| # 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 | |
| ```python | |
| 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)") | |
| ``` | |