMcAgAAAAAd4RAAAAAA6AiHAAAAAEBHOAQAAAAAOsIhAAAAANARDgEAAACAjnAIAAAAAHSEQwAAAACgIxwCAAAAAB3hEAAAAADoCIcAAAAAQEc4BAAAAAA6wiEAAAAA0BEOAQAAAICOcAgAAAAAdIRDAAAAAKAjHAIAAAAAHeEQAAAAAOgIhwAAAABARzgEAAAAADrCIQAAAADQEQ4BAAAAgI5wCAAAAAB0hEMAAAAAoCMcAgAAAAAd4RAAAAAA6AiHAAAAAEBnZOGwqi6uqu9X1Z3jxvaqquurau3wd89hvKrqgqpaV1W3V9Vh4845ZTh+bVWdMm788Kq6YzjngqqqUT0LAAAAAOxoRvnG4aVJlj5r7ENJbmitLUpyw/A9Sd6SZNHwOT3JhclYaExyTpJfTvK6JOc8ExuHY9477rxn3wsAAAAA+BmNLBy21m5K8uCzho9JctmwfVmSY8eNL29jbk4yp6penuToJNe31h5srT2U5PokS4d9u7fWbm6ttSTLx10LAAAAANhKU/0/Dl/aWrt/2P5ekpcO2/smuXfccRuGsecb3zDB+ISq6vSqWlVVqzZu3Lh1TwAAAAAAO4BpWxxleFOwTdG9Pt1aW9JaWzJv3rypuCUAAAAAvKBNdTh8YPiZcYa/3x/G70uy37jj5g9jzzc+f4JxAAAAAGAbmOpweHWSZ1ZGPiXJynHjJw+rKx+R5F+GnzRfl+SoqtpzWBTlqCTXDft+UFVHDKspnzzuWgAAAADAVtp5VBeuqi8keUOSvatqQ8ZWR/6TJJdX1WlJ/inJO4fDr03y1uT/tXfnwXZVBb7HfyuDGgSUKMEQQEWCBkK8JEH0ySRImkczqKAgQ0QGS5t+1d00YAsPlBAaEekKeaCA3bwG2w7PViBEMGo74NAgg4TBiCaEGWWGbnKRhGS9P3JIJa4E05Lck5t8PlWpu8/a65y79i2qTp0ve5+duUl6k3w8SWqtT5VSzkxyc2fepFrrSzdc+YssuXPzkCTf7vwDAAAAAFaDNRYOa60fXcmuvVYwtyY5fiWvc2mSS1cwfkuS0a9kjQAAAADAinXt5igAAAAAwNpLOAQAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwAAAACgIRwCAAAAAA3hEAAAAABoCIcAAAAAQEM4BAAAAAAawiEAAAAA0BAOAQAAAICGcAgAAAAANIRDAAAAAKAhHAIAAAAADeEQAAAAAGgIhwAAAABAQzgEAAAAABrCIQAAAADQEA4BAAAAgIZwCAAAAAA0hEMAAAAAoCEcAgAAAAAN4RAAAAAAaAiHAAAAAEBDOAQAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwAAAACgIRwCAAAAAA3hEAAAAABoCIcAAAAAQEM4BAAAAAAawiEAAAAA0BAOAQAAAICGcAgAAAAANIRDAAAAAKAhHAIAAAAADeEQAAAAAGgIhwAAAABAQzgEAAAAABrCIQAAAADQEA4BAAAAgIZwCAAAAAA0hEMAAAAAoCEcAgAAAAAN4RAAAAAAaAiHAAAAAEBDOAQAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwAAAACgIRwCAAAAAA3hEAAAAABoCIcAAAAAQEM4BAAAAAAawiEAAAAA0BAOAQAAAICGcAgAAAAANIRDAAAAAKAhHAIAAAAADeEQAAAAAGgIhwAAAABAQzgEAAAAABrCIQAAAADQEA4BAAAAgIZwCAAAAAA0hEMAAAAAoCEcAgAAAAAN4RAAAAAAaAiHAAAAAEBDOAQAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwAAAACgIRwCAAAAAA3hEAAAAABoCIcAAAAAQEM4BAAAAAAawiEAAAAA0BAOAQAAAICGcAgAAAAANIRDAAAAAKAhHAIAAAAADeEQAAAAAGgIhwAAAABAQzgEAAAAABrCIQAAAADQEA4BAAAAgIZwCAAAAAA0hEMAAAAAoCEcAgAAAAAN4RAAAAAAaAiHAAAAAEBDOAQAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwBgrXP00Udn2LBhGT169NKx0047LWPGjElPT08mTJiQRx55ZIXPHThwYHp6etLT05MDDjhg6fjhhx+eMWPG5JRTTlk6Nnny5Fx99dVr7kAAAKAfEw4BgLXOUUcdlZkzZy43dtJJJ+WOO+7IrFmzst9++2XSpEkrfO6QIUMya9aszJo1K9dcc02S5I477siQIUNyxx135Oabb86zzz6b3/72t/n5z3+eD3zgA2v8eAAAoD8SDgGAtc5uu+2WoUOHLje28cYbL92eP39+Simr/HqDBw/O888/n8WLF2fhwoUZOHBgTj/99