This project is a data analysis tool developed in Python aimed at understanding the reasons behind cancellations. It leverages various libraries to process, analyze, and visualize data effectively. By analyzing datasets on cancellations, the tool seeks to uncover patterns and insights that can inform strategies to reduce future cancellations.
Understanding why cancellations occur is critical for improving service quality, customer satisfaction, and reducing revenue loss. This project provides a structured approach to analyze cancellation data, identify underlying causes, and suggest actionable insights based on the analysis.
- Data import and cleaning using pandas and numpy
- Analysis of cancellation reasons using statistical methods
- Interactive visualizations using plotly
- Support for Jupyter Notebook formats with nbformat and ipykernel
The project is built using the following Python libraries:
- pandas: Data manipulation and analysis
- numpy: Numerical operations and array handling
- nbformat: Handling Jupyter Notebook files
- ipykernel: Kernel for Jupyter notebooks
- plotly: Interactive data visualization