kaggle住房预测项目——第1部分

来自 : www.qishunwang.net/news_show_4 发布时间：2021-03-25

IdMSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePricecount1460.0000001460.0000001201.0000001460.0000001460.0000001460.0000001460.0000001460.0000001452.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001379.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.0000001460.000000mean730.50000056.89726070.04995810516.8280826.0993155.5753421971.2678081984.865753103.685262443.63972646.549315567.2404111057.4294521162.626712346.9924665.8445211515.4636990.4253420.0575341.5650680.3828772.8664381.0465756.5178080.6130141978.5061641.767123472.98013794.24452146.66027421.9541103.40958915.0609592.75890443.4890416.3219182007.815753180921.195890std421.61000942.30057124.2847529981.2649321.3829971.11279930.20290420.645407181.066207456.098091161.319273441.866955438.705324386.587738436.52843648.623081525.4803830.5189110.2387530.5509160.5028850.8157780.2203381.6253930.64466624.6897250.747315213.804841125.33879466.25602861.11914929.31733155.75741540.177307496.1230242.7036261.32809579442.502883min1.00000020.00000021.0000001300.0000001.0000001.0000001872.0000001950.0000000.0000000.0000000.0000000.0000000.000000334.0000000.0000000.000000334.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000000.0000001900.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.0000002006.00000034900.00000025%365.75000020.00000059.0000007553.5000005.0000005.0000001954.0000001967.0000000.0000000.0000000.000000223.000000795.750000882.0000000.0000000.0000001129.5000000.0000000.0000001.0000000.0000002.0000001.0000005.0000000.0000001961.0000001.000000334.5000000.0000000.0000000.0000000.0000000.0000000.0000000.0000005.0000002007.000000129975.00000050%730.50000050.00000069.0000009478.5000006.0000005.0000001973.0000001994.0000000.000000383.5000000.000000477.500000991.5000001087.0000000.0000000.0000001464.0000000.0000000.0000002.0000000.0000003.0000001.0000006.0000001.0000001980.0000002.000000480.0000000.00000025.0000000.0000000.0000000.0000000.0000000.0000006.0000002008.000000163000.00000075%1095.25000070.00000080.00000011601.5000007.0000006.0000002000.0000002004.000000166.000000712.2500000.000000808.0000001298.2500001391.250000728.0000000.0000001776.7500001.0000000.0000002.0000001.0000003.0000001.0000007.0000001.0000002002.0000002.000000576.000000168.00000068.0000000.0000000.0000000.0000000.0000000.0000008.0000002009.000000214000.000000max1460.000000190.000000313.000000215245.00000010.0000009.0000002010.0000002010.0000001600.0000005644.0000001474.0000002336.0000006110.0000004692.0000002065.000000572.0000005642.0000003.0000002.0000003.0000002.0000008.0000003.00000014.0000003.0000002010.0000004.0000001418.000000857.000000547.000000552.000000508.000000480.000000738.00000015500.00000012.0000002010.000000755000.000000

data_train.head()