metadata
library_name: sklearn
tags:
- tabular-classification
- sklearn
- equipment-failure
- mining
- south-africa
Mining Equipment Failure Prediction Model
A GradientBoostingClassifier pipeline for predicting equipment failures, trained on South African mining data.
Intended Use
This model is intended for educational and demonstration purposes as part of an end-to-end ML pipeline showcasing Databricks, MLflow, Azure ML, and Hugging Face Hub integration.
Model Details
| Property | Value |
|---|---|
| Classifier | GradientBoostingClassifier |
| Pipeline steps | preprocessor -> classifier |
| Training samples | 12,000 |
| Test samples | 3,000 |
| Target column | target |
| Created | 2026-06-16T15:38:07.336452+00:00 |
Evaluation Metrics
| Metric | Score |
|---|---|
| Accuracy | 0.9337 |
| Precision | 0.7510 |
| Recall | 0.5884 |
| F1 | 0.6598 |
| ROC AUC | 0.9465 |
Confusion Matrix
ROC Curve
Feature Importance
Features
Numeric: temperature_celsius, vibration_mm_s, oil_pressure_kpa, rpm, operating_hours, days_since_maintenance, load_percentage, ambient_temperature_celsius, hydraulic_pressure_kpa, num_previous_failures
Categorical: equipment_type, mine_type, shift, province
Sample Usage
import joblib
from huggingface_hub import hf_hub_download
import pandas as pd
# Download and load the model
model_path = hf_hub_download(
repo_id="ThabangTheActuaryCoder/mining-equipment-failure-model",
filename="equipment_failure_model.joblib",
)
model = joblib.load(model_path)
# Create a sample input
sample = pd.DataFrame([{"temperature_celsius": 0, "vibration_mm_s": 0, "oil_pressure_kpa": 0, "rpm": 0, "operating_hours": 0, "days_since_maintenance": 0, "load_percentage": 0, "ambient_temperature_celsius": 0, "hydraulic_pressure_kpa": 0, "num_previous_failures": 0, "equipment_type": 0, "mine_type": 0, "shift": 0, "province": 0}])
# Predict
prediction = model.predict(sample)
probabilities = model.predict_proba(sample)
print(f"Prediction: {prediction}, Probabilities: {probabilities}")