Enhanced Diamond Price Prediction API Architecture

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

🕒 16:50–17:30
🏷️ Labels: API, Flask, Machine Learning, Software Architecture, Data Preprocessing
📂 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

• Designed an enhanced architecture for the API using Python and Flask, emphasizing modularity and best practices.
• Provided a detailed overview of the utils directory for organizing reusable utility functions and classes.
• Implemented a train_and_save_model function for RandomForestRegressor with hyperparameter tuning and model evaluation.
• Improved preprocessing for the diamonds dataset, including outlier handling and feature engineering.
• Developed an end-to-end model testing script integrating data preprocessing, model training, and prediction.
• Fixed an AttributeError related to OneHotEncoder in scikit-learn, updating deprecated methods.
• Adjusted the preprocess_data() function to separate features and labels.

Achievements

• Established a robust API architecture blueprint with a clear file structure.
• Enhanced model training with hyperparameter tuning and evaluation metrics.
• Improved data preprocessing techniques for better model performance.
• Resolved issues with deprecated methods in scikit-learn, ensuring code compatibility.

Pending Tasks

• Further testing and validation of the API architecture in a production environment.
• Continuous monitoring and adjustment of preprocessing strategies as needed.