Research Assistant

Hong Kong Baptist University

Assisted a tutor in processing large datasets from an electric company, focusing on data preprocessing, feature extraction, and fault analysis. Developed a Python application to automate the handling of fault values, ensuring efficient and accurate data processing. Handling Missing Values: Developed and implemented strategies to identify and manage missing data within the dataset. Applied techniques such as mean/mode imputation, forward/backward filling, and strategic deletion, tailored to the dataset's specific requirements and the tutor’s guidelines. Data Cleaning: Conducted comprehensive data cleaning, including the removal of outliers, normalization, and standardization, ensuring data consistency for accurate analysis. Python Application Development: Automating Fault Value Processing: Developed a Python application specifically designed to process fault values within the dataset. The application automated the identification, categorization, and reporting of fault values, significantly reducing manual effort and improving accuracy. Application Features: The application featured user-friendly interfaces for inputting data, customizable settings for fault detection parameters, and automated report generation, making it a valuable tool for ongoing dataset analysis. Excel Export: Processed and exported the cleaned and feature-engineered dataset to Excel, ensuring the files were well-organized and clearly labeled for easy interpretation. The exported data was used for further analysis and reporting by the tutor. Tutor Collaboration: Maintained regular communication with the tutor to ensure that project objectives were met. Adapted processing techniques and the Python application based on ongoing feedback, ensuring alignment with the project’s evolving needs. Timely Delivery: Managed and delivered tasks efficiently, contributing to the overall success of the research project by meeting all deadlines and exceeding expectations in the quality of work.

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