ch19_random_forest_classification.ipynb

{
  "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/develop/ch19_random_forest_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": [
        "# Random Forest Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWd1UlMnhT2s"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YvGPUQaHhXfL"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1VMqkGvhc3-"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "M52QDmyzhh9s"
      },
      "outputs": [],
      "source": [
        "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvxIPVyMhmKp"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "AVzJWAXIhxoC"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3nS3-6r1i2B",
        "outputId": "0114257d-aec9-49cf-8cfe-c1786b9a0ae3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ],
      "source": [
        "print(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8dpDLojm1mVG",
        "outputId": "f790a04c-e029-40e5-9461-3599d6a26055"
      },
      "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",
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            " 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"
          ]
        }
      ],
      "source": [
        "print(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qbb7i0DH1qui",
        "outputId": "77d3287c-3c3c-4cdc-9c00-3116836d3dc5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " [    42 104000]]\n"
          ]
        }
      ],
      "source": [
        "print(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kj1hnFAR1s5w",
        "outputId": "64a7db87-5de5-4617-d0b8-9e5c282b7750"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kW3c7UYih0hT"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "9fQlDPKCh8sc"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "syrnD1Op2BSR",
        "outputId": "68b1cfd1-355a-4d78-9b9e-387b68c3ba61"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUd6iBRp2C3L",
        "outputId": "5713fcb0-8458-482f-b1fb-c7cdb3a23c0a"
      },
      "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"
          ]
        }
      ],
      "source": [
        "print(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb6jCOCQiAmP"
      },
      "source": [
        "## Training the Random Forest Classification model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "e0pFVAmciHQs",
        "outputId": "aa6027ff-129f-4611-d1c2-e1f2574486e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', n_estimators=10, 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>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10, 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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyxW5b395mR2"
      },
      "source": [
        "## Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8YOXsQy58rP",
        "outputId": "bc9e0ff1-bbc4-4d7b-8d46-9caea1670066"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ],
      "source": [
        "print(classifier.predict(sc.transform([[30,87000]])))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKYVQH-l5NpE"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6VMTb2O4hwM",
        "outputId": "cf45ec17-95a7-436d-fae5-1f1b0f199b6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [1 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 0]\n",
            " [1 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 1]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [1 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [1 1]]\n"
          ]
        }
      ],
      "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))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4Hwj34ziWQW"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6bpZwUiiXic",
        "outputId": "e4c73ce8-2795-4ba9-929c-4671a3d331c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[63  5]\n",
            " [ 4 28]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.91"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OMC_P0diaoD"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "_NOjKvZRid5l",
        "outputId": "f1ed36b3-17a6-4811-ea0c-9268dfbf2502"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-f8e0d8b3db8b>: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": {}
        }
      ],
      "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('Random Forest Classification (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZ-j28aPihZx"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "qeTjz2vDilAC",
        "outputId": "e096fc75-974b-4a9b-85bb-c4ba18058ea0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-c8bf6ae292af>: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": {}
        }
      ],
      "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('Random Forest Classification (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}