
Developed an end-to-end data ingestion and preprocessing pipeline for Module 4 of the racousin/data_science_practice_2024 repository, consolidating data from CSV, JSON, Excel, API, and web scraping sources. The workflow automated data loading, cleaning, and preparation for downstream machine learning tasks, culminating in the generation of a formal submission artifact to streamline model evaluation. Leveraged Python, Pandas, and BeautifulSoup to integrate disparate data sources and ensure reproducibility through version-controlled commits. The work laid a robust foundation for modeling by producing clean, ready-to-use datasets and integrating a new submission.csv data source to support the model submission workflow.
November 2024 focused on delivering end-to-end data engineering for Module 4 of the data science practice project. Implemented a data ingestion and preprocessing pipeline that loads and consolidates data from multiple sources, prepares it for modeling, and generates a submission artifact to streamline evaluation.
November 2024 focused on delivering end-to-end data engineering for Module 4 of the data science practice project. Implemented a data ingestion and preprocessing pipeline that loads and consolidates data from multiple sources, prepares it for modeling, and generates a submission artifact to streamline evaluation.

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