license: mit
language:
- en
library_name: scikit-learn
pipeline_tag: tabular-classification
tags:
- predictive-maintenance
- classification
- scikit-learn
- tabular-data
metrics:
- accuracy
- precision
- recall
- f1
Predictive Maintenance Model
Overview
This repository contains the best-performing machine learning model developed for the predictive maintenance project.
Business Problem
The objective of this model is to classify whether an engine is operating normally or is likely to require maintenance based on sensor readings.
Input Features
- Engine_rpm
- Lub_oil_pressure
- Fuel_pressure
- Coolant_pressure
- lub_oil_temp
- Coolant_temp
Selected Model
AdaBoost
Evaluation Summary
{'Model': 'AdaBoost', 'Best_Parameters': "{'learning_rate': 0.05, 'n_estimators': 100}", 'CV_Best_F1': 0.7752, 'Test_Accuracy': 0.6304, 'Test_Precision': 0.6304, 'Test_Recall': 1.0, 'Test_F1': 0.7733}
Model Interpretation
The selected model was identified after comparing multiple tree-based algorithms using cross-validation and test-set evaluation.
Limitation
Although the selected model achieved the highest test F1-score, its confusion matrix shows that it predicted all observations as class 1. This means the model was very strong in identifying maintenance-required cases but weak in distinguishing normal operating cases.