Decision_Tree_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Decision_Tree_classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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/ansarimoiz/MachineLearning/blob/main/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": 25,
      "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": 26,
      "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": 27,
      "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": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb6jCOCQiAmP"
      },
      "source": [
        "## Training the Decision Tree Classification model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0pFVAmciHQs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc39149f-ddee-403f-981c-cdb926ca0b43"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicting test set result"
      ],
      "metadata": {
        "id": "ABqhjwTwlK6x"
      }
    },
    {
      "cell_type": "code",
      "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))"
      ],
      "metadata": {
        "id": "CIMQLst3lKYq"
      },
      "execution_count": 30,
      "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": "ed8c5f7e-07f8-4e86-dda5-6dc661051ebd"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 31,
      "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": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualising the Test set results"
      ],
      "metadata": {
        "id": "6633ps8Ilf5n"
      }
    },
    {
      "cell_type": "code",
      "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  ')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "c0LJfOMWlc2M",
        "outputId": "6636a175-ddf9-4379-a7dd-90e6ebb94277"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualising the Training set results"
      ],
      "metadata": {
        "id": "uugMqwpClom7"
      }
    },
    {
      "cell_type": "code",
      "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.figure(facecolor='white')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "Ki0q199MloFO",
        "outputId": "4bb9cc0a-6da5-46fa-fc1b-1a2f319bc691"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}