
Aniraj developed end-to-end data analytics and machine learning solutions for the Chameleon-company/MOP-Code repository over three months. He built a food security data pipeline and analytics suite, integrating Melbourne Open Data API with Python and Pandas for data fetching, cleaning, and visualization. He also delivered a unified AI Flask web application, combining food security dashboards and multiple AI project routes using HTML, JavaScript, and Flask. In addition, Aniraj implemented deep learning models for activity recognition with LSTM and GRU, organizing the project structure and documentation for reproducibility. His work demonstrated depth in full stack development, data science, and deployment readiness.

May 2025 performance summary for Chameleon-company/MOP-Code: Implemented and delivered the UC8 activity recognition submission using wearable sensor data, including data processing pipeline, model training with LSTM/GRU, evaluation scripts, and full documentation. Reorganized project structure and enhanced onboarding with updated README and folder layout. Performed repository hygiene by removing unnecessary artifacts and tightening .gitignore. The work establishes a reproducible baseline for UC8 submission and improves maintainability and handover readiness.
May 2025 performance summary for Chameleon-company/MOP-Code: Implemented and delivered the UC8 activity recognition submission using wearable sensor data, including data processing pipeline, model training with LSTM/GRU, evaluation scripts, and full documentation. Reorganized project structure and enhanced onboarding with updated README and folder layout. Performed repository hygiene by removing unnecessary artifacts and tightening .gitignore. The work establishes a reproducible baseline for UC8 submission and improves maintainability and handover readiness.
December 2024 — Delivered a unified AI Flask web application integrating food security analytics with routes for multiple AI projects (food security, health behavior, traffic analysis, vehicle classification) and a safety perception prediction module. Implemented the comprehensive Food Security Dashboard within the Flask app, including data fetching, preparation, and visualization for food security metrics. Included notebooks and code updates to support visualizations and dependencies, and added a dedicated requirements.txt for reproducibility and deployment readiness.
December 2024 — Delivered a unified AI Flask web application integrating food security analytics with routes for multiple AI projects (food security, health behavior, traffic analysis, vehicle classification) and a safety perception prediction module. Implemented the comprehensive Food Security Dashboard within the Flask app, including data fetching, preparation, and visualization for food security metrics. Included notebooks and code updates to support visualizations and dependencies, and added a dedicated requirements.txt for reproducibility and deployment readiness.
November 2024 monthly summary for Chameleon-company/MOP-Code: Delivered end-to-end food security data pipeline and analytics suite using the Melbourne Open Data API. Implemented data fetch, cleaning, analysis, visualization of food insecurity types and demographic distributions, and linear regression-based predictive trends. Added export capability for generated plots and refactored respondent group data for consistency. Prepared knowledge transfer documentation and included a corrected dataset.
November 2024 monthly summary for Chameleon-company/MOP-Code: Delivered end-to-end food security data pipeline and analytics suite using the Melbourne Open Data API. Implemented data fetch, cleaning, analysis, visualization of food insecurity types and demographic distributions, and linear regression-based predictive trends. Added export capability for generated plots and refactored respondent group data for consistency. Prepared knowledge transfer documentation and included a corrected dataset.
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