time-series-and-categorical-analysis

You can automate multi-dimensional trend analysis and visualization for time-series data to generate high-resolution reports. It extracts trends, parses percentages, and identifies statuses across business metrics.

Grouping categorical data enables performance grading and forecasting across departments. It aggregates metrics, applies grading models, and outputs growth analysis for direct performance comparison.

Yes, a Python data stack is required. You need pandas, numpy, and seaborn or matplotlib to handle data cleaning, percentage parsing, grouping, and high-resolution plotting.

Data cleaning and percentage parsing are applied directly to raw datasets before grouping. This prepares structured time-series and categorical inputs for multi-dimensional trend analysis and performance reporting.

Quarterly performance reports and forecasting values are generated by applying grading models to grouped data. It outputs growth analysis and high-resolution visualizations suitable for business metrics monitoring.