This project aims to detect anomalies in financial transaction data. By identifying unusual patterns or irregularities, the project helps in spotting potential fraudulent activities or errors in transaction records.
- Data Quality Checks: Includes steps to import data, check for null values, and generate descriptive statistics for a preliminary understanding of the dataset.
- Transaction Analysis: Visualizes the distribution of transaction amounts across different variables such as account type and user age group.
- Anomaly Detection: Implements methods to identify outliers or anomalies in transaction amounts, helping to detect potential fraud or data errors.