This case requires trainees to develop a model for predicting fraudulent transactions for a financial company and use insights from the model to develop an actionable plan. Data for the case is available in CSV format having 6362620 rows and 10 columns. Candidates can use whatever method they wish to develop their machine learning model. Following usual model development procedures, the model would be estimated on the calibration data and tested on the validation data. This case requires both statistical analysis and creativity/judgment. We recommend you spend time on both fine-tuning and interpreting the results of your machine learning model.

If you are running on your local machine,
Download the dataset: https://www.kaggle.com/datasets/chitwanmanchanda/fraudulent-transactions-data

Kaggle Notebook: https://www.kaggle.com/code/sauhardsaini/fraud-detection-dtrf/notebook