Georgia tech Study guides, Class notes & Summaries
Looking for the best study guides, study notes and summaries about Georgia tech? On this page you'll find 174 study documents about Georgia tech.
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Georgia tech ISYE 6501 Week 4 homework Exponential Smoothing Document Content and Description Below
- Exam (elaborations) • 96 pages • 2023
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Georgia tech ISYE 6501 Week 4 homework Exponential Smoothing 
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ISYE 6501 Week 4 homework Exponential Smoothing Exponential smoothing assists with change detection as it smoothes out the data. The benfits of exponential smoothing are that it gives you smoother data (less noisy data) and the ability to forecast using trends and seanolaity for time series data. Additionally, data can be made to be less noisy for more confidence in a CUSUM change detection mode...
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Georgia tech ISYE 6501 OAN - Homework Week 4, Questions and answers, Graded A+, 2022/2023
- Exam (elaborations) • 19 pages • 2023
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Georgia tech ISYE 6501 OAN - Homework Week 4, Questions and answers, Graded A+, 2022/2023 
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ISYE 6501 OAN - Homework Week 4 Contents 1 Question 7.1 1 2 Question 7.2 2 2.1 Set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Load and examine the data for this assignment . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.3 Convert temps data to vector . . . . . . . . . . . . . . . . . . . . . . . ...
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Georgia Tech Intro Analytics Modeling - ISYE-6501. 100% Accurate answers, rated A+
- Exam (elaborations) • 15 pages • 2023
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Georgia Tech Intro Analytics Modeling - ISYE-6501. 100% Accurate answers, rated A+ 
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Intro Analytics Modeling - ISYE-6501 Homework 7 Question 10.1 Using the same crime data set as in Questions 8.2 and 9.1, find the best model you can using (a) a regression tree model, and (b) a random forest model. In R, you can use the tree package or the rpart package, and the randomForest package. For each model, describe one or two qualitative takeaways you get from an...
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Georgia Tech Homework 2 Question 3.1, 100% Graded A+
- Exam (elaborations) • 7 pages • 2023
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Georgia Tech Homework 2 Question 3.1, 100% Graded A+ 
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Homework 2 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); and Using leave-one-out crossvalidation with different kernel for classification data <- ("credit_card_", header = TRUE, sep = "") # Splitting data for t...
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Georgia Tech SYE 6501 - Homework 2 Jacob Wilson September 6, 2018, 100% Pass rate
- Exam (elaborations) • 13 pages • 2023
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Georgia Tech SYE 6501 - Homework 2 Jacob Wilson September 6, 2018, 100% Pass rate 
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ISYE 6501 - Homework 2 Jacob Wilson September 6, 2018 Question 3.1(a) – KKNN with Cross Validation Answer: In this problem, I utilized the “caret” library and the train function with a “k knn” method to perform 10 -fold cross validation. The following is the results: The most accurate classification was at k=23 because the accuracy is the highest and the data is cl...
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GEORGIA Tech, ISYE Full course, Graded A+, 2022 update
- Exam (elaborations) • 85 pages • 2023
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GEORGIA Tech, ISYE Full course, Graded A+, 2022 update 
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Week 1 Why Analytics? 6 Data Vocabulary 7 Classification 8 Support Vector Machines 11 Scaling and Standardization 13 k-Nearest Neighbor (KNN) 13 Week 2 Model Validation 16 Validation and Test Sets 17 Splitting the Data 18 Cross-Validation 20 Clustering 21 Supervised vs. Unsupervised Learning 22 Week 3 Data Preparation 25 Introduction to Outliers 25 Change Detection 27 Week 4 Time Series Data 31 AutoRe...
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ISYE 6501- Homework 1 Michael Chapman August 23, 2018, Georgia Tech, Questions with accurate answers
- Exam (elaborations) • 11 pages • 2023
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ISYE 6501- Homework 1 Michael Chapman August 23, 2018, Georgia Tech, Questions with accurate answers 
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ISYE 6501- Homework 1 Michael Chapman August 23, 2018 Question 2.1 In my current job, I am responsible for understanding my company’s customer experience feedback when processing an insurance c laim on their mobile device. This feedback is typically in the form of a survey. The main metric we use to gauge customer satisfaction is Net Promoter Score (NPS)...
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Georgia Tech ISYE Midterm 1 Notes: Week 1 Classification:, Graded A+
- Exam (elaborations) • 14 pages • 2023
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Georgia Tech ISYE Midterm 1 Notes: Week 1 Classification:, Graded A+ 
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ISYE Midterm 1 Notes: Week 1 Classification: - Two main types of classifiers: o Hard Classifier: A classifier that perfectly separates data into 2 (or more) correct classes. This type of classifie r is rigid and is only applicable to perfectly separable datasets. o Soft Classifier: A classifier that does not perfectly separate data into perfectly correct classes. This type is used when a...
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Georgia Tech ISYE-ISYE-6501 Week 1 Assignment, 100% Accurate answers,
- Exam (elaborations) • 9 pages • 2023
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Georgia Tech ISYE-ISYE-6501 Week 1 Assignment, 100% Accurate answers, 
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ISYE-ISYE-6501 Week 1 Assignment Question 2.1 Describe a situation or problem from your job, everyday life, current events, etc., for which a classification model would be appropriate. List some pre dictors that you use. Designing user personas in product design developments The goal of user personas is to develop realistic representations of key audiences that give a clear picture of t...
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Georgia Tech WEEK 2 HOMEWORK – SAMPLE SOLUTIONS IMPORTANT NOTE, Rated A+, 2022.
- Exam (elaborations) • 7 pages • 2023
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Georgia Tech WEEK 2 HOMEWORK – SAMPLE SOLUTIONS IMPORTANT NOTE, Rated A+, 2022. 
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WEEK 2 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 s ubmit 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, ...
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