Seaborn Exercise Plots for Practice

This project provides a collection of Seaborn exercise plots implemented in Jupyter Notebook for practice. Seaborn is a powerful data visualization library in Python that offers a variety of statistical plots and visualization techniques. Through this project, we aim to enhance our skills in data visualization using Seaborn and gain hands-on experience with different plot types and customization options.

Prerequisites

Before running the code, make sure you have the following dependencies installed:

• Python (3.x)
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• Seaborn

Getting Started

To get started with the project, follow the steps below:

1. Clone the repository:

git clone https://github.com/shaadclt/Seaborn-Exercises.git

2. Change into the project directory:

cd Seaborn-Exercises

3. Install the required dependencies:

4. Run Jupyter Notebook:

jupyter notebook

5. Open the Seaborn Exercises.ipynb notebook in Jupyter.

6. Follow the instructions in the notebook to practice and explore different Seaborn exercise plots.

Project Overview

The notebook provides a series of exercises to practice different Seaborn plots and visualization techniques. The exercises include the following plot types:

1. Correlation Heatmap: Visualizing the correlation between variables in a dataset using a color-coded heatmap.
2. Joint Plot: Creating a scatter plot with additional marginal distributions for two numerical variables.
3. Pair Plot: Creating scatter plots and histograms for multiple numerical variables in a dataset.
4. Dist Plot: Visualizing the distribution of a single numerical variable using a histogram or kernel density estimation.
5. Count Plot: Creating a bar plot to display the count of categorical variables.
6. Bar Plot: Creating a bar plot to compare categorical variables with additional options for grouping and stacking.
7. Box Plot: Visualizing the distribution of numerical variables and detecting outliers using quartiles and whiskers.
8. Violin Plot: Combining a box plot and kernel density estimation for visualizing distributional information.

Each exercise includes explanations, code snippets, and sample datasets to practice and gain hands-on experience with Seaborn plots.

Results and Insights

As this project is for practice, the emphasis is on implementing and exploring different Seaborn exercise plots rather than providing specific results or insights. Each exercise will provide you with the opportunity to visualize and analyze datasets using different plot types and customization options. Feel free to experiment with different datasets, plot configurations, or explore additional Seaborn plots beyond the provided exercises.

Customization

You can customize the project by adding your own datasets, creating additional exercises, or exploring more advanced Seaborn plots. This project serves as a starting point for you to practice and enhance your data visualization skills using Seaborn.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgments

• This project is created for the purpose of practicing and exploring Seaborn exercise plots for data visualization.

Contributing

Contributions are welcome! If you find any issues, have suggestions for improvements, or want to add more exercises, please open an issue or submit a pull request.