IFRI-AI-Classes/ifri_mini_ml_lib
Apr 2025 – May 2025
2 Months active
Languages Used
Python
Technical Skills
Data ScienceLinear RegressionMachine LearningNumerical ComputingPolynomial RegressionDocumentation
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PROFILE
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Contributed to the IFRI-AI-Classes/ifri_mini_ml_lib repository by engineering and enhancing core regression models, focusing on Linear, Polynomial, and Support Vector Regression using Python. Refactored core modules to improve input handling, numerical stability, and code modularity, enabling more reliable analytics and streamlined integration into machine learning pipelines. Enhanced documentation, introduced type hints, and expanded unit testing to strengthen maintainability and type safety. Developed dedicated tests for polynomial feature generation and regression workflows, while refining hyperparameter handling for SVR. These efforts improved the library’s usability, reliability, and readiness for real-world deployment, supporting robust regression analysis and automated testing practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
4
Lines of code
1,652
Activity Months2
Your Network
11 peopleShared Repositories
11
Work History
May 2025
10 Commits • 3 Features
May 1, 2025May 2025: Delivered major feature improvements and stability enhancements for the IFRI mini ML library, focusing on polynomial, linear, and SVR models, with emphasis on documentation, typing, testing, and API usability.
10 Commits • 3 Features
May 1, 2025May 2025: Delivered major feature improvements and stability enhancements for the IFRI mini ML library, focusing on polynomial, linear, and SVR models, with emphasis on documentation, typing, testing, and API usability.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025Month 2025-04: Delivered a major regression model enhancement in IFRI-AI-Classes/ifri_mini_ml_lib, focusing on Linear and Polynomial Regression. Refactored regressions.py for improved input handling, robust numerical methods (least squares and gradient descent), and cleaner architecture. This work improves model reliability, accuracy, and usability for downstream analytics and deployment.
April 2025
1 Commits • 1 Features
Apr 1, 2025Month 2025-04: Delivered a major regression model enhancement in IFRI-AI-Classes/ifri_mini_ml_lib, focusing on Linear and Polynomial Regression. Refactored regressions.py for improved input handling, robust numerical methods (least squares and gradient descent), and cleaner architecture. This work improves model reliability, accuracy, and usability for downstream analytics and deployment.
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Quality Metrics
Correctness89.0%
Maintainability91.8%
Architecture85.4%
Performance80.0%
AI Usage25.4%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ScienceDocumentationFeature EngineeringHyperparameter TuningLinear RegressionMachine LearningNumerical ComputingPolynomial RegressionPythonPython DevelopmentRegression AnalysisSupport Vector RegressionTestingType HintingUnit Testing
Repositories Contributed To
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