Jxxxhmrbc0AALCuEQ4BgH7j1FNPzZZbbpmvfe1rKz3j8Pe//33Gjx+fd7/73UsvQx41alQ23XTTjB07Nvvvv3/mzp2bxYsXZ+zYsX25fAAA6FcGdXsBAACr6qyzzspZZ52Vs88+OxdccMEKzxi8//77M2LEiMybNy977rlndthhh7ztbW/LlClTls7Zf//9c/HFF+ess87K7bffnr333jvHHXdcXx4KAACs9ZxxCAD0O4cffni++c1vrnDfiBEjkiRbb7119thjj9x2223L7Z8+fXrGjRuX5557Lvfcc0++/vWv5xvf+EZ6e3vX+LoBAKA/EQ4BgH5hzpw5S7enT5+ed7zjHc2cp59+Oi+88EKS5IknnsjPfvazbLfddkv3L1y4MFOmTMnJJ5+c559/fun3JC5atCgLFixYw0cAAAD9i0uVAWA9Mu6ky7u9hFVy77e+lP968O68+PxzedVGQzP8vR/Mf867I79/6rdJKXnVxm/MVnt/LONOujzzf3dvnrj9B3nznx2T5x6ekwe+988ppaTWmmHjJuTI/3tLkluSJI/d+p0MfM022fWz30itNff9xy/zr5tumde9dUz2Ouua7h70eujWcyd2ewkAALwM4RAAWOu8db+/aMbeuMPuK5z72je9Na990zFJkg1HjMx2R5210tcdNu7Plm6XUlb4ewAAgCVcqgwAAAAANIRDAAAAAKAhHAIAAAAADeEQAAAAAGgIhwAAAABAQzgEAAAAABrCIQAAAADQEA4BAAAAgIZwCAAAAAA0hEMAAAAAoNGVcFhKua+UcmcpZVYp5ZbO2NBSyvdKKXM6PzfpjJdSytRSytxSyh2llLHLvM7HOvPnlFI+1o1jAQAAAIB1UTfPOHxfrbWn1jq+8/jvkny/1joyyfc7j5PkfyYZ2fn3iSRfTpaExiSfTbJzkncl+exLsREAAAAAeGXWpkuVD0xyWWf7siQfWGb88rrEjUleX0oZnuTPknyv1vpUrfXpJN9Lsk9fLxoAAAAA1kXdCoc1yXdLKbeWUj7RGdus1vrbzvbvkmzW2R6R5MFlnvtQZ2xl441SyidKKbeUUm55/PHHV9cxAAAAAMA6a1CXfu8utdaHSynDknyvlHL3sjtrrbWUUlfXL6u1XpLkkiQZP378antdAAAAAFhXdeWMw1rrw52fjyW5Kku+o/DRziXI6fx8rDP94SRbLvP0LTpjKxsHAAAAAF6hPg+HpZTXllI2emk7yYQkdyW5JslLd0b+WJLpne1rkkzs3F353Ume7VzS/J0kE0opm3RuijKhMwYAAAAAvELduFR5syRXlVJe+v3/WmudWUq5OcnXSynHJLk/yUc6869Lsm+SuUl6k3w8SWqtT5VSzkxyc2fepFrrU313GAAAAACw7urzcFhrnZfknSsYfzLJXisYr0mOX8lrXZrk0tW9RgAAAABY33XrrsoAAAAAwFpMOAQAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwAAAACgIRwCAAAAAA3hEAAAAABoCIcAAAAAQEM4BAAAAAAawiEAAAAA0BAOAQAAAICGcAgAAAAANIRDAAAAAKAhHAIAAAAADeEQAAAAAGgIh8A64fzzz8/o0aOz/fbbZ8qUKc3+6dOnZ8yYMenp6cn48ePz05/+NEny61//OuPGjcuYMWNyww03JElefPHFvP/9709vb2+fHgMAAACsTYRDoN+766678pWvfCU33XRTbr/99nzrW9/K3Llzl5uz11575fbbb8+sWbNy6aWX5thjj02SXHzxxTn//PNz3XXX5Ytf/GKS5Mtf/nKOOOKIbLDBBn1+LAAAALC2EA6Bfu9Xv/pVdt5552ywwQYZNGhQdt9991x55ZXLzdlwww1TSkmSzJ8/f+n24MGD09vbm97e3gwePDjPPPNMZsyYkYkTJ/b5cQAAAMDaZFC3FwDwSo0ePTqnnnpqnnzyyQwZMiTXXXddxo8f38y76qqr8pnPfCaPPfZYrr322iTJ8ccfn4kTJ+aFF17IxRdfnDPPPDOnnHJKBgzw/1UAAABYv/lkDPR7o0aNyqc//elMmDAh++yzT3p6ejJw4MBm3gc/+MHcfffdufrqq3PaaaclSbbaaqv86Ec/yg033JANNtggDz30UEaNGpUjjzwyhxxySH7zm9/09eEAAADAWkE4BNYJxxxzTG699db8+Mc/ziabbJJtt912pXN32223zJs3L0888cRy46eeemomT56cqVOn5thjj80XvvCFnHHGGWt66QAAALBWEg6BdcJjjz2WJHnggQdy5ZVX5rDDDltu/9y5c1NrTZL84he/yAsvvJA3vOENS/dff/312XzzzTNy5Mj09vZmwIABGTBggDsrAwAAsN7yHYfASj0waYduL2GVHfxP8/L084syeEDJ6fu8Kf85ddd86eankiRH7DQ0//STx/PN25/J4IElrx5UMnWfN+XBM8ckSWqt+d+X35cLP7xlHpj079nvxd/nU4dclEWLaybvt3m/+jusy7Y6/c5uLwEAAGC9IhwC64RvHLN1M3bETkOXbn9q103zqV03XeFzSyn52sfeuvTxyE1fk+s+uc3qXyQAAAD0Iy5VBgAAAAAawiEAAAAA0BAOAQAAAICGcAgAAAAANIRDAAAAAKAhHAIAAAAADeEQAAAAAGgIhwAAAABAQzgEAAAAABrCIQAAAADQEA4BAAAAgIZwCAAAAAA0hEMAAAAAoCEcAgAAAAAN4RAAAAAAaAiHAAAAAEBDOAQAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwAAAACgIRwCAAAAAA3hEAAAAABoCIcAAAAAQEM4BAAAAAAawiEAAAAA0BAOAQAAAICGcAgAAAAANIRDAAAAAKAhHAIAAAAADeEQAAAAAGgIhwAAAABAQzgEAAAAABrCIQAAAADQEA4BAAAAgIZwCAAAAAA0hEMAAAAAoCEcAgAAAAAN4RAAAAAAaAiHAAAAAEBDOAQAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwAAAACgIRwCAAAAAA3hEAAAAABoCIcAAAAAQEM4BAAAAAAawiEAAAAA0BAOAQAAAICGcAgAAAAANIRDAAAAAKAhHAIAAAAADeEQAAAAAGgIhwAAAABAQzgEAAAAABrCIQAAAADQEA4BAAAAgIZwCAAAAAA0hEMAAAAAoCEcAgAAAAAN4RAAAAAAaAiHAAAAAEBDOAQAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwAAANYbDz74YN73vvdlu+22y/bbb5/zzz+/mXP33XfnPe95T1796lfni1/84tLxxx9/PLvssktGjx6dq6++eun4gQcemEceeaRP1g/Ql4RDAAAA1huDBg3Keeedl9mzZ+fGG2/MhRdemNmzZy83Z+jQoZk6dWpOPPHE5canTZuWT37yk7npppsyZcqUJMmMGTOy4447ZvPNN++zYwDoK8IhAAAA643hw4dn7NixSZKNNtooo0aNysMPP7zcnGHDhmWnnXbK4MGDlxsfPHhwent788ILL2TgwIF58cUXM2XKlJx88sl9tn6AviQcAgAAsF667777ctttt2XnnXdepfmHHXZYpk+fnr333junnHJKvvSlL+XII4/MBhtssIZXCtAdwiEAAADrneeeey4HHXRQpkyZko033niVnvO6170u1157bW655ZaMHTs2M2bMyMEHH5zjjjsuBx98cG644YY1vGqAviUcAgDAyzj66KMzbNiwjB49eoX7zz333PT09KSnpyejR4/OwIED89RTT7mJAqzFFi5cmIMOOiiHH354PvShD/1Jr3HmmWfm1FNPzbRp07LLLrvksssuy+c+97nVu1CALhMOAQDgZRx11FGZOXPmSvefdNJJmTVrVmbNmpWzzz47u+++e4YOHeomCrCWqrXmmGOOyahRo3LCCSf8Sa8xZ86cPPTQQ9ljjz3S29ubAQMGpJSS559/fjWvFqC7BnV7AQAAsDbbbbfdct99963S3GnTpuWjH/1okpXfRGHGjBlrcLXQPeNOurzbS1glzz30m/zmiq/mNW/cIpf861VJks13PTgL/vPJJMmmPXtm4fxncvdXP5dFC55PKQNyyhl/n+0+fnYGvnpIkmTeNRdk810PzriTLs/C+QMzb/qkfOJv/i7D3/uhfvN3WNfdeu7Ebi8B1gnCIQAArAa9vb2ZOXNmLrjggiRLbqJw2GGH5ZJLLsk555zjJgqwlthwi20z9sTLXnbO4Ne+Pjt8cspK9299wF8uM3fjvP2w01bb+gDWJi5VBgCA1WDGjBl573vfm6FDhyZxEwUAoP8TDgEAYDW44oorll6m/IfcRAEA6I+EQwAAeIWeffbZXH/99TnwwAObfW6iAAD0V77jEACArnhg0g7dXsIq+V//9mBuuG9+nu59McNfNzh/s8ewvLh4yb4jdlpyWfK/3fZ0dhmxOE+e9+48+QfP/5uvP5CT9tosD0zaIbs+92KOO+/+nPm3x+WEPYf1m7/Bum6r0+/s9hIAYK0kHAIAwMv4Px/e8o/O+fCOm+TDO26ywn1f+shWS7ffuOGgXHXs21bb2gAA1iSXKgMAAAAADeEQAAAAAGgIhwAAAABAQzgEAAAAABrCIQAAAADQEA4BAAAAgIZwCAAAANAFM2fOzNvf/vZss802+fznP9/sv+iii7LDDjukp6cnu+yyS2bPnp0k+dnPfpYxY8Zk/PjxmTNnTpLkmWeeyYQJE7J48eI+PQbWbcIhAAAAQB9btGhRjj/++Hz729/O7NmzM23atKVh8CWHHXZY7rzzzsyaNSsnn3xyTjjhhCTJeeedl+uuuy5TpkzJRRddlCSZPHlyTjnllAwYIPWw+vivCQAAAKCP3XTTTdlmm22y9dZb51WvelUOPfTQTJ8+fbk5G2+88dLt+fPnp5SSJBk8eHB6e3vT29ubwYMH55577smDDz6YPfbYoy8PgfXAoG4vAAAAAGB98/DDD2fLLbdc+niLLbbIz3/+82behRdemH/4h3/IggUL8oMf/CBJ8pnPfCYTJ07MkCFD8tWvfjUnnnhiJk+e3GdrZ/3hjEMAAACAtdTxxx+fe+65J+ecc87SONjT05Mbb7wxP/zhDzNv3rwMHz48tdYccsghOeKII/Loo492edWsK4RDAAAAgD42YsSIPPjgg0sfP/TQQxkxYsRK5x966KG5+uqrlxurtWby5Mk57bTTcsYZZ+QLX/hCjjvuuEydOnWNrZv1i3AIAAAA0Md22mmnzJkzJ/fee28WLFiQK664IgcccMByc166Y3KSXHvttRk5cuRy+y+//PLsu+++GTp0aHp7ezNgwIAMGDAgvb29fXIMrPv6/XccllL2SXJ+koFJ/rHW2t6/HAAAAFhvPDBph24vYZWc/j8WZ6+d3pFFi2s+suMm2eibh+avjn80YzYfkr3fsXHOvu63+em85zJ4YMnGrxmYz//58KXH9vyCxbnoa/fnXya+JQ9M+qccPnR+9h4/MoMHlkw9eMs8MOnfu3x0JMlWp9/Z7SW8Iv06HJZSBia5MMneSR5KcnMp5Zpa6+yXfyYAAABAd+257UbZc9uNlhv72z03W7r9uX2Hr/S5Q141IP/v429d+vhdb35tvnv8yJXOhz9Ff79U+V1J5tZa59VaFyS5IsmBXV4TAAAAAPR7pdba7TX8yUopByfZp9Z6bOfxkUl2rrX+5R/M+0SST3Qevj3Jr/t0odB/vTHJE91eBADrLO8zAKwp3mNg1b251rrpinb060uVV1Wt9ZIkl3R7HdDflFJuqbWO7/Y6AFg3eZ8BYE3xHgOrR3+/VPnhJFsu83iLzhgAAAAA8Ar093B4c5KRpZS3llJeleTQJNd0eU0AAAAA0O/160uVa60vllL+Msl3kgxMcmmt9ZddXhasS1ziD8Ca5H0GgDXFewysBv365igAAAAAwJrR3y9VBgAAAADWAOEQAAAAAGgIhwAAAEC/UEo5qpRyQbfXAesL4RAAAAAAaAiHQJKklPKWUsqvSilfKaX8spTy3VLKkFJKTynlxlLKHaWUq0opm3R7rQCs/Uopk0opf73M47NKKX9VSjmplHJz533ljM6+15ZSri2l3F5KuauUckj3Vg5AX+p8DrlrmccnllI+V0r5USnlnFLKTaWU35RSdl3Bc/+8lHJDKeWNpZR/LqVMLaX8RyllXinl4M6cUko5t/P+cudL7zGllAtLKQd0tq8qpVza2T668561ws9HffNXgbWHcAgsa2SSC2ut2yd5JslBSS5P8ula65gkdyb5bBfXB0D/cWmSiUlSShmQ5NAkv8uS95p3JelJMq6UsluSfZI8Umt9Z611dJKZ3VkyAGuZQbXWdyX56/zB55BSygeT/F2SfWutT3SGhyfZJcl+ST7fGftQlrznvDPJ+5OcW0oZnuQnSV6KkSOSbNfZ3jXJjzvbK/p8BOsV4RBY1r211lmd7VuTvC3J62ut13fGLkuyW1dWBkC/Umu9L8mTpZQdk0xIcluSnZbZ/kWSd2TJh7I7k+zdObNk11rrs91ZNQBrmSs7P29N8pZlxvdM8ukkf15rfXqZ8atrrYtrrbOTbNYZ2yXJtFrrolrro0muz5L3o58k2bWUsl2S2Uke7QTF9yT5j85z//Dz0bJrgPXCoG4vAFirvLDM9qIkr+/WQgBYJ/xjkqOSvClLzkDcK8nZtdaL/3BiKWVskn2TTC6lfL/WOqkvFwpA17yY5U9qes0y2y99PlmU5fvFPUm2TrJtkltWMD9Jysv90lrrw6WU12fJWe8/TjI0yUeSPFdr/a9SyhvSfj5yqTLrHWccAi/n2SRPL/N9Ikdmyf+hA4BVcVWWfCDbKcl3Ov+OLqVsmCSllBGllGGllM2T9NZa/yXJuUnGdmvBAPS5R5MMK6W8oZTy6iy5zPiPuT+dr1UqpWz/R+b+JMkhpZSBpZRNs+QKqps6+27Mksugf9yZd2LnJ9DhjEPgj/lYkotKKRskmZfk411eDwD9RK11QSnlh0meqbUuSvLdUsqoJDeUUpLkuSRHJNkmS75zanGShUk+1a01A9C3aq0LSymTsiTmPZzk7lV83t2llMOT/FspZf+XmXpVllx+fHuSmuTkWuvvOvt+kmRCrXVuKeX+LDnrUDiEZZRaa7fXAADAOqhzU5RfJPlwrXVOt9cDAMB/j0uVAQBY7TpfNj83yfdFQwCA/skZhwAAAABAwxmHAAAAAEBDOAQAAAAAGsIhAAAAANAQDgEAWK1KKc91ew0AALxywiEAAAAA0BAOAQBYI8oS55ZS7iql