ai_ml_mini_project.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOsIBqPY574Irau9j4aBh4S",
      "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/AarnaSinghal/ML-projects-2023/blob/main/ai_ml_mini_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "LPyjxY3droJI"
      },
      "outputs": [],
      "source": [
        "import pandas\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import model_selection\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\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n"
      ],
      "metadata": {
        "id": "jOmqxNqjspKO"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']"
      ],
      "metadata": {
        "id": "zOxW_efmswiU"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe = pandas.read_csv(url, names=names)"
      ],
      "metadata": {
        "id": "exUy6Sf7swrc"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "array = dataframe.values\n",
        "X = array[:,0:8]\n",
        "Y = array[:,8]"
      ],
      "metadata": {
        "id": "oYI0GBjms3-p"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# prepare configuration for cross validation test harness"
      ],
      "metadata": {
        "id": "YUe8Ku8Gs8PK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seed = 7"
      ],
      "metadata": {
        "id": "E7AOW75us_W2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# prepare models"
      ],
      "metadata": {
        "id": "dthjdw7ntIez"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models = []"
      ],
      "metadata": {
        "id": "ANUmx7iKtJZ6"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models.append(('LR', LogisticRegression()))\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()))"
      ],
      "metadata": {
        "id": "_e3R8d6ttNC-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# evaluate each model in turn"
      ],
      "metadata": {
        "id": "idDIGbGKtU7J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = []"
      ],
      "metadata": {
        "id": "8ll37pintVuW"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "names = []"
      ],
      "metadata": {
        "id": "n9oEMfaxtZsj"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scoring = 'accuracy'"
      ],
      "metadata": {
        "id": "D4r3MD06tce9"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for name, model in models:\n",
        " kfold = model_selection.KFold(n_splits=10, random_state=None)\n",
        " cv_results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
        " results.append(cv_results)\n",
        " names.append(name)\n",
        " msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
        " print(msg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qulmMGoAtxEY",
        "outputId": "01537c56-ef59-4252-f848-d0c7409c4134"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR: 0.773428 (0.053375)\n",
            "LDA: 0.773462 (0.051592)\n",
            "KNN: 0.726555 (0.061821)\n",
            "CART: 0.693917 (0.059537)\n",
            "NB: 0.755178 (0.042766)\n",
            "SVM: 0.760424 (0.052931)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for name, model in models:\n",
        " kfold = model_selection.KFold(n_splits=10, random_state=None)\n",
        " cv_results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
        " results.append(cv_results)\n",
        " names.append(name)\n",
        " msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
        " print(msg)"
      ],
      "metadata": {
        "id": "xeC9IFa9uRPY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d5f84c5-aea6-4745-c6e9-c62d4dd24c84"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR: 0.773428 (0.053375)\n",
            "LDA: 0.773462 (0.051592)\n",
            "KNN: 0.726555 (0.061821)\n",
            "CART: 0.691353 (0.062828)\n",
            "NB: 0.755178 (0.042766)\n",
            "SVM: 0.760424 (0.052931)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# boxplot algorithm comparison"
      ],
      "metadata": {
        "id": "WEQhVHYaua3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "fig.suptitle('Algorithm Comparison')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.boxplot(results)\n",
        "ax.set_xticklabels(names)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "A60Gzm41ubwL",
        "outputId": "e635078a-590c-4346-b1f4-d4a8a03777bd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}