F1 Car Transition
Initializing telemetry...
Machine Learning × Formula 1

PREDICT THE
NEXT PIT STOP

Machine Learning Powered Formula 1 Strategy Analysis. Real-time pit stop predictions using advanced Random Forest models trained on race telemetry.

Race Intelligence

Engineered for precision strategy decisions

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ML Predictions

Random Forest model trained on real F1 telemetry data. Predicts optimal pit windows with high confidence.

📊

Data Driven

Analyzed thousands of race laps across multiple seasons. Tire degradation, position pressure, and more.

Real-Time

Instant strategy recommendations. Input current race conditions and get pit stop predictions in milliseconds.

🏁

Strategy Edge

Compare aggressive, balanced, and conservative strategies. Confidence scoring and risk assessment built in.

Live Telemetry

Simulated race engineering data

Tire Temperatures
FL
98°C
FR
105°C
RL
96°C
RR
108°C
Sector Times
S1
28.341
S2
34.129
S3
25.872
LAP TIME
1:28.342
Fuel Load
70% REMAINING
Track Position
DRS
LAP 42 / 57

About Project

Engineering meets data science

Machine Learning for F1 Strategy

PitVision AI uses a Random Forest Classifier trained on real Formula 1 race data to predict optimal pit stop timing. The model analyzes tire degradation patterns, race position dynamics, lap time deltas, and compound-specific wear rates to deliver actionable strategy recommendations.

Built with Python's scikit-learn ecosystem, the model processes 20+ engineered features including tyre stress indices, degradation rates, and position pressure metrics to achieve 98.47% accuracy on validation data.

🐍 Python 🧠 Scikit-Learn 🐼 Pandas 🔢 NumPy 🌐 Flask 📊 Plotly.js 💾 Joblib
System Architecture
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Race Data
⚙️
Features
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RF Model
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Flask API
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Dashboard