racousin/data_science_practice_2024
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
API IntegrationBeautifulSoupCross-ValidationData AnalysisData CleaningData Collection
nacefbm
PROFILE
Nacefbm
During November 2024, Nacef Ben Mansour developed end-to-end machine learning workflows in the racousin/data_science_practice_2024 repository, focusing on both deep learning and data engineering. He built a complete MNIST digit recognition suite, covering model training, evaluation, and automated prediction generation using Python, PyTorch, and Jupyter Notebooks. In parallel, he engineered a sales data processing pipeline that integrated data from APIs and web scraping with BeautifulSoup, emphasizing robust data cleaning and feature engineering for downstream ML tasks. He also maintained educational content by updating notebooks and exercises, and improved project hygiene by removing deprecated artifacts and finalizing submission datasets.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
4
Lines of code
70,343
Activity Months1
Work History
November 2024
9 Commits • 4 Features
Nov 1, 2024November 2024 performance summary for racousin/data_science_practice_2024 focused on delivering end-to-end ML content, stabilizing artifacts, and enabling scalable learning workflows. Key features delivered include the MNIST Digit Recognition Deep Learning Suite (training, evaluation, and generation of submission predictions) and the Sales Data Processing Pipeline for ML predictions (end-to-end data collection, cleaning, aggregation from APIs and web sources, plus feature engineering). Educational Notebooks and Exercises were created/updated across modules (stock market prediction and evaluation metrics), and ML exercise artifacts were cleaned up to finalize submissions by removing obsolete notebooks and updating submission data.
9 Commits • 4 Features
Nov 1, 2024November 2024 performance summary for racousin/data_science_practice_2024 focused on delivering end-to-end ML content, stabilizing artifacts, and enabling scalable learning workflows. Key features delivered include the MNIST Digit Recognition Deep Learning Suite (training, evaluation, and generation of submission predictions) and the Sales Data Processing Pipeline for ML predictions (end-to-end data collection, cleaning, aggregation from APIs and web sources, plus feature engineering). Educational Notebooks and Exercises were created/updated across modules (stock market prediction and evaluation metrics), and ML exercise artifacts were cleaned up to finalize submissions by removing obsolete notebooks and updating submission data.
November 2024
Activity
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Quality Metrics
Correctness82.2%
Maintainability82.2%
Architecture82.2%
Performance68.8%
AI Usage29.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
API IntegrationBeautifulSoupCross-ValidationData AnalysisData CleaningData CollectionData EngineeringData PreprocessingData ScienceData VisualizationDeep LearningExploratory Data Analysis (EDA)Jupyter NotebooksLightGBMMachine Learning
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