R语言HousePrice预测房价数据挖掘分析报告附代码数据

1、【原创】R语言报告论文（附代码数据）有问题到淘宝找“大数据部落”就可以了R语言HousePrice预测房价分析报告在这个分析中，我们将尝试预测房子的交易价格。因为有这么多变量，一些收缩回归可能是很好的选择以下是分析过程DataOverview数据概述加载要求的包library(dplyr)# #Attachingpackage:'dplyr'# #Thefollowingobjectsaremaskedfrom'package:stats':# # #filter,lag# #Thefollowingobjectsaremaskedfrom'packag

2、e:base':# # #intersect,setdiff,setequal,unionlibrary(ggplot2)library(mice)library(e1071)library(caret)# #Loadingrequiredpackage:latticelibrary(glmnet)# #Loadingrequiredpackage:Matrix# #Loadingrequiredpackage:foreach# #Loadedglmnet2.0-13library(xgboost)【原创】R语言报告论文(附代码数据)有问题到淘宝找“大数据部落”就可以了# #Attac

3、hingpackage:'xgboost'# #Thefollowingobjectismaskedfrom'package:dplyr':# #sliceImportthedataandcreateacombineddataset.Browsethedatasort(names(full)str(full)nacol=which(colSums(is.na(full)>0)sort(colSums(sapply(fullnacol,is.na),decreasing=T)# #PoolQCMiscFeatureAlleyFenceFireplaceQu#

4、 #29092814272123481420# #LotFrontageGarageYrBltGarageFinishGarageQualGarageCond# #486159159159159# #GarageTypeBsmtCondBsmtExposureBsmtQualBsmtFinType2# #BsmtFinType1MasVnrTypeMasVnrAreaMSZoningUtilities# #79242342# #BsmtFullBathBsmtHalfBathFunctionalExterior1stExterior2nd# #22211# #Bsm

5、tFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSFElectrical# #11111【原创】R语言报告论文（附代码数据）有问题到淘宝找“大数据部落”就可以了# #KitchenQualGarageCarsGarageAreaSaleType# #1111【原创】R语言报告论文（附代码数据）有问题到淘宝找“大数据部落”就可以了BIEoca若fl言山Is,Mr力£菖击n里总tflst<zMMdzM归一生54MNEEffi003，目需口-¥c器>WE名J名E-l品£盘6甘用N-Hep匚nreoirder(NeighbortKjQd,

6、-Price3营*05AmountGaraqeYrBItMissingValue缺失值【原创】R语言报告论文（附代码数据）有问题到淘宝找“大数据部落”就可以了PoolQCPoolQC中缺少2909个。我们推断的原因是大多数家庭没有泳池。所以我们将看到是否有任何PoolArea不是0与NA池QC然后我们根据PoolArea填充三个PoolQC,另一个填充没有。poolna=which(is.na(full$PoolQC)full(full$PoolArea)>0&is.na(full$PoolQC),c("PoolArea","PoolQC"

7、)# #PoolAreaPoolQC# #2421368<NA># #2504444<NA># #2600561<NA>full%>%elect(PoolArea,PoolQC)%>%group_by(PoolQC)%>%summarise(mean=mean(PoolArea),count=n()# #Atibble:4x3# #PoolQCmeancount# #<chr><dbl><int># #1Ex359.75000004# #2Fa583.50000002# #3Gd648.50000004#

8、 #4<NA>0.47198352909full$PoolQCc(2421,2504)="Ex"full$PoolQC2600="Fa"full$PoolQCis.na(full$PoolQC)="None"MiscFeature当我们谈论MiscFeature时，只有一个MiscVal>0与NAMiscFeature。通过MiscVal对数据进行排序，我们发现最广泛的MiscVal来自第二个车库。所以我们用“Gar2”填写了丢失的MiscFeature。misna=which(is.na(full$MiscFeature)fullfull$MiscVal>0&is.na(full$MiscFeature),c("MiscFeature","MiscVa