Predictive Maintenance using Automated Machine Learning
Predicting Air Pressure System (APS) failures in Scania trucks using a TPOT AutoML pipeline
This classification task is based on the Industrial Challenge 2016 at The 15th International Symposium on Intelligent Data Analysis (IDA). It was published in the UCI Machine Learning Library and is available on Kaggle.
Results of the original challenge:
| Top 3 contestants | Score | Type 1 faults | Type 2 faults |
|---|---|---|---|
| Camila F. Costa and Mario A. Nascimento | 9920 | 542 | 9 |
| Christopher Gondek, Daniel Hafner and Oliver R. Sampson | 10900 | 490 | 12 |
| Sumeet Garnaik, Sushovan Das, Rama Syamala Sreepada and Bidyut Kr. Patra | 11480 | 398 | 15 |
Automated machine learning (AutoML)
TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Usage
This repository contains
- a module for preprocessing the data set preprocessing.py.
- a script for executing the TPOT process
- a notebook for evaluating the results of the TPOT process
Results
| Score | Type 1 faults | Type 2 faults |
|---|---|---|
| 11960 | 646 | 11 |