AI-Driven Race Strategy Simulator

Bringing Formula 1-level strategic thinking to AI-powered simulations. The AI-Driven Race Strategy Simulator is a cutting-edge web application designed to revolutionize race strategy analysis by leveraging real Formula 1 data and machine learning models. It allows users—whether motorsport enthusiasts, data scientists, or professional strategists—to simulate race scenarios and predict optimal strategies based on lap times, tire wear, pit stops, weather conditions, and circuit characteristics. By integrating Flask (backend) with HTML/CSS (frontend) and machine learning models trained on F1 race data, the system provides real-time AI-driven strategy recommendations to enhance race performance simulations. Key Components 1. Race Data Processing: Uses Formula 1 World Championship (1950–2024) dataset to analyze historical trends. Merges lap times, pit stops, race details, and circuit attributes to extract meaningful insights. 2 .Machine Learning-Based Strategy Predictions: Trained with a Random Forest Classifier (expandable to XGBoost or Neural Networks). Predicts race strategy decisions—whether to pit, push, or hold position—based on real-world track conditions. 3.Web Dashboard for Interactive Simulations: A user-friendly Flask-powered interface for real-time race simulations. Allows input of lap time, tire degradation, circuit selection, and weather conditions to generate AI-driven race strategies. Does it work? User Input: The system takes lap times, tire wear percentage, track type, and weather as input. AI Model Processing: A trained ML model analyzes race conditions and driver performance. Predicts Strategy: Outputs recommendations like "Pit Now", "Push Harder", "Hold Position". Visualizations: Displays real-time lap deltas, pit stop windows, and tire performance graphs using libraries like Plotly.