Advanced Regression Assignment for Upgrad by Aashna Behl
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 company is looking at prospective properties to buy to enter the market.
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.
Business Goal
The goal is 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.
The most significant features areLinear Relationship established. Significant Variables
The top influencing factors as per both Ridge and Lasso are:
- GrLivArea
- OverallQual
- age_since_built
- Neighborhood
- BsmtQual
- GarageArea
- BsmtFinSF1
- 1stFlrSF
- MSSubClass
- BldgType
- KitchenQual
- Exterior1st
- Functional
- BsmtExposure
- LotArea
- 2ndFlrSF
python: 3.11.4 numpy: 1.24.3 pandas: 1.5.3 matplotlib: 3.7.1 seaborn: 0.12.2 sklearn: 1.3.0 statsmodels: 0.14.0
- This project was based on the Advance Regression assignment given by UpGrad as a part of the AI and ML course from IIIT Bangalore.
Created by [AashnaBehl17] - feel free to contact me!