R语言房价回归预测案例报告-附代码数据.docx

【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语言房价回归预测案例报告 首先，我们加载数据和必要的软件包： load("ames_train.Rdata") library(MASS) library(dplyr) ## Warning: package 'dplyr' was built under R version 3.3.3 library(ggplot2) ## Warning: package 'ggplot2' was built under R version 3.3.3 library(devtools) ## Warning: package 'devtools' was built under R version 3.3.3 library(statsr)library(lubridate) ## Warning: package 'lubridate' was built under R version 3.3.3 library(tidyr) ## Warning: package 'tidyr' was built under R version 3.3.3 library(gridExtra) ## Warning: package 'gridExtra' was built under R version 3.3.3 1 在数据集中建立房屋的年龄标签直方图（30个箱），并描述分布。 # type your code for Question 1 here, and Knit ggplot(data = ames_train, aes(x=year(today())-Year.Built))+ geom_histogram(bins = 30, fill="blue", colour="white")+labs(title="House counts by age", x="House age", y="House count")+ geom_vline(xintercept = median(year(today())-ames_train$Year.Built), colour="red")+ annotate("text", x= median(year(today())-ames_train$Year.Built)-4, y=c(100,-10) , label=c("Median", median(year(today())-ames_train$Year.Built)) , colour="red", angle=c(90,0))+ geom_vline(xintercept = mean(year(today())-ames_train$Year.Built), colour="#41c42f")+ annotate("text", x= mean(year(today())-ames_train$Year.Built)+4, y=c(100,-10) , label=c("Mean", round(mean(year(today())-ames_train$Year.Built), 1)) , colour="#41c42f", angle=c(-90,0))+ theme(plot.title = element_text(hjust=0.5) ,title = element_text(hjust = 0.5, colour = "red", size = rel(2)) , axis.title =element_text(size = rel(1), colour = "blue", hjust = .5) , legend.position = "none" ,panel.background = element_rect(fill = "grey", colour = "black") , panel.grid = element_line(color = "blue")) 上面绘制的房屋年龄分布是非常正确的。 我们看到三个峰值，表明分布是多模态的。 这个数据集中的大部分房子（约140个）都是10-15岁。 第二类房屋（约80人）年龄在55-60岁之间，分布右边的第三类房屋（约37户）的年龄在90-95岁之间。 这可能表示指定期间房地产业务的繁荣。 分配表明，超过45％的房屋建于不到45年前。 2 房地产的咒语