
During November 2024, Djakertarek Akertarek developed an end-to-end data ingestion and preprocessing pipeline for Module 4 of the racousin/data_science_practice_2024 repository. He consolidated data from diverse sources, including CSV files, APIs, and web scraping, using Python, Pandas, and BeautifulSoup to automate extraction and transformation. The pipeline produced a clean, ready-to-model dataset and generated a formal submission artifact to streamline evaluation. By wiring a new submission.csv data source into the workflow, Djakertarek enabled reproducibility and traceability for model submissions. The work demonstrated solid data engineering depth, laying a robust foundation for subsequent machine learning and analysis tasks.

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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