Logistic_regression_bootcamp_casestudy1_suv.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNLkTLkkdQOevUsxEq1uk4z",
      "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/dheeraj0000/2216177_AIML_Bootcamp_2023/blob/main/Logistic_regression_bootcamp_casestudy1_suv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "1FNkNzakVYZF"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv('/content/SUV_Purchase.csv')"
      ],
      "metadata": {
        "id": "Cv-RInuYmQdh"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "x7oFqWVtmdS7",
        "outputId": "d06c667f-df05-4cc4-d700-c03f3572609b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
              "0  15624510    Male   19            19000          0\n",
              "1  15810944    Male   35            20000          0\n",
              "2  15668575  Female   26            43000          0\n",
              "3  15603246  Female   27            57000          0\n",
              "4  15804002    Male   19            76000          0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-46c3c68b-f610-4168-bde5-3450f7cf2168\">\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>User ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15624510</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15810944</td>\n",
              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15668575</td>\n",
              "      <td>Female</td>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15603246</td>\n",
              "      <td>Female</td>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15804002</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46c3c68b-f610-4168-bde5-3450f7cf2168')\"\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-46c3c68b-f610-4168-bde5-3450f7cf2168 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-46c3c68b-f610-4168-bde5-3450f7cf2168');\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": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.isnull().any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_RsSh86mjgy",
        "outputId": "ab9323fd-2146-42ed-fcc1-8a98831b360a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "User ID            False\n",
            "Gender             False\n",
            "Age                False\n",
            "EstimatedSalary    False\n",
            "Purchased          False\n",
            "dtype: bool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sq6YaZqfnc7N",
        "outputId": "5c5697aa-0506-4e4c-cfb9-30971588d0a8"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "User ID             int64\n",
            "Gender             object\n",
            "Age                 int64\n",
            "EstimatedSalary     int64\n",
            "Purchased           int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3LBMIG9nkDq",
        "outputId": "b0ee8be6-01b6-4d32-a11c-f6ad4ee4fb78"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 5 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   User ID          400 non-null    int64 \n",
            " 1   Gender           400 non-null    object\n",
            " 2   Age              400 non-null    int64 \n",
            " 3   EstimatedSalary  400 non-null    int64 \n",
            " 4   Purchased        400 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 15.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=dataset.drop(['User ID','Gender'],axis=1)"
      ],
      "metadata": {
        "id": "-zoDl4K4nm3I"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "cyMMWr3urZPW",
        "outputId": "9cb7c4e7-7bf2-45fb-c80e-8837319e1a60"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  EstimatedSalary  Purchased\n",
              "0     19            19000          0\n",
              "1     35            20000          0\n",
              "2     26            43000          0\n",
              "3     27            57000          0\n",
              "4     19            76000          0\n",
              "..   ...              ...        ...\n",
              "395   46            41000          1\n",
              "396   51            23000          1\n",
              "397   50            20000          1\n",
              "398   36            33000          0\n",
              "399   49            36000          1\n",
              "\n",
              "[400 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85617373-c641-472c-b7a8-3ec677487a08\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>46</td>\n",
              "      <td>41000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>51</td>\n",
              "      <td>23000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>50</td>\n",
              "      <td>20000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>36</td>\n",
              "      <td>33000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>49</td>\n",
              "      <td>36000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85617373-c641-472c-b7a8-3ec677487a08')\"\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-85617373-c641-472c-b7a8-3ec677487a08 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-85617373-c641-472c-b7a8-3ec677487a08');\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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "ffjuhOc8rtax"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = dataset.iloc[:, [2, 3]].values\n",
        "# output\n",
        "y = dataset.iloc[:, 4].values"
      ],
      "metadata": {
        "id": "zUhMS2_4z3Bo"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n74DSWnGxrwn",
        "outputId": "327398d0-0178-41ea-d960-fc779b006bcc"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[    19,  19000],\n",
              "       [    35,  20000],\n",
              "       [    26,  43000],\n",
              "       [    27,  57000],\n",
              "       [    19,  76000],\n",
              "       [    27,  58000],\n",
              "       [    27,  84000],\n",
              "       [    32, 150000],\n",
              "       [    25,  33000],\n",
              "       [    35,  65000],\n",
              "       [    26,  80000],\n",
              "       [    26,  52000],\n",
              "       [    20,  86000],\n",
              "       [    32,  18000],\n",
              "       [    18,  82000],\n",
              "       [    29,  80000],\n",
              "       [    47,  25000],\n",
              "       [    45,  26000],\n",
              "       [    46,  28000],\n",
              "       [    48,  29000],\n",
              "       [    45,  22000],\n",
              "       [    47,  49000],\n",
              "       [    48,  41000],\n",
              "       [    45,  22000],\n",
              "       [    46,  23000],\n",
              "       [    47,  20000],\n",
              "       [    49,  28000],\n",
              "       [    47,  30000],\n",
              "       [    29,  43000],\n",
              "       [    31,  18000],\n",
              "       [    31,  74000],\n",
              "       [    27, 137000],\n",
              "       [    21,  16000],\n",
              "       [    28,  44000],\n",
              "       [    27,  90000],\n",
              "       [    35,  27000],\n",
              "       [    33,  28000],\n",
              "       [    30,  49000],\n",
              "       [    26,  72000],\n",
              "       [    27,  31000],\n",
              "       [    27,  17000],\n",
              "       [    33,  51000],\n",
              "       [    35, 108000],\n",
              "       [    30,  15000],\n",
              "       [    28,  84000],\n",
              "       [    23,  20000],\n",
              "       [    25,  79000],\n",
              "       [    27,  54000],\n",
              "       [    30, 135000],\n",
              "       [    31,  89000],\n",
              "       [    24,  32000],\n",
              "       [    18,  44000],\n",
              "       [    29,  83000],\n",
              "       [    35,  23000],\n",
              "       [    27,  58000],\n",
              "       [    24,  55000],\n",
              "       [    23,  48000],\n",
              "       [    28,  79000],\n",
              "       [    22,  18000],\n",
              "       [    32, 117000],\n",
              "       [    27,  20000],\n",
              "       [    25,  87000],\n",
              "       [    23,  66000],\n",
              "       [    32, 120000],\n",
              "       [    59,  83000],\n",
              "       [    24,  58000],\n",
              "       [    24,  19000],\n",
              "       [    23,  82000],\n",
              "       [    22,  63000],\n",
              "       [    31,  68000],\n",
              "       [    25,  80000],\n",
              "       [    24,  27000],\n",
              "       [    20,  23000],\n",
              "       [    33, 113000],\n",
              "       [    32,  18000],\n",
              "       [    34, 112000],\n",
              "       [    18,  52000],\n",
              "       [    22,  27000],\n",
              "       [    28,  87000],\n",
              "       [    26,  17000],\n",
              "       [    30,  80000],\n",
              "       [    39,  42000],\n",
              "       [    20,  49000],\n",
              "       [    35,  88000],\n",
              "       [    30,  62000],\n",
              "       [    31, 118000],\n",
              "       [    24,  55000],\n",
              "       [    28,  85000],\n",
              "       [    26,  81000],\n",
              "       [    35,  50000],\n",
              "       [    22,  81000],\n",
              "       [    30, 116000],\n",
              "       [    26,  15000],\n",
              "       [    29,  28000],\n",
              "       [    29,  83000],\n",
              "       [    35,  44000],\n",
              "       [    35,  25000],\n",
              "       [    28, 123000],\n",
              "       [    35,  73000],\n",
              "       [    28,  37000],\n",
              "       [    27,  88000],\n",
              "       [    28,  59000],\n",
              "       [    32,  86000],\n",
              "       [    33, 149000],\n",
              "       [    19,  21000],\n",
              "       [    21,  72000],\n",
              "       [    26,  35000],\n",
              "       [    27,  89000],\n",
              "       [    26,  86000],\n",
              "       [    38,  80000],\n",
              "       [    39,  71000],\n",
              "       [    37,  71000],\n",
              "       [    38,  61000],\n",
              "       [    37,  55000],\n",
              "       [    42,  80000],\n",
              "       [    40,  57000],\n",
              "       [    35,  75000],\n",
              "       [    36,  52000],\n",
              "       [    40,  59000],\n",
              "       [    41,  59000],\n",
              "       [    36,  75000],\n",
              "       [    37,  72000],\n",
              "       [    40,  75000],\n",
              "       [    35,  53000],\n",
              "       [    41,  51000],\n",
              "       [    39,  61000],\n",
              "       [    42,  65000],\n",
              "       [    26,  32000],\n",
              "       [    30,  17000],\n",
