手把手教你用python开始第一个机器学习项目

1、安装Python

安装python-m pip安装–用户麻木scipy matplotlib IPython jupyter熊猫症状鼻[en]Install python-m pip install– user numpy scipy matplotlib ipython jupyter pandas sympy nose

pip install -U scikit-learn

效果图:[en]Effect picture:

手把手教你用python开始第一个机器学习项目

运行结果:[en]Running result:

手把手教你用python开始第一个机器学习项目

完整代码:[en]Complete code:

python;gutter:true; from pandas import read_csv from pandas.plotting import scatter_matrix from matplotlib import pyplot from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.model_selection import StratifiedKFold 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</p> <p>print("------------------------------------------------")</p> <h1>Load dataset</h1> <h1>url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"</h1> <p>names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class'] dataset = read_csv("C:\Users\Administrator\Downloads\iris.data", names=names)</p> <h1>shape</h1> <p>print("------------------------------------------------") print(dataset.shape)</p> <h1>print(dataset.head(20))</h1> <h1>descriptions</h1> <p>print("------------------------------------------------") print(dataset.describe())</p> <h1>classdistribution</h1> <p>print("------------------------------------------------") print(dataset.groupby('class').size())</p> <h1>boxand whisker plots</h1> <p>print("------------------------------------------------")</p> <h1>dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)</h1> <h1>pyplot.show()</h1> <p>print("------------------------------------------------")</p> <h1>histograms</h1> <h1>dataset.hist()</h1> <h1>pyplot.show()</h1> <p>print("------------------------------------------------")</p> <h1>scatter plot matrix</h1> <h1>scatter_matrix(dataset)</h1> <h1>pyplot.show()</h1> <p>print("------------------------------------------------")</p> <h1>Split-out validation dataset</h1> <p>array = dataset.values X = array[:,0:4] y = array[:,4] X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1)</p> <p>models = [] models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr'))) models.append(('LDA', LinearDiscriminantAnalysis())) models.append(('KNN', KNeighborsClassifier())) models.append(('CART', DecisionTreeClassifier())) models.append(('NB', GaussianNB())) models.append(('SVM', SVC(gamma='auto')))</p> <h1>evaluate each model in turn</h1> <p>results = [] names = [] for name, model in models: kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True) cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy') results.append(cv_results) names.append(name) print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))</p> <h1>Compare Algorithms</h1> <p>pyplot.boxplot(results, labels=names) pyplot.title('Algorithm Comparison') pyplot.show()</p> <h1>Make predictions on validation dataset</h1> <p>model = SVC(gamma='auto') model.fit(X_train, Y_train) predictions = model.predict(X_validation)</p> <h1>Evaluate predictions</h1> <p>print(accuracy_score(Y_validation, predictions)) print(confusion_matrix(Y_validation, predictions)) print(classification_report(Y_validation, predictions))

参考资料:[en]Reference:

英文:https://machinelearningmastery.com/machine-learning-in-python-step-by-step/

中文: https://www.jianshu.com/p/711488d85e00

Original: https://www.cnblogs.com/linlf03/p/16117231.html
Author: work hard work smart
Title: 手把手教你用python开始第一个机器学习项目

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