Mindinventory/Bank-Marketing-Data-Visualisation

This repository contains Python code for visualizing the Bank Marketing dataset using various data visualization techniques. The dataset is loaded from a CSV file, and both numerical and categorical features are explored using popular libraries such as Pandas, Matplotlib, Seaborn, and Plotly.

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Implements exploratory data analysis through univariate, bivariate, and multivariate visualization workflows, including correlation heatmaps and parallel coordinate plots for pattern detection across the 16 marketing features and binary deposit subscription target. The codebase is structured as a runnable Jupyter notebook with pre-built visualization templates using Pandas for data wrangling and Seaborn/Plotly for interactive and static charting. Optimized for Google Colab deployment with dependencies limited to standard scientific Python stack (NumPy, Matplotlib, Seaborn, Plotly).

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Jan 11, 2024

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