
Developed a pedestrian traffic forecasting workflow for the Chameleon-company/MOP-Code repository, focusing on data preparation and the implementation of an initial GRU-based deep learning model. Leveraged Python and Jupyter Notebook to build a clean, reproducible data pipeline that included data loading, cleaning, exploratory data analysis, time-based feature extraction, normalization, and sequential data creation for model input. Established a robust train/test split to enable reliable evaluation of forecasting performance on held-out data. Applied skills in Pandas, TensorFlow, and Keras to address time series analysis challenges, delivering a foundation for future model improvements and supporting business decision-making through data-driven insights.
Concise monthly summary for 2025-05 focusing on business value and technical achievements for the Chameleon-company/MOP-Code project. Highlights include delivered data preparation workflow and an initial GRU-based pedestrian traffic forecasting model, with a clean data pipeline and reproducible train/test split. No major bugs reported this period.
Concise monthly summary for 2025-05 focusing on business value and technical achievements for the Chameleon-company/MOP-Code project. Highlights include delivered data preparation workflow and an initial GRU-based pedestrian traffic forecasting model, with a clean data pipeline and reproducible train/test split. No major bugs reported this period.

Overview of all repositories you've contributed to across your timeline