ch18_decision_tree_classification.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ThatNinjaGuy/Machine-learning-A-Z-Course/blob/main/ch18_decision_tree_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MRC0e0KhQ0S"
      },
      "source": [
        "# Decision Tree Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWd1UlMnhT2s"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvGPUQaHhXfL"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1VMqkGvhc3-"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M52QDmyzhh9s"
      },
      "source": [
        "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvxIPVyMhmKp"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVzJWAXIhxoC"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3nS3-6r1i2B",
        "outputId": "c47d1c4e-6c6f-4727-d88c-185643b6a2b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dpDLojm1mVG",
        "outputId": "95e92118-d5e7-4ab7-c6bd-b20605ecd2e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(y_train)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 1\n",
            " 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1\n",
            " 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0\n",
            " 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0\n",
            " 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0\n",
            " 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0\n",
            " 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0\n",
            " 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1\n",
            " 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbb7i0DH1qui",
        "outputId": "2d003278-f545-4c0b-bbe3-6296923a27de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(X_test)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[    30  87000]\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kj1hnFAR1s5w",
        "outputId": "3fa32363-97e8-4411-f9d4-68c1f03736db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(y_test)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0\n",
            " 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n",
            " 0 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 1 0 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kW3c7UYih0hT"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fQlDPKCh8sc"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syrnD1Op2BSR",
        "outputId": "92083504-ef46-400e-e2a1-c05bf501ba00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.58164944 -0.88670699]\n",
            " [-0.60673761  1.46173768]\n",
            " [-0.01254409 -0.5677824 ]\n",
            " [-0.60673761  1.89663484]\n",
            " [ 1.37390747 -1.40858358]\n",
            " [ 1.47293972  0.99784738]\n",
            " [ 0.08648817 -0.79972756]\n",
            " [-0.01254409 -0.24885782]\n",
            " [-0.21060859 -0.5677824 ]\n",
            " [-0.21060859 -0.19087153]\n",
            " [-0.30964085 -1.29261101]\n",
            " [-0.30964085 -0.5677824 ]\n",
            " [ 0.38358493  0.09905991]\n",
            " [ 0.8787462  -0.59677555]\n",
            " [ 2.06713324 -1.17663843]\n",
            " [ 1.07681071 -0.13288524]\n",
            " [ 0.68068169  1.78066227]\n",
            " [-0.70576986  0.56295021]\n",
            " [ 0.77971394  0.35999821]\n",
            " [ 0.8787462  -0.53878926]\n",
            " [-1.20093113 -1.58254245]\n",
            " [ 2.1661655   0.93986109]\n",
            " [-0.01254409  1.22979253]\n",
            " [ 0.18552042  1.08482681]\n",
            " [ 0.38358493 -0.48080297]\n",
            " [-0.30964085 -0.30684411]\n",
            " [ 0.97777845 -0.8287207 ]\n",
            " [ 0.97777845  1.8676417 ]\n",
            " [-0.01254409  1.25878567]\n",
