
Sercan Gulluoglu developed an end-to-end parking occupancy analytics and forecasting pipeline for the Chameleon-company/MOP-Code repository, integrating City of Melbourne API data with Google BigQuery for storage and analysis. He engineered data cleaning, timezone normalization, and aggregation processes, then applied LSTM-based forecasting to predict occupancy trends. Using Python, SQL, and TensorFlow, Sercan visualized results with Leaflet.js and Matplotlib, delivering actionable insights into parking usage. He also improved repository maintainability by standardizing pipelines, cleaning up artifacts, and updating documentation. His work emphasized security by hardening credential management and enhanced onboarding efficiency through clear commit history and organized project structure.

December 2024 performance summary for Chameleon-company/MOP-Code: Delivered end-to-end Parking Occupancy Analysis and Forecasting with data cleaning, timezone normalization, hourly/monthly aggregations, correlation analysis, and LSTM-based forecasts; completed Playground Cleanup and Documentation for Parking Slot Occupancy Detection; improved data quality and maintainability through pipeline standardization and repository hygiene. These efforts deliver actionable occupancy insights, reduce operational risk, and enhance onboarding efficiency.
December 2024 performance summary for Chameleon-company/MOP-Code: Delivered end-to-end Parking Occupancy Analysis and Forecasting with data cleaning, timezone normalization, hourly/monthly aggregations, correlation analysis, and LSTM-based forecasts; completed Playground Cleanup and Documentation for Parking Slot Occupancy Detection; improved data quality and maintainability through pipeline standardization and repository hygiene. These efforts deliver actionable occupancy insights, reduce operational risk, and enhance onboarding efficiency.
Month: 2024-11 — Focused on delivering business-value analytics for parking occupancy, hardening credentials, and tidying project artifacts in Chameleon MOP-Code. End-to-end pipeline fetches occupancy data from City of Melbourne API, loads into BigQuery, analyzes occupancy and peak usage, and visualizes results on a Leaflet map. Security hardening removed sensitive credential files and updated gitignore; service account authentication set up for Google Cloud. Completed artifacts cleanup, dataset organization, and documentation updates to improve reproducibility and maintainability.
Month: 2024-11 — Focused on delivering business-value analytics for parking occupancy, hardening credentials, and tidying project artifacts in Chameleon MOP-Code. End-to-end pipeline fetches occupancy data from City of Melbourne API, loads into BigQuery, analyzes occupancy and peak usage, and visualizes results on a Leaflet map. Security hardening removed sensitive credential files and updated gitignore; service account authentication set up for Google Cloud. Completed artifacts cleanup, dataset organization, and documentation updates to improve reproducibility and maintainability.
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