KNN_vs_DecisionTree.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KNN_vs_DecisionTree.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1n-llYffk6C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing datasets and library required\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier as DTC\n",
        "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split as tts\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcTfR3iJgXbZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "K=np.array([3,5,7,9]) #different K values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VS-e385EhIuC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d6221416-5762-4692-c213-70a119cd276b"
      },
      "source": [
        "#taking % input from user \n",
        "percnt=int(input(\"Enter % of test data eg:- 10, 20 \"))/100"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter % of test data eg:- 10, 20 20\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwzgO1KCiBCM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#loading data and spliting it\n",
        "Iris=load_iris()\n",
        "data=Iris.data\n",
        "answer=Iris.target\n",
        "\n",
        "train_data,test_data,train_target,test_target=tts(data,answer,test_size=percnt)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pY8AEylLhXCE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating DTC classifers\n",
        "\n",
        "DTC_class=DTC()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWb1EFGqixP2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#now training classifier initially only DecisionTree as it does not have any KNN like input\n",
        "\n",
        "DTC_iris=DTC_class.fit(train_data,train_target)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AF55YsU5j4Rq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#list for storing accuracy of KNN and DTC\n",
        "KNN_accuracy=[]\n",
        "DTC_accuracy=[]\n",
        "\n",
        "for i in K :\n",
        "  KNN_class=KNN(n_neighbors=i)\n",
        "  KNN_iris=KNN_class.fit(train_data,train_target)  \n",
        "  \n",
        "  #Now KNN and DTC are trained no time for predection\n",
        "  \n",
        "  DTC_predict=DTC_iris.predict(test_data)\n",
        "  KNN_predict=KNN_iris.predict(test_data)\n",
        "  \n",
        "  #storing there accuracy \n",
        "  KNN_accuracy.append(accuracy_score(KNN_predict,test_target))\n",
        "  DTC_accuracy.append(accuracy_score(DTC_predict,test_target))\n",
        " \n",
        "\n",
        "  \n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcOCYbF4mMZ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#time for plotting graphs \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VojSGXaym853",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "2191ea46-faa9-4eaa-8f7e-53a509175c19"
      },
      "source": [
        "plt.title(\"KNN_vs_DTC at test data \"+str(percnt*100)+\"%\")\n",
        "plt.bar(K-0.25,KNN_accuracy,width=0.5,label=\"KNN\")\n",
        "plt.bar(K+0.25,DTC_accuracy,width=0.5,label=\"DTC\")\n",
        "plt.xlabel(\"KValues\")\n",
        "plt.ylabel(\"Accuracy_score\")\n",
        "plt.legend()\n",
        "plt.plot()\n"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BNZnZOqxnz54MHTr0g+n9x95TyOvMqxlVyHY5d0kx261QdFfS5sD8iun6dF6lc4GjJNWT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\nuaxERvNZtAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EgA0ph99nFi2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}