This project has been developed as part of my Masters in Artificial Inteligence and Machine Learning course with Upgrad
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 was provided in the train.csv file
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:
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Which variables are significant in predicting the price of a house, and
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How well those variables describe the price of a house.
- The project will compare both ridge and lasso models
- We will use hyper parameter tuning to identify the optimal value of lamda (often also refered to as alpha)
- We will attempt to find the 5 most important features impacting house prices
The top 5 features identified in the Lasso model where:
- GrLivArea ($212375.99)
- OverallQualSq ($115221.72)
- LotArea ($111092.41)
- BsmtFinSF1 ($78209.38)
- Neighborhood_StoneBr ($57023.84)
The values in brackets show how much value the feature can add to the sale price of a property
- Python - version 3.11.2
- Numpy - version 1.23.5
- Matplotlib - version 3.7.1
- Scipy - version 1.10.1
- Seaborne - version 0.12.2
Created by [@KaneAI] - feel free to contact me!