LCIT-AISC-T3-S25/Group4
May 2025 – Jul 2025
3 Months active
Languages Used
HTMLJupyter NotebookMarkdownPythonJavaScript
Technical Skills
Data AnalysisData VisualizationDeep LearningDocumentationJupyter NotebookKeras
nidhichakrani
PROFILE
Nidhichakrani
Nidhi Chakrani developed a suite of machine learning and NLP solutions for the LCIT-AISC-T3-S25/Group4 repository, focusing on end-to-end workflows from data preprocessing to deployment. She built Jupyter Notebooks for sentiment analysis using GRU and transformer models, implemented image classification with VGG16, and introduced generative modeling via WGANs. Leveraging Python, TensorFlow, and Keras, she established reproducible experimentation pipelines and integrated model interpretability with LIME. Nidhi also created web interfaces using Streamlit and Flask for interactive demos, while maintaining rigorous documentation and repository hygiene. Her work demonstrated depth in model evaluation, hyperparameter tuning, and practical deployment readiness.
Overall Statistics
Feature vs Bugs
85%Features
Repository Contributions
23Total
Bugs
2
Commits
23
Features
11
Lines of code
250,265
Activity Months3
Work History
July 2025
10 Commits • 5 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group4: Delivered a cohesive suite of NLP capabilities across modeling, evaluation, interpretability, and deployment interfaces. Implemented causal transformer NLP models for sentiment analysis and biomedical text generation with interactive components, a WGAN-based generative modeling workflow with IS/FID evaluation, LIME-based model interpretability, reinforcement learning and bandit techniques for prompt optimization, and web/API interfaces (Streamlit and Flask) for practical demos and explanations. Notebook cleanup removed deprecated assets to improve maintainability and reduce drift.
10 Commits • 5 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group4: Delivered a cohesive suite of NLP capabilities across modeling, evaluation, interpretability, and deployment interfaces. Implemented causal transformer NLP models for sentiment analysis and biomedical text generation with interactive components, a WGAN-based generative modeling workflow with IS/FID evaluation, LIME-based model interpretability, reinforcement learning and bandit techniques for prompt optimization, and web/API interfaces (Streamlit and Flask) for practical demos and explanations. Notebook cleanup removed deprecated assets to improve maintainability and reduce drift.
July 2025
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for LCIT-AISC-T3-S25/Group4: Delivered two end-to-end ML notebooks for sentiment analysis tuning and image classification, and performed repository cleanup to reduce deployment risk. Established a reproducible ML experimentation workflow in the Group4 project, enabling faster iteration, evaluation, and handoff to deployment. Demonstrated strong data handling, model development, training orchestration, and evaluation capabilities using TensorFlow/Keras and Keras Tuner, with a focus on business value and technical rigor.
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for LCIT-AISC-T3-S25/Group4: Delivered two end-to-end ML notebooks for sentiment analysis tuning and image classification, and performed repository cleanup to reduce deployment risk. Established a reproducible ML experimentation workflow in the Group4 project, enabling faster iteration, evaluation, and handoff to deployment. Demonstrated strong data handling, model development, training orchestration, and evaluation capabilities using TensorFlow/Keras and Keras Tuner, with a focus on business value and technical rigor.
May 2025
10 Commits • 4 Features
May 1, 2025May 2025 monthly performance summary for LCIT-AISC-T3-S25/Group4 focusing on delivering data quality tooling, model evaluation capabilities, NLP preprocessing, and governance documentation. No major bug fixes were recorded this month; the work centered on building reusable notebooks and refining MECE documentation to support ongoing analytics and process clarity.
10 Commits • 4 Features
May 1, 2025May 2025 monthly performance summary for LCIT-AISC-T3-S25/Group4 focusing on delivering data quality tooling, model evaluation capabilities, NLP preprocessing, and governance documentation. No major bug fixes were recorded this month; the work centered on building reusable notebooks and refining MECE documentation to support ongoing analytics and process clarity.
May 2025
Activity
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Quality Metrics
Correctness85.6%
Maintainability85.2%
Architecture85.2%
Performance80.4%
AI Usage29.6%
Skills & Technologies
Programming Languages
HTMLJavaScriptJupyter NotebookMarkdownPython
Technical Skills
API IntegrationChatbot DevelopmentCode RefactoringData AnalysisData PreprocessingData ScienceData VisualizationDeep LearningDocumentationEnvironment DesignEvaluation MetricsFastTextFile ManagementFlaskGRU
Repositories Contributed To
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