Using machine learning technique to detect fraud behaviour
The IEEE-CIS Fraud Detection competition on Kaggle was a highly anticipated event for data scientists and machine learning enthusiasts around the world. With a dataset provided by Vesta Corporation, the competition challenged participants to develop innovative solutions for detecting fraudulent transactions in online financial services. The competition ran for several months and attracted over 3,500 participants, who used a variety of machine learning techniques and feature engineering approaches to tackle the problem. The competition was notable for its large and complex dataset, which required participants to develop creative solutions to extract meaningful insights and build accurate models. In this competition, participants not only had the opportunity to showcase their skills and knowledge, but also to contribute to the development of more effective fraud detection systems for online financial services. https://www.kaggle.com/c/ieee-fraud-detection
The dataset used in the IEEE-CIS Fraud Detection competition on Kaggle was provided by Vesta Corporation, a leading provider of online payment solutions. The dataset was a large and complex collection of transactional data, consisting of two main sets: transactional information and identity information. The transactional information set contained over 50 million individual transactions, with a wide range of features related to the transaction itself, such as time, amount, and product type. The identity information set contained over 400,000 individual identities, with features related to the user's identity, such as name, address, and device information. The dataset was highly imbalanced, with only a small percentage of transactions labeled as fraudulent. This made it a challenging problem for participants, as they needed to develop effective techniques to identify fraudulent transactions while avoiding false positives. Overall, the dataset was a rich and complex source of information, requiring participants to use a range of data preprocessing and feature engineering techniques to extract meaningful insights and build accurate models. More detail: https://www.kaggle.com/c/ieee-fraud-detection/discussion/101203
The file is too large to upload directly to the github server. Here I provide the way to download the files
Enter the following command in the terminal： kaggle competitions download -c ieee-fraud-detection