Feature Selection is the process of identifying the most relevant variables from a dataset and removing features that provide little or redundant information. By keeping only useful features, machine learning models can focus on the data that contributes most to accurate predictions.
For example, a predictive maintenance model may have dozens of measurements, but only temperature, vibration, and operating hours may strongly indicate equipment condition. Selecting these important features can simplify the model, reduce processing requirements, improve accuracy, and make results easier to interpret.
