Predictive maintenance is one of the highest-impact applications of data science

in industry — and one of the least saturated. As a mechatronics engineer with

real experience in industrial plant maintenance, I wanted to build a project that

reflects what actual failure analysis looks like, not just a generic ML exercise.

This is the EDA (Exploratory Data Analysis) phase of a full predictive maintenance

pipeline I'm building as part of my Master's in Data Science & AI.

The Dataset

I used the AI4I 2020 Predictive Maintenance dataset from the UCI Machine Learning

Repository — 10,000 records of synthetic industrial sensor data with 5 labeled

failure types: Tool Wear Failure (TWF), Heat Dissipation Failure (HDF), Power

Failure (PWF), Overstrain Failure (OSF), and Random Failure (RNF).

The dataset is highly imbalanced: only 3.39% of records are failures (339 out of

10,000).…