LCIT-AISC-T3-S25/Group1
May 2025 – Jul 2025
3 Months active
Languages Used
CSSHTMLJupyter NotebookPythonKerasShell
Technical Skills
Batch NormalizationClass Imbalance HandlingComputer VisionData AnalysisData CleaningData Preprocessing
anjusunnyc
PROFILE
Anjusunnyc
Over a three-month period, this developer delivered six features to the LCIT-AISC-T3-S25/Group1 repository, focusing on robust machine learning and data processing pipelines. They built end-to-end workflows for tweet dataset cleaning, emoji-based analysis, and color image classification using Python, Keras, and TensorFlow. Their work included RNN and transformer-based sentiment analysis with advanced preprocessing, as well as VGG and diffusion models for image tasks, leveraging data augmentation and model tuning for improved accuracy and stability. Emphasizing reproducibility and interpretability, they established experiment workflows and SHAP-based visualizations, enabling faster iteration and data-driven decision making without introducing major bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
6
Lines of code
21,188
Activity Months3
Your Network
7 peopleWork History
July 2025
4 Commits • 2 Features
Jul 1, 2025July 2025 focused on delivering high-value NLP capabilities and a reproducible diffusion-model experimentation workflow for LCIT-AISC-T3-S25/Group1. Key features shipped include a causal transformer for sentiment analysis with comprehensive data preprocessing (biomedical text transformations, negation handling, spelling correction, lemmatization), model definition with positional encoding, training/evaluation, and SHAP-based interpretability visualizations. In addition, a Jupyter Notebook for tuning a Diffusion Probabilistic Model (TunedUNet) was delivered, featuring data augmentation, a complete training loop, and evaluation metrics (FID, Inception Score). The work establishes a robust, interpretable sentiment analysis pipeline and an reproducible diffusion-model experimentation framework, enabling faster iteration and data-driven decision making.
4 Commits • 2 Features
Jul 1, 2025July 2025 focused on delivering high-value NLP capabilities and a reproducible diffusion-model experimentation workflow for LCIT-AISC-T3-S25/Group1. Key features shipped include a causal transformer for sentiment analysis with comprehensive data preprocessing (biomedical text transformations, negation handling, spelling correction, lemmatization), model definition with positional encoding, training/evaluation, and SHAP-based interpretability visualizations. In addition, a Jupyter Notebook for tuning a Diffusion Probabilistic Model (TunedUNet) was delivered, featuring data augmentation, a complete training loop, and evaluation metrics (FID, Inception Score). The work establishes a robust, interpretable sentiment analysis pipeline and an reproducible diffusion-model experimentation framework, enabling faster iteration and data-driven decision making.
July 2025
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 (2025-06) monthly summary for LCIT-AISC-T3-S25/Group1 focused on delivering high-impact ML capabilities for NLP and CV tasks, with emphasis on business value and robustness.
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 (2025-06) monthly summary for LCIT-AISC-T3-S25/Group1 focused on delivering high-impact ML capabilities for NLP and CV tasks, with emphasis on business value and robustness.
May 2025
4 Commits • 2 Features
May 1, 2025May 2025 performance review for LCIT-AISC-T3-S25/Group1: Focused on delivering business-value data processing and ML capabilities. Key features include a Python-based tweet dataset cleaning and emoji-based analysis workflow, and an end-to-end color image classification DNN pipeline with tuning. No documented major bugs; stability and data-quality improvements were achieved through code enhancements and thorough preprocessing. Impact: improved data quality for social analytics, enhanced image classification readiness, and stronger foundations for analytics deployment. Technologies demonstrated: Python, CSV data processing, data cleaning, emoji analysis, DNN training, model tuning (batch size, class weights, batch normalization).
4 Commits • 2 Features
May 1, 2025May 2025 performance review for LCIT-AISC-T3-S25/Group1: Focused on delivering business-value data processing and ML capabilities. Key features include a Python-based tweet dataset cleaning and emoji-based analysis workflow, and an end-to-end color image classification DNN pipeline with tuning. No documented major bugs; stability and data-quality improvements were achieved through code enhancements and thorough preprocessing. Impact: improved data quality for social analytics, enhanced image classification readiness, and stronger foundations for analytics deployment. Technologies demonstrated: Python, CSV data processing, data cleaning, emoji analysis, DNN training, model tuning (batch size, class weights, batch normalization).
May 2025
Activity
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Quality Metrics
Correctness70.0%
Maintainability76.4%
Architecture70.0%
Performance62.8%
AI Usage32.8%
Skills & Technologies
Programming Languages
CSSHTMLJupyter NotebookKerasPythonShell
Technical Skills
Batch NormalizationBiomedical NLPClass Imbalance HandlingComputer VisionData AnalysisData AugmentationData CleaningData PreprocessingData VisualizationDeep LearningDiffusion ModelsDropout RegularizationImage ClassificationImage GenerationJupyter Notebook
Repositories Contributed To
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