Untitled.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled",
      "provenance": [],
      "authorship_tag": "ABX9TyNi4x+Bq1LLqgX5CQ5ArRVO",
      "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/ghenwafkh/miniproject/blob/master/Untitled.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zEa5_YicApQA",
        "colab_type": "text"
      },
      "source": [
        "**Automate the ML process using pipelines**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8KWvxFDAt46",
        "colab_type": "text"
      },
      "source": [
        "*Data Preparation and Modeling Pipeline*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4saTJvEcA-5E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a pipeline that standardizes the data then creates a model\n",
        "#Load libraries for data processing\n",
        "import pandas as pd #data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "import sklearn\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import GridSearchCV\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\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "# visualization\n",
        "import seaborn as sns \n",
        "plt.style.use('fivethirtyeight')\n",
        "sns.set_style(\"white\")\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (8,4) \n",
        "#plt.rcParams['axes.titlesize'] = 'large'"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mHZkNlACE8n",
        "colab_type": "text"
      },
      "source": [
        "**Validation Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NT_W4tlbCGvb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#load data\n",
        "data = pd.read_csv('data/clean-data.csv', index_col=False)\n",
        "data.drop('Unnamed: 0',axis=1, inplace=True)\n",
        "\n",
        "# Split-out validation dataset\n",
        "array = data.values\n",
        "X = array[:,1:31]\n",
        "y = array[:,0]\n",
        "\n",
        "# Divide records in training and testing sets.\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=7)\n",
        "\n",
        "#transform the class labels from their original string representation (M and B) into integers\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tE_VtmqtCUtN",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "**2.0 Evaluate Algorithms: Baseline**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7TZL6LZCXza",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a4fa41eb-a102-44a2-c581-41baead33fc5"
      },
      "source": [
        "# Spot-Check Algorithms\n",
        "models = []\n",
        "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()))\n",
        "\n",
        "# Test options and evaluation metric\n",
        "num_folds = 10\n",
        "num_instances = len(X_train)\n",
        "seed = 7 \n",
        "scoring =  'accuracy'\n",
        "\n",
        "# Test options and evaluation metric\n",
        "num_folds = 10\n",
        "num_instances = len(X_train)\n",
        "seed = 7 \n",
        "scoring =  'accuracy'\n",
        "results = []\n",
        "names = []\n",
        "for name, model in models:\n",
        " kfold = KFold(n_splits=num_folds, random_state=seed)\n",
        " cv_results = cross_val_score(model, X_train, y_train, 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)\n",
        "print('-> 10-Fold cross-validation accurcay score for the training data for six classifiers')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: 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",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "LR: 0.939872 (0.033780)\n",
            "LDA: 0.957372 (0.029611)\n",
            "KNN: 0.929872 (0.036675)\n",
            "CART: 0.907115 (0.049999)\n",
            "NB: 0.934679 (0.044038)\n",
            "SVM: 0.899679 (0.043197)\n",
            "-> 10-Fold cross-validation accurcay score for the training data for six classifiers\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNnljYJVE5Se",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "outputId": "d1061802-bf97-4b4d-a765-0ebd8991c6fa"
      },
      "source": [
        "# Compare Algorithms\n",
        "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()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}