            " [-0.90383437  2.27354572]\n",
            " [-1.20093113 -1.58254245]\n",
            " [ 2.1661655  -0.79972756]\n",
            " [-1.39899564 -1.46656987]\n",
            " [ 0.38358493  2.30253886]\n",
            " [ 0.77971394  0.76590222]\n",
            " [-1.00286662 -0.30684411]\n",
            " [ 0.08648817  0.76590222]\n",
            " [-1.00286662  0.56295021]\n",
            " [ 0.28455268  0.07006676]\n",
            " [ 0.68068169 -1.26361786]\n",
            " [-0.50770535 -0.01691267]\n",
            " [-1.79512465  0.35999821]\n",
            " [-0.70576986  0.12805305]\n",
            " [ 0.38358493  0.30201192]\n",
            " [-0.30964085  0.07006676]\n",
            " [-0.50770535  2.30253886]\n",
            " [ 0.18552042  0.04107362]\n",
            " [ 1.27487521  2.21555943]\n",
            " [ 0.77971394  0.27301877]\n",
            " [-0.30964085  0.1570462 ]\n",
            " [-0.01254409 -0.53878926]\n",
            " [-0.21060859  0.1570462 ]\n",
            " [-0.11157634  0.24402563]\n",
            " [-0.01254409 -0.24885782]\n",
            " [ 2.1661655   1.11381995]\n",
            " [-1.79512465  0.35999821]\n",
            " [ 1.86906873  0.12805305]\n",
            " [ 0.38358493 -0.13288524]\n",
            " [-1.20093113  0.30201192]\n",
            " [ 0.77971394  1.37475825]\n",
            " [-0.30964085 -0.24885782]\n",
            " [-1.6960924  -0.04590581]\n",
            " [-1.00286662 -0.74174127]\n",
            " [ 0.28455268  0.50496393]\n",
            " [-0.11157634 -1.06066585]\n",
            " [-1.10189888  0.59194336]\n",
            " [ 0.08648817 -0.79972756]\n",
            " [-1.00286662  1.54871711]\n",
            " [-0.70576986  1.40375139]\n",
            " [-1.29996338  0.50496393]\n",
            " [-0.30964085  0.04107362]\n",
            " [-0.11157634  0.01208048]\n",
            " [-0.30964085 -0.88670699]\n",
            " [ 0.8787462  -1.3505973 ]\n",
            " [-0.30964085  2.24455257]\n",
            " [ 0.97777845  1.98361427]\n",
            " [-1.20093113  0.47597078]\n",
            " [-1.29996338  0.27301877]\n",
            " [ 1.37390747  1.98361427]\n",
            " [ 1.27487521 -1.3505973 ]\n",
            " [-0.30964085 -0.27785096]\n",
            " [-0.50770535  1.25878567]\n",
            " [-0.80480212  1.08482681]\n",
            " [ 0.97777845 -1.06066585]\n",
            " [ 0.28455268  0.30201192]\n",
            " [ 0.97777845  0.76590222]\n",
            " [-0.70576986 -1.49556302]\n",
            " [-0.70576986  0.04107362]\n",
            " [ 0.48261718  1.72267598]\n",
            " [ 2.06713324  0.18603934]\n",
            " [-1.99318916 -0.74174127]\n",
            " [-0.21060859  1.40375139]\n",
            " [ 0.38358493  0.59194336]\n",
            " [ 0.8787462  -1.14764529]\n",
            " [-1.20093113 -0.77073441]\n",
            " [ 0.18552042  0.24402563]\n",
            " [ 0.77971394 -0.30684411]\n",
            " [ 2.06713324 -0.79972756]\n",
            " [ 0.77971394  0.12805305]\n",
            " [-0.30964085  0.6209365 ]\n",
            " [-1.00286662 -0.30684411]\n",
            " [ 0.18552042 -0.3648304 ]\n",
            " [ 2.06713324  2.12857999]\n",
            " [ 1.86906873 -1.26361786]\n",
            " [ 1.37390747 -0.91570013]\n",
            " [ 0.8787462   1.25878567]\n",
            " [ 1.47293972  2.12857999]\n",
            " [-0.30964085 -1.23462472]\n",
            " [ 1.96810099  0.91086794]\n",
            " [ 0.68068169 -0.71274813]\n",
            " [-1.49802789  0.35999821]\n",
            " [ 0.77971394 -1.3505973 ]\n",
            " [ 0.38358493 -0.13288524]\n",
            " [-1.00286662  0.41798449]\n",
            " [-0.01254409 -0.30684411]\n",
            " [-1.20093113  0.41798449]\n",
