Formula-1-Prediction-Model

Formula 1 Prediction Model 🏎️

F1 Prediction Dashboard is an interactive web app that predicts Formula 1 race outcomes using machine learning, real qualifying data, and live weather conditions.

Features

• Race Time Prediction: Uses an XGBoost regression model trained on historical qualifying and race data to predict median race lap times for each driver.

• Live & Hypothetical Modes: Analyze real races (2023–2024) or generate hypothetical scenarios for the 2025 season with customizable weather and track conditions.

• Weather Integration: Fetches live weather data via WeatherAPI to enhance prediction accuracy.

• Data Visualization: Presents results and podium predictions with interactive Plotly charts.

• User-Friendly Interface: Built with Streamlit for an intuitive, responsive dashboard experience.

How It Works

The backend fetches and processes qualifying and race data using FastF1.

The model is trained and saved with train_and_save_model.py using features like sector times and weather.

The Streamlit app (streamlit_app.py) loads the model, collects user inputs, and displays predictions.

💻 Technologies

Here is a list of the main technologies used in your F1 Advanced Prediction Dashboard project:

• Streamlit – for building the interactive web dashboard.

• Pandas – for data manipulation and analysis.

• NumPy – for numerical operations and random data -generation.

• Joblib – for saving and loading the trained machine learning model.

• Requests – for making HTTP requests to fetch live weather data.

• FastF1 – for accessing and processing Formula 1 session data (qualifying, race, weather).

• Plotly – for interactive data visualization within the dashboard.

• XGBoost – as the core machine learning algorithm for race time prediction.

• Scikit-learn – for model evaluation and data splitting (train/test split).

• WeatherAPI – (via HTTP requests) for integrating live weather data into predictions

🚀 Getting started

Step 1

Clone the repository:

git clone https://github.com/aryannverse/Formula-1-Prediction-Model.git

Step 2

Install all the libraries mentioned in the 'Requirements.txt'

pip install -r Requirements.txt

Step 3

In the 'Streamlit_app.py' file, insert your WeatherAPI key in line 76

params = {"key": "", "q": location} #insert your key here from http://api.weatherapi.com

Running the app:

Enter this line in the terminal to launch the dashboard in your browser:

streamlit run 'streamlit_app.py'