{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Decision Tree\n"
      ],
      "metadata": {
        "id": "olXHFemFxw_A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "yqTvhnXux1j1"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "PlayTennis = pd.read_csv(\"PlayTennis.csv\")"
      ],
      "metadata": {
        "id": "z7V8SbmZyAhL"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(PlayTennis.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhEpzCP5yL6G",
        "outputId": "b7ed02e5-d284-4635-93cf-231a88d297e5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    outlook  temp humidity  windy play\n",
            "0     sunny   hot     high  False   no\n",
            "1     sunny   hot     high   True   no\n",
            "2  overcast   hot     high  False  yes\n",
            "3     rainy  mild     high  False  yes\n",
            "4     rainy  cool   normal  False  yes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "Le = LabelEncoder()"
      ],
      "metadata": {
        "id": "qQ9W2kmkyN2F"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Le)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_dgr7CtyQYk",
        "outputId": "014dd3aa-3940-4016-9768-3b6781bf95cf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LabelEncoder()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PlayTennis['outlook'] = Le.fit_transform(PlayTennis['outlook'])\n",
        "PlayTennis['temp'] = Le.fit_transform(PlayTennis['temp'])\n",
        "PlayTennis['humidity'] = Le.fit_transform(PlayTennis['humidity'])\n",
        "PlayTennis['windy'] = Le.fit_transform(PlayTennis['windy'])\n",
        "PlayTennis['play'] = Le.fit_transform(PlayTennis['play'])"
      ],
      "metadata": {
        "id": "inlqtnhqyRuU"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = PlayTennis['play']\n",
        "X = PlayTennis.drop(['play'],axis=1)"
      ],
      "metadata": {
        "id": "a7c0nbWUyT8U"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVHsazo0yYOS",
        "outputId": "cde7346c-889f-453e-bee9-3e8c64993d31"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0     0\n",
            "1     0\n",
            "2     1\n",
            "3     1\n",
            "4     1\n",
            "5     0\n",
            "6     1\n",
            "7     0\n",
            "8     1\n",
            "9     1\n",
            "10    1\n",
            "11    1\n",
            "12    1\n",
            "13    0\n",
            "Name: play, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree"
      ],
      "metadata": {
        "id": "8xFHA0EVyYzc"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = tree.DecisionTreeClassifier(criterion = 'entropy')\n",
        "clf = clf.fit(X, y)"
      ],
      "metadata": {
        "id": "6-AWugJxya2K"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tree.plot_tree(clf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "nXM-fQdqyeS2",
        "outputId": "e45e526b-4550-4640-8a6a-b7708011aef2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.4, 0.9, 'x[0] <= 0.5\\nentropy = 0.94\\nsamples = 14\\nvalue = [5, 9]'),\n",
              " Text(0.3, 0.7, 'entropy = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
              " Text(0.5, 0.7, 'x[2] <= 0.5\\nentropy = 1.0\\nsamples = 10\\nvalue = [5, 5]'),\n",
              " Text(0.3, 0.5, 'x[0] <= 1.5\\nentropy = 0.722\\nsamples = 5\\nvalue = [4, 1]'),\n",
              " Text(0.2, 0.3, 'x[3] <= 0.5\\nentropy = 1.0\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.1, 0.1, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.3, 0.1, 'entropy = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.4, 0.3, 'entropy = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
              " Text(0.7, 0.5, 'x[3] <= 0.5\\nentropy = 0.722\\nsamples = 5\\nvalue = [1, 4]'),\n",
              " Text(0.6, 0.3, 'entropy = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
              " Text(0.8, 0.3, 'x[0] <= 1.5\\nentropy = 1.0\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.7, 0.1, 'entropy = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.9, 0.1, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1]')]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZUaiidQqyfW1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}