LCIT-AISC-T3-S25/Group4
May 2025 – Jul 2025
3 Months active
Languages Used
CSSHTMLJavaScriptJupyter NotebookMarkdownPythonDockerfileText
Technical Skills
CNNCSS StylingComputer VisionData AnalysisData AugmentationData Cleaning
smit-8001-patel
PROFILE
Smit-8001-patel
Over three months, contributed to the LCIT-AISC-T3-S25/Group4 repository by building end-to-end machine learning workflows and improving project maintainability. Developed and deployed a GRU-based sentiment analysis model using Python, Keras, and Flask, enabling rapid textual insight generation and production readiness through Docker-based deployment. Established project scaffolding and asset onboarding for multiple case studies, streamlining onboarding and reducing maintenance friction. Enhanced repository hygiene by cleaning up obsolete files and directories, fixing broken paths, and updating documentation for consistency. Leveraged skills in data preprocessing, deep learning, and frontend development to deliver features that supported scalable, well-documented, and production-ready solutions.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
50Total
Bugs
4
Commits
50
Features
8
Lines of code
612,411
Activity Months3
Your Network
9 peopleShared Repositories
9
Work History
July 2025
16 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group4: Focused on asset onboarding for NLP Case Study 2 and repository hygiene to accelerate case-study work and reduce maintenance overhead. Key work included consolidating asset uploads for NLP Case Study 2 across multiple commits, establishing initial project scaffolding and asset uploads, and cleaning up outdated directories to fix broken paths in NLP Case Study 2 and IoT Case Study 2.
16 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group4: Focused on asset onboarding for NLP Case Study 2 and repository hygiene to accelerate case-study work and reduce maintenance overhead. Key work included consolidating asset uploads for NLP Case Study 2 across multiple commits, establishing initial project scaffolding and asset uploads, and cleaning up outdated directories to fix broken paths in NLP Case Study 2 and IoT Case Study 2.
July 2025
June 2025
12 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for LCIT-AISC-T3-S25/Group4: Focused on delivering an end-to-end sentiment analysis capability and enabling deployment readiness, plus repository hygiene improvements. Key outcomes include feature work for sentiment modeling and deployment, as well as cleanup of obsolete notebooks. Demonstrated ML lifecycle execution and deployment engineering, delivering business value through faster textual insight generation and streamlined production readiness.
June 2025
12 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for LCIT-AISC-T3-S25/Group4: Focused on delivering an end-to-end sentiment analysis capability and enabling deployment readiness, plus repository hygiene improvements. Key outcomes include feature work for sentiment modeling and deployment, as well as cleanup of obsolete notebooks. Demonstrated ML lifecycle execution and deployment engineering, delivering business value through faster textual insight generation and streamlined production readiness.
May 2025
22 Commits • 4 Features
May 1, 2025May 2025 monthly performance for LCIT-AISC-T3-S25/Group4 focused on establishing a scalable foundation for the Smit Case Study, improving documentation clarity, and reducing repo maintenance friction. Key outcomes include project scaffolding and assets for the Smit Case Study, cleanup of obsolete Smit files, MECE Table documentation updates reflecting the latest structure, and initial assets uploaded to the repository. These workstreams enable faster onboarding, consistent documentation, and lower risk of broken references as the Case Study progresses.
22 Commits • 4 Features
May 1, 2025May 2025 monthly performance for LCIT-AISC-T3-S25/Group4 focused on establishing a scalable foundation for the Smit Case Study, improving documentation clarity, and reducing repo maintenance friction. Key outcomes include project scaffolding and assets for the Smit Case Study, cleanup of obsolete Smit files, MECE Table documentation updates reflecting the latest structure, and initial assets uploaded to the repository. These workstreams enable faster onboarding, consistent documentation, and lower risk of broken references as the Case Study progresses.
May 2025
Activity
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Quality Metrics
Correctness84.2%
Maintainability84.0%
Architecture83.4%
Performance79.2%
AI Usage32.0%
Skills & Technologies
Programming Languages
CSSDockerfileHTMLJavaScriptJupyter NotebookMarkdownPythonText
Technical Skills
API DevelopmentAPI IntegrationArray ManipulationAsynchronous ProgrammingBrowser CompatibilityCNNCSS StylingCode CleanupCode ManagementComputer VisionConfiguration ManagementD3.jsData AnalysisData AugmentationData Cleaning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline