Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
3rd Edition
ISBN: 9781118729274
Author: Galit Shmueli, Peter C. Bruce, Nitin R. Patel
Publisher: WILEY
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Chapter 2, Problem 6P
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Given: A specific company made the training data by using the internal data including the purchase and demographic information. The future data to be arranged will include the demographic data of the list of purchases from other sources. In the training data, the predictor, “refund issued�, was a useful predictor.
To find: The explanation on why the predictor named “refund issued� in the training data cannot be considered as an appropriate variable to be incorporated in the model...
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A manager of a chain of 20 Sports Bars would like to be able to predict daily revenue for each restaurant based on several factors. Recognizing that each sports bar is different, the manager assigns each an id number from 1 to 20 . What is the problem with including that id number as an attribute in a model to predict daily revenue?
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The intent behind using the data is to predict fraudulent transactions accurately. The number of records used to train the model is exactly 1/6 of the entire annual transactions count, which was 312 million. The model is able to predict a total of 2400 records correctly as fraudulent , while it fails to sensitively predict the remaining 6600 fraudulent transactions correctly. The type 1 error count comes to be just a meagre 1% of the non-fraudulent cases present in the training data. Construct the confusion matrix using the above information. Assume fraudulent transactions as the positive class. Calculate Accuracy of the Trained Model, Misclassification rate, Precision, Sensitivity, F1-Score
Question 8
Each regression model can only have one dependent variable.
Question 8 options:
True
False
Chapter 2 Solutions
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
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