There are many factors that impact the price of housing.

A housing dataset was downloaded from this link:

https://www.kaggle.com/datasets/yasserh/housing-prices-dataset?resource=download

housing<-read.csv("Housing.csv")
summary(housing)

##      price               area          bedrooms        parking      
##  Min.   : 1750000   Min.   : 1650   Min.   :1.000   Min.   :0.0000  
##  1st Qu.: 3430000   1st Qu.: 3600   1st Qu.:2.000   1st Qu.:0.0000  
##  Median : 4340000   Median : 4600   Median :3.000   Median :0.0000  
##  Mean   : 4766729   Mean   : 5151   Mean   :2.965   Mean   :0.6936  
##  3rd Qu.: 5740000   3rd Qu.: 6360   3rd Qu.:3.000   3rd Qu.:1.0000  
##  Max.   :13300000   Max.   :16200   Max.   :6.000   Max.   :3.0000  
##  furnishingstatus  
##  Length:545        
##  Class :character  
##  Mode  :character  
##                    
##                    
## 

• This Shiny application predicts housing price using linear regression.
• Once you enter the housing characteristics, a predicted price will appear.
• This application only considers the following factors: area of the house, number of bedrooms, number of parking spaces, and the furnishing status.

Click this link to see the Shiny Application:

https://arunparmar.shinyapps.io/PricePredictorforHousing/

Click this link to see the compiled project files on Github:

https://github.com/arunp6708/Developing-Data-Products