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Regression Model Implementation for Melbourne Housing Dataset #5
This pull request details the development and refinement of a regression model for the Melbourne Housing dataset. It outlines the journey from an initial Ordinary Least Squares (OLS) model to a more robust LASSO model, addressing several key issues encountered along the way.
Background and Initial Model Creation
The branch began with the creation of an OLS regression model (3.1_ols_regression_model.py). The 3_regression_model.py file was initially empty, and the OLS model was the first attempt to analyze the dataset.
Approach and Methodology
Initial OLS Model Development:
Developed the OLS model using 'Suburb', but due to a high number of variables, switched to 'RegionName'.
Issues with the OLS model were identified post-implementation, including non-normality of residuals, mild autocorrelation, and potential multicollinearity.
Transition to LASSO Model:
Implemented the LASSO model (3.2_lasso_regression_model.py).
Faced challenges in alpha selection and decided to use 'Region' for simplification.
ElasticNet Model Comparison:
Implemented ElasticNet (3.3_elastic_net_regression_model.py) to compare with the LASSO model.
The ElasticNet model demonstrated a strong preference for the LASSO component, with an L1 ratio of 0.9500000000000001, highlighting the effectiveness of LASSO in feature selection for our dataset.
The model's preference for lower alpha values in both LASSO and ElasticNet, and the high L1 ratio in ElasticNet, suggested that a LASSO approach, emphasizing feature selection, was more beneficial for our dataset.
Analysis and Findings
Key Improvements and Diagnostics:
Residual analysis in the LASSO model showed normal distribution, addressing one of the OLS model's main issues.
The Durbin-Watson statistic indicated an improvement in autocorrelation.
Final Model Selection:
The LASSO model was finalized with an effective alpha, maintaining all variables while benefiting from regularization.
ElasticNet comparison favoured LASSO, confirming its suitability for our dataset.
Conclusion and Future Directions
The LASSO model successfully addressed the issues found in the initial OLS model and proved to be the most suitable for our dataset. This process highlighted the importance of iterative modelling and diagnostics in regression analysis. Future considerations include the exploration of machine learning models to potentially enhance our analytical capabilities further.
Technical Details and Code Changes
Files Modified:
Initial file: 3_regression_model.py.
Developed OLS model: 3.1_ols_regression_model.py.
Files Added:
LASSO model implementation: 3.2_lasso_regression_model.py.
ElasticNet model for comparison: 3.3_elastic_net_regression_model.py.
Testing and Validation
The LASSO model underwent rigorous testing and validation.
Performance metrics and residual checks demonstrated its superiority over the initial OLS approach.
Introduction
This pull request details the development and refinement of a regression model for the Melbourne Housing dataset. It outlines the journey from an initial Ordinary Least Squares (OLS) model to a more robust LASSO model, addressing several key issues encountered along the way.
Background and Initial Model Creation
The branch began with the creation of an OLS regression model (
3.1_ols_regression_model.py). The3_regression_model.pyfile was initially empty, and the OLS model was the first attempt to analyze the dataset.Approach and Methodology
Initial OLS Model Development:
Transition to LASSO Model:
3.2_lasso_regression_model.py).ElasticNet Model Comparison:
3.3_elastic_net_regression_model.py) to compare with the LASSO model.Analysis and Findings
Key Improvements and Diagnostics:
Final Model Selection:
Conclusion and Future Directions
The LASSO model successfully addressed the issues found in the initial OLS model and proved to be the most suitable for our dataset. This process highlighted the importance of iterative modelling and diagnostics in regression analysis. Future considerations include the exploration of machine learning models to potentially enhance our analytical capabilities further.
Technical Details and Code Changes
Files Modified:
3_regression_model.py.3.1_ols_regression_model.py.Files Added:
3.2_lasso_regression_model.py.3.3_elastic_net_regression_model.py.Testing and Validation