ISYE 6501 Homework 12 Latest Update

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Homework 12 Question 18.1 Describe analytics models and data that could be used to make good recommendations to the power company. Here are some questions to consider: • The bottom-line questio... n is which shutoffs should be done each month, given the capacity constraints. One consideration is that some of the capacity – the workers’ time – is taken up by travel, so maybe the shutoffs can be scheduled in a way that increases the number of them that can be done. • Not every shutoff is equal. Some shutoffs shouldn’t be done at all, because if the power is left on, those people are likely to pay the bill eventually. How can you identify which shutoffs should or shouldn’t be done? And among the ones to shut off, how should they be prioritized? The main goal of this analysis is that we want to find which shutoffs should be done each month. To define which shutoff should be done or not, we need to find or to predict which customers will not pay. Thus, the first step in my approach will be to analyze the customer base of the company and try to define different groups of clients: - A group of regular and good payers - A group of non-regular payers I) Find the delinquent customers For this first classification, I would use logistic regression. Given: - Credit score - Income - Historic default on payment (all power companies) - Average time of bill payment (- x days before the due date or + x days after the due date) - Average monthly bill Use: Support Vector Machine To: Define the classification for each customer into 2 groups: “will pay” and “will not pay” the bills. Now, I will focus on the “will not pay” group defined. This study source was downloaded by 100000834091502 from CourseHero.com on 05-16-2022 06:47:13 GMT -05:00 https://www.coursehero.com/file/57498559/HW12power-companypdf/ In this group, there are also different sub-groups of potential non-payers with different profiles. And regarding these different profiles, the target for shutoff will not be the same because some of them will eventually pay: Profiles of non-payers Perspectives Target for shutoff Customers with variable incomes (seasonality) Eventually will pay late No Disorganized customers (forget everything!) Eventually will pay late No Customers with financial inability to pay Will not pay No, because of the special aid program Customers with financial ability to pay but don’t pay Will not pay Yes Then, on this group of customers, I want to extract the “will not pay” customers with Target “Yes”. Assuming there is a special program to help people with financial inability, I’ll be able to extract this special group using the criteria required to be in the program and set them aside. Then, on the rest of the group and based on the idea that some of them will pay after a while: Given: - Credit score - Income - Historic default on payment (all power companies) - Average time of bill payment (- x days before the due date or + x days after the due date) - Average monthly bill Use: Logistic Regression To: Define the probability that a customer will pay after a while (for example using the average time of bill payment when the due date [Show More]

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