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.
Problem Statement Data understanding Create Dummy Varibales Plotting heatmap to check the corelation between varables Let us check the distribution of our target variable before scaling and Splitting Prepairing Data For Modeling Rescalling The Numeric Factors Training The Model RFE- Recursive Feature Elimination Building model using statsmodel, for the detailed statistics Rebuild the model without GarageType_NA Residual analysis of Train Data Evaluate R2 of Train model Calculate r2 score of Test mode Final evalution Ridge Regression Lasso Regression Lets observe the changes in the coefficients after regularization The company wants to know:
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.
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.
-Final evalution No of Variables = 21
VIF < 5 p-value < 0.05 r2 score for Train Data set model = 0.80 r2 score for Test Data set model = 0.82
Coeff values are deceresing as alpha will increase.r2_score of train data is also drop from .839 to 0.835
As alpha value increse But r2score is also dropped by .839 to .833
- python 3.0
- pandas
- numpy
- matplotlib
- seaborn
- sklearn.linear_model
- LinearRegression, Ridge, Lasso
- GridSearchCV
- statsmodels
Give credit here.
- This project was inspired by IIIT banglore assigment
- upgrad
- This project was based on assignment form upgrad
Created by [@karansbisht] - feel free to contact me!