Advanced-Linear-Regression

Advanced Linear Regression Assignment Upgrad

Problem Statement

Surprise Housing, a US-based company specializing in data-driven housing investments, seeks to enter the Australian market. The company aims to leverage its data analytics capabilities to purchase properties below market value and sell them at a profit. To facilitate this expansion, Surprise Housing has obtained a dataset containing information on house sales in Australia. The objective is to develop a regression model with regularization techniques to predict the true market value of prospective properties, enabling informed investment decisions.

Business Goals

1. Predictive Modeling: Develop a regression model using regularization techniques to accurately predict the actual value of properties in the Australian housing market.

2. Identify Significant Variables: Determine the variables significantly influencing house prices in the Australian market to focus on key predictors.

3. Performance Assessment: Evaluate the effectiveness of the regression model in describing the relationship between independent variables and house prices.

4. Optimization of Regularization Parameters: Determine optimal values of lambda for ridge and lasso regression techniques to enhance model performance.

5. Strategic Decision-Making: Empower management with actionable insights derived from the regression model to formulate data-driven investment strategies.

6. Market Understanding: Provide comprehensive insights into the pricing dynamics of the Australian housing market to guide strategic planning and market entry.

Expected Outcome

The developed regression model will serve as a valuable tool for Surprise Housing to make informed investment decisions in the Australian housing market, optimize resource allocation, and maximize returns on investments.

Tools and Technologies

• Python
• Pandas
• Scikit-learn
• Regression Techniques (Ridge, Lasso)
• Git

Contributors

• Garv Daga