Build a advanced linear regression model using Ridge and Lasso for the prediction of house price for a US based housing company who has decided to enter Australian market.
Problem Statement:
A US-Based housing company, Surprise Housing has decided to enter the Australian market. The company is looking at prospective properties to buy to enter the market. The business model of the company is that it purchase houses at a price below their actual values and flip them on at a higher price. The company wants to build a model to predict the actual value of the prospective properties and decide whether to invest in them or not.
Goal:
Build a regression model using regularisation and find:
- 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 and lasso regression.
The solution is divided into the following sections:
- Data understanding and exploration
- Data cleaning
- Data preparation
- Model building and evaluation
- What is the dataset that is being used? 'train.csv'
- Overall we have a decent Ridge and Lasso models with optimal alpha and coeffients for variables.
- python 3.x
- pandas
- Scikit-learn
- The Python notebook containing 'House Price Prediction' can be found here (https://github.com/suniljadhav/HousePricePrediction/blob/13246d9b7e1e05590193125c798fcd8ae1329759/Advanced%20Regression%20Assignment%20-%20House%20Price%20Prediction%20(Part%20I)%20%20-%20Model.ipynb).
- The answers to the 'Advanced Regression Subjective Questions' can be found here (https://github.com/suniljadhav/HousePricePrediction/blob/13246d9b7e1e05590193125c798fcd8ae1329759/Advanced%20Regression%20Assignment%20-%20House%20Price%20Prediction%20(Part%20II)%20%E2%80%93%20Subjective%20Questions.pdf)
- This project is part of the ML/AI masters programme.
Created by [@suniljadhav] - feel free to contact me!