3FlKOaQzvmEp5fullF90xg/sjL+llPKrUspXSim/LKV8t5QypLtHAQCw/hIOAQBYUz6UpCfJO5O8P8m5pZThSX6f5IO11rFJ3pfkvFJK6TxnZJILa63bJ3kmyUF9v2wAABLhEACANWeXJNNqrYtqrY8muT7JTklKkr8vpdyR5N+TjEiyWec599ZaZ3W2b03ylr5dMgAALxnU7QUAALDeOTzJpknG1VoXllLuS/Kazr4Xlpm3KIlLlQEAusQZhwAArCk/SXJIKWVgKWXTJLsluSnJ65I81omG70vy5m4uEgCAFXPGIQAAa8pVSd6T5PYkNcnJtdbflVK+lmRGKeXOJLckubuLawQAYCVKrbXbawAAAAAA1jIuVQYAAAAAGsIhAAAAANAQDgEAAACAhnAIAAAAADSEQwAAAACgIRwCAAAAAA3hEAAAAABo/H9uHw5t65cgngAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = contact (Categorical)"
      ],
      "metadata": {
        "id": "9a3sUvk-3UI0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"contact\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "YMxclRkKtZvd",
        "outputId": "0c0b3ffb-1b33-45ff-c33c-68816bee9c83"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"contact\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "2FQGjdiK3lkZ",
        "outputId": "a30a1681-36de-4fce-e830-a22f4343322a"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = month (Categorical)"
      ],
      "metadata": {
        "id": "G0uamHbX3uv6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"month\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "y1rgMV3o3lrb",
        "outputId": "76c29c22-5e96-4263-f36f-aed2f6fe7575"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"month\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "hHvDXBpi3lyb",
        "outputId": "d7ca981f-0d7c-4233-d122-0edffc45cbf6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = day_of_week (Categorical)"
      ],
      "metadata": {
        "id": "JoYKguGk4JkB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"day_of_week\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "q7hbatZD3l2z",
        "outputId": "fd9d5534-b194-4b27-d24c-1fa1ba3ff047"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"day_of_week\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "UL9-FOvp3l92",
        "outputId": "19256df8-04a8-4630-a9c7-5385655b0d78"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####The day of the week seems to be irrelevent as we have the same amount of data for all the days of the week, and no:yes ratio is also almost same.\n",
        " "
      ],
      "metadata": {
        "id": "3jitIDj44lc7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = poutcome (Categorical)"
      ],
      "metadata": {
        "id": "rVs3H3da4wAO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This feature indicates the outcome of the previous marketing campaign"
