liquidinstruments/moku-examples
Dec 2024 – Dec 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data ScienceData SplittingData VisualizationJupyter NotebookMachine LearningModel Evaluation
EC2 Default User
PROFILE
Ec2 Default User
During December 2024, ec2-user@ip-172-16-57-101.ap-southeast-2.compute.internal developed a robust classification model validation workflow for the liquidinstruments/moku-examples repository. They refactored the data splitting process in Python and Jupyter Notebook, reserving the second half of generated signals as a dedicated test set to ensure reliable evaluation on unseen data. By visualizing model accuracy on this new test set, they improved the clarity of model performance and supported better deployment decisions. Their work demonstrated solid skills in data science, machine learning, and model evaluation, resulting in a more trustworthy validation process and enhanced data integrity for future model readiness.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,282
Activity Months1
Your Network
13 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12. Focused on delivering a robust classification model validation workflow in liqidinstruments/moku-examples. Implemented a dedicated test set and evaluated the model on unseen data, with visualization of accuracy on the new test set. No major bugs reported this month. Impact includes more reliable validation, better data integrity for model readiness, and clearer signals for deployment decisions. Technologies and skills demonstrated include Python, Jupyter notebooks, data splitting strategies, model evaluation metrics, and data visualization within ML notebooks.
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12. Focused on delivering a robust classification model validation workflow in liqidinstruments/moku-examples. Implemented a dedicated test set and evaluated the model on unseen data, with visualization of accuracy on the new test set. No major bugs reported this month. Impact includes more reliable validation, better data integrity for model readiness, and clearer signals for deployment decisions. Technologies and skills demonstrated include Python, Jupyter notebooks, data splitting strategies, model evaluation metrics, and data visualization within ML notebooks.
December 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data ScienceData SplittingData VisualizationJupyter NotebookMachine LearningModel EvaluationPython
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