Seaborn Data Visualization Projects 📊

This repository is a collection of Jupyter Notebooks dedicated to exploring the Seaborn library for statistical data visualization in Python. As part of my journey in AIML, I'm using this space to practice creating a wide variety of plots, understanding their nuances, and documenting my learning process.

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📈 Visualizations Explored

This collection aims to cover a broad range of Seaborn's capabilities, from basic plots to more complex statistical visualizations.

• Relational Plots: Understanding relationships between variables using scatterplot() and lineplot().
• Categorical Plots: Visualizing categorical data with plots like boxplot(), violinplot(), countplot(), and barplot().
• Distribution Plots: Analyzing the distribution of data using histplot() (histograms), kdeplot() (kernel density estimates), and displot().
• Matrix Plots: Visualizing matrix data with heatmap() and clustermap() to see patterns across many variables.
• Regression Plots: Exploring linear relationships with regplot() and lmplot().

(This collection will be updated with more advanced plots and techniques as my skills develop.)

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📚 Datasets Used

Many of the notebooks utilize Seaborn's convenient built-in datasets for quick and easy practice, including:

• tips
• titanic
• iris

Additionally, external datasets like the California Housing dataset from scikit-learn are used for practical regression analysis visualizations.

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🛠️ Technologies Used

• Python 3.13
• Seaborn: The core library for statistical plotting.
• Matplotlib: The foundational library upon which Seaborn is built.
• Pandas: For data manipulation and creating the DataFrames used in plotting.
• NumPy: For numerical operations.
• Scikit-learn: For loading sample datasets.
• Jupyter Notebook (run within VS Code)

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🚀 Getting Started

To run these notebooks on your local machine, follow these steps:

1. Clone the repository:

   git clone [https://github.com/parikhdev/your-seaborn-repo.git](https://github.com/parikhdev/your-seaborn-repo.git)

2. Navigate to the directory:

   cd your-seaborn-repo

3. Install the required libraries:

   pip install seaborn pandas matplotlib scikit-learn jupyterlab

4. Launch Jupyter Lab and open a notebook:

   jupyter lab

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👤 Author

• Yash Parikh - parikhdev