MohammedSohail/engine_maintenance_model

Predictive Maintenance Model – RandomForest

This model predicts Engine Condition using multiple engine sensor parameters.

Model Information

• Best Model: RandomForest
• Algorithm: Tuned RandomForest Classifier
• Training Method: GridSearchCV + Cross Validation
• Task Type: Classification

Evaluation Metrics

	F1-Score
RandomForest	0.963115
GradientBoosting	0.775914
AdaBoost	0.76212

Dataset

Dataset used is hosted in the Hugging Face dataset hub:

• https://huggingface.co/datasets/MohammedSohail/engine_maintenance_data

How to Use

from huggingface_hub import hf_hub_download
import joblib
import pandas as pd

model_path = hf_hub_download(
    repo_id="MohammedSohail/engine_maintenance_model",
    filename="models/best_model.pkl" 
)

model = joblib.load(model_path)

# new_data = pd.DataFrame([
#     [750, 3.5, 6.0, 2.5, 80.0, 75.0] # Example sensor values
# ], columns=['Engine rpm', 'Lub oil pressure', 'Fuel pressure', 'Coolant pressure', 'lub oil temp', 'Coolant temp'])
# prediction = model.predict(new_data)
# print(f"Predicted Engine Condition: {prediction[0]} (0=Normal, 1=Faulty)")