Decision_Tree.ipynb

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  "metadata": {
    "colab": {
      "name": "Decision Tree.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "JD6YLcs6vs6f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "993bb4cc-9a69-4146-ce9c-5fbaf8d76825"
      },
      "source": [
        "pip install mglearn"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting mglearn\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/65/38/8aced26fce0b2ae82c3c87cd3b6105f38ca6d9d51704ecc44aa54473e6b9/mglearn-0.1.9.tar.gz (540kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from mglearn) (1.18.5)\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mglearn) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mglearn) (1.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mglearn) (2.8.1)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->mglearn) (1.4.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->mglearn) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler->mglearn) (1.15.0)\n",
            "Building wheels for collected packages: mglearn\n",
            "  Building wheel for mglearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mglearn: filename=mglearn-0.1.9-py2.py3-none-any.whl size=582639 sha256=5592a5f1a85b258e1b7d49b7fa231a4946a58838cc1c814ada638fb5749bcca1\n",
            "  Stored in directory: /root/.cache/pip/wheels/eb/a6/ea/a6a3716233fa62fc561259b5cb1e28f79e9ff3592c0adac5f0\n",
            "Successfully built mglearn\n",
            "Installing collected packages: mglearn\n",
            "Successfully installed mglearn-0.1.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5pHWPkWjM0P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import mglearn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import math\n",
        "%matplotlib inline\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import tree"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPemPfd2mGLk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ae3d113a-455a-4e80-ef8d-f520a77d8f95"
      },
      "source": [
        "file = 'https://raw.githubusercontent.com/jigar-pandit/ml1/master/Tree.csv'\n",
        "df = pd.read_csv(file)\n",
        "df.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Day</th>\n",
              "      <th>Outlook</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Play</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>D1</td>\n",
              "      <td>Sunny</td>\n",
              "      <td>High</td>\n",
              "      <td>Weak</td>\n",
              "      <td>Hot</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>D2</td>\n",
              "      <td>Sunny</td>\n",
              "      <td>High</td>\n",
              "      <td>Strong</td>\n",
              "      <td>Hot</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>D3</td>\n",
              "      <td>Overcast</td>\n",
              "      <td>High</td>\n",
              "      <td>Weak</td>\n",
              "      <td>Hot</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>D4</td>\n",
              "      <td>Rain</td>\n",
              "      <td>High</td>\n",
              "      <td>Weak</td>\n",
              "      <td>Mild</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>D5</td>\n",
              "      <td>Rain</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Weak</td>\n",
              "      <td>Cool</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Day   Outlook Humidity    Wind Temperature Play\n",
              "0  D1     Sunny     High    Weak         Hot   No\n",
              "1  D2     Sunny     High  Strong         Hot   No\n",
              "2  D3  Overcast     High    Weak         Hot  Yes\n",
              "3  D4      Rain     High    Weak        Mild  Yes\n",
              "4  D5      Rain   Normal    Weak        Cool  Yes"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEhVzaDGwTHR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e445029e-a3f8-4ebc-dc94-8261c6ae6f4c"
      },
      "source": [
        "df = df.drop('Day',axis=1)\n",
        "\n",
        "#Encoding categorical data values\n",
        "df['Outlook'] = LabelEncoder().fit_transform(df['Outlook'])\n",
        "df['Humidity'] = LabelEncoder().fit_transform(df['Humidity'])\n",
        "df['Wind'] = LabelEncoder().fit_transform(df['Wind'])\n",
        "df['Temperature'] = LabelEncoder().fit_transform(df['Temperature'])\n",
        "df['Play'] = LabelEncoder().fit_transform(df['Play'])\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Outlook</th>\n",
              "      <th>Humidity</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Play</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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            ],
            "text/plain": [
              "   Outlook  Humidity  Wind  Temperature  Play\n",
              "0        2         0     1            1     0\n",
              "1        2         0     0            1     0\n",
              "2        0         0     1            1     1\n",
              "3        1         0     1            2     1\n",
