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Fraud Credit Card Detection

This project utilizes machine learning techniques to detect fraudulent credit card transactions. By analyzing transaction data, the model aims to accurately identify fraudulent activities, providing a robust solution for fraud detection in financial systems.

Dataset

To run this project, you'll need to download the dataset from the following link:

Download Dataset

Note: Place the downloaded CSV file in the same directory as your main and test files.

Project Overview

The project consists of two main files:

main.py

This file contains the main code for training the machine learning model and evaluating its performance. It performs the following tasks:

  • Loads the dataset (creditcard.csv) into a Pandas DataFrame.
  • Explores and visualizes the dataset to understand its structure and characteristics.
  • Prepares the data for training by splitting it into features (X) and labels (Y).
  • Divides the dataset into training and testing sets using Scikit-learn's train_test_split function.
  • Builds a Random Forest Classifier model and trains it on the training data.
  • Evaluates the model's performance using various metrics such as accuracy, precision, recall, F1-score, and Matthews correlation coefficient.
  • Generates a confusion matrix to visualize the classification results.

test.py

This file contains code for testing the trained model on a separate dataset or real-time transactions. It performs similar tasks to main.py but is focused on applying the model to new data rather than training it.

Usage

  1. Download Dataset: Download the dataset from the provided link and place it in the project directory.
  2. Install Dependencies: Make sure you have all the required Python libraries installed, including NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
  3. Run main.py: Execute the main.py file to train the machine learning model and evaluate its performance.
  4. Run test.py: Optionally, run the test.py file to test the trained model on new data or real-time transactions.

License

This project is licensed under the MIT License.