HumanMachine74/engine-performance-data-model

Random Forest Classifier for Engine Condition Prediction

This repository contains a trained RandomForestClassifier model for predicting engine condition (Normal vs. Faulty) based on various engine parameters.

Model Details

• Algorithm: RandomForestClassifier
• Framework: scikit-learn

Performance Metrics (on Test Set)

• Accuracy: 0.6629
• Precision: 0.6890
• Recall: 0.8482
• F1-Score: 0.7603

Hyperparameters

{
  "max_depth": 10,
  "min_samples_leaf": 1,
  "min_samples_split": 10,
  "n_estimators": 200
}

Usage

To load and use this model:

import joblib
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="HumanMachine74/engine-performance-data-model", filename="random_forest_model.joblib")
model = joblib.load(model_path)

# Example prediction (assuming X_new is your new data)
# predictions = model.predict(X_new)