Welcome to the K-Means Credit Card Anomaly Detection project! This project demonstrates the application of the K-means clustering algorithm for detecting anomalies or outliers within a credit card transaction dataset. By grouping similar transactions together, we can identify unusual transactions that deviate from the norm.
Credit card transaction data often contains a mix of legitimate and fraudulent activities. Anomaly detection using K-means clustering can help identify potential fraudulent transactions based on their deviation from normal patterns. This project showcases how K-means clustering can be used to identify anomalies in such data.
- Python
- Scikit-learn
- Jupyter Notebook
- Data preprocessing and exploration
- Application of K-means clustering algorithm
- Visualization of clusters and anomalies
- Identification of potentially fraudulent transactions
To explore and run this project:
- Clone the repository to your local machine:
git clone https://github.com/your-username/K_Means_Credit_Card_Anamoly_Detection.git
- Navigate to the project folder:
cd K_Means_Credit_Card_Anamoly_Detection
3. Open the Jupyter Notebook file (K_Means_Credit_Card_Anamoly_Detection.ipynb
) using Jupyter Notebook or any compatible environment.
- Run the code cells in the notebook to see the K-means clustering algorithm in action, understand how anomalies are identified, and analyze the results.
If you have any questions or want to connect, you can reach me at:
- LinkedIn: Md Amanullah
- Email: [email protected]
Happy learning and exploring the project! ๐