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
Worked on the racousin/data_science_practice_2024 repository to deliver end-to-end machine learning workflows, focusing on both deep learning and data engineering. Developed a comprehensive MNIST digit recognition suite, covering model training, evaluation, and automated prediction generation using Python, PyTorch, and Jupyter Notebooks. Built a sales data processing pipeline that integrated data collection from APIs and web scraping with BeautifulSoup and Selenium, followed by cleaning, aggregation, and feature engineering for downstream ML tasks. Updated educational notebooks on stock market prediction and evaluation metrics, and improved project maintainability by cleaning obsolete artifacts and streamlining data preparation for reproducible, scalable learning modules.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
4
Lines of code
70,343
Activity Months1
Your Network
15 peopleShared Repositories
15
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
Repositories Contributed To
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