SpikyCherry/DSA3101_group9
Mar 2025 – Apr 2025
2 Months active
Languages Used
CSVJupyter NotebookPythonSQLMarkdown
Technical Skills
Data AnalysisData CleaningData EngineeringData PreprocessingData VisualizationExploratory Data Analysis (EDA)
alvislow
PROFILE
Alvislow
Alvis Low developed core data science and analytics features for the SpikyCherry/DSA3101_group9 repository, focusing on customer conversion and churn prediction pipelines. He engineered end-to-end workflows for data preprocessing, feature engineering, and model training using Python and Scikit-learn, integrating Logistic Regression and Random Forest with hyperparameter tuning and KPI visualization. Alvis enhanced data cleaning and encoding logic, introduced binary encoding, and built a Streamlit-based experimentation tool to streamline model evaluation and collaboration. His work improved data governance, reproducibility, and deployment readiness, while restructuring project assets and documentation to support maintainability and cross-team collaboration throughout the two-month development period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
9
Lines of code
43,998
Activity Months2
Your Network
8 people
Work History
April 2025
8 Commits • 5 Features
Apr 1, 2025Month: 2025-04. This month focused on delivering core data science pipeline features, enhancing encoding capabilities, building an interactive experimentation UI, and simplifying project structure with documentation updates. Key improvements include binary encoding support, a Streamlit notebook viewer with ML experimentation, churn model pipeline refactor and training script, marketing data analysis workflow, and targeted cleanup to improve maintainability and reproducibility. These efforts improved model iteration speed, data governance, and deployment readiness across the SpikyCherry/DSA3101_group9 project.
8 Commits • 5 Features
Apr 1, 2025Month: 2025-04. This month focused on delivering core data science pipeline features, enhancing encoding capabilities, building an interactive experimentation UI, and simplifying project structure with documentation updates. Key improvements include binary encoding support, a Streamlit notebook viewer with ML experimentation, churn model pipeline refactor and training script, marketing data analysis workflow, and targeted cleanup to improve maintainability and reproducibility. These efforts improved model iteration speed, data governance, and deployment readiness across the SpikyCherry/DSA3101_group9 project.
April 2025
March 2025
5 Commits • 4 Features
Mar 1, 2025March 2025 monthly summary for SpikyCherry/DSA3101_group9. Delivered end-to-end analytics and data-prep enhancements that drive customer acquisition insights and cross-team collaboration. Key features delivered include: (1) customer conversion prediction models with tuning and KPI insights; (2) shared folders to enable collaborative file access; (3) flexible encoding options and adaptive feature scaling in data preprocessing; (4) SubA Qn4 analytics with risk scoring and call effectiveness analysis. The work emphasizes business value by enabling data-driven decisions, reducing preprocessing friction, and improving collaboration across the team. Technologies demonstrated include Logistic Regression and Random Forest modeling, hyperparameter tuning, KPI extraction/visualization, exploratory data analysis, encoding strategies, and data-cleaning improvements.
March 2025
5 Commits • 4 Features
Mar 1, 2025March 2025 monthly summary for SpikyCherry/DSA3101_group9. Delivered end-to-end analytics and data-prep enhancements that drive customer acquisition insights and cross-team collaboration. Key features delivered include: (1) customer conversion prediction models with tuning and KPI insights; (2) shared folders to enable collaborative file access; (3) flexible encoding options and adaptive feature scaling in data preprocessing; (4) SubA Qn4 analytics with risk scoring and call effectiveness analysis. The work emphasizes business value by enabling data-driven decisions, reducing preprocessing friction, and improving collaboration across the team. Technologies demonstrated include Logistic Regression and Random Forest modeling, hyperparameter tuning, KPI extraction/visualization, exploratory data analysis, encoding strategies, and data-cleaning improvements.
Activity
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Quality Metrics
Correctness84.6%
Maintainability84.6%
Architecture83.0%
Performance81.6%
AI Usage27.6%
Skills & Technologies
Programming Languages
CSVJupyter NotebookMarkdownPythonSQL
Technical Skills
Data AnalysisData CleaningData EngineeringData PreprocessingData ScienceData VisualizationDocumentationExploratory Data AnalysisExploratory Data Analysis (EDA)Feature EngineeringHyperparameter TuningJupyter NotebookLogistic RegressionMachine LearningMatplotlib
Repositories Contributed To
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