              "4        1         1     1            0     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TbXtyBhyQN0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "f6e24a7e-9ee1-4d5c-c670-11443db4095b"
      },
      "source": [
        "X = df.drop('Play',axis=1)\n",
        "y = df['Play']\n",
        "play_tree = DecisionTreeClassifier(random_state=0,criterion='entropy')\n",
        "play_tree.fit(X.astype(int), y.astype(int))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=0, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QugutMPUwYDA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import export_graphviz\n",
        "dot = export_graphviz(play_tree, out_file=None, \n",
        "                           feature_names=X.columns,  \n",
        "                           class_names=[\"no\", \"yes\"],  \n",
        "                           filled=True, rounded=True,  \n",
        "                           special_characters=True) "
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kd-hfpgy4Yg-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "outputId": "ca6f7949-66e1-4ed1-cb52-b86d6dff75f9"
      },
      "source": [
        "import graphviz\n",
        "graph = graphviz.Source(dot) \n",
        "graph"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7fc9ba306be0>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"551pt\" height=\"552pt\"\n viewBox=\"0.00 0.00 551.00 552.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 548)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-548 547,-548 547,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#a7d3f3\" stroke=\"#000000\" d=\"M269.5,-544C269.5,-544 183.5,-544 183.5,-544 177.5,-544 171.5,-538 171.5,-532 171.5,-532 171.5,-473 171.5,-473 171.5,-467 177.5,-461 183.5,-461 183.5,-461 269.5,-461 269.5,-461 275.5,-461 281.5,-467 281.5,-473 281.5,-473 281.5,-532 281.5,-532 281.5,-538 275.5,-544 269.5,-544\"/>\n<text text-anchor=\"start\" x=\"183.5\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Outlook ≤ 0.5</text>\n<text text-anchor=\"start\" x=\"179.5\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.94</text>\n<text text-anchor=\"start\" x=\"182.5\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 14</text>\n<text text-anchor=\"start\" x=\"185\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 9]</text>\n<text text-anchor=\"start\" x=\"190\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = yes</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#399de5\" stroke=\"#000000\" d=\"M204,-417.5C204,-417.5 127,-417.5 127,-417.5 121,-417.5 115,-411.5 115,-405.5 115,-405.5 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x=\"244.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 10</text>\n<text text-anchor=\"start\" x=\"247\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5, 5]</text>\n<text text-anchor=\"start\" x=\"255\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = no</text>\n</g>\n<!-- 0&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>0&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M248.1846,-460.8796C252.6423,-452.3236 257.3834,-443.2238 261.9894,-434.3833\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"265.2062,-435.7838 266.7229,-425.2981 258.9983,-432.5494 265.2062,-435.7838\"/>\n<text text-anchor=\"middle\" x=\"274.2322\" y=\"-445.4436\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">False</text>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#eca06a\" 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class=\"edge\">\n<title>3&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M178.8796,-222.8796C169.6931,-213.6931 159.8798,-203.8798 150.4336,-194.4336\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"152.8441,-191.8943 143.2981,-187.2981 147.8943,-196.8441 152.8441,-191.8943\"/>\n</g>\n<!-- 7 -->\n<g id=\"node8\" class=\"node\">\n<title>7</title>\n<path fill=\"#e58139\" stroke=\"#000000\" d=\"M259,-179.5C259,-179.5 182,-179.5 182,-179.5 176,-179.5 170,-173.5 170,-167.5 170,-167.5 170,-123.5 170,-123.5 170,-117.5 176,-111.5 182,-111.5 182,-111.5 259,-111.5 259,-111.5 265,-111.5 271,-117.5 271,-123.5 271,-123.5 271,-167.5 271,-167.5 271,-173.5 265,-179.5 259,-179.5\"/>\n<text text-anchor=\"start\" x=\"178\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"start\" x=\"181\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3</text>\n<text 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font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n<text text-anchor=\"start\" x=\"453\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3</text>\n<text text-anchor=\"start\" x=\"451\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 3]</text>\n<text text-anchor=\"start\" x=\"456\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = yes</text>\n</g>\n<!-- 8&#45;&gt;12 -->\n<g id=\"edge12\" class=\"edge\">\n<title>8&#45;&gt;12</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M404.0662,-222.8796C417.5128,-211.1138 432.1347,-198.3197 445.5451,-186.5855\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"448.0619,-189.034 453.2829,-179.8149 443.4524,-183.766 448.0619,-189.034\"/>\n</g>\n<!-- 10 -->\n<g id=\"node11\" class=\"node\">\n<title>10</title>\n<path fill=\"#e58139\" stroke=\"#000000\" 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
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}