1使用逻辑回归进行二分类预测
- 1导入所需模块：from sklearn.linear_model import LogisticRegression；from sklearn.model_selection import train_test_split
- 2加载数据集（如鸢尾花数据），划分训练集和测试集：X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
- 3创建模型并训练：model = LogisticRegression(); model.fit(X_train, y_train)
- 4预测并评估：y_pred = model.predict(X_test); from sklearn.metrics import accuracy_score; print(accuracy_score(y_test, y_pred))
2使用K-Means进行客户分群聚类
- 1导入模块：from sklearn.cluster import KMeans
- 2准备特征数据（如客户消费数据），并标准化：from sklearn.preprocessing import StandardScaler; X_scaled = StandardScaler().fit_transform(X)
- 3训练聚类模型并指定簇数：kmeans = KMeans(n_clusters=3, random_state=42); kmeans.fit(X_scaled)
- 4获取聚类结果并分析：labels = kmeans.labels_; 可将标签用于业务分组
3使用线性回归预测房价（回归任务）
- 1导入模块：from sklearn.linear_model import LinearRegression; from sklearn.model_selection import train_test_split
- 2加载房价数据（如波士顿房价数据集），划分训练集和测试集
- 3训练模型：model = LinearRegression(); model.fit(X_train, y_train)
- 4预测并计算均方误差：y_pred = model.predict(X_test); from sklearn.metrics import mean_squared_error; print(mean_squared_error(y_test, y_pred))