racousin/data_science_practice_2025
Sep 2025 – Oct 2025
2 Months active
Languages Used
CSVJupyter NotebookPythonText
Technical Skills
BeautifulSoupData AggregationData AnalysisData CleaningData CollectionData Engineering
wangke
PROFILE
Wangke
During a two-month period, Wang Ke developed a suite of end-to-end data science modules in the racousin/data_science_practice_2025 repository, focusing on practical machine learning workflows. He built Jupyter Notebooks for tasks such as house price, quantity sold, and electricity demand prediction, implementing data collection, preprocessing, and model training using Python, pandas, and scikit-learn. His work included a reusable math utilities library and a regression experiment pipeline with cross-validation and hyperparameter optimization. By emphasizing reproducibility and modularity, Wang Ke enabled streamlined benchmarking and results tracking. The engineering demonstrated depth in data engineering, model evaluation, and production-like artifact creation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
6
Lines of code
19,959
Activity Months2
Your Network
22 people
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for racousin/data_science_practice_2025: Key feature delivered: Module 6 ML Regression Notebook for Module 6 exercises, including data collection, model building, cross-validation, and hyperparameter optimization across multiple regression algorithms. The release includes a submission.csv scaffold and v2 notebook updates that improve reproducibility and results tracking. Commit references: daa374334d5680748a77d5caf7146a6151cca7e4 (add module6_exercise notebook with submission.csv) and 7df918b1202703c4bd5d5fb01bc2ea42ad6cbc72 (module6: update notebook and submission (v2)). Major bugs fixed: none reported; minor cleanup and documentation improvements included in the v2 update. Overall impact: accelerates learning and benchmarking for Module 6 by providing a reusable, end-to-end regression experiment pipeline, enabling faster decision-making and clearer performance insights. Technologies/skills demonstrated: Python, Jupyter Notebooks, pandas, scikit-learn, cross-validation, hyperparameter optimization, notebook-based data collection, version control.
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for racousin/data_science_practice_2025: Key feature delivered: Module 6 ML Regression Notebook for Module 6 exercises, including data collection, model building, cross-validation, and hyperparameter optimization across multiple regression algorithms. The release includes a submission.csv scaffold and v2 notebook updates that improve reproducibility and results tracking. Commit references: daa374334d5680748a77d5caf7146a6151cca7e4 (add module6_exercise notebook with submission.csv) and 7df918b1202703c4bd5d5fb01bc2ea42ad6cbc72 (module6: update notebook and submission (v2)). Major bugs fixed: none reported; minor cleanup and documentation improvements included in the v2 update. Overall impact: accelerates learning and benchmarking for Module 6 by providing a reusable, end-to-end regression experiment pipeline, enabling faster decision-making and clearer performance insights. Technologies/skills demonstrated: Python, Jupyter Notebooks, pandas, scikit-learn, cross-validation, hyperparameter optimization, notebook-based data collection, version control.
October 2025
September 2025
7 Commits • 5 Features
Sep 1, 2025September 2025 monthly summary for racousin/data_science_practice_2025. Delivered a cohesive set of feature-rich modules and a reusable utilities library, establishing end-to-end notebooks from data collection to submission. No explicit bug fixes were documented in the provided data; primary focus was on feature delivery, packaging readiness, and production-like artifacts to support ongoing practice and assessment.
September 2025
7 Commits • 5 Features
Sep 1, 2025September 2025 monthly summary for racousin/data_science_practice_2025. Delivered a cohesive set of feature-rich modules and a reusable utilities library, establishing end-to-end notebooks from data collection to submission. No explicit bug fixes were documented in the provided data; primary focus was on feature delivery, packaging readiness, and production-like artifacts to support ongoing practice and assessment.
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance82.2%
AI Usage24.4%
Skills & Technologies
Programming Languages
CSVJupyter NotebookPythonText
Technical Skills
Bayesian OptimizationBeautifulSoupCross-ValidationData AggregationData AnalysisData CleaningData CollectionData EngineeringData PreprocessingData ScienceFile ManagementHyperparameter TuningLightGBMLinear RegressionMachine Learning
Repositories Contributed To
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