Chameleon-company/MOP-Code
Sep 2025 – Sep 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data PreprocessingData VisualizationFeature EngineeringHyperparameter TuningMachine LearningMatplotlib
Vinithaa2025
PROFILE
Vinithaa2025
Vinitha Aveeramani developed an end-to-end Physical Activity Recognition notebook for the Chameleon-company/MOP-Code repository, implementing a complete machine learning pipeline in Python using Jupyter Notebook. She handled data loading, cleaning, feature engineering, and scaling, followed by model training with Random Forest and hyperparameter tuning via GridSearchCV. Evaluation was performed using accuracy metrics, classification reports, and confusion matrices, ensuring reproducible and traceable analytics. Additionally, Vinitha improved repository hygiene by removing unnecessary documentation files, streamlining the project structure. Her work established a scalable workflow for physical activity analytics, demonstrating depth in data preprocessing, visualization with Matplotlib and Seaborn, and robust model evaluation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,217
Activity Months1
Your Network
98 people
Work History
September 2025
2 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for Chameleon-company/MOP-Code: Delivered an end-to-end Physical Activity Recognition notebook implementing a complete ML pipeline (data loading, cleaning, preparation, feature scaling, model training with RandomForest, hyperparameter tuning via GridSearchCV, and evaluation using accuracy, classification reports, and confusion matrices). Performed repository hygiene improvement by removing stray Readmefile.pdf in the Physical Activity Recognition directory to streamline the repository structure. This period improved reproducibility and established a scalable analytics workflow with clear traceability to commits, enhancing both business value and technical reliability.
2 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for Chameleon-company/MOP-Code: Delivered an end-to-end Physical Activity Recognition notebook implementing a complete ML pipeline (data loading, cleaning, preparation, feature scaling, model training with RandomForest, hyperparameter tuning via GridSearchCV, and evaluation using accuracy, classification reports, and confusion matrices). Performed repository hygiene improvement by removing stray Readmefile.pdf in the Physical Activity Recognition directory to streamline the repository structure. This period improved reproducibility and established a scalable analytics workflow with clear traceability to commits, enhancing both business value and technical reliability.
September 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data PreprocessingData VisualizationFeature EngineeringHyperparameter TuningMachine LearningMatplotlibPandasRandom ForestScikit-learnSeaborn
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