Muhammad Huzaifa Aftab completed Predictive Maintenance: Industrial ML for Fault Detection

Built a compact bearing-fault detector end to end: plotted a faulty vibration snapshot, used time-domain features including kurtosis, trained a shallow decision tree on training bearings only, and evaluated it on a held-out set with catch-rate and false-alarm rate. Also produced a maintenance memo defending a 0.50 alarm threshold with explicit cost trade-offs and a plain-language explanation for operations staff.

• Leakage-Free Model Training
• Honest Held-Out Evaluation
• Cost-Based Operational Decision-Making