Credit-Card-Fraud-Detection-using-ML
The aim of this project is to detect fraudulent credit card transactions using Machine Learning models. We implement four machine learning algorithms namely Logistic Regression, K Nearest Neighbour, Naive Bayes and Decision Tree. The main aim is to compare these machine learning models and find the most effective and accurate predictive model based on three evaluation metrics- Accuracy Score, F1 score and ROC curve.
Content of this repository :
- Dataset : Contains the description of dataset used for this project
- Help Document : Contains execution steps and packages used for the code
- FDS_025_217.ipynb : Contains python code of the project
- Methodology : Contains description of methods and techniques used
- Results : Contains results obtained from the project
Group Members
- Tauqir Waqar Panvelkar
- Rohan Ganesh Modak
Supervisor
Dr. Siddhaling Urolagin,
PhD, Post-Doc, Machine Learning and Data Science Expert,
Passionate Researcher, Focus on Deep Learning and its applications,
dr.siddhaling@gmail.com