machine learning, data science, and cybersecurity
Credit card fraud is a growing concern, with millions of dollars lost annually due to unauthorized transactions. Traditional rule-based fraud detection systems are ineffective against sophisticated fraud schemes. This project aims to develop a machine learning-based fraud detection system that analyzes transaction data, identifies fraudulent behavior, and minimizes false positives. Techniques such as logistic regression, random forests, support vector machines (SVM), and deep learning models can be employed to improve fraud detection accuracy.
Programming Languages: Python, R, SQL Libraries & Frameworks: Pandas, NumPy, Scikit-learn, TensorFlow/Keras, XGBoost, PyCaret
Databases: MySQL, PostgreSQL, MongoDB APIs & Data Sources: Kaggle credit card fraud datasets, financial transaction APIs Tools & Platforms: Jupyter Notebook, Google Colab, AWS/GCP for cloud computing Before Commencing the project the following links have to be examined.
https://www.kaggle.com/
https://seon.io/resources/credit-card-fraud-detection/
https://www.datacamp.com/datalab/datasets/
https://www.acfe.com/
