The `AutoViz` architecture is designed as a streamlined data visualization pipeline, centered around the `AutoViz API & Orchestrator` which acts as a facade for automated exploratory data analysis. Data flows sequentially, beginning with the `Data Loader & Classifier` for ingestion and type inference, then moving to `Data Preprocessing & Feature Engineering` for refinement. Text-specific data is routed through the `NLP Data Processor`. Depending on the visualization requirements, processed data is fed into either the `Matplotlib/Seaborn Plotting Engine` for static outputs or the `HoloViews/hvPlot Plotting Engine` for interactive visualizations. All generated plots converge at the `Visualization Output & Export Manager` for final display and saving, providing a comprehensive and automated EDA workflow.

Components

AutoViz API & Orchestrator

The primary user-facing interface and control center, orchestrating the entire EDA and visualization pipeline.

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Data Loader & Classifier

Handles initial data loading and automatic classification of column types.

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Data Preprocessing & Feature Engineering

Manages advanced data preparation, including cleaning, transformation, and feature selection.

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NLP Data Processor

Specialized module for text data cleaning and text-specific visualization generation.

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Matplotlib/Seaborn Plotting Engine

Generates static statistical and relational plots.

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HoloViews/hvPlot Plotting Engine

Provides an interactive visualization backend for dynamic plots.

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Visualization Output & Export Manager

Manages saving and displaying all generated visualizations.

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