The competition is designed for those passionate about leveraging machine learning to make significant predictions in financial behavior. This challenge offers a unique opportunity to predict users' future transaction volumes—classifying them as either "high" or "low" compared to their peers in the subsequent month. By analyzing a comprehensive dataset of user profiles and their recent transaction history, competitors will unlock key insights and identify patterns that influence transactional behavior.
The goal is to build a model that not only predicts high vs. low transaction volumes with high accuracy but also sheds light on the underlying factors that contribute to higher financial engagement among users. This insight is invaluable for businesses aiming to tailor their services, optimize marketing strategies, and enhance customer engagement by identifying and understanding their most valuable customers.
As part of this Kaggle competition, everyone MUST leverage Large Language Models (LLMs) or other Generative AI techniques throughout their competition. This requirement aims to:
- Familiarize participants with real-world AI tool integration.
- Explore innovative ways to combine traditional ML with modern AI tools.
- Develop best practices for AI-assisted data science.
Predicting "whales" has far-reaching implications across various business functions:
- Marketing and Sales: Knowing which users are likely to transact more can help in crafting personalized marketing campaigns, optimizing resource allocation, and improving sales strategies to maximize ROI.
- Customer Experience: Insights into the transaction behavior can guide the development of tailored products and services, enhancing the user experience and fostering loyalty among high-value customers.
- Risk Management: Understanding the transaction patterns of users can aid in assessing financial risks and fraud detection, leading to more robust risk management strategies.
- Strategic Planning: Businesses can use predictions to inform their strategic decisions, focusing on areas with the highest potential for growth and investment.
Important Notes:
- Upon signing up for Kaggle and joining the competition, kindly change the Display Name in your Kaggle profile and your Team Name under the Team tab to your NUS student number, e.g., "A0123456Z".
- Do not sign up using more than one Kaggle account.
- This is a competition between individual participants.
- Please read the Overview, Submission Guidelines, Grading, Data, and Rules pages carefully.
- Please note that the End Time of this competition (see Timeline) is specified in UTC.
Entries will be evaluated on the Area Under the ROC Curve (AUC) metric, emphasizing the model's precision in distinguishing between the two classes of transaction volumes.