End To End Data Preprocessing & Exploration Questions

Follow a systematic, tool-agnostic data pipeline before deeper analysis: load the data, check shape and dtypes, identify missing values and duplicates, explore distributions, check for outliers, understand class or category balance where relevant, and summarize key statistics. Document findings and build visualizations that surface relationships in the data. This exploration is the foundation for whatever comes next: feature engineering and model selection for predictive/ML work, or clean aggregations and trustworthy KPIs for dashboards and reporting.