Credit_LDA_SVM.ipynb

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  "metadata": {
    "colab": {
      "name": "Credit LDA SVM.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNdTmhfGOcQfvi+mFtGJEEi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/philsaurabh/Machine-Learning-Tutorials/blob/main/Credit_LDA_SVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries"
      ],
      "metadata": {
        "id": "de85fWTURf7g"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "5IoSep5PLIm1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import dataset"
      ],
      "metadata": {
        "id": "AXEiN-_tRiyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/philsaurabh/Machine-Learning-Tutorials/main/Credit.csv')"
      ],
      "metadata": {
        "id": "rqvFKocFLwot"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "h2ebyRl7L3uw",
        "outputId": "6e294179-030d-4559-dcb7-41960090e5e9"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Income</th>\n",
              "      <th>Limit</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Cards</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Student</th>\n",
              "      <th>Married</th>\n",
              "      <th>Ethnicity</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Defaultee</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>14.891</td>\n",
              "      <td>3606</td>\n",
              "      <td>283</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>11</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>333</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>106.025</td>\n",
              "      <td>6645</td>\n",
              "      <td>483</td>\n",
              "      <td>3</td>\n",
              "      <td>82</td>\n",
              "      <td>15</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Asian</td>\n",
              "      <td>903</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>104.593</td>\n",
              "      <td>7075</td>\n",
              "      <td>514</td>\n",
              "      <td>4</td>\n",
              "      <td>71</td>\n",
              "      <td>11</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>580</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>148.924</td>\n",
              "      <td>9504</td>\n",
              "      <td>681</td>\n",
              "      <td>3</td>\n",
              "      <td>36</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>964</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>55.882</td>\n",
              "      <td>4897</td>\n",
              "      <td>357</td>\n",
              "      <td>2</td>\n",
              "      <td>68</td>\n",
              "      <td>16</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>331</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "   Unnamed: 0   Income  Limit  Rating  ...  Married  Ethnicity  Balance Defaultee\n",
              "0           1   14.891   3606     283  ...      Yes  Caucasian      333         0\n",
              "1           2  106.025   6645     483  ...      Yes      Asian      903         0\n",
              "2           3  104.593   7075     514  ...       No      Asian      580         0\n",
              "3           4  148.924   9504     681  ...       No      Asian      964         0\n",
              "4           5   55.882   4897     357  ...      Yes  Caucasian      331         0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
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        "df.shape"
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      "outputs": [
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          "data": {
            "text/plain": [
              "(400, 13)"
            ]
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          "metadata": {},
          "execution_count": 71
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      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Income', 'Limit', 'Rating', 'Cards', 'Age', 'Education',\n",
              "       'Gender', 'Student', 'Married', 'Ethnicity', 'Balance', 'Defaultee'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
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      "source": [
        "df.describe()"
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              "      <th>Unnamed: 0</th>\n",
              "      <th>Income</th>\n",
              "      <th>Limit</th>\n",
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              "      <th>Defaultee</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>200.500000</td>\n",
              "      <td>45.218885</td>\n",
              "      <td>4735.600000</td>\n",
              "      <td>354.940000</td>\n",
              "      <td>2.957500</td>\n",
              "      <td>55.667500</td>\n",
              "      <td>13.450000</td>\n",
              "      <td>520.015000</td>\n",
              "      <td>0.180000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>115.614301</td>\n",
              "      <td>35.244273</td>\n",
              "      <td>2308.198848</td>\n",
              "      <td>154.724143</td>\n",
              "      <td>1.371275</td>\n",
              "      <td>17.249807</td>\n",
              "      <td>3.125207</td>\n",
              "      <td>459.758877</td>\n",
              "      <td>0.384669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.354000</td>\n",
              "      <td>855.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>100.750000</td>\n",
              "      <td>21.007250</td>\n",
              "      <td>3088.000000</td>\n",
              "      <td>247.250000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>41.750000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>68.750000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>200.500000</td>\n",
              "      <td>33.115500</td>\n",
              "      <td>4622.500000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>459.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>300.250000</td>\n",
              "      <td>57.470750</td>\n",
              "      <td>5872.750000</td>\n",
