This project focuses on the analysis of payment transaction data for specific hours over three days (Feb 12, 13, and 14). It aims to identify factors affecting the success rate and provides an intuitive dashboard for visualizing key metrics.
The dataset used in this project contains payment transaction data for Feb 12, 13, and 14, hour-wise. It includes columns such as 'hr' (transaction hour), 'pmt' (payment method type), 'pg' (payment gateway), 'bank' (customer bank), and 'sub_type' (payment subtype).
- Data analysis using Python (Pandas)
- Data visualization with Matplotlib
- Proactive issue detection strategies
- Interactive dashboard creation with Plotly Dash
You can use the project to:
- Analyze payment transaction success rates by various dimensions.
- Explore the interactive dashboard for real-time monitoring.
- Implement proactive issue detection strategies for your own datasets.