
Worked on the racousin/data_science_practice_2025 repository, delivering four new features focused on machine learning pipeline development and data engineering. Developed user seed data to streamline onboarding processes and introduced a reusable Python utility library for safe arithmetic operations. Built end-to-end pipelines for sale price and quantity sold prediction, handling data collection, cleaning, feature engineering, and linear regression modeling using Python, Pandas, and Scikit-learn. Integrated multiple data sources, including CSV, JSON, and APIs, and ensured robust preprocessing for model training. The work emphasized modularity and reusability, supporting both user management and predictive analytics within a structured data science workflow.
Month: 2025-09 Scope: racousin/data_science_practice_2025; Feature delivery and ML pipeline development across modules. This month focused on seeding user data for Module1, introducing a reusable utility library, and building end-to-end ML pipelines for Module 3 (sale price) and Module 4 (quantity_sold).
Month: 2025-09 Scope: racousin/data_science_practice_2025; Feature delivery and ML pipeline development across modules. This month focused on seeding user data for Module1, introducing a reusable utility library, and building end-to-end ML pipelines for Module 3 (sale price) and Module 4 (quantity_sold).

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