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README.md

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+# Predictive Maintenance – Gradient Boosting Model
+
+## Model Overview
+This model is a recall-optimized Gradient Boosting classifier developed to support predictive maintenance for engine systems. The primary objective is to identify engines likely to require maintenance before failure occurs.
+
+## Training Data
+The model was trained on a prepared engine sensor dataset sourced from the Hugging Face Dataset Hub. The dataset contains structured numeric sensor readings representing engine operating conditions.
+
+## Objective
+- Minimize missed engine failures (false negatives)
+- Prioritize recall for the faulty engine class
+
+## Evaluation Metrics
+- Recall (Faulty): ~0.84
+- ROC-AUC: ~0.70
+- PR-AUC: ~0.80
+
+## Intended Use
+This model is intended for:
+- Predictive maintenance decision support
+- Risk-based maintenance scheduling
+- Offline or batch inference scenarios
+
+## Limitations
+- Trained on a static, pre-processed dataset
+- Does not incorporate temporal or sequential dependencies
+- Threshold selection may require calibration based on operational risk tolerance
+
+## Model Artifacts
+The repository contains a serialized `joblib` model file that can be loaded directly for inference in Python-based environments.