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TILet’s build a machine learning mode to make predictions about F1s upcoming Japan GP 🏎️
These are super popular projects right now (thanks ChatGPT for the suggestion) and I wanted to put my learnings to the test and see what I could do!
GitHub for the project: GitHub.com/tiffintech/f1_predictions
💡save this so you can reference it later!
Here is what is going on…
1. Data Collection
- Uses FastF1 API to fetch qualifying session data
- Collects data from recent 2025 races (rounds 1-4)
- Includes 2024 Japanese GP data as reference
2. Data Processing
- Converts lap times from timedelta to seconds
- Handles missing values using SimpleImputer
- Cleans and structures data for analysis
3. Model Development
- Uses Linear Regression to establish baseline predictions
- Features: Q1 and Q2 times
- Target: Q3 times
- Includes train-test split for validation
4. Performance Factors
- Implements team-specific performance coefficients
- Adds driver-specific performance adjustments
- Base lap time calibrated to ~89.5 seconds
- Includes small random variation for realism
5. Prediction System
- Combines model predictions with performance factors
- Accounts for 2025 driver-team combinations
- Sorts and displays predicted qualifying order
6. Validation
- Calculates Mean Absolute Error (MAE)
- Provides R² score for model accuracy
- Visualizes qualifying time distributions
#tech #technology #coding #stem #developer
@tiffintech
