Data on the amount of the customers' shopping by using an account card and whether they decided to upgrade their account from silver status to platinum status after receiving the upgrade offer is shown in Table 2. Construct the k nearest neighbors scheme for predicting
Data on the amount of the customers' shopping by using an account card and whether they decided to upgrade their account from silver status to platinum status after receiving the upgrade offer is shown in Table 2. Construct the k nearest neighbors scheme for predicting
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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- Data on the amount of the customers' shopping by using an account card and whether they decided to upgrade their account from silver status to platinum status after receiving the upgrade offer is shown in Table 2.
- Construct the k nearest neighbors scheme for predicting upgrades based on the data given in Table 2, and interpret the confusion matrix.
- According to the data given in part 1, estimate the probability of upgrade for each division of the purchase amount, using Bayes' theorem.
Please provide your work in detail and include in-text citations. At least five (5) references are required for PA’s and CLA’s
Transcribed Image Text:Below is the transcription of the tabulated data suitable for inclusion on an educational website. Each column represents specific data fields, which are likely part of a dataset used for analysis or research purposes.
**Table: Dataset Overview**
| UpGrade | Purchases | PlatProfile | RowNear 1 | RowNear 2 | RowNear 3 | RowNear 4 |
|---------|-----------|-------------|-----------|-----------|-----------|-----------|
| 0 | 7.471 | 0 | 7 | 40 | 26 | 29 |
| 0 | 21.142 | 0 | 33 | 10 | 26 | 37 |
| 1 | 39.925 | 1 | 17 | 19 | 35 | 2 |
| 1 | 32.450 | 1 | 32 | 22 | 39 | 4 |
| 1 | 48.950 | 1 | 34 | 1 | 32 | 11 |
| 0 | 28.520 | 1 | 35 | 20 | 32 | 4 |
| 0 | 7.822 | 0 | 40 | 1 | 26 | 29 |
| 0 | 26.548 | 0 | 33 | 6 | 40 | 19 |
| 1 | 48.831 | 1 | 5 | 11 | 31 | 24 |
| 1 | 49.820 | 1 | 2 | 3 | 32 | 15 |
| 1 | 46.294 | 0 | 21 | 18 | 6 | 23 |
| 0 | 50.450 | 0 | 1 | 25 | 31 | 3 |
| 0 | 28.175 | 0 | 36 | 8 | 34 | 24 |
| 0 | 16.
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