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Building classification model with Python Step by Step

Bài cuối 08-02-2018 10:46 AM của chucnv. 0 trả lời.
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Sắp xếp bài viết: Trước Tiếp theo
  • 08-02-2018 10:46 AM

    • chucnv
    • 10 thành viên năng nổ nhất
    • Tham gia 12-05-2008
    • Điểm 9,950

    Building classification model with Python Step by Step

    Building classification model with Python Step by Step

    Chuc1803@gmail.com

    Bài viết này giới thiệu cách xây dựng các mô hình phân lớp dữ liệu bằng ngôn ngữ Python theo các bước sau:

    1.     Loading the dataset.

    2.     Summarizing the dataset.

    3.     Visualizing the dataset.

    4.     Building and Evaluating some classification algorithms.

    5.     Making some predictions.

    Yêu cầu:

    Python software installed

    Dataset: iris (download here)

    1.     Loading the dataset

    # Load libraries

    import pandas as pd

    from pandas.plotting import scatter_matrix

    import matplotlib.pyplot as plt

    from sklearn import model_selection

    from sklearn.metrics import classification_report

    from sklearn.metrics import confusion_matrix

    from sklearn.metrics import accuracy_score

    from sklearn.linear_model import LogisticRegression

    from sklearn.tree import DecisionTreeClassifier

    from sklearn.neighbors import KNeighborsClassifier

    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

    from sklearn.naive_bayes import GaussianNB

    from sklearn.svm import SVC

    # Load dataset

    dataset = np.read_csv("D:/Python_Pro/iris.csv")

    2.     Summarizing the dataset

     


     


     

    3.     Visualizing the dataset


     


     

     

    4.     Building and Evaluating some classification algorithms

    # Split dataset into train, test and validation sets

    array = dataset.values

    X = array[:,0:4]

    Y = array[:,4]

    X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=0.2, random_state=7)

    # Building and evaluating  classification Algorithms

    models=[

    models.append(('LR', LogisticRegression()))

    models.append(('LDA', LinearDiscriminantAnalysis()))

    models.append(('KNN', KNeighborsClassifier()))

    models.append(('CART', DecisionTreeClassifier()))

    models.append(('NB', GaussianNB()))

    models.append(('SVM', SVC()))

    # evaluate each model in turn

    results=[

    names=[

    for name, model in models:

        kfold = model_selection.KFold(n_splits=10, random_state=7)

        cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')

        results.append(cv_results)

        names.append(name)

        msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())

        print(msg)

    # Compare Algorithms

    fig = plt.figure()

    fig.suptitle('Algorithm Comparison')

    ax = fig.add_subplot(111)

    plt.boxplot(results)

    ax.set_xticklabels(names)

    plt.show()


     

    5.     Making some predictions


     


     

     

    Download Code file Here

    View Video Here

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