馬拉松**博覽會參訪動線類別預測 (Behavior Classification of Exposition Visitors)
- The goal of project is to predict the best route among five routing options.
- Team member: Jack Liu, Barron Chang, Michael Chung, Leo
Dataset consists of "sniffer_loc", "created_time". We focus on using the information about "sniffer_loc".
- RandomForest
- Ensemble of multiple decision trees (tree-based)
- LSTM/RNN
- Traditional sequence prediction method
- CatBoost
- Gradient boosting tree-based method
- Transformer-based (BERT & XLNet)
- With the help of Multi-head self attention mechanism
Note: Under our attempts, we found that transformer-based models have better result and may have higher potential
- Our team reaches the top-3 on this leader board.
- The statistics on Aidea platform by 2022.6.13
Other details and discussion are stored in the .pdf file. Please find reference there if you're interested.
- Reference/note.md: the report.pdf and poster.pdf can help you understand more details.
- src/note.md: Summarize how our code works and the purposes of each files.