Q3. [2 pt] (submit to this assignment item) An analyst in a company's sales department collected...

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Q3. [2 pt] (submit to this assignment item) An analyst in a company's sales department collected data (Customers_class_tr.csv) from sampled customers in the company's customer database and a survey to build a prediction model for the levels of satisfaction of the new customers. For the prediction (classification), the analyst considers k-nearest neighbor methods for classification. Choose the model parameter k from a positive integer number, and make predictions for the new customers in (Customers_class_new.csv) after applying a standardization or normalization. Then, report the chosen k and the result by summarizing in the following table, each of whose element is for the count of the corresponding cases. Submit your R codes too. Predicted Satisfaction Level Very Dissatisfied Somewhat Dissatisfied Neutral Somewhat Satisfied Very Satisfied Actual Satisfaction Level Neutral Very Somewhat Dissatisfied Dissatisfied Somewhat Very Satisfied Satisfied Hint: Use table () function to summarize such counts from a data.frame for the actual and predicted categorical data. Q3. [2 pt] (submit to this assignment item) An analyst in a company's sales department collected data (Customers_class_tr.csv) from sampled customers in the company's customer database and a survey to build a prediction model for the levels of satisfaction of the new customers. For the prediction (classification), the analyst considers k-nearest neighbor methods for classification. Choose the model parameter k from a positive integer number, and make predictions for the new customers in (Customers_class_new.csv) after applying a standardization or normalization. Then, report the chosen k and the result by summarizing in the following table, each of whose element is for the count of the corresponding cases. Submit your R codes too. Predicted Satisfaction Level Very Dissatisfied Somewhat Dissatisfied Neutral Somewhat Satisfied Very Satisfied Actual Satisfaction Level Neutral Very Somewhat Dissatisfied Dissatisfied Somewhat Very Satisfied Satisfied Hint: Use table () function to summarize such counts from a data.frame for the actual and predicted categorical data.

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To solve this problem we can use the knearest ... View the full answer