Refactor metric calculation in calcular_metricas_oulad function to include handling for 'sum_click' column. This enhances robustness by allowing the average clicks to be computed from either 'clicks' or 'sum_click' based on availability.
Refactor metric calculation in obter_metricas_principais_oulad function to handle multiple click columns. Now checks for 'clicks' and 'sum_click' to compute the average clicks, ensuring robustness in data processing.
Enhance API key initialization and validation process. Automatically test the OpenAI key if not previously validated, and provide user feedback on key status. Update sidebar to reflect validation results and allow re-testing of the key.
Update landing page and dashboard to implement unified template generation with top 2 features from UCI and OULAD; add new dashboard page for consolidated dataset analysis; enhance OpenAI integration for automatic insights and improve user experience with detailed instructions and error handling.
Enhance OULAD visualizations and metrics calculations to focus on unique student counts across various categories. Update histograms and bar plots for scores, age, gender, region, and final results to reflect unique student data, improving accuracy and insights in the educational dashboard. Adjust utility functions to ensure consistent handling of unique student metrics.
Update UCI and OULAD metrics calculations to be dynamic, enhancing the educational dashboard with real-time data insights. Refactor visualizations to reflect actual dataset values, improving user experience and accuracy in performance metrics. Add error handling for data loading processes.
Refactor feature importance calculations for UCI and OULAD datasets: optimize caching durations, enhance progress indicators, and streamline data processing with improved sampling and parallel execution. Update documentation for clarity.
Update feature importance calculations to utilize permutation importance for real data analysis. Improve PyGWalker integration with dataset selection for interactive analysis.
Add script to generate optimized pickle for OULAD dataset and update data loading functions for improved performance and memory efficiency. Include caching mechanisms and detailed logging for data processing.
Implement feature importance analysis for UCI and OULAD datasets in the educational dashboard, including new visualizations and interactive PyGWalker section. Update model training functions with caching improvements and enhance data loading mechanisms.
Enhance educational dashboard sidebar: add detailed dataset descriptions, new metrics for UCI and OULAD, and key insights while removing outdated filters for improved user experience.
Refactor educational dashboard: replace metrics display with informative cards, add new insights and visualizations for UCI and OULAD datasets, and streamline sidebar functionality.
