Welcome to the Formula One Machine Learning Workshop GitHub repository! This workshop is designed to introduce participants to the exciting application of machine learning in the world of Formula One racing. Through hands-on projects, we'll explore how to use historical race data, driver performance, and car telemetry to make informed predictions and optimizations.
This workshop is part of a multi-session event where participants will collaborate to build and refine machine learning models using Formula One data. Our aim is to apply these models to solve real-world problems like optimizing race strategies, predicting race outcomes, and improving team performance.
- Session 1: Introduction to Formula One Data and Machine Learning
- Session 2: Data Collection and Preprocessing
- Session 3: Building Predictive Models
- Session 4: Model Evaluation and Refinement
- Session 5: Final Presentations and Discussion
- Understand the role of data in Formula One racing.
- Learn to process and cleanse data specific to Formula One.
- Develop predictive models to analyze driver performance and race outcomes.
- Evaluate and refine machine learning models.
- Present model insights and implications.
Participants are expected to have a basic understanding of Python and machine learning concepts. Familiarity with tools like Jupyter Notebooks, pandas, and scikit-learn will be beneficial.
- Python: Main programming language used.
- Jupyter Notebook: For interactive coding sessions.
- pandas: For data manipulation and analysis.
- scikit-learn: For building machine learning models.
- Matplotlib/Seaborn: For data visualization.
To get started with the project, clone this repository and install the required Python packages:
git clone https://github.com/yourgithubusername/formula-one-ml-workshop.git
cd formula-one-ml-workshop
pip install -r requirements.txt