            " [-0.90383437 -1.20563157]\n",
            " [-0.11157634  0.04107362]\n",
            " [-1.59706014 -0.42281668]\n",
            " [ 0.97777845 -1.00267957]\n",
            " [ 1.07681071 -1.20563157]\n",
            " [-0.01254409 -0.13288524]\n",
            " [-1.10189888 -1.52455616]\n",
            " [ 0.77971394 -1.20563157]\n",
            " [ 0.97777845  2.07059371]\n",
            " [-1.20093113 -1.52455616]\n",
            " [-0.30964085  0.79489537]\n",
            " [ 0.08648817 -0.30684411]\n",
            " [-1.39899564 -1.23462472]\n",
            " [-0.60673761 -1.49556302]\n",
            " [ 0.77971394  0.53395707]\n",
            " [-0.30964085 -0.33583725]\n",
            " [ 1.77003648 -0.27785096]\n",
            " [ 0.8787462  -1.03167271]\n",
            " [ 0.18552042  0.07006676]\n",
            " [-0.60673761  0.8818748 ]\n",
            " [-1.89415691 -1.40858358]\n",
            " [-1.29996338  0.59194336]\n",
            " [-0.30964085  0.53395707]\n",
            " [-1.00286662 -1.089659  ]\n",
            " [ 1.17584296 -1.43757673]\n",
            " [ 0.18552042 -0.30684411]\n",
            " [ 1.17584296 -0.74174127]\n",
            " [-0.30964085  0.07006676]\n",
            " [ 0.18552042  2.09958685]\n",
            " [ 0.77971394 -1.089659  ]\n",
            " [ 0.08648817  0.04107362]\n",
            " [-1.79512465  0.12805305]\n",
            " [-0.90383437  0.1570462 ]\n",
            " [-0.70576986  0.18603934]\n",
            " [ 0.8787462  -1.29261101]\n",
            " [ 0.18552042 -0.24885782]\n",
            " [-0.4086731   1.22979253]\n",
            " [-0.01254409  0.30201192]\n",
            " [ 0.38358493  0.1570462 ]\n",
            " [ 0.8787462  -0.65476184]\n",
            " [ 0.08648817  0.1570462 ]\n",
            " [-1.89415691 -1.29261101]\n",
            " [-0.11157634  0.30201192]\n",
            " [-0.21060859 -0.27785096]\n",
            " [ 0.28455268 -0.50979612]\n",
            " [-0.21060859  1.6067034 ]\n",
            " [ 0.97777845 -1.17663843]\n",
            " [-0.21060859  1.63569655]\n",
            " [ 1.27487521  1.8676417 ]\n",
            " [-1.10189888 -0.3648304 ]\n",
            " [-0.01254409  0.04107362]\n",
            " [ 0.08648817 -0.24885782]\n",
            " [-1.59706014 -1.23462472]\n",
            " [-0.50770535 -0.27785096]\n",
            " [ 0.97777845  0.12805305]\n",
            " [ 1.96810099 -1.3505973 ]\n",
            " [ 1.47293972  0.07006676]\n",
            " [-0.60673761  1.37475825]\n",
            " [ 1.57197197  0.01208048]\n",
            " [-0.80480212  0.30201192]\n",
            " [ 1.96810099  0.73690908]\n",
            " [-1.20093113 -0.50979612]\n",
            " [ 0.68068169  0.27301877]\n",
            " [-1.39899564 -0.42281668]\n",
            " [ 0.18552042  0.1570462 ]\n",
            " [-0.50770535 -1.20563157]\n",
            " [ 0.58164944  2.01260742]\n",
            " [-1.59706014 -1.49556302]\n",
            " [-0.50770535 -0.53878926]\n",
            " [ 0.48261718  1.83864855]\n",
            " [-1.39899564 -1.089659  ]\n",
            " [ 0.77971394 -1.37959044]\n",
            " [-0.30964085 -0.42281668]\n",
            " [ 1.57197197  0.99784738]\n",
            " [ 0.97777845  1.43274454]\n",
            " [-0.30964085 -0.48080297]\n",
            " [-0.11157634  2.15757314]\n",
            " [-1.49802789 -0.1038921 ]\n",
            " [-0.11157634  1.95462113]\n",
            " [-0.70576986 -0.33583725]\n",
            " [-0.50770535 -0.8287207 ]\n",
            " [ 0.68068169 -1.37959044]\n",
            " [-0.80480212 -1.58254245]\n",
            " [-1.89415691 -1.46656987]\n",
            " [ 1.07681071  0.12805305]\n",
            " [ 0.08648817  1.51972397]\n",
            " [-0.30964085  0.09905991]\n",
