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Key Advanced Improvements
Cross-Validation and Model Selection:
Implemented a method to split the data into training and test sets, ensuring that the model's performance can be evaluated on unseen data. Logging:
Added logging statements to track the loading of data, errors, and the safety check results. This is useful for debugging and understanding the flow of execution. Visualization:
Added a plotting function to visualize the predicted vs. actual user counts, helping to understand the model's performance visually. Modularization:
Split the code into smaller functions for better readability and maintainability, making it easier to test individual components. Enhanced Exception Handling:
Improved exception handling for loading data, making it robust against common file issues. Pipeline and Hyperparameter Tuning:
While the code uses simpler models for demonstration, integrating GridSearchCV into the support vector regressor can optimize parameters. You can implement this based on your specific needs.
Describe your change:
Checklist: