Bank Marketing Data Analysis Project

The notebook begins by loading the “bank.csv” dataset (semicolon-delimited) into pandas and displaying its first few rows . It proceeds with exploratory data analysis—visualizations and mermaid diagrams for workflow documentation—then preprocesses features (e.g. encoding categorical variables), splits into train/test sets, and applies classification algorithms (e.g., Logistic Regression, Random Forest), evaluating via accuracy, confusion matrices, and classification reports.