Enhanced Diamond Price Prediction API Architecture

• Day: 2024-04-06
• Time: 16:50 to 17:30
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: API, Machine Learning, Python, Data Preprocessing, Software Architecture

Description

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.

Evidence

• source_file=2024-04-06.sessions.jsonl, line_number=1, event_count=0, session_id=1dbf25276db7200190607ecd4efd7c40e711c73364a921b5e1d48fb02c153a58
• event_ids: []