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zayedansari/
HamiltonMLModel

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F1MLModel

Predictive models for Lewis Hamilton / Ferrari performance in the 2025 F1 season.

This repository contains a small machine-learning project used to train and persist models that predict race-related outcomes (classification and regression) and a ready-to-use serialized predictor for making predictions.

hamilton_ferrari_predictor.pkl — A serialized (pickled) model or pipeline ready for inference.
imputer.pkl — Serialized imputer used during preprocessing.
scaler.pkl — Serialized scaler used during preprocessing.
hamilton_ferrari_2025_predictions.csv — Example / output predictions produced by the model.
model/ — Source code for model classes and training/experiment code.
- ClassificationModel.py
- HalfDataModel.py
- RegressionModel.py
data/ — Raw and preprocessed CSV datasets used for training and analysis (races, drivers, lap times, etc.).
requirements.txt — Python dependencies for running and developing with this project.

Project goals

Provide a compact pipeline to predict aspects of race performance using historical F1 data.
Ship a ready-to-run predictor for quick inference on new examples.

Quick start

Create and activate a virtual environment (recommended):

bash

python3 -m venv .venv
source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Run an interactive Python session or script to load the predictor and call .predict.

Example usage (replace X with your feature matrix / DataFrame formatted for the model):

python

import joblib
import pandas as pd

# Load artifacts
predictor = joblib.load('hamilton_ferrari_predictor.pkl')
scaler = joblib.load('scaler.pkl')
imputer = joblib.load('imputer.pkl')

# Prepare input. The model expects the same features used during training.
# Here we load a CSV from the `data/` folder as an example — adapt to your features.
df = pd.read_csv('data/lewishamilton.csv')

# Example preprocessing pipeline (adapt to the project's feature set):
# - fill missing values with the imputer
# - scale numeric features with the scaler
# The exact columns used by the pipeline depend on how the model was trained. See `model/` for details.

# This is a placeholder; replace with the real column list used by the model
# features = df[ ['col1', 'col2', 'col3'] ]

# features = imputer.transform(features)
# features = scaler.transform(features)

# Make predictions
# preds = predictor.predict(features)

# Save predictions
# pd.DataFrame({'prediction': preds}).to_csv('my_predictions.csv', index=False)

print('Loaded predictor; adapt input features and uncomment prediction lines to run inference.')

Notes:

The pickled predictor may be a scikit-learn Pipeline, a single model object, or a custom class — call .predict or .predict_proba depending on the object's API.
Inspect the files in model/ to determine the expected input feature names and preprocessing steps.

Reproducing / Training

The model/ directory contains the model code. Use those files as a starting point to retrain or modify models. Typical steps:

Inspect and prepare data in data/ (join race, qualifying, driver stats, etc.).
Implement preprocessing (imputation, scaling, feature engineering) consistent with the serialized artifacts.
Train a model and persist artifacts using joblib.dump.

If you add a training script, consider producing the following artifacts and committing them:

*.pkl for the trained model/pipeline
a small notebooks/ or scripts/ folder with reproducible steps (data cleaning, feature engineering, training)

Files of interest

hamilton_ferrari_predictor.pkl — inference artifact. Load with joblib.load.
model/ClassificationModel.py — classification training/definition code.
model/RegressionModel.py — regression training/definition code.

Tips and troubleshooting

If joblib.load fails with a ModuleNotFoundError, ensure you are running inside the python environment that has the same package structure and that the model package path is available (run from the repository root).
If predictions don't match expectations, re-check feature ordering and preprocessing steps (scaler and imputer must be applied in the same way as during training).
To inspect the trained pipeline's feature names (if it is a scikit-learn Pipeline with a ColumnTransformer), try:

python

import joblib
pipe = joblib.load('hamilton_ferrari_predictor.pkl')
print(pipe)

License

This project currently has is under the Apache 2.0 License.

zayedansari/HamiltonMLModel