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
Alen Charuvila Saji developed a diverse suite of machine learning and data science features for the LCIT-AISC-T3-S25/Group1 repository over three months, focusing on sentiment analysis, image classification, and multimodal prediction. Leveraging Python, Jupyter Notebooks, and frameworks such as TensorFlow and PyTorch, Alen implemented LSTM and transformer-based models for text and image data, introduced PCA-driven SVM pipelines, and enhanced interpretability with LIME. The work included robust data preprocessing, visualization utilities, and model evaluation strategies, addressing challenges like overfitting and cross-modal learning. Alen’s contributions demonstrated technical depth through end-to-end workflows, model tuning, and integration of advanced deep learning techniques.
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
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