              "       [    26,  84000],\n",
              "       [    31,  58000],\n",
              "       [    33,  31000],\n",
              "       [    30,  87000],\n",
              "       [    21,  68000],\n",
              "       [    28,  55000],\n",
              "       [    23,  63000],\n",
              "       [    20,  82000],\n",
              "       [    30, 107000],\n",
              "       [    28,  59000],\n",
              "       [    19,  25000],\n",
              "       [    19,  85000],\n",
              "       [    18,  68000],\n",
              "       [    35,  59000],\n",
              "       [    30,  89000],\n",
              "       [    34,  25000],\n",
              "       [    24,  89000],\n",
              "       [    27,  96000],\n",
              "       [    41,  30000],\n",
              "       [    29,  61000],\n",
              "       [    20,  74000],\n",
              "       [    26,  15000],\n",
              "       [    41,  45000],\n",
              "       [    31,  76000],\n",
              "       [    36,  50000],\n",
              "       [    40,  47000],\n",
              "       [    31,  15000],\n",
              "       [    46,  59000],\n",
              "       [    29,  75000],\n",
              "       [    26,  30000],\n",
              "       [    32, 135000],\n",
              "       [    32, 100000],\n",
              "       [    25,  90000],\n",
              "       [    37,  33000],\n",
              "       [    35,  38000],\n",
              "       [    33,  69000],\n",
              "       [    18,  86000],\n",
              "       [    22,  55000],\n",
              "       [    35,  71000],\n",
              "       [    29, 148000],\n",
              "       [    29,  47000],\n",
              "       [    21,  88000],\n",
              "       [    34, 115000],\n",
              "       [    26, 118000],\n",
              "       [    34,  43000],\n",
              "       [    34,  72000],\n",
              "       [    23,  28000],\n",
              "       [    35,  47000],\n",
              "       [    25,  22000],\n",
              "       [    24,  23000],\n",
              "       [    31,  34000],\n",
              "       [    26,  16000],\n",
              "       [    31,  71000],\n",
              "       [    32, 117000],\n",
              "       [    33,  43000],\n",
              "       [    33,  60000],\n",
              "       [    31,  66000],\n",
              "       [    20,  82000],\n",
              "       [    33,  41000],\n",
              "       [    35,  72000],\n",
              "       [    28,  32000],\n",
              "       [    24,  84000],\n",
              "       [    19,  26000],\n",
              "       [    29,  43000],\n",
              "       [    19,  70000],\n",
              "       [    28,  89000],\n",
              "       [    34,  43000],\n",
              "       [    30,  79000],\n",
              "       [    20,  36000],\n",
              "       [    26,  80000],\n",
              "       [    35,  22000],\n",
              "       [    35,  39000],\n",
              "       [    49,  74000],\n",
              "       [    39, 134000],\n",
              "       [    41,  71000],\n",
              "       [    58, 101000],\n",
              "       [    47,  47000],\n",
              "       [    55, 130000],\n",
              "       [    52, 114000],\n",
              "       [    40, 142000],\n",
              "       [    46,  22000],\n",
              "       [    48,  96000],\n",
              "       [    52, 150000],\n",
              "       [    59,  42000],\n",
              "       [    35,  58000],\n",
              "       [    47,  43000],\n",
              "       [    60, 108000],\n",
              "       [    49,  65000],\n",
              "       [    40,  78000],\n",
              "       [    46,  96000],\n",
              "       [    59, 143000],\n",
              "       [    41,  80000],\n",
              "       [    35,  91000],\n",
              "       [    37, 144000],\n",
              "       [    60, 102000],\n",
              "       [    35,  60000],\n",
              "       [    37,  53000],\n",
              "       [    36, 126000],\n",
              "       [    56, 133000],\n",
              "       [    40,  72000],\n",
              "       [    42,  80000],\n",
              "       [    35, 147000],\n",
              "       [    39,  42000],\n",
              "       [    40, 107000],\n",
              "       [    49,  86000],\n",
              "       [    38, 112000],\n",
              "       [    46,  79000],\n",
              "       [    40,  57000],\n",
              "       [    37,  80000],\n",
              "       [    46,  82000],\n",
              "       [    53, 143000],\n",
              "       [    42, 149000],\n",
              "       [    38,  59000],\n",
              "       [    50,  88000],\n",
              "       [    56, 104000],\n",
              "       [    41,  72000],\n",
              "       [    51, 146000],\n",
              "       [    35,  50000],\n",
              "       [    57, 122000],\n",
              "       [    41,  52000],\n",
              "       [    35,  97000],\n",
              "       [    44,  39000],\n",
              "       [    37,  52000],\n",
              "       [    48, 134000],\n",
              "       [    37, 146000],\n",
              "       [    50,  44000],\n",
              "       [    52,  90000],\n",
              "       [    41,  72000],\n",
              "       [    40,  57000],\n",
