PROG8430 – Data Analysis, Modeling and Algorithms Assignment 4 Clustering: K-Means DUE BEFORE APRIL 4, 2021; 10PM 1. Submission Guidelines All assignments must be submitted via the econestoga course...

Assignment


PROG8430 – Data Analysis, Modeling and Algorithms Assignment 4 Clustering: K-Means DUE BEFORE APRIL 4, 2021; 10PM 1. Submission Guidelines All assignments must be submitted via the econestoga course website before the due date in to the assignment folder. You may make multiple submissions, but only the most current submission will be graded. Late assignments will receive a penalty of 20%. SUBMISSIONS In the Assignment 4 Folder submit: 1. Your R Code 2. Your report in Word, following the template from previous lectures. PLEASE DO NOT SUBMIT ZIPPED FILES All variables in your code must abide by the naming convention [variable_name]_[intials]. For example, a variable I create for State would be State_DM. You may only use the ‘R’ packages discussed and demonstrated in class: 1. ggplot2 2. cluster 3. factoextra 4. dplyr THIS IS AN INDIVIDUAL ASSIGNMENT. UNAUTHORIZED COLLABORATION IS AN ACADEMIC OFFENSE. Please see the Conestoga College Academic Integrity Policy for details. 2. Grading This assignment will be marked out of 15 and is worth 5% of your total grade in the course. 3. Data Each student will be using one dataset: PROG8430_Assign_Clstr.Rdata 4. Background The data summarizes the expenses of randomly selected participants. Each column represents the percentage of income devoted each expense category. The data dictionary is in the Appendix. Your task is to use k-means clustering to segment these reviewers in to distinct clusters. Your work should follow the format of the sample report used previously. 5. Assignment Tasks Nbr Description Marks 1 Data Transformation 1. Standardize all of the variables using either of the two functions demonstrated in class. Describe why you chose the method you did. 1 2 Descriptive Data Analysis 1. Create graphical summaries of the data (as demonstrated in class: boxplots or histograms) and comment on any observations you make. 1 3 Clustering Using the K-Means procedure as demonstrated in class, create clusters with k=2,3,4,5,6. You will be using only two variables as your centroids (Food and Tran) 1. Create segmentation/cluster schemes for k=2,3,4,5,6. 2. Create the WSS plots as demonstrated in class and select a suitable k value based on the “elbow”. [NOTE – It is easiest to create this in Excel or some other spreadsheet program] 2 2 4 Evaluation of Clusters 1. Based on the “k” chosen above, create a scatter plot showing the clusters and colour-coded datapoints for each of “k-1”, “k”, “k+1”. For example, if you think the “elbow” is at k=4 create the charts for k=3, k=4 and k=5. 2. Based on the WSS plot (3.2) and the charts (4.1) choose one set of clusters that best describes the data. 3. Create summary tables for the segmentation/clustering scheme (selected in step 4.2). 4. Create suitable descriptive names for each cluster. 5. Suggest possible uses for this clustering scheme. 2 1 2 1 1 5 Professionalism and Clarity 2 APPENDIX ONE: DATA DICTIONARY Name Description ID Unique Identifier of each user Food Percentage of income spent on Food. Entr Percentage of income spent on Entertainment. Educ Percentage of income spent on Education. Trans Percentage of income spent on Transportation. Work Percentage of income spent on Work Related Expenses. Hous Percentage of income spent on Housing. Other Percentage of income spent on Other Expenses.