Machine Failure Prediction using Machine LearningMachine Learning is used to have a more systematic approach to fault diagnosis to proactively identify issues and take actions before a machine's actual failure. In this implementation, the aim is to predict whether a machine would experience a failure based on parameters such as process temperature, rotational speed, etc. The tasks implemented using Fabric Notebook are as follows: Install custom libraries, Load and process the data, Train machine learning models and track experiments, Score the trained model, Select the best model, Load and Generate the model for predictions. NOTE: The below demo video is hosted on YouTube and incase you are unable to view this demo video then access has to be specifically provided.
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