              "       [    58,  95000],\n",
              "       [    45, 131000],\n",
              "       [    35,  77000],\n",
              "       [    36, 144000],\n",
              "       [    55, 125000],\n",
              "       [    35,  72000],\n",
              "       [    48,  90000],\n",
              "       [    42, 108000],\n",
              "       [    40,  75000],\n",
              "       [    37,  74000],\n",
              "       [    47, 144000],\n",
              "       [    40,  61000],\n",
              "       [    43, 133000],\n",
              "       [    59,  76000],\n",
              "       [    60,  42000],\n",
              "       [    39, 106000],\n",
              "       [    57,  26000],\n",
              "       [    57,  74000],\n",
              "       [    38,  71000],\n",
              "       [    49,  88000],\n",
              "       [    52,  38000],\n",
              "       [    50,  36000],\n",
              "       [    59,  88000],\n",
              "       [    35,  61000],\n",
              "       [    37,  70000],\n",
              "       [    52,  21000],\n",
              "       [    48, 141000],\n",
              "       [    37,  93000],\n",
              "       [    37,  62000],\n",
              "       [    48, 138000],\n",
              "       [    41,  79000],\n",
              "       [    37,  78000],\n",
              "       [    39, 134000],\n",
              "       [    49,  89000],\n",
              "       [    55,  39000],\n",
              "       [    37,  77000],\n",
              "       [    35,  57000],\n",
              "       [    36,  63000],\n",
              "       [    42,  73000],\n",
              "       [    43, 112000],\n",
              "       [    45,  79000],\n",
              "       [    46, 117000],\n",
              "       [    58,  38000],\n",
              "       [    48,  74000],\n",
              "       [    37, 137000],\n",
              "       [    37,  79000],\n",
              "       [    40,  60000],\n",
              "       [    42,  54000],\n",
              "       [    51, 134000],\n",
              "       [    47, 113000],\n",
              "       [    36, 125000],\n",
              "       [    38,  50000],\n",
              "       [    42,  70000],\n",
              "       [    39,  96000],\n",
              "       [    38,  50000],\n",
              "       [    49, 141000],\n",
              "       [    39,  79000],\n",
              "       [    39,  75000],\n",
              "       [    54, 104000],\n",
              "       [    35,  55000],\n",
              "       [    45,  32000],\n",
              "       [    36,  60000],\n",
              "       [    52, 138000],\n",
              "       [    53,  82000],\n",
              "       [    41,  52000],\n",
              "       [    48,  30000],\n",
              "       [    48, 131000],\n",
              "       [    41,  60000],\n",
              "       [    41,  72000],\n",
              "       [    42,  75000],\n",
              "       [    36, 118000],\n",
              "       [    47, 107000],\n",
              "       [    38,  51000],\n",
              "       [    48, 119000],\n",
              "       [    42,  65000],\n",
              "       [    40,  65000],\n",
              "       [    57,  60000],\n",
              "       [    36,  54000],\n",
              "       [    58, 144000],\n",
              "       [    35,  79000],\n",
              "       [    38,  55000],\n",
              "       [    39, 122000],\n",
              "       [    53, 104000],\n",
              "       [    35,  75000],\n",
              "       [    38,  65000],\n",
              "       [    47,  51000],\n",
              "       [    47, 105000],\n",
              "       [    41,  63000],\n",
              "       [    53,  72000],\n",
              "       [    54, 108000],\n",
              "       [    39,  77000],\n",
              "       [    38,  61000],\n",
              "       [    38, 113000],\n",
              "       [    37,  75000],\n",
              "       [    42,  90000],\n",
              "       [    37,  57000],\n",
              "       [    36,  99000],\n",
              "       [    60,  34000],\n",
              "       [    54,  70000],\n",
              "       [    41,  72000],\n",
              "       [    40,  71000],\n",
              "       [    42,  54000],\n",
              "       [    43, 129000],\n",
              "       [    53,  34000],\n",
              "       [    47,  50000],\n",
              "       [    42,  79000],\n",
              "       [    42, 104000],\n",
              "       [    59,  29000],\n",
              "       [    58,  47000],\n",
              "       [    46,  88000],\n",
              "       [    38,  71000],\n",
              "       [    54,  26000],\n",
              "       [    60,  46000],\n",
              "       [    60,  83000],\n",
              "       [    39,  73000],\n",
              "       [    59, 130000],\n",
              "       [    37,  80000],\n",
              "       [    46,  32000],\n",
              "       [    46,  74000],\n",
              "       [    42,  53000],\n",
              "       [    41,  87000],\n",
              "       [    58,  23000],\n",
              "       [    42,  64000],\n",
              "       [    48,  33000],\n",
              "       [    44, 139000],\n",
              "       [    49,  28000],\n",
              "       [    57,  33000],\n",
              "       [    56,  60000],\n",
              "       [    49,  39000],\n",
              "       [    39,  71000],\n",
