Question
Sensor data can be used in predictive modeling in various ways. For instance, it can be used to predict equipment failure in industries by monitoring the condition of the equipment and predicting its failure based on the data collected. It can also be used in healthcare to predict patient health outcomes based on data collected from wearable devices. In agriculture, sensor data can be used to predict crop yields based on soil and weather conditions. In transportation, it can be used to predict traffic patterns and optimize routes.
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As an example, Berinato uses Tesla Motors' Data Scientist, Anmol Garg. Garg has used visual exploration to tap into the vast amount of sensor data Tesla cars produce. He developed an interactive chart that shows the pressure in a car's tires over time. "In true exploratory form, first created the visualizations and then found a variety of uses for them: to see whether tires are properly inflated when a car leaves the factory, how often customers reinflate them, and how long customers take to respond to a low-pressure alert; to find leak rates; and to do some predictive modeling on when tires are likely to go flat. The pressure of all four tires is visualized on a scatter plot, which, however inscrutable to a general audience, is clear to its intended audience," Berinato writes.
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