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
Contributed to the SpikyCherry/DSA3101_group9 repository by building end-to-end data science features focused on customer analytics and model development. Developed and tuned machine learning pipelines for customer conversion and churn prediction using Python, Pandas, and Scikit-learn, incorporating Logistic Regression and Random Forest models with hyperparameter optimization. Enhanced data preprocessing through flexible encoding strategies and adaptive feature scaling, while improving team collaboration with shared folders and streamlined documentation. Introduced a Streamlit-based experimentation tool for interactive model evaluation and visualization. Refactored project structure for maintainability, updated marketing data analysis workflows, and improved reproducibility, emphasizing data-driven decision-making and efficient model iteration.
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 peopleShared Repositories
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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
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