The case study introduces the learner to the concept of Clustering and focuses on explaining how clustering can be used to group the drivers with similar behavior based on telematics data of the vehicles.
The case introduces the learner to apply Data Pre-processing techniques for telematics data and prepares it before applying any machine learning technique.
Deep-Dive to the concept of data scaling, that generalizes data points so that the distance between them will be minimized.
The case exposes a learner to gain hands-on experience on how to detect and remove missing & outlier values that reduce the overall performance of the Machine learning model.
The case helps the learner to implement various techniques of clustering which looks for structures in the data and create groups such that data within a cluster are grouped in order to be more comparable to one another than to data from other clusters.
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Difficulty: Advanced
Duration: Approximately 3 hours