            " [ 0.08648817  0.04107362]\n",
            " [-1.39899564 -1.3505973 ]\n",
            " [ 0.28455268  0.07006676]\n",
            " [-0.90383437  0.38899135]\n",
            " [ 1.57197197 -1.26361786]\n",
            " [-0.30964085 -0.74174127]\n",
            " [-0.11157634  0.1570462 ]\n",
            " [-0.90383437 -0.65476184]\n",
            " [-0.70576986 -0.04590581]\n",
            " [ 0.38358493 -0.45180983]\n",
            " [-0.80480212  1.89663484]\n",
            " [ 1.37390747  1.28777882]\n",
            " [ 1.17584296 -0.97368642]\n",
            " [ 1.77003648  1.83864855]\n",
            " [-0.90383437 -0.24885782]\n",
            " [-0.80480212  0.56295021]\n",
            " [-1.20093113 -1.5535493 ]\n",
            " [-0.50770535 -1.11865214]\n",
            " [ 0.28455268  0.07006676]\n",
            " [-0.21060859 -1.06066585]\n",
            " [ 1.67100423  1.6067034 ]\n",
            " [ 0.97777845  1.78066227]\n",
            " [ 0.28455268  0.04107362]\n",
            " [-0.80480212 -0.21986468]\n",
            " [-0.11157634  0.07006676]\n",
            " [ 0.28455268 -0.19087153]\n",
            " [ 1.96810099 -0.65476184]\n",
            " [-0.80480212  1.3457651 ]\n",
            " [-1.79512465 -0.59677555]\n",
            " [-0.11157634  0.12805305]\n",
            " [ 0.28455268 -0.30684411]\n",
            " [ 1.07681071  0.56295021]\n",
            " [-1.00286662  0.27301877]\n",
            " [ 1.47293972  0.35999821]\n",
            " [ 0.18552042 -0.3648304 ]\n",
            " [ 2.1661655  -1.03167271]\n",
            " [-0.30964085  1.11381995]\n",
            " [-1.6960924   0.07006676]\n",
            " [-0.01254409  0.04107362]\n",
            " [ 0.08648817  1.05583366]\n",
            " [-0.11157634 -0.3648304 ]\n",
            " [-1.20093113  0.07006676]\n",
            " [-0.30964085 -1.3505973 ]\n",
            " [ 1.57197197  1.11381995]\n",
            " [-0.80480212 -1.52455616]\n",
            " [ 0.08648817  1.8676417 ]\n",
            " [-0.90383437 -0.77073441]\n",
            " [-0.50770535 -0.77073441]\n",
            " [-0.30964085 -0.91570013]\n",
            " [ 0.28455268 -0.71274813]\n",
            " [ 0.28455268  0.07006676]\n",
            " [ 0.08648817  1.8676417 ]\n",
            " [-1.10189888  1.95462113]\n",
            " [-1.6960924  -1.5535493 ]\n",
            " [-1.20093113 -1.089659  ]\n",
            " [-0.70576986 -0.1038921 ]\n",
            " [ 0.08648817  0.09905991]\n",
            " [ 0.28455268  0.27301877]\n",
            " [ 0.8787462  -0.5677824 ]\n",
            " [ 0.28455268 -1.14764529]\n",
            " [-0.11157634  0.67892279]\n",
            " [ 2.1661655  -0.68375498]\n",
            " [-1.29996338 -1.37959044]\n",
            " [-1.00286662 -0.94469328]\n",
            " [-0.01254409 -0.42281668]\n",
            " [-0.21060859 -0.45180983]\n",
            " [-1.79512465 -0.97368642]\n",
            " [ 1.77003648  0.99784738]\n",
            " [ 0.18552042 -0.3648304 ]\n",
            " [ 0.38358493  1.11381995]\n",
            " [-1.79512465 -1.3505973 ]\n",
            " [ 0.18552042 -0.13288524]\n",
            " [ 0.8787462  -1.43757673]\n",
            " [-1.99318916  0.47597078]\n",
            " [-0.30964085  0.27301877]\n",
            " [ 1.86906873 -1.06066585]\n",
            " [-0.4086731   0.07006676]\n",
            " [ 1.07681071 -0.88670699]\n",
            " [-1.10189888 -1.11865214]\n",
            " [-1.89415691  0.01208048]\n",
            " [ 0.08648817  0.27301877]\n",
            " [-1.20093113  0.33100506]\n",
            " [-1.29996338  0.30201192]\n",
            " [-1.00286662  0.44697764]\n",
            " [ 1.67100423 -0.88670699]\n",
            " [ 1.17584296  0.53395707]\n",
