LCIT-AISC-T3-S25/Group1
May 2025 – Jul 2025
3 Months active
Languages Used
JavaScriptJupyter NotebookPython
Technical Skills
Data AnalysisData ManipulationFunctional ProgrammingJavaScriptLodashPandas
HARSHAVARDHANREDDY5
PROFILE
Harshavardhanreddy5
Over a three-month period, contributed to the LCIT-AISC-T3-S25/Group1 repository by building six features spanning data analytics, visualization, and machine learning. Developed Python-based utilities for CSV URL analysis and a comprehensive toolkit to streamline data transformation. Enhanced D3.js charting with new visualizations and performance improvements using JavaScript. Advanced model interpretability by integrating LIME with VGG16 image classification and SVM analytics. Tuned a BiLSTM model for improved neutral sentiment detection and implemented a retrieval-augmented QA system leveraging FAISS, Sentence Transformers, and Llama-2. Demonstrated depth in data preprocessing, deep learning, and information retrieval, focusing on scalable, maintainable solutions without reported bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
6
Lines of code
238,372
Activity Months3
Your Network
7 peopleWork History
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 — LCIT-AISC-T3-S25/Group1: Two high-impact features delivering business value and strong technical execution: 1) Neutral sentiment classification BiLSTM tuning to improve neutral handling (capacity adjustments, relaxed dropout, improved pooling); commit e0b866b8ebc429f299df626ad054cc83a8ced11f. 2) RAG-based retrieval QA system with FAISS: end-to-end pipeline (data loading, embeddings via Sentence Transformers, FAISS index, Llama-2 integration with domain prompts and out-of-context rejection); commit 0b4fe873611a3ec2918ddf6378376c2219f802f1. These efforts lay a scalable foundation for domain-specific QA and sentiment analytics, enabling faster decision support and improved user satisfaction.
2 Commits • 2 Features
Jul 1, 2025July 2025 — LCIT-AISC-T3-S25/Group1: Two high-impact features delivering business value and strong technical execution: 1) Neutral sentiment classification BiLSTM tuning to improve neutral handling (capacity adjustments, relaxed dropout, improved pooling); commit e0b866b8ebc429f299df626ad054cc83a8ced11f. 2) RAG-based retrieval QA system with FAISS: end-to-end pipeline (data loading, embeddings via Sentence Transformers, FAISS index, Llama-2 integration with domain prompts and out-of-context rejection); commit 0b4fe873611a3ec2918ddf6378376c2219f802f1. These efforts lay a scalable foundation for domain-specific QA and sentiment analytics, enabling faster decision support and improved user satisfaction.
July 2025
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for LCIT-AISC-T3-S25/Group1: Delivered two major features with measurable business value and initiated foundational work for model interpretability and scalable visualization. Key outcomes include enriched data visualization capabilities for diverse chart types and integrated model explainability to support data-driven decision making.
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for LCIT-AISC-T3-S25/Group1: Delivered two major features with measurable business value and initiated foundational work for model interpretability and scalable visualization. Key outcomes include enriched data visualization capabilities for diverse chart types and integrated model explainability to support data-driven decision making.
May 2025
2 Commits • 2 Features
May 1, 2025May 2025 Monthly Summary: Focused on delivering high-value data analytics capabilities and foundational utilities to accelerate development and data quality insights. Delivered two key features and laid ML scaffolding for future predictive workflows.
2 Commits • 2 Features
May 1, 2025May 2025 Monthly Summary: Focused on delivering high-value data analytics capabilities and foundational utilities to accelerate development and data quality insights. Delivered two key features and laid ML scaffolding for future predictive workflows.
May 2025
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Quality Metrics
Correctness84.2%
Maintainability82.8%
Architecture82.8%
Performance82.8%
AI Usage34.4%
Skills & Technologies
Programming Languages
JavaScriptJupyter NotebookPython
Technical Skills
Computer VisionD3.jsData AnalysisData ManipulationData PreprocessingData ScienceData VisualizationDeep LearningFine-tuningFunctional ProgrammingHyperparameter TuningImage AugmentationImage ClassificationInformation RetrievalInterpretability (LIME)
Repositories Contributed To
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