To create regression model to predict housing price using regularisation.
A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below.
The company is looking at prospective properties to buy to enter the market. You are required to build a regression model using regularisation in order to predict the actual value of the prospective properties and decide whether to invest in them or not.
The company wants to know:
Which variables are significant in predicting the price of a house, and
How well those variables describe the price of a house.
Also, determine the optimal value of lambda for ridge and lasso regression.
- Using GridSearchCV the model arrives at a final optimum lambda to determine the house prices using ridge and lasso regression.
- pandas
- seaborn
- matplotlib
- statsmodels
- sci-kit learn
- numpy
Created by Indhumathi C R- feel free to contact me!