LCIT-AISC-T3-S25/Group4
May 2025 – Jul 2025
3 Months active
Languages Used
JSONPythonJavaScriptShellYAMLCSVGit ConfigurationJupyter Notebook
Technical Skills
Data AnalysisData PreprocessingData ScienceData VisualizationExplainable AI (XAI)LIME
AeshaSavani
PROFILE
Aeshasavani
Aesha Savani contributed to the LCIT-AISC-T3-S25/Group4 repository by developing machine learning features such as SVM-based sentiment analysis with GloVe embeddings and integrating CBOW models for improved text representation. She implemented data reduction workflows to streamline downstream processing and built end-to-end pipelines for SVM experimentation, including explainable AI components using LIME and SHAP. Her work included deploying models with Flask and automating CI/CD using GitHub Actions, with code written primarily in Python and Jupyter Notebooks. These efforts enhanced model evaluation, deployment consistency, and repository hygiene, demonstrating depth in data preprocessing, model training, and collaborative version control practices.
Overall Statistics
Feature vs Bugs
87%Features
Repository Contributions
28Total
Bugs
2
Commits
28
Features
13
Lines of code
253,328
Activity Months3
Work History
July 2025
19 Commits • 8 Features
Jul 1, 2025July 2025 — Business value and technical accomplishments for LCIT-AISC-T3-S25/Group4: - Data Reduction Enhancements: Implemented initial data reduction and updates to the reduced dataset, enabling faster analyses and reduced storage for downstream workflows. - DDPM Model Development and Classifier Evaluation: Completed Ayesha's updated DDPM workflow (full DDPM lifecycle) with classifier training and confusion matrix evaluation to improve model accuracy assessment. - CBOW Model Integration: Integrated CBOW-based embeddings into the project to enhance representational quality for downstream tasks. - Flask Integration: RAG, GLIDE, and UI-GLIDE integration to deliver end-to-end inference interfaces and UI components. - Bug Fix and Repository Hygiene: Fixed folder deletion issues triggered by invalid issues and updated .gitignore to exclude datasets, reducing risk of data loss and keeping releases clean. Impact: These deliveries increase data processing efficiency, broaden ML/model capabilities, enable cohesive deployment interfaces, and strengthen release discipline and data protection.
19 Commits • 8 Features
Jul 1, 2025July 2025 — Business value and technical accomplishments for LCIT-AISC-T3-S25/Group4: - Data Reduction Enhancements: Implemented initial data reduction and updates to the reduced dataset, enabling faster analyses and reduced storage for downstream workflows. - DDPM Model Development and Classifier Evaluation: Completed Ayesha's updated DDPM workflow (full DDPM lifecycle) with classifier training and confusion matrix evaluation to improve model accuracy assessment. - CBOW Model Integration: Integrated CBOW-based embeddings into the project to enhance representational quality for downstream tasks. - Flask Integration: RAG, GLIDE, and UI-GLIDE integration to deliver end-to-end inference interfaces and UI components. - Bug Fix and Repository Hygiene: Fixed folder deletion issues triggered by invalid issues and updated .gitignore to exclude datasets, reducing risk of data loss and keeping releases clean. Impact: These deliveries increase data processing efficiency, broaden ML/model capabilities, enable cohesive deployment interfaces, and strengthen release discipline and data protection.
July 2025
June 2025
4 Commits • 3 Features
Jun 1, 2025June 2025: Delivered a production-ready ML feature (SVM-based sentiment analysis with GloVe), established deployment scaffolding for multiple architectures, and implemented CI/CD automation for the test branch. Also streamlined the code-review workflow by removing an obsolete Dockerfile check. These efforts enhanced sentiment insights, standardized deployment across model families, and accelerated and stabilized release processes for LCIT-AISC-T3-S25/Group4.
June 2025
4 Commits • 3 Features
Jun 1, 2025June 2025: Delivered a production-ready ML feature (SVM-based sentiment analysis with GloVe), established deployment scaffolding for multiple architectures, and implemented CI/CD automation for the test branch. Also streamlined the code-review workflow by removing an obsolete Dockerfile check. These efforts enhanced sentiment insights, standardized deployment across model families, and accelerated and stabilized release processes for LCIT-AISC-T3-S25/Group4.
May 2025
5 Commits • 2 Features
May 1, 2025May 2025 monthly summary for LCIT-AISC-T3-S25/Group4 focusing on SVM experimentation initiatives. Delivered end-to-end notebooks, documentation, and reproducible pipelines to accelerate model evaluation and decision-making. Highlights include dataset setup, evaluation on small datasets, and explainable AI components integrated into the SVM workstreams.
5 Commits • 2 Features
May 1, 2025May 2025 monthly summary for LCIT-AISC-T3-S25/Group4 focusing on SVM experimentation initiatives. Delivered end-to-end notebooks, documentation, and reproducible pipelines to accelerate model evaluation and decision-making. Highlights include dataset setup, evaluation on small datasets, and explainable AI components integrated into the SVM workstreams.
May 2025
Activity
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Quality Metrics
Correctness77.8%
Maintainability78.6%
Architecture76.4%
Performance70.6%
AI Usage36.4%
Skills & Technologies
Programming Languages
CSVGit ConfigurationJSONJavaScriptJupyter NotebookPythonShellYAML
Technical Skills
AutomationCI/CDConditional GenerationData AnalysisData AugmentationData LoadingData PreprocessingData ScienceData VisualizationDataset CurationDataset ReductionDeep LearningDiffusion ModelsExplainable AI (XAI)FID Score
Repositories Contributed To
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