HWTeng-Teaching/202509-ML-FinTech
Sep 2025 – Dec 2025
4 Months active
Languages Used
Jupyter NotebookMarkdownPythonUnknownCSVNonePDF
Technical Skills
ClusteringData AnalysisData CleaningData VisualizationDocumentationExploratory Data Analysis
menselsantoso
PROFILE
Menselsantoso
Mensel Santoso developed a suite of data analysis and machine learning workflows for the HWTeng-Teaching/202509-ML-FinTech repository over four months, focusing on reproducible analytics and robust project structure. He built Jupyter Notebooks for exploratory data analysis, clustering, PCA, and regression, integrating Python, pandas, and scikit-learn to support hands-on learning and data-driven experimentation. Mensel established project scaffolding, streamlined data ingestion, and maintained repository hygiene by removing deprecated files and clarifying dataset ownership. His work enabled rapid experimentation, improved documentation, and facilitated onboarding, demonstrating depth in data preprocessing, visualization, and model evaluation while ensuring maintainable, well-organized code and datasets.
Overall Statistics
Feature vs Bugs
76%Features
Repository Contributions
60Total
Bugs
4
Commits
60
Features
13
Lines of code
70,570
Activity Months4
Your Network
19 people
Work History
December 2025
21 Commits • 4 Features
Dec 1, 2025December 2025 for HWTeng-Teaching/202509-ML-FinTech delivered foundational ML project scaffolding, data ingestion capabilities, and repository hygiene improvements to enable scalable, repeatable experimentation and cleaner data governance. Key features include project scaffolding with initial data uploads, asdf project skeleton/files with dataset integration, and a Preprocessed dataset structure. Major fixes involved removing deprecated notebooks/test datasets and cleaning up asdf components within the TCRI project to reduce maintenance risk. The work positions the team for faster onboarding, reproducible pipelines, and clearer ownership of data assets.
21 Commits • 4 Features
Dec 1, 2025December 2025 for HWTeng-Teaching/202509-ML-FinTech delivered foundational ML project scaffolding, data ingestion capabilities, and repository hygiene improvements to enable scalable, repeatable experimentation and cleaner data governance. Key features include project scaffolding with initial data uploads, asdf project skeleton/files with dataset integration, and a Preprocessed dataset structure. Major fixes involved removing deprecated notebooks/test datasets and cleaning up asdf components within the TCRI project to reduce maintenance risk. The work positions the team for faster onboarding, reproducible pipelines, and clearer ownership of data assets.
December 2025
November 2025
19 Commits • 5 Features
Nov 1, 2025November 2025 performance summary for HWTeng-Teaching/202509-ML-FinTech: Delivered a suite of data-analysis notebooks spanning stock market analysis, statistical learning, wage modeling, and credit risk, established repository hygiene, and prepared groundwork for future components. These efforts advance analytical capabilities, reproducibility, and developer velocity, enabling faster experimentation and better decision support. Key technologies include Python notebooks, pandas, scikit-learn, cross-validation, polynomial regression, and data visualization.
November 2025
19 Commits • 5 Features
Nov 1, 2025November 2025 performance summary for HWTeng-Teaching/202509-ML-FinTech: Delivered a suite of data-analysis notebooks spanning stock market analysis, statistical learning, wage modeling, and credit risk, established repository hygiene, and prepared groundwork for future components. These efforts advance analytical capabilities, reproducibility, and developer velocity, enabling faster experimentation and better decision support. Key technologies include Python notebooks, pandas, scikit-learn, cross-validation, polynomial regression, and data visualization.
October 2025
4 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for HWTeng-Teaching/202509-ML-FinTech focusing on delivering hands-on learning artifacts and improving repo hygiene.
4 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for HWTeng-Teaching/202509-ML-FinTech focusing on delivering hands-on learning artifacts and improving repo hygiene.
October 2025
September 2025
16 Commits • 3 Features
Sep 1, 2025September 2025 – Monthly summary for HWTeng-Teaching/202509-ML-FinTech: Key features delivered - README: Created and updated documentation to include author and team LinkedIn profile links, improving visibility and contactability (7 commits across multiple updates). - EDA notebook: Added a Jupyter Notebook for College.csv with data reading/cleaning, numerical summaries, visualizations, and creation of an Elite variable to compare elite vs non-elite universities (2 uploads). - HW0915 scaffolding: Established scaffolding and clustering notebooks for HW0915, including placeholder files, artifact cleanup, and clustering experiments (K-means and hierarchical). Major bugs fixed - No major bugs fixed this month. Maintenance focused on documentation, data exploration setup, and scaffolding. Overall impact and accomplishments - Strengthened external visibility and collaboration by enriching README metadata. - Accelerated data-driven analytics with a ready-to-run EDA notebook and Elite variable concept. - Enabled rapid experimentation for clustering workflows with dedicated HW0915 notebooks. Technologies/skills demonstrated - Python, Jupyter Notebooks, pandas, data visualization (Matplotlib/Seaborn), and scikit-learn clustering (K-means, hierarchical). - Git workflows, commit discipline, and documentation practices.
September 2025
16 Commits • 3 Features
Sep 1, 2025September 2025 – Monthly summary for HWTeng-Teaching/202509-ML-FinTech: Key features delivered - README: Created and updated documentation to include author and team LinkedIn profile links, improving visibility and contactability (7 commits across multiple updates). - EDA notebook: Added a Jupyter Notebook for College.csv with data reading/cleaning, numerical summaries, visualizations, and creation of an Elite variable to compare elite vs non-elite universities (2 uploads). - HW0915 scaffolding: Established scaffolding and clustering notebooks for HW0915, including placeholder files, artifact cleanup, and clustering experiments (K-means and hierarchical). Major bugs fixed - No major bugs fixed this month. Maintenance focused on documentation, data exploration setup, and scaffolding. Overall impact and accomplishments - Strengthened external visibility and collaboration by enriching README metadata. - Accelerated data-driven analytics with a ready-to-run EDA notebook and Elite variable concept. - Enabled rapid experimentation for clustering workflows with dedicated HW0915 notebooks. Technologies/skills demonstrated - Python, Jupyter Notebooks, pandas, data visualization (Matplotlib/Seaborn), and scikit-learn clustering (K-means, hierarchical). - Git workflows, commit discipline, and documentation practices.
Activity
Loading activity data...
Quality Metrics
Correctness89.4%
Maintainability88.2%
Architecture88.2%
Performance88.0%
AI Usage22.0%
Skills & Technologies
Programming Languages
CSVJupyter NotebookMarkdownNonePDFPythonUnknown
Technical Skills
ClusteringData AnalysisData CleaningData ManagementData SimulationData VisualizationDocumentationExploratory Data AnalysisFile ManagementHierarchical ClusteringJupyterJupyter NotebookK-Means ClusteringMachine LearningMatplotlib
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline