ThabangTheActuaryCoder/mining-equipment-failure-model

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}")