            " [ 1.07681071  0.53395707]\n",
            " [ 1.37390747  2.331532  ]\n",
            " [-0.30964085 -0.13288524]\n",
            " [ 0.38358493 -0.45180983]\n",
            " [-0.4086731  -0.77073441]\n",
            " [-0.11157634 -0.50979612]\n",
            " [ 0.97777845 -1.14764529]\n",
            " [-0.90383437 -0.77073441]\n",
            " [-0.21060859 -0.50979612]\n",
            " [-1.10189888 -0.45180983]\n",
            " [-1.20093113  1.40375139]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUd6iBRp2C3L",
        "outputId": "cf24af26-5d97-4716-f096-1717d9249493",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(X_test)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.80480212  0.50496393]\n",
            " [-0.01254409 -0.5677824 ]\n",
            " [-0.30964085  0.1570462 ]\n",
            " [-0.80480212  0.27301877]\n",
            " [-0.30964085 -0.5677824 ]\n",
            " [-1.10189888 -1.43757673]\n",
            " [-0.70576986 -1.58254245]\n",
            " [-0.21060859  2.15757314]\n",
            " [-1.99318916 -0.04590581]\n",
            " [ 0.8787462  -0.77073441]\n",
            " [-0.80480212 -0.59677555]\n",
            " [-1.00286662 -0.42281668]\n",
            " [-0.11157634 -0.42281668]\n",
            " [ 0.08648817  0.21503249]\n",
            " [-1.79512465  0.47597078]\n",
            " [-0.60673761  1.37475825]\n",
            " [-0.11157634  0.21503249]\n",
            " [-1.89415691  0.44697764]\n",
            " [ 1.67100423  1.75166912]\n",
            " [-0.30964085 -1.37959044]\n",
            " [-0.30964085 -0.65476184]\n",
            " [ 0.8787462   2.15757314]\n",
            " [ 0.28455268 -0.53878926]\n",
            " [ 0.8787462   1.02684052]\n",
            " [-1.49802789 -1.20563157]\n",
            " [ 1.07681071  2.07059371]\n",
            " [-1.00286662  0.50496393]\n",
            " [-0.90383437  0.30201192]\n",
            " [-0.11157634 -0.21986468]\n",
            " [-0.60673761  0.47597078]\n",
            " [-1.6960924   0.53395707]\n",
            " [-0.11157634  0.27301877]\n",
            " [ 1.86906873 -0.27785096]\n",
            " [-0.11157634 -0.48080297]\n",
            " [-1.39899564 -0.33583725]\n",
            " [-1.99318916 -0.50979612]\n",
            " [-1.59706014  0.33100506]\n",
            " [-0.4086731  -0.77073441]\n",
            " [-0.70576986 -1.03167271]\n",
            " [ 1.07681071 -0.97368642]\n",
            " [-1.10189888  0.53395707]\n",
            " [ 0.28455268 -0.50979612]\n",
            " [-1.10189888  0.41798449]\n",
            " [-0.30964085 -1.43757673]\n",
            " [ 0.48261718  1.22979253]\n",
            " [-1.10189888 -0.33583725]\n",
            " [-0.11157634  0.30201192]\n",
            " [ 1.37390747  0.59194336]\n",
            " [-1.20093113 -1.14764529]\n",
            " [ 1.07681071  0.47597078]\n",
            " [ 1.86906873  1.51972397]\n",
            " [-0.4086731  -1.29261101]\n",
            " [-0.30964085 -0.3648304 ]\n",
            " [-0.4086731   1.31677196]\n",
            " [ 2.06713324  0.53395707]\n",
            " [ 0.68068169 -1.089659  ]\n",
            " [-0.90383437  0.38899135]\n",
            " [-1.20093113  0.30201192]\n",
            " [ 1.07681071 -1.20563157]\n",
            " [-1.49802789 -1.43757673]\n",
            " [-0.60673761 -1.49556302]\n",
            " [ 2.1661655  -0.79972756]\n",
            " [-1.89415691  0.18603934]\n",
            " [-0.21060859  0.85288166]\n",
            " [-1.89415691 -1.26361786]\n",
            " [ 2.1661655   0.38899135]\n",
            " [-1.39899564  0.56295021]\n",
            " [-1.10189888 -0.33583725]\n",
            " [ 0.18552042 -0.65476184]\n",
            " [ 0.38358493  0.01208048]\n",
            " [-0.60673761  2.331532  ]\n",
