This repository contains code for performing Bayesian regression on Olympic data. The project demonstrates how to estimate the parameters of a linear regression model using a Bayesian approach and visualize the results.

• Introduction
• Installation
• Usage
• Repository Structure
• License

Linear regression is a fundamental statistical technique for modeling the relationship between a dependent variable (target) and one or more independent variables (features). In this project, we apply Bayesian regression to analyze Olympic data. The goal is to estimate the parameters of a linear model while accounting for uncertainty.

This repository includes Jupyter Notebook files that guide you through the following steps:

• Loading and preprocessing Olympic data.
• Defining the Bayesian linear regression model.
• Estimating the posterior distribution of model parameters.
• Visualizing the results, including posterior mean and uncertainty.

To run the code in this repository, you need Python and Jupyter Notebook installed. You can create a virtual environment and install the required packages using the following commands:

# Create a virtual environment (optional but recommended)
python -m venv venv

# Activate the virtual environment
source venv/bin/activate  # On Windows, use 'venv\Scripts\activate'

# Install required packages
pip install -r requirements.txt

1. Clone the repository to your local machine:

git clone https://github.com/abelxmendoza/Bayesian_Regression_for_the_Olympic_Data_Checkpoint.git

2. Change the working directory to the cloned repository:

cd Bayesian_Regression_for_the_Olympic_Data_Checkpoint

3. Launch Jupyter Notebook to interact with the project:

jupyter notebook

4. Open the provided Jupyter Notebook files to run the code step by step.
5. Follow the instructions in the Jupyter Notebook to perform Bayesian regression on the Olympic data and visualize the results.

The repository is organized as follows:

• data/: Contains the Olympic data in CSV format.
• notebooks/: Jupyter Notebook files for data analysis and Bayesian regression.
• requirements.txt: Lists the required Python packages and their versions.
• example.png: An example output plot generated from the Jupyter Notebook.

This project is licensed under the MIT License. You are free to use, modify, and distribute the code, but please provide attribution to the original author.

Feel free to reach out if you have any questions or need further assistance with this project.

Happy coding!