Decision_Tree.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOjD15dk/5vcFJAnJDxTWbO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mostafizur1997/Machine-Learning-Project/blob/main/Decision_Tree.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#import Library"
      ],
      "metadata": {
        "id": "AKBKIqnu4pwg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "9r28myaH3bfM"
      },
      "outputs": [],
      "source": [
        "#import library\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/game_data.csv')"
      ],
      "metadata": {
        "id": "JUe9vGJF5IPM"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#chek the data \n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "72SSO5d65S1k",
        "outputId": "f58a34e4-1bd7-4264-c950-bb6e97a012a9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Days Outlook Temprature  Routine Wear Jacket?\n",
              "0     1   Sunny       Cold   Indoor           No\n",
              "1     2  Cloudy       Cold   Indoor          Yes\n",
              "2     3  Cloudy       Warm  Outdoor           No\n",
              "3     4   Sunny       Cold  Outdoor          Yes\n",
              "4     5  Cloudy       Cold  Outdoor          Yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bea4688f-e6ee-45ce-ab59-4ad6cf91ccbf\">\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>Days</th>\n",
              "      <th>Outlook</th>\n",
              "      <th>Temprature</th>\n",
              "      <th>Routine</th>\n",
              "      <th>Wear Jacket?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Warm</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bea4688f-e6ee-45ce-ab59-4ad6cf91ccbf')\"\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-bea4688f-e6ee-45ce-ab59-4ad6cf91ccbf 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-bea4688f-e6ee-45ce-ab59-4ad6cf91ccbf');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x= df.drop('Days', axis=1, inplace= True)"
      ],
      "metadata": {
        "id": "3WfN-YFg5ZrP"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.head()"
      ],
      "metadata": {
        "id": "bN4um9t75jGI"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XXSuSHJo_VMp",
        "outputId": "f058aef4-f74e-4da8-a4d0-397f36885f97"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Outlook Temprature  Routine Wear Jacket?\n",
              "0   Sunny       Cold   Indoor           No\n",
              "1  Cloudy       Cold   Indoor          Yes\n",
              "2  Cloudy       Warm  Outdoor           No\n",
              "3   Sunny       Cold  Outdoor          Yes\n",
              "4  Cloudy       Cold  Outdoor          Yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f277efa1-253d-41dd-b5bb-e6289ba43446\">\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>Outlook</th>\n",
              "      <th>Temprature</th>\n",
              "      <th>Routine</th>\n",
              "      <th>Wear Jacket?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Warm</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f277efa1-253d-41dd-b5bb-e6289ba43446')\"\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-f277efa1-253d-41dd-b5bb-e6289ba43446 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-f277efa1-253d-41dd-b5bb-e6289ba43446');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#again drop the output column(Wear Jaket?)\n",
        "x= df.drop('Wear Jacket?', axis=1)"
      ],
      "metadata": {
        "id": "JWAhXAza5lAh"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dBEF43dqAX-y",
        "outputId": "8d609eab-244b-4f19-e381-b2c3e01fcc20"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Outlook Temprature  Routine\n",
              "0   Sunny       Cold   Indoor\n",
              "1  Cloudy       Cold   Indoor\n",
              "2  Cloudy       Warm  Outdoor\n",
              "3   Sunny       Cold  Outdoor\n",
              "4  Cloudy       Cold  Outdoor"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-913733d3-ca71-4f96-89f3-886fa9426fcd\">\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>Outlook</th>\n",
              "      <th>Temprature</th>\n",
              "      <th>Routine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Warm</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-913733d3-ca71-4f96-89f3-886fa9426fcd')\"\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-913733d3-ca71-4f96-89f3-886fa9426fcd 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-913733d3-ca71-4f96-89f3-886fa9426fcd');\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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xZUEDZ5gAFPe",
        "outputId": "82b62282-db06-4882-a5eb-6c11a6ee0482"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Outlook Temprature  Routine Wear Jacket?\n",
              "0   Sunny       Cold   Indoor           No\n",
              "1  Cloudy       Cold   Indoor          Yes\n",
              "2  Cloudy       Warm  Outdoor           No\n",
              "3   Sunny       Cold  Outdoor          Yes\n",
              "4  Cloudy       Cold  Outdoor          Yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b77f322-8d4d-42f5-a8e6-c541a16d00b9\">\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>Outlook</th>\n",
              "      <th>Temprature</th>\n",
              "      <th>Routine</th>\n",
              "      <th>Wear Jacket?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Warm</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b77f322-8d4d-42f5-a8e6-c541a16d00b9')\"\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-6b77f322-8d4d-42f5-a8e6-c541a16d00b9 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-6b77f322-8d4d-42f5-a8e6-c541a16d00b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df[['Wear Jacket?']]"