            " [-0.30964085  0.21503249]\n",
            " [-1.59706014 -0.19087153]\n",
            " [ 0.68068169 -1.37959044]\n",
            " [-1.10189888  0.56295021]\n",
            " [-1.99318916  0.35999821]\n",
            " [ 0.38358493  0.27301877]\n",
            " [ 0.18552042 -0.27785096]\n",
            " [ 1.47293972 -1.03167271]\n",
            " [ 0.8787462   1.08482681]\n",
            " [ 1.96810099  2.15757314]\n",
            " [ 2.06713324  0.38899135]\n",
            " [-1.39899564 -0.42281668]\n",
            " [-1.20093113 -1.00267957]\n",
            " [ 1.96810099 -0.91570013]\n",
            " [ 0.38358493  0.30201192]\n",
            " [ 0.18552042  0.1570462 ]\n",
            " [ 2.06713324  1.75166912]\n",
            " [ 0.77971394 -0.8287207 ]\n",
            " [ 0.28455268 -0.27785096]\n",
            " [ 0.38358493 -0.16187839]\n",
            " [-0.11157634  2.21555943]\n",
            " [-1.49802789 -0.62576869]\n",
            " [-1.29996338 -1.06066585]\n",
            " [-1.39899564  0.41798449]\n",
            " [-1.10189888  0.76590222]\n",
            " [-1.49802789 -0.19087153]\n",
            " [ 0.97777845 -1.06066585]\n",
            " [ 0.97777845  0.59194336]\n",
            " [ 0.38358493  0.99784738]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb6jCOCQiAmP"
      },
      "source": [
        "## Training the Decision Tree Classification model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0pFVAmciHQs",
        "outputId": "9371a015-6cc0-46ed-aaba-9dfb9937ff91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ],
            "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(criterion=&#x27;entropy&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-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(criterion=&#x27;entropy&#x27;, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyxW5b395mR2"
      },
      "source": [
        "## Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8YOXsQy58rP",
        "outputId": "5f664b72-ec76-4a7d-b946-84fa0c0d6e21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(classifier.predict(sc.transform([[30,87000]])))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKYVQH-l5NpE"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6VMTb2O4hwM",
        "outputId": "c4c7db43-3117-43f7-d430-c4a17695d458",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0]\n",
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            " [1 1]\n",
            " [1 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4Hwj34ziWQW"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6bpZwUiiXic",
        "outputId": "1b99b171-9c47-4ff2-8606-fe1e9d02b730",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[62  6]\n",
            " [ 3 29]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.91"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OMC_P0diaoD"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NOjKvZRid5l",
        "outputId": "23d30110-1916-46cb-b3a2-04f0f8a863cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(X_train), y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Decision Tree Classification (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-be05346e83e3>:10: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "  plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZ-j28aPihZx"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeTjz2vDilAC",
        "outputId": "2bbea8d6-d4c0-4fb6-a5b4-e779e924f293",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(X_test), y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Decision Tree Classification (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-656d7b6695af>:10: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "  plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}