TCS-2021/Data-Mining-Project
Mar 2025 – Mar 2025
1 Month active
Languages Used
CSVPython
Technical Skills
Data MiningData VisualizationPandasPythonSequential Pattern MiningStreamlit
SherinJA
PROFILE
Sherinja
Developed an end-to-end sequential pattern mining feature for the TCS-2021/Data-Mining-Project repository, focusing on implementing the Generalized Sequential Patterns (GSP) algorithm core in Python. Leveraging Pandas for data handling and Streamlit for the user interface, the work enabled users to upload CSV datasets, configure minimum support thresholds, and interactively visualize frequent sequences in a tabular format. This integration streamlined the data mining workflow, reducing the need for manual scripting and making pattern discovery more accessible. The feature was delivered through two commits, demonstrating proficiency in data mining, data visualization, and building interactive applications with modern Python frameworks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
401
Activity Months1
Your Network
20 peopleWork History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary highlighting key feature delivery, bug status, impact, and skills demonstrated for business performance reviews.
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary highlighting key feature delivery, bug status, impact, and skills demonstrated for business performance reviews.
March 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVPython
Technical Skills
Data MiningData VisualizationPandasPythonSequential Pattern MiningStreamlit
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