Enhanced Diamond Price Prediction API Architecture

📅 2024-04-06 — Session: Enhanced Diamond Price Prediction API Architecture

🕒 16:50–17:30
🏷️ Labels: API, Machine Learning, Python, Data Preprocessing, Software Architecture
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to improve the architecture of a diamond price prediction API, focusing on modularization, maintainability, and scalability.

Key Activities

• Outlined an enhanced architecture for the API, including a proposed file structure and synthesized code for data preprocessing, model training, and API routes.
• Discussed the importance of the utils directory for organizing reusable utility functions in software projects.
• Implemented a train_and_save_model function for the RandomForestRegressor, incorporating hyperparameter tuning and performance evaluation.
• Improved the preprocessing of the diamonds dataset, focusing on outlier handling, feature engineering, and specific imputation strategies.
• Developed an end-to-end Python script for model testing, integrating data preprocessing, model training, saving, loading, and prediction.
• Resolved an AttributeError with the OneHotEncoder by updating to get_feature_names_out().
• Adjusted the preprocess_data() function to return both features and labels for the diamonds dataset.

Achievements

• Successfully outlined a scalable and maintainable architecture for the diamond price prediction API.
• Enhanced the model training pipeline with hyperparameter tuning and evaluation metrics.
• Improved data preprocessing techniques for better model performance.

Pending Tasks

• Further testing and validation of the API architecture and model performance in a production-like environment.
• Documentation of the new architecture and preprocessing methods for future reference.