      ],
      "metadata": {
        "id": "DIVZhj2d-1NC"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#check the dataset\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UPdbKcfw58kA",
        "outputId": "3dd7df50-1e04-4c6e-cd86-02b0687cfe02"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Wear Jacket?\n",
              "0           No\n",
              "1          Yes\n",
              "2           No\n",
              "3          Yes\n",
              "4          Yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-51900d10-7a46-4622-a924-c54715afd929\">\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>Wear Jacket?</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51900d10-7a46-4622-a924-c54715afd929')\"\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-51900d10-7a46-4622-a924-c54715afd929 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-51900d10-7a46-4622-a924-c54715afd929');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sCFkM6p1Fefk",
        "outputId": "c8e4ef30-713d-4052-fd7d-be71cc03f78b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Outlook Temprature  Routine\n",
              "0   Sunny       Cold   Indoor\n",
              "1  Cloudy       Cold   Indoor\n",
              "2  Cloudy       Warm  Outdoor\n",
              "3   Sunny       Cold  Outdoor\n",
              "4  Cloudy       Cold  Outdoor"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d449f5c7-a5c9-4869-92bb-793a416186a1\">\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>Outlook</th>\n",
              "      <th>Temprature</th>\n",
              "      <th>Routine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Warm</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d449f5c7-a5c9-4869-92bb-793a416186a1')\"\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-d449f5c7-a5c9-4869-92bb-793a416186a1 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-d449f5c7-a5c9-4869-92bb-793a416186a1');\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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Encoding"
      ],
      "metadata": {
        "id": "3hd6HgHHH_q0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import library\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "#import pandas library to check the data type\n",
        "from pandas.core.dtypes.common import is_numeric_dtype\n"
      ],
      "metadata": {
        "id": "OPJtXXO6592h"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in x.columns:\n",
        "  if is_numeric_dtype(x[col]):\n",
        "    continue\n",
        "  else:\n",
        "    x[col]=le.fit_transform(x[col])"
      ],
      "metadata": {
        "id": "ES6hY_pY6ury"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Wvu1uVJe8Aep",
        "outputId": "d3f3a76f-a786-4dbe-9b4c-c1c260a5b2cf"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Outlook  Temprature  Routine\n",
              "0        1           0        0\n",
              "1        0           0        0\n",
              "2        0           1        1\n",
              "3        1           0        1\n",
              "4        0           0        1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-acb255a7-48f5-4961-a554-1423a7727faf\">\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>Outlook</th>\n",
              "      <th>Temprature</th>\n",
              "      <th>Routine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-acb255a7-48f5-4961-a554-1423a7727faf')\"\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-acb255a7-48f5-4961-a554-1423a7727faf 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-acb255a7-48f5-4961-a554-1423a7727faf');\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": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#In here\n",
        "##1= sunny\n",
        "##0= cloudy\n",
        "##0= cold\n",
        "##1= warm\n",
        "##0= indoor\n",
        "##1= outdoor"
      ],
      "metadata": {
        "id": "ipI0pbTh9SzQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Apply DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "AAtJWA8YIDx6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import library\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "clf= DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "hPuDC4tg9CC9"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "OIHAtp3t-f46",
        "outputId": "74e5b968-0fe9-4237-f3dd-d23db6d7ac4c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict([[1,0,0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "teBRo615FDIG",
        "outputId": "93dda01b-3f16-4f86-af55-853991099b31"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict([[1,0,1]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YfcXKqJ-GaHe",
        "outputId": "793debf9-abc7-4cb6-e2fb-923c59fd04e6"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict([[1,1,0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FhzQTPx_Ggc0",
        "outputId": "53e6c319-5960-4c90-d06f-a6354eb76493"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualization"
      ],
      "metadata": {
        "id": "y3immE7qG5dd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import library\n",
        "from sklearn import tree\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "G9ZYFzcyGmdI"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tree.plot_tree(clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "hwXTd8lQG-DO",
        "outputId": "12c86805-6b5f-4882-84b7-3e732be6c066"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.6, 0.875, 'x[1] <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
              " Text(0.4, 0.625, 'x[0] <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
              " Text(0.2, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
              " Text(0.6, 0.375, 'x[2] <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.4, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.8, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.8, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "tree.plot_tree(clf, feature_names= x.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "96Tq1Z28HDdE",
        "outputId": "8670de70-fc1d-4640-a081-3a97ef5003f0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.6, 0.875, 'Temprature <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
              " Text(0.4, 0.625, 'Outlook <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
              " Text(0.2, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
              " Text(0.6, 0.375, 'Routine <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.4, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.8, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.8, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "tree.plot_tree(clf, feature_names= x.columns, rounded= True, filled = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "5cqUppnRHRRH",
        "outputId": "22d20392-4de9-41fc-9f28-27e8b9e72aa2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.6, 0.875, 'Temprature <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
              " Text(0.4, 0.625, 'Outlook <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
              " Text(0.2, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
              " Text(0.6, 0.375, 'Routine <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.4, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.8, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.8, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ucHaSUVHHs8o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}