      ],
      "metadata": {
        "id": "dhNgBc704wM3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"poutcome\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "uX_F554r3mEH",
        "outputId": "6f9554aa-cc4c-4d1a-ea35-421767ee230c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"poutcome\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "90WHMTKE5Cps",
        "outputId": "b02b722e-483d-4d94-8be6-e58cc996a106"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = Age (Numeric)"
      ],
      "metadata": {
        "id": "8EDCv4Aa5Tez"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "sns.boxplot(data=data, x=\"y\", y=\"age\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "L0B5U2BI5Cyn",
        "outputId": "5807a8cb-74e3-4136-f587-486452abedaa"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above boxplot we know that for both the customers that subscibed or didn't subscribe a term deposit, has a median age of around 38-40. And the boxplot for both the classes overlap quite a lot, which means that age isn't necessarily a good indicator for which customer will subscribe and which customer will not."
      ],
      "metadata": {
        "id": "quHQWoFo5j06"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "sns.distplot(data[\"age\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "DzgYd5Yq5C8X",
        "outputId": "8fa52e42-e0d1-44f9-859c-6796749f1434"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d396f8b10>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see in the above distribution also, that most of the customers are in the age range of 30-40."
      ],
      "metadata": {
        "id": "yeNYnwt55yu5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = duration (numeric)"
      ],
      "metadata": {
        "id": "hGajEMaZ5_2d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "sns.boxplot(data=data, x=\"y\", y=\"duration\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "_zgNrMQE5DEq",
        "outputId": "f5ed0627-628c-4eb9-a3c0-af73055c2875"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above plot it is clear that, the duration (last contact duration) of a customer can be useful for predicting the target variable. It is expected because it is already mentioned in the data overview that this field highely affects the target variable and should only be used for benchmark purposes."