              "      <td>437.250000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>863.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>186.634000</td>\n",
              "      <td>13913.000000</td>\n",
              "      <td>982.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-029ead90-c72b-4b8c-bfd7-aac3a6ed1836');\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",
              "  "
            ],
            "text/plain": [
              "       Unnamed: 0      Income  ...      Balance   Defaultee\n",
              "count  400.000000  400.000000  ...   400.000000  400.000000\n",
              "mean   200.500000   45.218885  ...   520.015000    0.180000\n",
              "std    115.614301   35.244273  ...   459.758877    0.384669\n",
              "min      1.000000   10.354000  ...     0.000000    0.000000\n",
              "25%    100.750000   21.007250  ...    68.750000    0.000000\n",
              "50%    200.500000   33.115500  ...   459.500000    0.000000\n",
              "75%    300.250000   57.470750  ...   863.000000    0.000000\n",
              "max    400.000000  186.634000  ...  1999.000000    1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Cleaning and Preprocessing"
      ],
      "metadata": {
        "id": "TygAZSxsRobW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wiNVywfgPbAZ",
        "outputId": "96d326a8-f116-4be3-d616-75b94d189d4c"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('Unnamed: 0', axis=1, inplace=True)\n",
        "df['Student'] = df['Student'].map({'Yes': 1, 'No': 0})\n",
        "df['Married'] = df['Married'].map({'Yes': 1, 'No': 0})\n",
        "df['Gender'] = df['Gender'].map({'Male': 1, 'Female': 0})\n",
        "df['Ethnicity'] = df['Ethnicity'].map({'Caucasian': 1, 'Asian': 0,'African American':2})"
      ],
      "metadata": {
        "id": "5obnbEvlPiBv"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BUTUwj_iPrrq",
        "outputId": "95140917-f672-406e-bf16-c24795ce20d2"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8b2f1dfb-85e4-493d-936a-c9c070da91f1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Income</th>\n",
              "      <th>Limit</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Cards</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Student</th>\n",
              "      <th>Married</th>\n",
              "      <th>Ethnicity</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Defaultee</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.891</td>\n",
              "      <td>3606</td>\n",
              "      <td>283</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>106.025</td>\n",
              "      <td>6645</td>\n",
              "      <td>483</td>\n",
              "      <td>3</td>\n",
              "      <td>82</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>903</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>104.593</td>\n",
              "      <td>7075</td>\n",
              "      <td>514</td>\n",
              "      <td>4</td>\n",
              "      <td>71</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>580</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>148.924</td>\n",
              "      <td>9504</td>\n",
              "      <td>681</td>\n",
              "      <td>3</td>\n",
              "      <td>36</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>964</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55.882</td>\n",
              "      <td>4897</td>\n",
              "      <td>357</td>\n",
              "      <td>2</td>\n",
              "      <td>68</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>331</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b2f1dfb-85e4-493d-936a-c9c070da91f1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8b2f1dfb-85e4-493d-936a-c9c070da91f1 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-8b2f1dfb-85e4-493d-936a-c9c070da91f1');\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",
              "  "
            ],
            "text/plain": [
              "    Income  Limit  Rating  Cards  ...  Married  Ethnicity  Balance  Defaultee\n",
              "0   14.891   3606     283      2  ...        1          1      333          0\n",
              "1  106.025   6645     483      3  ...        1          0      903          0\n",
              "2  104.593   7075     514      4  ...        0          0      580          0\n",
              "3  148.924   9504     681      3  ...        0          0      964          0\n",
              "4   55.882   4897     357      2  ...        1          1      331          0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sum()\n",
        "#mean_value=df['Column_name'].mean()\n",
        "#df['Column_name'].fillna(value=mean_value, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONx9XgOZPs9e",
        "outputId": "9f9ea33c-d531-4a46-c48f-9ac26014fe20"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, ConfusionMatrixDisplay, precision_score, recall_score, f1_score, classification_report, roc_curve, plot_roc_curve, auc, precision_recall_curve, plot_precision_recall_curve, average_precision_score\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "lkreEtCGP30H"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns = ['Defaultee'])\n",
        "y = df['Defaultee'].values"
      ],
      "metadata": {
        "id": "NAuANBsEP60p"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCcm_yOfP9gO",
        "outputId": "145c7e79-dfb8-4564-a2e7-fa227db06610"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Scaler = StandardScaler()\n",
        "X = Scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "553KTpPoP_s7"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzAEshfUQBvp",
        "outputId": "428f4555-be27-4785-a738-09679534cd9d"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.86158299, -0.48999879, -0.46553881, ...,  0.79539491,\n",
              "         0.01058078, -0.40727698],\n",
              "       [ 1.72743711,  0.82826106,  0.82870309, ...,  0.79539491,\n",
              "        -1.40018942,  0.83405596],\n",
              "       [ 1.68675551,  1.01478681,  1.02931059, ..., -1.25723711,\n",
