简单粗暴理解与实现机器学习之逻辑回归（三）：案例：癌症分类预测-良／恶性乳腺癌肿瘤预测

获取数据
    2.基本数据处理
    2.1 缺失值处理
    2.2 确定特征值,目标值
    2.3 分割数据
    3.特征工程(标准化)
    4.机器学习(逻辑回归)
    5.模型评估

    import pandas as pd
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LogisticRegression
    
    import ssl
    ssl._create_default_https_context = ssl._create_unverified_context
    # 1.获取数据
    names = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape',
                   'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin',
                   'Normal Nucleoli', 'Mitoses', 'Class']
    
    data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data",
                  names=names)
    data.head()
    # 2.基本数据处理
    # 2.1 缺失值处理
    data = data.replace(to_replace="?", value=np.NaN)
    data = data.dropna()
    # 2.2 确定特征值,目标值
    x = data.iloc[:, 1:10]
    x.head()
    y = data["Class"]
    y.head()
    # 2.3 分割数据
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=22)
    # 3.特征工程(标准化)
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.transform(x_test)
    # 4.机器学习(逻辑回归)
    estimator = LogisticRegression()
    estimator.fit(x_train, y_train)
    # 5.模型评估
    y_predict = estimator.predict(x_test)
    y_predict
    estimator.score(x_test, y_test)