smvicky / pixel_test_submissions

House Prices - Advanced Regression Techniques

pixel_test_submissions

This repository explores house price prediction using linear regression techniques on the Kaggle dataset, "House Prices - Advanced Regression Techniques" https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques. The goal is to estimate the final selling price of residential properties in Ames, Iowa, based on various explanatory variables.

Model and Techniques

The core model employed is linear regression from sklearn.linear_model.LinearRegression. To handle categorical features effectively, one-hot encoding is implemented with OneHotEncoder from sklearn.preprocessing. Additionally, missing values are addressed using SimpleImputer from sklearn.impute.

Current Performance

Validation Set Accuracy: 73% (indicates a promising model structure) Test Set Score: 60% (further improvement is necessary for reliable predictions) Visualization

Most Significant Features Graph: An image is included to visualize the features that have the strongest influence on the predicted house prices. This helps in understanding the model's decision-making process and identifying potential areas for feature engineering. Future Directions

This project serves as a foundation for further exploration of house price prediction with advanced regression techniques. Here are some potential areas for improvement:

Feature Engineering: Feature selection, transformation, and creation of new features from existing ones can significantly enhance model performance. Hyperparameter Tuning: Optimizing hyperparameters of the linear regression model (e.g., regularization parameters) may lead to better generalization.

Ensemble Learning: Combining predictions from multiple models (e.g., Random Forest, Gradient Boosting) could potentially boost accuracy. Evaluation Metrics: Exploring alternative metrics like Mean Absolute Error (MAE) or using stratified K-Fold cross-validation might provide more robust performance assessment.

Collaboration and Contribution

This repository is open for collaboration and suggestions. Feel free to fork it, make improvements, and submit pull requests to enhance the capabilities of the model. By working together, we can continuously refine the house price prediction approach.

Additional Considerations

Clear and Concise Language: Strive for a balance between technical accuracy and readability for a wider audience. Reproducibility: Ensure code clarity and proper documentation for easy replication of results. Data Exploration: Include a brief section on data exploration (if applicable) to showcase initial insights into the dataset. Continuous Improvement: Regularly update the README file with the latest results, methodologies, and future directions. I hope this improved README file provides a valuable structure for your repository!

Most significant features graph.(significant coefficient)