              "        -1.40018942,  0.13063396],\n",
              "       ...,\n",
              "       [ 0.35946155, -0.24491264, -0.21963285, ...,  0.79539491,\n",
              "         0.01058078, -0.83194351],\n",
              "       [-0.21280808, -0.95891584, -1.05441888, ...,  0.79539491,\n",
              "         0.01058078, -1.13247675],\n",
              "       [-0.75334493,  0.34199278,  0.38866085, ..., -1.25723711,\n",
              "        -1.40018942,  0.97125591]])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "pxcTp-fEQF8m"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpDK35icQJz4",
        "outputId": "a2a4ba89-1e8d-4fae-a752-c283e05923f6"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((320, 11), (80, 11), (320,), (80,))"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold"
      ],
      "metadata": {
        "id": "ldYEid1M0OFO"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naive Bayes"
      ],
      "metadata": {
        "id": "l2B7tIDgkDNo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "model = GaussianNB()#KNeighborsClassifier()#SVC#RandomForestClassifier()#AdaBoostClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "score = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "roc = roc_auc_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cv_score = cross_val_score(model, X_train, y_train, cv = 6)\n",
        "kfold_validation=KFold(10)\n",
        "k_fold =cross_val_score(model,X,y,cv=kfold_validation)\n",
        "print ('Train score is', score)\n",
        "print ('--')\n",
        "print ('Precision score is ', precision)\n",
        "print ('--')\n",
        "print ('ROC Score is', roc)\n",
        "print ('--')\n",
        "print ('Recall Score is ', recall)\n",
        "print ('--')\n",
        "print ('Confusion Matrix\\n\\n', cm)\n",
        "print ('--')\n",
        "print ('CV score is', cv_score)\n",
        "print(np.mean(cv_score))\n",
        "print ('K fold score is', k_fold)\n",
        "print(np.mean(k_fold))"
      ],
      "metadata": {
        "id": "E3OVQmjrkCpb",
        "outputId": "7550b94f-ec6c-41f0-be62-816dccea9b03",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train score is 0.9375\n",
            "--\n",
            "Precision score is  0.7333333333333333\n",
            "--\n",
            "ROC Score is 0.9289215686274509\n",
            "--\n",
            "Recall Score is  0.9166666666666666\n",
            "--\n",
            "Confusion Matrix\n",
            "\n",
            " [[64  4]\n",
            " [ 1 11]]\n",
            "--\n",
            "CV score is [0.92592593 0.90740741 0.94339623 0.86792453 0.90566038 0.94339623]\n",
            "0.9156184486373166\n",
            "K fold score is [0.9   0.95  0.975 0.925 0.95  0.925 0.95  0.925 0.975 0.825]\n",
            "0.9299999999999999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Compute fpr, tpr, thresholds and roc auc\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.plot(fpr, tpr, label='ROC curve (area = %0.3f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # random predictions curve\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('False Positive Rate or (1 - Specifity)')\n",
        "plt.ylabel('True Positive Rate or (Sensitivity)')\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.legend(loc=\"lower right\")"
      ],
      "metadata": {
        "id": "hjyfLd9wkj4U",
        "outputId": "e10245c4-54c5-4d53-b98f-5da1229059fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc106b39ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SVM"
      ],
      "metadata": {
        "id": "fOb6FNKlek75"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = SVC()#KNeighborsClassifier()#SVC#RandomForestClassifier()#AdaBoostClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "score = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "roc = roc_auc_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cv_score = cross_val_score(model, X_train, y_train, cv = 6)\n",
        "kfold_validation=KFold(10)\n",
        "k_fold =cross_val_score(model,X,y,cv=kfold_validation)\n",
        "print ('Train score is', score)\n",
        "print ('--')\n",
        "print ('Precision score is ', precision)\n",
        "print ('--')\n",
        "print ('ROC Score is', roc)\n",
        "print ('--')\n",
        "print ('Recall Score is ', recall)\n",
        "print ('--')\n",
        "print ('Confusion Matrix\\n\\n', cm)\n",
        "print ('--')\n",
        "print ('CV score is', cv_score)\n",
        "print(np.mean(cv_score))\n",
        "print ('K fold score is', k_fold)\n",
        "print(np.mean(k_fold))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkTY9D6zQN08",
        "outputId": "5834b9c3-db6d-446a-f846-e9cd0e8527ae"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train score is 0.9625\n",
            "--\n",
            "Precision score is  1.0\n",
            "--\n",
            "ROC Score is 0.875\n",
            "--\n",
            "Recall Score is  0.75\n",
            "--\n",
            "Confusion Matrix\n",
            "\n",
            " [[68  0]\n",
            " [ 3  9]]\n",
            "--\n",
            "CV score is [0.90740741 0.94444444 0.94339623 0.94339623 0.88679245 0.94339623]\n",
            "0.928138830654554\n",
            "K fold score is [0.925 0.975 0.975 0.975 1.    0.95  0.9   0.975 0.975 0.9  ]\n",
            "0.9550000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute fpr, tpr, thresholds and roc auc\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.plot(fpr, tpr, label='ROC curve (area = %0.3f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # random predictions curve\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('False Positive Rate or (1 - Specifity)')\n",
        "plt.ylabel('True Positive Rate or (Sensitivity)')\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.legend(loc=\"lower right\")"
      ],
      "metadata": {
        "id": "UZi8eRApkMf_",
        "outputId": "e862a8df-0951-41bf-af6b-5dc723dcded5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc106b25a90>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}