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machine-learning-project-predicting-house-prices icon machine-learning-project-predicting-house-prices

机器学习项目:研究美国爱荷华州艾姆斯市2006年至2010年的住房数据,来建立预测模型 方法: I.通过特征工程来 a.去除缺失值超过5%的特征 b.去除有缺失值的非数值特征 c.其他有缺失值的数值列填充众数 d.转换时间列 e.去除明显无关特征 II.通过特征选择来 a.去除和目标相关系数小于0.4的特征 b.去除共线性特征(通过heatmap图) c.去除低方差/变化(小于0.015)特征 d.类型类特征的虚拟化(dummy code,去除分类超过10的特征) III.通过scikit-learn线性回归模型来训练和预测 IV.通过KFolds方法来检验证Mean Squared Error

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