      ],
      "metadata": {
        "id": "XRJzkuwB6UTb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "sns.distplot(data[\"duration\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "-BLYjz3Z5DJL",
        "outputId": "614343f9-848f-4612-d53f-fbd3edbbba7d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This seems like a powerlaw distribution where most the values are very low and very few have high values."
      ],
      "metadata": {
        "id": "bY4LbKoP6kuV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature = pdays (numeric)"
      ],
      "metadata": {
        "id": "TEnzyZh16tvn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"pdays\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DA_m7Pz85DOM",
        "outputId": "1d59fbe0-ddc3-4271-e42b-d5b30d379371"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([999,   6,   4,   3,   5,   1,   0,  10,   7,   8,   9,  11,   2,\n",
              "        12,  13,  14,  15,  16,  21,  17,  18,  22,  25,  26,  19,  27,\n",
              "        20])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"pdays\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "veLFrNw83mKE",
        "outputId": "89d8b0c7-0ab3-4847-9749-3f47e1da8bce"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "999    39673\n",
              "3        439\n",
              "6        412\n",
              "4        118\n",
              "9         64\n",
              "2         61\n",
              "7         60\n",
              "12        58\n",
              "10        52\n",
              "5         46\n",
              "13        36\n",
              "11        28\n",
              "1         26\n",
              "15        24\n",
              "14        20\n",
              "8         18\n",
              "0         15\n",
              "16        11\n",
              "17         8\n",
              "18         7\n",
              "22         3\n",
              "19         3\n",
              "21         2\n",
              "25         1\n",
              "26         1\n",
              "27         1\n",
              "20         1\n",
              "Name: pdays, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most of the values are 999, which means that the most of the customers have never been contacted before."
      ],
      "metadata": {
        "id": "-Z6FZOH57CgA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "sns.boxplot(data=data, x=\"y\", y=\"pdays\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "BTjujzWF6_gW",
        "outputId": "459ad20a-9952-42de-80b2-9bb01e3352ac"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.distplot(data[data[\"y\"]==\"yes\"][\"pdays\"])\n",
        "sns.distplot(data[data[\"y\"]==\"no\"][\"pdays\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "lEgH0NxZ6_or",
        "outputId": "da1dfaab-bc1c-4d7a-93eb-26e9bfe07dac"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Feature: previous (numeric)"
      ],
      "metadata": {
        "id": "uW3UYEpZABue"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"previous\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2N-PWz16_x8",
        "outputId": "fcfaff12-c4d4-43ff-b44d-37cb582ebd20"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data[\"y\"]==\"yes\"][\"previous\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_PInSDwI6_6m",
        "outputId": "efbc4e9c-c424-4376-e693-4b9084ab1d83"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3141\n",
              "1     967\n",
              "2     350\n",
              "3     128\n",
              "4      38\n",
              "5      13\n",
              "6       3\n",
              "Name: previous, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data[\"y\"]==\"no\"][\"previous\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aOHWK-gk6__a",
        "outputId": "8b18a793-7997-440a-d9ab-9fc3ffbed11f"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    32422\n",
              "1     3594\n",
              "2      404\n",
              "3       88\n",
              "4       32\n",
              "5        5\n",
              "6        2\n",
              "7        1\n",
              "Name: previous, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.distplot(data[\"previous\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "hLyKDcWa7AEo",
        "outputId": "53f82df6-9430-4401-887d-f4277dc74f78"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.distplot(data[data[\"y\"]==\"yes\"][\"previous\"])\n",
        "sns.distplot(data[data[\"y\"]==\"no\"][\"previous\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "L_x0Auhq7AKI",
        "outputId": "0b039baa-b6bc-44b5-fdec-b254ebc1f571"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d38b81050>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The previous feature is very similarly distributed for both the classes in the target variable. From basic EDA it is not sure how much value this individual feature have on the target variable"
      ],
      "metadata": {
        "id": "jFeXaG2TBdXw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "countplot(\"previous\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "zyjE-HM47APh",
        "outputId": "a7edafb8-7695-47aa-e128-8887aa6a6ad0"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countplot_withY(\"previous\", data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "oGusIUiQ7AVP",
        "outputId": "1f510270-6c19-4976-83b5-6bd3d43dcfaf"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Correlation matrix of numerical features"
      ],
      "metadata": {
        "id": "S-3b8diPB9_W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr = data.corr()\n",
        "f, ax = plt.subplots(figsize=(10,12))\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "_ = sns.heatmap(corr, cmap=\"YlGn\", square=True, ax=ax, annot=True, linewidth=0.1)\n",
        "plt.title(\"Pearson correlation of Features\", y=1.05, size=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "yVVoEEEB7Aai",
        "outputId": "69813201-ee83-4ff5-b4a5-b20f9740320d"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Pearson correlation of Features')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "From the above heatmap we can see that there are some numerical features which share a high correlation between them, e.g nr.employed and euribor3m these features share a correlation value of 0.95, and euribor3m and emp.var.rate share a correlation of 0.97, which is very high compared to the other features that we see in the heatmap."
