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.

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.

Build predictive model that will be used by the management to understand how exactly the prices vary with the variables. So, they can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns.

1. Process data (convert columns to appropriate formats, handle missing values, etc.)

2. Conduct exploratory analysis to extract useful insights (whether directly useful for business or for eventual modelling/feature engineering).

3. Derive new features.

4. Performed Scalling and RFE.

5. Build Model using Ridge and Lasso Regression.

6. Evaluate the Model