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Create two Supervised Artificial Intelligent (AI) models to make predictions. Starting with the Naïve Bayes model described in Chapter 3, create two Supervised Bayes AI models. The first Supervised AI model is a document classifier based on internal organizational emails. The second Supervised AI model is a document classifier based on incoming customer emails.
Your first Bayes (AI) model must describe the organization’s internal mail system, the number of users, and if known, an estimate of the number of daily emails (volume). Next present the “training data” i.e. classified examples (words) in these documents to the training algorithm, so the Bayes (AI) model can classify new emails into these categories using its knowledge. As indicated in the textbook, input to Bayes (AI) models are the “training data” words indicating which are relevant/valued/etc. or irrelevant/unvalued etc. Your table listing of words must classify the word into organizationally relevant categories that you define e.g. maintenance complaint, IT problem, etc. (create training data words that are relevant to your organization).
Your second Bayes (AI) model must describe the organization’s external email system, does the organization have a web page script email system, can customer send emails to anyone directly and if known, an estimate of the number of daily emails (volume). Next present the “training data” i.e. classified examples (words) in these documents to the training algorithm, so the Bayes (AI) model can classify new emails into these categories using its knowledge. As indicated in the textbook, input to Bayes (AI) models are the “training data” words indicating which are relevant/valued/etc. or irrelevant/unvalued etc. Your table listing of words must classify the word into organizationally relevant categories that you define e.g. complaints, orders, reorders, returns, requests for more information, etc. (create training data word that is relevant to your organization).
