Enhanced Dynamic Model Retraining and API Integration

📅 2024-04-13 — Session: Enhanced Dynamic Model Retraining and API Integration

🕒 21:40–22:30
🏷️ Labels: Flask, Javascript, Machine Learning, API, Data Visualization
📂 Project: Dev

Session Goal

The session aimed to enhance dynamic model retraining, improve [[data visualization]], and integrate robust API endpoints in a web application for predicting diamond prices.

Key Activities

• Organized Git Commits: Structured commits by functionality and ensured meaningful commit messages for better version control.
• Technical Overview: Reviewed the architecture and session plan for a diamond price prediction application using Flask, Pandas, and Scikit-Learn.
• Development Preparation: Focused on UI enhancements, testing, and documentation for the development session.
• Model Optimization: Optimized dynamic model retraining and visualization strategies, emphasizing real-time updates and data handling.
• API and Frontend Development: Created a fetchModels function and Flask API endpoint for dynamic [[data visualization]], ensuring integration between frontend JavaScript and backend Flask.
• Error Handling: Resolved JavaScript error Uncaught TypeError: document.getElementById(...) is null by ensuring element existence and managing script loading order.
• Flask Endpoint Fix: Adjusted /[[api]]/plot-data endpoint to include model_name parameter, updating frontend JavaScript accordingly.

Achievements

• Successfully implemented dynamic model retraining and visualization features.
• Integrated robust API endpoints for data fetching and visualization.
• Enhanced error handling in JavaScript and Flask applications.

Pending Tasks

• Further testing and validation of the integrated features in a production environment.
• Continuous monitoring and optimization of model retraining strategies.