LCIT-AISC-T3-S25/Group1
May 2025 – Jul 2025
3 Months active
Languages Used
Jupyter NotebookPythonMarkdown
Technical Skills
Computer VisionData AnalysisData PreprocessingData VisualizationJupyter NotebooksMachine Learning
Alen Saji
PROFILE
Alen Saji
Over a three-month period, contributed to the LCIT-AISC-T3-S25/Group1 repository by developing eleven features spanning computer vision, natural language processing, and data visualization. Built end-to-end machine learning workflows for sentiment analysis and multimodal prediction, leveraging Python, TensorFlow, and PyTorch. Implemented and tuned models such as LSTM with Word2Vec embeddings, SVM with PCA for image classification, and transformer-based sentiment models, addressing challenges like overfitting and interpretability using LIME. Enhanced data exploration and visualization capabilities with Jupyter Notebooks and D3.js, and introduced image generation models including VAE and DDIM. No bugs were reported or fixed during this period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
20Total
Bugs
0
Commits
20
Features
11
Lines of code
759,579
Activity Months3
Your Network
11 peopleSame Organization
@loyalistcollege.com4
Work History
July 2025
15 Commits • 7 Features
Jul 1, 2025Summary for 2025-07: Delivered a broad feature set across NLP, data visualization, computer vision, and QA for LCIT-AISC-T3-S25/Group1. Key contributions include tuning a causal transformer sentiment model with improved generalization and neutral-class performance, complemented by LIME-based interpretability visuals; refactored model architecture and training pipelines to boost accuracy and efficiency; enhanced D3.js toolkit for scales, layouts, SVG path generation, transitions, and time utilities to enable complex data visualizations; expanded data utilities and visualization helpers to support robust data analysis; implemented and evaluated Image Generation Models (VAE and DDIM) with encoder/decoder, sampling, and FID/Inception metrics; TinyLlama QA model fine-tuning with LoRA and evaluation of semantic similarity using ROUGE-L and BERTScore; added a Documentation placeholder for Computer_Vision/Case_Study_2 to support future work. Notable commits include multiple instances of "Model with Tuning" across sentiment tuning and architecture; VAE/DDIM model files added; and a README placeholder created.
15 Commits • 7 Features
Jul 1, 2025Summary for 2025-07: Delivered a broad feature set across NLP, data visualization, computer vision, and QA for LCIT-AISC-T3-S25/Group1. Key contributions include tuning a causal transformer sentiment model with improved generalization and neutral-class performance, complemented by LIME-based interpretability visuals; refactored model architecture and training pipelines to boost accuracy and efficiency; enhanced D3.js toolkit for scales, layouts, SVG path generation, transitions, and time utilities to enable complex data visualizations; expanded data utilities and visualization helpers to support robust data analysis; implemented and evaluated Image Generation Models (VAE and DDIM) with encoder/decoder, sampling, and FID/Inception metrics; TinyLlama QA model fine-tuning with LoRA and evaluation of semantic similarity using ROUGE-L and BERTScore; added a Documentation placeholder for Computer_Vision/Case_Study_2 to support future work. Notable commits include multiple instances of "Model with Tuning" across sentiment tuning and architecture; VAE/DDIM model files added; and a README placeholder created.
July 2025
June 2025
2 Commits • 2 Features
Jun 1, 20252025-06 monthly summary for LCIT-AISC-T3-S25/Group1. Delivered two key features enabling faster, more accurate sentiment insights and multimodal prediction capabilities. No major bugs reported this month. Impact: established end-to-end ML workflows, improved user feedback analytics, and cross-modal prediction for Q1.
June 2025
2 Commits • 2 Features
Jun 1, 20252025-06 monthly summary for LCIT-AISC-T3-S25/Group1. Delivered two key features enabling faster, more accurate sentiment insights and multimodal prediction capabilities. No major bugs reported this month. Impact: established end-to-end ML workflows, improved user feedback analytics, and cross-modal prediction for Q1.
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary focused on delivering core data science features and evaluating model performance within LCIT-AISC-T3-S25/Group1. The month emphasized enabling data exploration for Case Study 1 (Q2/Q3), and establishing an evaluation path for image classification using SVM with PCA; no critical bugs were reported or fixed this month. The work provides immediate business value by accelerating exploratory data analysis, informing data-driven decisions for the Case Study, and outlining a tuning plan to improve model generalization in image classification.
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary focused on delivering core data science features and evaluating model performance within LCIT-AISC-T3-S25/Group1. The month emphasized enabling data exploration for Case Study 1 (Q2/Q3), and establishing an evaluation path for image classification using SVM with PCA; no critical bugs were reported or fixed this month. The work provides immediate business value by accelerating exploratory data analysis, informing data-driven decisions for the Case Study, and outlining a tuning plan to improve model generalization in image classification.
May 2025
Activity
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Quality Metrics
Correctness77.0%
Maintainability70.0%
Architecture79.0%
Performance66.0%
AI Usage25.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
Computer VisionData AnalysisData LoadingData PreprocessingData VisualizationDeep LearningDocumentationGensimHugging Face TransformersImage GenerationJupyter NotebooksKerasLSTMMachine LearningMatplotlib
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline