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.
You are required to model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for management to understand the pricing dynamics of a new market. Ridge and Lasso Regeression
- Reading, understanding and visualizing the data
- Preparing data for modelling - Encoding, Rescaling and Train-Test split
- Training the model on train set
- Residual analysis on Train set
- Prediction and Evaluation on Test set
- Build models using Liner Regression, Ridge Regression and Lasso Regression
- Compare metrices such as RMSE, R2 score of all 3 models
- Find the best model among the three
Conclusion from the analysis are:
- Which model is best among the three in terms of accuracy and which model is more generalised
- Top 5 most important predictor variables
Created by [@Yogesh Ghate] - feel free to contact me!