decision_tree_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of decision_tree_classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/sandeep8064/Machine-learning-/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": 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": "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": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb6jCOCQiAmP"
      },
      "source": [
        "## Training the Decision Tree Classification model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier(criterion='entropy',random_state=0)\n",
        "classifier.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "vXItI7bMN4Rk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fcf289b-d955-4236-f423-97387316a9d4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyxW5b395mR2"
      },
      "source": [
        "## Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8YOXsQy58rP",
        "outputId": "82cafc84-8e93-4b17-c5c1-8cbe80abb08b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(classifier.predict(sc.transform([[30,87000]])))"
      ],
      "execution_count": 6,
      "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": "fcbd255f-59de-4341-d848-95889eee117b",
        "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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4Hwj34ziWQW"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6bpZwUiiXic",
        "outputId": "3fdfe568-85b3-4af0-deee-49d07af0c8a5",
        "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": 8,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OMC_P0diaoD"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NOjKvZRid5l",
        "outputId": "3fa97869-4606-4535-93e1-9434ede56c7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "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": 9,
      "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",
      "metadata": {
        "id": "SZ-j28aPihZx"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeTjz2vDilAC",
        "outputId": "c5dbdf04-d9a7-428e-d543-384a16ff62bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "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": 10,
      "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"
          }
        }
      ]
    }
  ]
}