LCIT-AISC-T3-S25/Group1
May 2025 – Jul 2025
3 Months active
Languages Used
JavaScriptJupyter NotebookPythonSQL
Technical Skills
Computer VisionD3.jsData AnalysisData PreprocessingData ScienceData Visualization
SereneSebastian
PROFILE
Serenesebastian
Serene Sebastian developed end-to-end machine learning and data science features for the LCIT-AISC-T3-S25/Group1 repository over three months, focusing on engagement analytics, computer vision, and sentiment analysis. She built data analysis notebooks for IP and hashtag extraction, implemented SVM and VGG16-based image classifiers with interpretability enhancements, and delivered a transformer model for sentiment analysis using FastText embeddings. Her work included robust data preprocessing, model tuning, and deployment-ready pipelines, leveraging Python, TensorFlow, and JavaScript. By integrating utility libraries and reproducible workflows, Serene ensured scalable, transparent analytics and model explainability, demonstrating depth in both technical implementation and end-to-end solution delivery.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
8
Lines of code
163,994
Activity Months3
Your Network
11 peopleSame Organization
@loyalistcollege.com4
Work History
July 2025
5 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group1: Delivered three core capabilities that directly enable business value: sentiment monitoring, content generation, and robust data utilities. All work progressed from data preprocessing through model construction and training to deployment readiness, with the final models prepared for production use and clear handoffs to deployment pipelines.
5 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for LCIT-AISC-T3-S25/Group1: Delivered three core capabilities that directly enable business value: sentiment monitoring, content generation, and robust data utilities. All work progressed from data preprocessing through model construction and training to deployment readiness, with the final models prepared for production use and clear handoffs to deployment pipelines.
July 2025
June 2025
3 Commits • 2 Features
Jun 1, 20252025-06 monthly summary for LCIT-AISC-T3-S25/Group1: Delivered two high-impact ML features with measurable business value. Feature 1: Model tuning and interpretability enhancements, including tuning iterations #2–#5 and a final model release, improving performance and decision transparency. Feature 2: Custom VGG16-based image classifier with caption text embeddings; trained and evaluated with improvements in accuracy, F1, and AUC. Major bugs fixed: none documented this period. Overall impact: stronger predictive capabilities, better explainability for stakeholders, and readiness for deployment. Technologies/skills demonstrated: model tuning, interpretability, transfer learning with VGG16, text embeddings, model evaluation metrics (accuracy, F1, AUC), and disciplined commit-driven development.
June 2025
3 Commits • 2 Features
Jun 1, 20252025-06 monthly summary for LCIT-AISC-T3-S25/Group1: Delivered two high-impact ML features with measurable business value. Feature 1: Model tuning and interpretability enhancements, including tuning iterations #2–#5 and a final model release, improving performance and decision transparency. Feature 2: Custom VGG16-based image classifier with caption text embeddings; trained and evaluated with improvements in accuracy, F1, and AUC. Major bugs fixed: none documented this period. Overall impact: stronger predictive capabilities, better explainability for stakeholders, and readiness for deployment. Technologies/skills demonstrated: model tuning, interpretability, transfer learning with VGG16, text embeddings, model evaluation metrics (accuracy, F1, AUC), and disciplined commit-driven development.
May 2025
5 Commits • 3 Features
May 1, 2025May 2025 performance summary for LCIT-AISC-T3-S25/Group1. Delivered end-to-end data science and ML capabilities with a focus on engagement analytics and transparent computer vision models. Implemented a data analysis notebook for IP address and hashtag analysis, a robust SVM-based image classification pipeline with PCA and data augmentation, and added interpretability features to the vision case study to increase model transparency. No major defects were reported; emphasis was on delivering business value and scalable analytics workflows that can be extended in Q2.
5 Commits • 3 Features
May 1, 2025May 2025 performance summary for LCIT-AISC-T3-S25/Group1. Delivered end-to-end data science and ML capabilities with a focus on engagement analytics and transparent computer vision models. Implemented a data analysis notebook for IP address and hashtag analysis, a robust SVM-based image classification pipeline with PCA and data augmentation, and added interpretability features to the vision case study to increase model transparency. No major defects were reported; emphasis was on delivering business value and scalable analytics workflows that can be extended in Q2.
May 2025
Activity
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Quality Metrics
Correctness77.0%
Maintainability77.0%
Architecture75.4%
Performance69.2%
AI Usage26.2%
Skills & Technologies
Programming Languages
JavaScriptJupyter NotebookPythonSQL
Technical Skills
Computer VisionD3.jsData AnalysisData LoadingData ManipulationData PreprocessingData ScienceData VisualizationDeep LearningFastTextFastText EmbeddingsIP Address HandlingImage GenerationInterpretabilityJavaScript
Repositories Contributed To
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