Advanced Linear Regression Assignment for Housing Data in Australia

Surprise Housing, a US-based company specializing in data-driven house flipping, is entering the Australian market. This assignment focuses on building a regression model to predict house prices and assess investment potential.

Table of Contents

1. Overview
2. Technologies Used
3. Objectives
4. Modeling Approach
5. Conclusions
6. Acknowledgements
7. Contact

Overview

• Objective: This project aims to equip Surprise Housing with predictive insights to optimize house purchases in the Australian market, maximizing profitability through accurate pricing strategies.
• Significance: By identifying key variables influencing house prices, the project enhances decision-making capabilities, ensuring informed investments.

Technologies Used

• Python: 3.12.2
• Jupyter Notebook: 7.4.2
• Anaconda: 2023.10
• Libraries:
  • Numpy: 1.26.2
  • Pandas: 2.1.4
  • Plotly: 5.18.0
  • Matplotlib: 3.6.2
  • Seaborn: 0.12.2
  • Statsmodels: 0.14.0
  • Scikit-learn (Sklearn): 1.2.0

Objectives

• Identify Significant Variables: Determine features with statistically significant impacts on house prices using regression analysis.
• Model Accuracy Assessment: Evaluate the predictive performance of linear, polynomial, ridge, and lasso regression models.
• Optimize Regularization Parameters: Fine-tune lambda parameters for ridge and lasso models to achieve optimal predictive power.

Modeling Approach

• Data Preparation: Cleaned and prepared the train.csv dataset provided by UpGrad, ensuring data integrity and consistency.
• Visualization: Visualized data patterns and insights to understand relationships between variables.
• Model Building:
  • Utilized Recursive Feature Elimination (RFE) to select relevant features for linear regression.
  • Applied polynomial regression (degree 2) and addressed overfitting with ridge and lasso regression techniques.
• Model Validation: Conducted residual analysis to validate model assumptions and ensure robustness across training and test datasets.

Conclusions

• Data Preparation: Ensured dataset cleanliness and preparedness for accurate modeling.
• Model Performance: Developed robust regression models (linear, polynomial, ridge, lasso) validated through rigorous analysis.
• Insights: Derived actionable insights into Australian housing market dynamics, enabling strategic decision-making for Surprise Housing.

Acknowledgements

• Resources: Leveraged resources from:
  • Google
  • Stack Overflow
  • YouTube
  • UpGrad
  • OpenAI
  • Wikipedia
  • Stack Exchange

Contact

Created by @SandeepGitGuy - Feel free to reach out!