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:

1. Clone the repository to your local machine:

git clone https://github.com/your-username/K_Means_Credit_Card_Anamoly_Detection.git

2. 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.

4. 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! 🚀