f47c7d7196
添加完整的客户流失预测系统，包括数据处理、模型训练、预测和行动建议功能。主要包含以下模块： 1. 数据预处理流水线（Polars + Pandera） 2. 机器学习模型训练（LightGBM + Logistic Regression） 3. AI Agent预测和建议工具 4. Streamlit交互式Web界面 5. 完整的课程设计报告文档
129 lines
5.0 KiB
Python
129 lines
5.0 KiB
Python
import polars as pl
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import pandera.pandas as pa
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from pandera.pandas import Column, DataFrameSchema, Check
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import numpy as np
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# 使用Pandera定义数据Schema
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telco_schema = DataFrameSchema({
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"customerID": Column(str, nullable=False),
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"gender": Column(str, Check.isin(["Male", "Female"]), nullable=False),
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"SeniorCitizen": Column(int, Check.isin([0, 1]), nullable=False),
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"Partner": Column(str, Check.isin(["Yes", "No"]), nullable=False),
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"Dependents": Column(str, Check.isin(["Yes", "No"]), nullable=False),
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"tenure": Column(int, Check.ge(0), nullable=False),
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"PhoneService": Column(str, Check.isin(["Yes", "No"]), nullable=False),
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"MultipleLines": Column(str, Check.isin(["Yes", "No", "No phone service"]), nullable=False),
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"InternetService": Column(str, Check.isin(["DSL", "Fiber optic", "No"]), nullable=False),
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"OnlineSecurity": Column(str, Check.isin(["Yes", "No", "No internet service"]), nullable=False),
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"OnlineBackup": Column(str, Check.isin(["Yes", "No", "No internet service"]), nullable=False),
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"DeviceProtection": Column(str, Check.isin(["Yes", "No", "No internet service"]), nullable=False),
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"TechSupport": Column(str, Check.isin(["Yes", "No", "No internet service"]), nullable=False),
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"StreamingTV": Column(str, Check.isin(["Yes", "No", "No internet service"]), nullable=False),
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"StreamingMovies": Column(str, Check.isin(["Yes", "No", "No internet service"]), nullable=False),
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"Contract": Column(str, Check.isin(["Month-to-month", "One year", "Two year"]), nullable=False),
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"PaperlessBilling": Column(str, Check.isin(["Yes", "No"]), nullable=False),
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"PaymentMethod": Column(str, nullable=False),
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"MonthlyCharges": Column(float, Check.ge(0), nullable=False),
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"TotalCharges": Column(float, Check.ge(0), nullable=False),
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"Churn": Column(str, Check.isin(["Yes", "No"]), nullable=False)
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})
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# 数据处理流水线
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def data_processing_pipeline(file_path: str):
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# 1. 读取数据
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# 先将TotalCharges作为字符串读取，以便处理空值
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df = pl.read_csv(file_path, schema_overrides={"TotalCharges": pl.Utf8})
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# 2. 数据清洗
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# 处理TotalCharges列中的空值（转换为0或均值）
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df = df.with_columns(
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pl.col("TotalCharges")
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.str.strip_chars()
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.replace("", None)
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.cast(pl.Float64)
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)
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# 填充缺失值（使用0填充，因为 tenure=0 时 TotalCharges 可能为0）
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df = df.with_columns(
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pl.col("TotalCharges").fill_null(0.0)
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)
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# 3. 验证数据Schema
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# 转换为pandas DataFrame进行Pandera验证
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df_pandas = df.to_pandas()
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validated_df_pandas = telco_schema.validate(df_pandas)
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# 转换回Polars DataFrame
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df = pl.from_pandas(validated_df_pandas)
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# 4. 特征工程
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# 将Churn列转换为0/1
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df = df.with_columns(
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pl.col("Churn").replace({"Yes": 1, "No": 0}).alias("Churn").cast(pl.Int64)
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)
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# 5. 分离特征和目标变量
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X = df.drop(["customerID", "Churn"])
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y = df.select("Churn")
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return X, y, df
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# 全局变量，用于存储特征处理信息
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_encoded_columns = None
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# 数据预处理（用于模型训练）
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def preprocess_data(X: pl.DataFrame, y: pl.DataFrame):
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global _encoded_columns
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# 分类特征和数值特征
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categorical_cols = X.select(pl.col(pl.Utf8)).columns
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numerical_cols = X.select(pl.col(pl.Int64, pl.Float64)).columns
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# 对分类特征进行独热编码
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X_encoded = X.to_dummies(columns=categorical_cols)
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# 保存编码后的列名
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_encoded_columns = X_encoded.columns
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# 转换为numpy数组
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X_np = X_encoded.to_numpy()
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y_np = y.to_numpy().ravel()
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return X_np, y_np
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# 数据预处理（用于单个客户预测）
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def preprocess_single_customer(customer_data: pl.DataFrame):
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global _encoded_columns
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if _encoded_columns is None:
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# 如果还没有编码列信息，加载训练数据并处理
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_, _, df = data_processing_pipeline("data/Telco-Customer-Churn.csv")
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X_train = df.drop(["customerID", "Churn"])
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y_train = df.select("Churn")
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preprocess_data(X_train, y_train)
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# 对分类特征进行独热编码
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categorical_cols = customer_data.select(pl.col(pl.Utf8)).columns
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X_encoded = customer_data.to_dummies(columns=categorical_cols)
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# 确保编码后的列与训练时的列一致
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for col in _encoded_columns:
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if col not in X_encoded.columns:
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X_encoded = X_encoded.with_columns(pl.lit(0).alias(col))
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# 按照训练时的列顺序排序
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X_encoded = X_encoded.select(_encoded_columns)
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# 转换为numpy数组
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X_np = X_encoded.to_numpy()
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return X_np
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if __name__ == "__main__":
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# 测试数据处理流水线
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X, y, df = data_processing_pipeline("data/Telco-Customer-Churn.csv")
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print("数据处理完成！")
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print(f"特征数据形状: {X.shape}")
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print(f"目标变量形状: {y.shape}")
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print(f"清洗后的数据行数: {df.shape[0]}")
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