https://github.com/dr-saad-la/stats-modeling-with-python

Statistical Modeling with Python
https://github.com/dr-saad-la/stats-modeling-with-python

python statistical-learning statistical-models stats statsmodels

Last synced: 8 months ago
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Statistical Modeling with Python

• Host: GitHub
• URL: https://github.com/dr-saad-la/stats-modeling-with-python
• Owner: dr-saad-la
• License: mit
• Created: 2024-06-15T23:03:23.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-19T17:44:56.000Z (over 1 year ago)
• Last Synced: 2025-01-04T04:33:12.300Z (9 months ago)
• Topics: python, statistical-learning, statistical-models, stats, statsmodels
• Language: HTML
• Homepage: https://dr-saad-la.github.io/Stats-Modelling-with-Python/
• Size: 1.57 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Statistical Modeling with Python

**Statistical Modeling with Python** is an online book in the form of a guide, which is designed to provide students with the essential tools and knowledge to perform statistical modeling using Python. This book offers valuable insights and practical examples to enhance the understanding and application of statistical methods using the open source tools:

## Table of Contents

1. **Introduction**

   - Overview of Statistical Modeling

   - Why Python for Statistical Modeling?

   - Structure of the Book

2. **Getting Started with Python**

   - Installing Python and Required Libraries

   - Basic Python Programming Concepts

   - Introduction to Jupyter Notebooks

3. **Exploratory Data Analysis (EDA)**

   - Understanding Your Data

   - Descriptive Statistics

   - Data Visualization Techniques

4. **Probability and Distributions**

   - Basic Probability Concepts

   - Common Probability Distributions

   - Sampling Methods

5. **Statistical Inference**

   - Point Estimation

   - Confidence Intervals

   - Hypothesis Testing

6. **Regression Analysis**

   - Simple Linear Regression

   - Multiple Linear Regression

   - Diagnostics and Model Validation

7. **Classification Techniques**

   - Logistic Regression

   - k-Nearest Neighbors

   - Support Vector Machines

8. **Advanced Modeling Techniques**

   - Decision Trees and Random Forests

   - Gradient Boosting Machines

   - Neural Networks

9. **Time Series Analysis**

   - Introduction to Time Series Data

   - Time Series Decomposition

   - ARIMA Models

10. **Practical Applications**

    - Case Studies

    - Real-world Examples

    - Best Practices

11. **Appendix**

    - Mathematical Foundations

    - Python Resources

    - Further Reading

## About the Author

The author of this book is an experienced data scientist with extensive knowledge in statistical modeling and Python programming. With a passion for teaching and simplifying complex concepts, the author aims to equip readers with practical skills and a solid understanding of statistical methods.

## How to Use This Book

This book is structured to guide you from basic concepts to advanced techniques in statistical modeling. Each chapter builds upon the previous one, introducing new concepts and providing hands-on examples. To get the most out of this book, it is recommended to follow the chapters in sequence and actively participate in the coding exercises.

## Contributing

Contributions to this book are welcome. If you find any errors or have suggestions for improvements, please open an issue or submit a pull request on the [GitHub repository](https://github.com/your-repo-link).

## License

This book is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). You are free to share and adapt the material for non-commercial purposes, provided you give appropriate credit and distribute your contributions under the same license.

## Acknowledgments

We would like to thank the Python community and the developers of the various libraries used in this book. Their contributions make statistical modeling accessible and efficient.

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Thank you for choosing **Statistical Modeling with Python**. We hope this book serves as a valuable resource in your journey to mastering statistical modeling.

For any queries or further information, please contact [dr.saad.laouadi@gmail.com](mailto:dr.saad.laouadi@gmail.com).

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_Last Updated: [19/06/2024]_