Credit Card Fraud Detection

This project uses machine learning to detect fraudulent credit card transactions. It aims to classify transactions as either fraudulent or legitimate based on patterns in the data, ultimately creating a reliable system for fraud detection.

Project Overview

The project utilizes a dataset of credit card transactions, where each transaction is labeled as either fraudulent or legitimate. We apply machine learning algorithms to detect patterns in the data that indicate fraud.

Technologies Used

• Python
• Pandas (for data manipulation)
• NumPy (for numerical operations)
• Scikit-learn (for machine learning models and evaluation)
• Matplotlib & Seaborn (for data visualization)
• Jupyter Notebook (for interactive data analysis)

Features

• Data Preprocessing: Handling missing data, scaling features, and encoding categorical variables.
• Exploratory Data Analysis (EDA): Analyzing data trends and identifying class imbalances.
• Modeling: Implementing machine learning models such as Logistic Regression, Random Forest, etc.
• Model Evaluation: Evaluating models using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.
• Hyperparameter Tuning: Optimizing models using GridSearchCV or RandomizedSearchCV.
• Fraud Detection: Classifying transactions as fraudulent or legitimate.

Getting Started

Prerequisites

Make sure you have the following installed:

• Python 3.x
• pip (Python package manager)

Install the required libraries using:

pip install -r requirements.txt