house-features:创建了一个回归模型来预测爱荷华州埃姆斯市房屋的销售价格在实际价值的25,000美元以内,并确定对销售价格贡献最大的交互条件

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预测房屋售价 技术与技能 技术技能:回归,数据清理,探索性数据分析(EDA),数据可视化,机器学习,偏差方差折衷,插补方法,模型验证,统计,特征工程,正则化,集成模型,k均值聚类,管道,网格搜索,转学 技术: Python,Jupyter Notebook,GitHub,Git Python库: Pandas,numpy,sklearn,matplotlib,seaborn,scipy 模型:多元线性回归,岭回归,LASSO回归,k近邻回归,随机森林回归,额外树木回归,支持向量回归,XGBoost回归,主成分回归 概述 该项目将涵盖以下部分: 问题陈述 执行摘要 结论 数据源 数据字典 问题陈述 我对该项目的目标是建立一个可以预测爱荷华州艾姆斯房屋实际售价在25,000美元以内的回归模型。 我将用来评估模型准确性的主要指标包括均方根误差(RMSE)和确定系数(R平方)。 RMSE代表

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