OBJECTIVE: At some point in their lives, everyone must deal with the real estate or housing market. It will be easier to buy or sell a house on the market at the right price if you have a thorough understanding of the market.
In this project, estimate the cost per square foot of homes based on their attributes.
Data Preprocessing:
Drop Outliers: There were outliers in house prices, unit area, longitude, and distance to the nearest MRT station.
Check Correlation:
The number of convenience stores is moderately correlated to the price of unit area, while the distance to the nearest MRT station negatively correlated.
Preprocessing Data: Convert transaction date to day, month and year columns Scaling the data
Model used: -Polynomial Regresdsion -XGB Regression -Ridge Regression -Lasso Regression
Result: XGB Regression performs the best:
-R2 Score: 0.780957
-RMSE: 6.09142