prudhvi6e / advanced-regression

Surprise-Housing - Advanced-Regression

Advanced Regression Problem Statement: 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 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.

Approach: Build a regression model using Advance regression methods like Ridge and Lasso regression to predict 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.

Model Objective: Which variables are significant in predicting the price of a house How well those variables describe the price of a house. Also, determine the optimal value of lambda for ridge and lasso regression. The tasks performed in the model presented below are: Importing and understading the data. Miising value treatment and outlier analysis Exploratory data analysis to find out the inference about the data and its correlation with the target variables. Tranformation of the target variable to handle the data skewness. Data preprocession like Label encoding and ceration of dummies. Test train split and Feature scaling Data modelling using RFE to identify the top 30 variables. Ridge and Lasso Regression to find the top feature variables and finding the optimal alpha value