# 📊 Seaborn Statistical Visualization

Statistical data visualization with Seaborn including distribution plots, correlation matrices, box plots, and statistical charts.

## 📝 Project Description

This project demonstrates Seaborn, a statistical visualization library built on Matplotlib. It covers distribution plots, correlation heatmaps, categorical plots, regression plots, and advanced statistical visualizations. Perfect for exploratory data analysis and statistical insights.

## ✨ Features

- Statistical distribution plots
- Correlation matrices and heatmaps
- Categorical and relational plots
- Regression visualization
- Advanced statistical charts
- Q-Q plots and ECDF (Empirical Cumulative Distribution Function)
- Statistical tests and annotations (t-tests, ANOVA)
- Multiple styling options and themes
- Matrix plots and multi-variable visualizations
- Statistical summary tables and visualizations

## 🛠️ Technologies

Python Seaborn Matplotlib Pandas NumPy Jupyter Notebook

## 📚 Notebooks

- 01_distribution_plots.ipynb - Histograms, KDE plots, rug plots, and distribution comparisons
- 02_correlation_heatmaps.ipynb - Correlation matrices, heatmaps, and cluster maps
- 03_categorical_plots.ipynb - Bar plots, count plots, box plots, violin plots, and swarm plots
- 04_regression_plots.ipynb - Linear regression, polynomial regression, and residual plots
- 05_advanced_visualizations.ipynb - Pair plots, joint plots, and faceted grids
- 06_advanced_statistical_analysis.ipynb - Q-Q plots, ECDF, statistical tests, and annotations
- 07_styling_and_themes.ipynb - Different styles, color palettes, contexts, and custom styling
- 08_matrix_plots.ipynb - Matrix plots and multi-variable visualizations
- complete_guide.ipynb - Comprehensive guide covering all visualization types

## 🚀 Getting Started

1. Install required packages: `pip install -r requirements.txt`
2. Start Jupyter Notebook: `jupyter notebook`
3. Open any notebook from the `notebooks/` directory
4. Run cells sequentially to see visualizations