House Prices - Advanced Regression Techniques

Project overview

As part of the "House Prices - Advanced Regression Techniques" Kaggle competition, I developed a machine learning model in Python to predict house prices based on various features like size, location, and age of the house. My solution achieved Top-8% on the Kaggle leader board. The project is based on publicly available dataset https://www.kaggle.com/c/house-prices-advanced-regression-techniques.

Code: https://www.kaggle.com/code/nikitakrasnytskyi/compilation-of-best-kernels-top-8-2023

Business Value

The developed model can be used to help homeowners and real estate companies to estimate the market value of houses with much higher accuracy than using traditional methods like mean or median. Additionally, it could also help homebuyers to determine whether a house is priced reasonably, thus avoiding overpaying for properties.

The model can also be utilized to optimize house flipping by determining the features that make a significant impact on the house's price. This information can be used to upgrade or downgrade specific features and estimate how it will affect the house's selling price.

Technical details

I applied various regression techniques, such as linear regression, ridge regression, and gradient boosting regression, to the Kaggle dataset. The dataset contained 79 explanatory variables, including location, age, and size of the house. Preprocessing of the dataset included handling missing values and encoding categorical variables. I also experimented with feature engineering adding new features to improve model performance.

Moreover, through this project, I learned the importance of careful feature engineering and handling missing data. I found that some features had more than 80% missing values and used to drop them, but after reading the dataset description, I found that NAs sometimes represent a separate label for categorical data, and the feature is not corrupt.

During the experimentation phase, I observed that the most significant feature in predicting the house price was the size of the house. Other important features included the number of rooms, location, age, and the overall condition of the house. To further improve model performance, I used techniques like regularization and cross-validation to avoid overfitting and increase the model's accuracy. I also used ensemble methods like bagging and boosting to reduce model variance and bias.

I also learned about the power of Model Blending and Model Stacking. I combined 8 models, including Ridge, Lasso, and XGBoost, and set a meta-model on top. Then, I blended results of each model separately and stacked model to reduce overfitting.

Results

The final model achieved an RMSE of 0.1217 on the test dataset, which placed my solution in the Top-8% on the Kaggle leaderboard. The model was able to accurately predict the house prices based on the given features, making it a powerful tool for predicting real estate market values.

Future Works

There are several possible directions for further development, such as exploring deep learning models like neural networks and applying techniques like transfer learning to improve model performance. Another potential avenue for exploration is using different methods of handling missing data or exploring different feature engineering techniques. Additionally, incorporating external data sources such as economic indicators or demographic data could further enhance the model's accuracy. Overall, this project taught me valuable lessons about feature engineering, missing data handling, and ensemble methods, and there is much potential for future improvements in the field of house price prediction.