Enhanced Diamond Price Prediction Web Application

📅 2024-04-13 — Session: Enhanced Diamond Price Prediction Web Application

🕒 21:40–22:30
🏷️ Labels: Flask, Javascript, Machine Learning, Web Development, API
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal: The session aimed to enhance a web-based application for predicting diamond prices using machine learning, focusing on improving model retraining, visualization, and API integration.

Key Activities:

• Organized Git commits to ensure clarity and maintainability, emphasizing best practices in version control.
• Provided a technical overview of the diamond price prediction application, detailing its architecture and technologies such as Flask, Pandas, and Scikit-Learn.
• Prepared for development by reviewing current implementations and enhancing UI features, testing, and documentation.
• Implemented strategies for optimizing dynamic model retraining and visualization, focusing on data management and real-time updates.
• Created a JavaScript function fetchModels and a corresponding Flask API endpoint for fetching and serving plotting data.
• Developed a Flask API endpoint to serve actual vs. predicted price data and a JavaScript function for dynamic data visualization.
• Updated the get_latest_model_predictions function for dynamic loading and error handling in Flask.
• Resolved a JavaScript error related to DOM manipulation by ensuring proper element existence and script loading order.
• Fixed the /api/plot-data endpoint in the Flask application to include a model_name parameter for fetching model predictions.

Achievements:

• Successfully implemented and integrated dynamic model retraining and visualization features.
• Enhanced the application’s architecture and functionality through improved API and frontend-backend integration.

Pending Tasks:

• Further testing and validation of the enhanced features to ensure robustness and reliability.