Machine Fault Prediction

Project overview

Comparing models for machine-status classification.

An ensemble-classification study for a strongly imbalanced machine-fault dataset. The workflow separates training, threshold selection and final evaluation, reporting fault-detection trade-offs rather than accuracy alone.

• Python
• Machine Learning
• Ensemble Learning
• Evaluation

Inside this build

• Prepared 20,000 records with 678 faults and 2,579 missing cells using training-fitted preprocessing.
• Combined decision tree, random forest, gradient boosting, AdaBoost, K-nearest-neighbour and logistic-regression estimators.
• Separated 12,000 training, 4,000 threshold-validation and 4,000 test records with a fixed random seed.
• Selected a 0.455 decision threshold by validation F1 across 181 candidates.
• Reported held-out precision 0.5932, recall 0.7721 and F1 0.6709, detecting 105 of 136 test faults with 72 false positives.
• Added 17 tests and documented the single-dataset, non-temporal evaluation limit; external-factory validation and calibration remain unestablished.

Source code & links