Python线性回归预测任务标价：基于商品和会员信息的建模分析

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.preprocessing import MinMaxScaler

# 读取Excel文件
data = pd.read_excel(r'D:\pythonProject3\会员信息\附件二：会员信息数据.xlsx')
data.dropna(inplace=True)

# 选择特征和目标变量
X = data[['商品周围商品个数', '会员点个数']]
y = data['任务标价']

# 填充缺失值
X.fillna(X.mean(), inplace=True)
y.fillna(y.mean(), inplace=True)

# 数据归一化
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)

# 创建线性回归模型
model = LinearRegression()

# 训练模型
model.fit(X_scaled, y)

# 输出方程系数
coefficients = model.coef_
intercept = model.intercept_
print('方程：y = {}x1 + {}x2 + {}'.format(coefficients[0], coefficients[1], intercept))

# 预测值
y_pred = model.predict(X_scaled)

# 求拟合优度
r2 = r2_score(y, y_pred)
print('拟合优度：', r2)