random_forest_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "random_forest_classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/antonio2505/SuperDataScience_Projects_ML/blob/main/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",
      "metadata": {
        "id": "YvGPUQaHhXfL"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 11,
      "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[:, [2, 3]].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 12,
      "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": 13,
      "outputs": []
    },
    {
      "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": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb6jCOCQiAmP"
      },
      "source": [
        "## Training the Random Forest Classification model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pFEV88gA3h7_",
        "outputId": "0754136a-9c38-44fa-a6bb-6080e05c020a"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "classifier = RandomForestClassifier(n_estimators = 100, criterion = 'entropy', random_state = 0 )\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='entropy', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=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, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmePIEiiiKyw"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWK4AcCgiNwF"
      },
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4Hwj34ziWQW"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6bpZwUiiXic",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2f74b66-2443-489e-ce17-eef59e0b79a9"
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[63  5]\n",
            " [ 4 28]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.91"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OMC_P0diaoD"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NOjKvZRid5l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "9b46bdf9-9eba-418a-d294-17865bf12680"
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_train, y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(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],\n",
        "                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()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*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 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*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 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZ-j28aPihZx"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeTjz2vDilAC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "5d6ef391-e618-4155-dce6-59b14b89ef43"
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(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],\n",
        "                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()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*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 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "*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 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}