⚫ This project is NGIDS (Next-Generation Intrusion Detection System) based on Machine Learning.◼ Extract key features from IoT network traffic through “data visualization” such as “information graph”, “sankey diagram”, etc.◼ Data preprocess and fix imbalance data issue, such as “data normalization”, “SMOTE sampling”, etc.◼ Train and evaluate 9 detection models with AutoML. Such as “Neural network”, “Random forest”, “SVM”,“XGBoost”,etc.◼ Research on feature embedding and IoT attack behavior modeling with LLMs.

⚫ This project analyzes customer consume behavior with ChatGPT and helps vendors to find right customers.◼ Generate customer labels based on consumer spending behavior with ChatGPT.◼ Generate product labels based on product selling conditions with ChatGPT.◼ Predict customers' purchasing intentions and recommend key customers.◼ Provide web UI for above functions

⚫ Processed over 2 million rows of patient data with over 30 different features using R dplyr and delivered a clean CSV data file to my supervisor, ready for database creation and statistical modeling.⚫ Developed name searching algorithms with an accuracy rate of 98% using Python, and created a user-friendly search interface with great accessibility for clients.⚫ Kept clear and organized track of project progress with weekly PowerPoint reports, building an efficient communication with my supervisor and colleagues