      ],
      "metadata": {
        "id": "u9knn2ajCXRl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "8vCzj5SP7A2Z"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4O6YdzT13mUg"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sDgZ6vvJ2_o"
      },
      "source": [
        "### Problem 5: Engineering Features\n",
        "\n",
        "Now that you understand your business objective, we will build a basic model to get started.  Before we can do this, we must work to encode the data.  Using just the bank information features (columns 1 - 7), prepare the features and target column for modeling with appropriate encoding and transformations."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "GhM7wR0ACofz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "QXbWIa71J2_o"
      },
      "outputs": [],
      "source": [
        "# Import the libraries\n",
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "matplotlib.use(u'nbAgg')\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pickle\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn import preprocessing\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV \n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.calibration import CalibratedClassifierCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "Z2Tc-FLzJ2_p"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "WSXJs-ryJ2_p"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8562km4MJ2_p"
      },
      "source": [
        "### Problem 6: Train/Test Split\n",
        "\n",
        "With your data prepared, split it into a train and test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "USdhjcNAJ2_q"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2hn04QmGJ2_q"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F860zV7VJ2_r"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcbqaXIuJ2_r"
      },
      "source": [
        "### Problem 7: A Baseline Model\n",
        "\n",
        "Before we build our first model, we want to establish a baseline.  What is the baseline performance that our classifier should aim to beat?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m0ntQSlbJ2_r"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KzcfGwcQJ2_s"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_r1KmBQJJ2_s"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b66-K0sKJ2_s"
      },
      "source": [
        "### Problem 8: A Simple Model\n",
        "\n",
        "Use Logistic Regression to build a basic model on your data.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dt8lAmJUJ2_t"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mm70_UujJ2_t"
      },
      "source": [
        "### Problem 9: Score the Model\n",
        "\n",
        "What is the accuracy of your model?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "48tGJ7MQJ2_t"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LR7jahYfJ2_t"
      },
      "source": [
        "### Problem 10: Model Comparisons\n",
        "\n",
        "Now, we aim to compare the performance of the Logistic Regression model to our KNN algorithm, Decision Tree, and SVM models.  Using the default settings for each of the models, fit and score each.  Also, be sure to compare the fit time of each of the models.  Present your findings in a `DataFrame` similar to that below:\n",
        "\n",
        "| Model | Train Time | Train Accuracy | Test Accuracy |\n",
        "| ----- | ---------- | -------------  | -----------   |\n",
        "|     |    |.     |.     |"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IzwGMBGCJ2_u"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kmS7I8TPJ2_u"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4RkrDWG9J2_u"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgwZLuM0J2_u"
      },
      "source": [
        "### Problem 11: Improving the Model\n",
        "\n",
        "Now that we have some basic models on the board, we want to try to improve these.  Below, we list a few things to explore in this pursuit.\n",
        "\n",
        "- More feature engineering and exploration.  For example, should we keep the gender feature?  Why or why not?\n",
        "- Hyperparameter tuning and grid search.  All of our models have additional hyperparameters to tune and explore.  For example the number of neighbors in KNN or the maximum depth of a Decision Tree.  \n",
        "- Adjust your performance metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xukk1hOBJ2_v"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kFsAaViZJ2_v"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iUMaiS99J2_v"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RruQYogYJ2_v"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PZ8kc8-oJ2_v"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1wjIdh2YJ2_v"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xOmGUPTNJ2_w"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AMOvKGZvJ2_w"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q0p6O0GZJ2_w"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vMZJg_BOJ2_w"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6tqMfrNRJ2_x"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIdq-FwXJ2_x"
      },
      "source": [
        "##### Questions"
      ]
    }
  ],
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