Isye 6501 homework 2 - Study guides, Class notes & Summaries

ISYE-6501 – HOMEWORK WEEK #14 Question 19.1, Questions with accurate solutions, Graded A+ Document Content and Description Below ISYE-6501 – HOMEWORK WEEK #14 Question 19.1 Describe analytics models and data that could be used to make good recommendations to the retailer. How much shelf space should the company have, to ma ximize their sales or their profit? Of course, there are some restrictions – for each product type, the retailer imposed a minimum amount of shelf space required, and ...

ISYE 6501 Homework 12: Power Company Case In approaching the power company case study, I find it best to break the problem into three more discrete and manageable parts. For each of these parts we can then better define the specific data needs, model usage, and results for each individual part. Then, it is possible bring these individually addressed issues together to create a cohesive analysis for the case. The three primary addressable parts are the following: 1) Classifying a non-paying...

Week 2 Homework - Fall 2020 Ques%on 3.1 Using the same data set (credit_card_ or credit_card_) as in Ques%on 2.2, use the ksvm or kknn func%on to find a good classifier: I started by installing and running the “kknn” package. I then loaded the credit card data in to the “cd_info” variable. I then proceeded with the cross valida<on process using the “” func<on with a “k” value of “30”. (I had a small error, but was then able to run the code). I ended up with a ...

Question 3.1 Using the same data set (credit_card_ or credit_card_) as in Question 2.2, use the ksvm or kknn function to find a good classifier: (a) using cross-validation (do this for the k-nearest-neighbors model; SVM is optional); Cross Validation is a model evaluation method to see how well our model has performed and prevent over fitting. Supposed we receive a dataset with n data points, we do not use all these data points for training instead we split them into training data and tes...

NAME____________________________ ISYE 6501, Introduction to Analytics Modeling Midterm #2 – Friday, November 8, 2019 50 minute time limit INSTRUCTIONS • Work alone. Do not collaborate with or copy from anyone else. • Write all of your answers on the answer sheet. • You may use any of the following resources: o One sheet (both sides) of handwritten (not photocopied or scanned) notes o Scratch paper • If any question seems ambiguous, use the most reasonable interpretation (i.e....

ISYE-6501- Homework 2

ISYE 6501 WEEK 1 HOMEWORK – SAMPLE SOLUTIONS IMPORTANT NOTE These homework solutions show multiple approaches and some optional extensions for most of the questions in the assignment. You don’t need to submit all this in your assignments; they’re included here just to help you learn more – because remember, the main goal of the homework assignments, and of the entire course, is to help you learn as much as you can, and develop your analytics skills as much as possible! Q...

ISYE 6501 WEEK 1 HOMEWORK – SAMPLE SOLUTIONS IMPORTANT NOTE These homework solutions show multiple approaches and some optional extensions for most of the questions in the assignment. You don’t need to submit all this in your assignments; they’re included here just to help you learn more – because remember, the main goal of the homework assignments, and of the entire course, is to help you learn as much as you can, and develop your analytics skills as much as possible! ...

ISYE-6501 Week #13 Homework, Questions and answers, 100% Accurate. Document Content and Description Below ISYE-6501 Week #13 Homework 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 botto m-line question 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 m...

ISYE 6501 Module 15 Reference Material Slide 1 - Lesson 15.1 (M): Introduction to Optimization Document Content and Description Below ISYE 6501 Module 15 Reference Material Slide 1 - Lesson 15.1 (M): Introduction to Optimization In quite a few of the lesson in this class, we've seen various models that I've noted can be solved us ing optimization. You've probably heard me refer to optimization in lessons on classification. Clustering, regression, variable selection, time series analysis and...