10_Comparing_ML_Algorithm.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN+DRNQFT3adqivTWEoixcW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/satwiksps/Machine_Learning/blob/main/10_Comparing_ML_Algorithm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Compare Machine Learning Algorithms**"
      ],
      "metadata": {
        "id": "vY8RoW2gVexc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "TPuH5ViJPY16"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pima-Indian-Diabetes Dataset\n",
        "from pandas import read_csv\n",
        "url = 'https://raw.githubusercontent.com/erojaso/MLMasteryEndToEnd/master/data/pima-indians-diabetes.data.csv' #Load CSV using Pandas\n",
        "column_names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] #Define column names\n",
        "data = read_csv(url, names=column_names) #Load CSV using Pandas\n",
        "array = data.values #Convert to NumPy array\n",
        "X = array[:,0:8] #Split into input column\n",
        "Y = array[:,8] #Split into output column"
      ],
      "metadata": {
        "id": "pCLnubMuUz_j"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prepare models\n",
        "models = []\n",
        "models.append(('LR', LogisticRegression(solver='liblinear')))\n",
        "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
        "models.append(('KNN', KNeighborsClassifier()))\n",
        "models.append(('CART', DecisionTreeClassifier()))\n",
        "models.append(('NB', GaussianNB()))\n",
        "models.append(('SVM', SVC(gamma='scale')))"
      ],
      "metadata": {
        "id": "EE2X_JdLVJnw"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = []\n",
        "names = []\n",
        "scoring = 'accuracy'\n",
        "for name, model in models:\n",
        "    kfold = KFold(n_splits=10)\n",
        "    cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
        "    results.append(cv_results)\n",
        "    names.append(name)\n",
        "    msg = \"%s -> accuracy: (%f), Standard Deviation: (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
        "    print(msg)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-6Zig3lU-fC",
        "outputId": "5ceedf57-5b23-4995-cd01-be84b4338bc6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR -> accuracy: (0.769515), Standard Deviation: (0.048411)\n",
            "LDA -> accuracy: (0.773462), Standard Deviation: (0.051592)\n",
            "KNN -> accuracy: (0.726555), Standard Deviation: (0.061821)\n",
            "CART -> accuracy: (0.687457), Standard Deviation: (0.060036)\n",
            "NB -> accuracy: (0.755178), Standard Deviation: (0.042766)\n",
            "SVM -> accuracy: (0.760424), Standard Deviation: (0.052931)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# boxplot algorithm comparison\n",
        "fig = pyplot.figure()\n",
        "fig.suptitle('Algorithm Comparison')\n",
        "ax = fig.add_subplot(111)\n",
        "pyplot.boxplot(results)\n",
        "ax.set_xticklabels(names)\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "T45rQ8BVVNqv",
        "outputId": "4f1df326-91ec-4aa2-c3d1-9c5fb99e5a34"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}