LCIT-AISC-T3-S25/Group4
May 2025 – May 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data PreprocessingLemmatizationNLTKNatural Language ProcessingPandasStemming
GITHUB43769
PROFILE
Github43769
Nithish developed two feature assets for the LCIT-AISC-T3-S25/Group4 repository, focusing on enhancing analytics readiness and supporting NLP experimentation. He created Jupyter notebooks that implement data preprocessing, stemming, and lemmatization workflows using Python, Pandas, and NLTK, applied to the Data-2.csv dataset. These notebooks enable reproducible, end-to-end NLP preprocessing demonstrations for analytics teams. Additionally, Nithish updated the MECE.xlsx workbook to reflect the latest MECE structure, ensuring data alignment for decision-support processes. All work was carefully documented with clear commit references, providing traceability and facilitating repeatable demos, though the scope was limited to feature delivery without bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
372
Activity Months1
Work History
May 2025
2 Commits • 2 Features
May 1, 2025May 2025: Delivered two feature assets for LCIT-AISC-T3-S25/Group4 that strengthen analytics readiness and NLP experimentation. Implemented NLP data preprocessing notebooks (data preprocessing, stemming, lemmatization) using NLTK on Data-2.csv and updated the MECE.xlsx workbook to reflect the latest MECE structure. All work is archived with clear commit references for traceability, enabling repeatable demos and decision-support workflows.
2 Commits • 2 Features
May 1, 2025May 2025: Delivered two feature assets for LCIT-AISC-T3-S25/Group4 that strengthen analytics readiness and NLP experimentation. Implemented NLP data preprocessing notebooks (data preprocessing, stemming, lemmatization) using NLTK on Data-2.csv and updated the MECE.xlsx workbook to reflect the latest MECE structure. All work is archived with clear commit references for traceability, enabling repeatable demos and decision-support workflows.
May 2025
Activity
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Quality Metrics
Correctness70.0%
Maintainability70.0%
Architecture70.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data PreprocessingLemmatizationNLTKNatural Language ProcessingPandasStemming
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