This project aims to build a Credit Card Fraud Detection model using data science and machine learning techniques. The goal is to identify and prevent fraudulent credit card transactions, thus ensuring the security of financial transactions for users.
Credit card fraud is a significant concern in the financial industry. This project leverages machine learning techniques to detect patterns associated with fraudulent transactions, providing an additional layer of security for credit card users.
- Data Preprocessing: Clean and prepare the dataset for training and testing.
- Exploratory Data Analysis (EDA): Understand the distribution of features and relationships within the dataset.
- Feature Engineering: Create relevant features to enhance model performance.
- Model Building: Utilize machine learning algorithms to train a fraud detection model.
- Model Evaluation: Assess the model's performance using appropriate metrics.
- Deployment (Optional): If applicable, deploy the model for real-time fraud detection.
The dataset used for this project is [provide dataset source/link]. It contains [brief description of dataset].
Ensure you have the following dependencies installed:
- Python 3.x
- Jupyter Notebooks (optional but recommended)