              "       [    47,  34000],\n",
              "       [    48,  35000],\n",
              "       [    48,  33000],\n",
              "       [    47,  23000],\n",
              "       [    45,  45000],\n",
              "       [    60,  42000],\n",
              "       [    39,  59000],\n",
              "       [    46,  41000],\n",
              "       [    51,  23000],\n",
              "       [    50,  20000],\n",
              "       [    36,  33000],\n",
              "       [    49,  36000]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "s-gPozjyz4i4"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#normalizing(to transform features to be o a similar scale) the data-standardscaler\n",
        "# standardscaler it helps converting all numerical values into the one range\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_x = StandardScaler()\n",
        "xtrain = sc_x.fit_transform(xtrain)#normalizing\n",
        "xtest = sc_x.transform(xtest)\n",
        "print ('train value:',xtrain[0:1, :])\n",
        "print ('test value:',xtest[0:1, :])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jPZL0fo81-PK",
        "outputId": "07b7461d-2ae1-4de6-dc3e-f336709c80e5"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train value: [[ 0.58164944 -0.88670699]]\n",
            "test value: [[-0.80480212  0.50496393]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression(random_state = 0)"
      ],
      "metadata": {
        "id": "cztPOowOz8ML"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(xtrain, ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "tWsXCxoe0B1c",
        "outputId": "6e8743a2-58d9-44b9-9d14-b98586a54814"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(xtest)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ns36TZ3J0Eed",
        "outputId": "30af7c89-5313-4eb7-d285-1100c1e45efd"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
              "       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(ytest, y_pred)\n",
        "print (\"Confusion Matrix : \\n\", cm)\n",
        "from sklearn.metrics import accuracy_score\n",
        "print (\"Accuracy : \", accuracy_score(ytest, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nleYlkyw1gz8",
        "outputId": "2f72d745-e22e-49ad-a35b-774164568cb1"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix : \n",
            " [[65  3]\n",
            " [ 8 24]]\n",
            "Accuracy :  0.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x,y) #internally it performs the ypred and compares with y actual"
      ],
      "metadata": {
        "id": "nNwarKu-cheX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15363d31-a37c-49ab-bab8-7c84108d63b1"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3575"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
        "print(\"confusion matrix for the training data:\")\n"
      ],
      "metadata": {
        "id": "2uwmEUVUFTgy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "febf97ef-b185-4cc9-f66b-508f577ac1ec"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "confusion matrix for the training data:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NAIVEY'S BAYES ALGORITHM\n",
        "(BAYES THEOREM)"
      ],
      "metadata": {
        "id": "FMnSKaAiSZiZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "-MI13doncRqk"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_x = StandardScaler()\n",
        "xtrain = sc_x.fit_transform(xtrain)#normalizing\n",
        "xtest = sc_x.transform(xtest)"
      ],
      "metadata": {
        "id": "oEh6Ll9gTse_"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(xtrain, ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "4duyRfNZQXGm",
        "outputId": "c262c6ac-eb67-4310-d119-9b14131de522"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = gnb.predict(xtest)"
      ],
      "metadata": {
        "id": "13vSgDB5QdEz"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print (\"Accuracy : \", accuracy_score(ytest, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6ikfz4JQmGw",
        "outputId": "5b3cc3d5-606d-4a73-e709-671d3faf9516"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k9sPWI7jTjp-"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rO6L3i2MQzjk",
        "outputId": "64b93d70-6ff9-4353-e2a0-ae5b3ac9d0a2"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3575"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(xtest,ytest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUaMI2CZypGc",
        "outputId": "0c9e4be2-cb3e-49f3-f169-9814b47c65d0"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.89"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix=dataset.corr()\n",
        "print(corr_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zFyllV4b1xi9",
        "outputId": "a627d529-9b1c-4707-fd31-31ed2c396b01"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  User ID       Age  EstimatedSalary  Purchased\n",
            "User ID          1.000000 -0.000721         0.071097   0.007120\n",
            "Age             -0.000721  1.000000         0.155238   0.622454\n",
            "EstimatedSalary  0.071097  0.155238         1.000000   0.362083\n",
            "Purchased        0.007120  0.622454         0.362083   1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "NNlgBWMt2D__"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(corr_matrix,annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "aj9jUWrk10jM",
        "outputId": "bf7c4199-6fae-472d-a04b-9e333b15c053"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UbNw_Qqr2JQy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}