上机题：调试运行讲解的示例题，并发挥自己的创意，改写相应程序。

#py601.py    Scikit-learn 机器学习步骤  示例

# 导入 sklearn

from sklearn import neighbors, datasets, preprocessing

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

print('===========  机器学习步骤  示例 =========')

# 加载数据

print('------加载数据-----------')

iris = datasets.load_iris()

print(iris)

# 划分训练集与测试集

print('-----划分训练集与测试集-------------')

X, y = iris.data[:, :2], iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)

print(X,y)

print(X_train, X_test, y_train, y_test)

# 数据预处理

print('-----数据预处理-------------')

scaler = preprocessing.StandardScaler().fit(X_train)

X_train = scaler.transform(X_train)

X_test = scaler.transform(X_test)

print(scaler)

print(X_train)

print(X_test)

# 创建模型

print('----- 创建模型-------------')

knn = neighbors.KNeighborsClassifier(n_neighbors=5)

print(knn)

# 模型拟合

print('----模型拟合--------------')

knn.fit(X_train, y_train)

print(knn.fit)

# 预测

print('-----预测-------------')

y_pred = knn.predict(X_test)

print(y_pred)

# 评估

print('------评估------------')

accuracy_score(y_test, y_pred)

print(accuracy_score(y_test, y_pred))

上机实训参考资源包：