The task is to build a fraud detection model using a dataset of credit card transactions. The challenge lies in accurately identifying fraudulent transactions while dealing with an imbalanced dataset and limited information about the features.
To solve the problem, we have developed a fraud detection model using machine learning techniques. The dataset consists of anonymized features resulting from PCA transformation, along with the 'Time' and 'Amount' features. We have employed a combination of over and undersampling techniques to tackle the class imbalance. Exploratory data analysis was conducted to understand feature relationships, detect outliers, and create new features using 'Time' and 'Amount'. A pipeline was constructed to preprocess the data and train various classification algorithms. Model performance was evaluated using the area under the precision-recall curve (AUPRC). Our results indicate that the Random Forest Classifier is the best model for fraud detection.
To install the project, please follow these steps:
Clone the repository Install the required dependencies: pip install -r requirements.txt
To use the project, follow these instructions:
Preprocess the data: python preprocess.py Train the model: python train.py Make predictions on new data: python predict.py Make sure to provide the appropriate input data paths and adjust the hyperparameters as needed.
Umar Kabir Made at Qwasar SV -- Software Engineering School <img alt='Qwasar SV -- Software Engineering School's Logo' src='https://storage.googleapis.com/qwasar-public/qwasar-logo_50x50.png' width='20px'>
