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Post by account_disabled on Dec 14, 2023 4:00:08 GMT -5
Group customers based on their purchasing behavior Find correlations in customer data (for example, someone who buys a certain type of bag might also be interested in a certain type of shoes) Segment data based on purchase history Classify people based on different interests Group inventory based on production and sales metrics Wood told us he worked for a pharmaceutical. Company with manufacturing facilities around the world. The software the company used to record errors found in its factories did not have a drop-down menu with the most common errors. For Job Function Email List this reason, employees documented errors in plain text files (in English or the local language). The company wanted to know the causes of the most frequent production problems, but without categorizing the errors it was impossible. Perform statistical analysis on the data. Wood used an unsupervised learning algorithm to discover the common characteristics of various errors. This allowed us to identify the most relevant areas and produce statistics in the form of graphs of the company's most common production problems. As Wood states: “This gave the company an immediate overview of the problems, which would otherwise have required a lot of manual work.
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