Write a python program to cluster candidates into 2/3 groups. The program performs dimensionality reduction, then clustering of candidates based on top 2 PCs. The results are plotted to visually show the performance of candidates. The program performs the following tasks: • Read input "pca cluster.csv" file. Use Panda library. Example of the file: candl cand2 cand3 cand4 cand5 cad6 9. 3 experience education health interview 10 10 8. 10 8 10 4 6. 8. 9. 9. 9 6. 6. 8. test 4 6. 10 9. communication skills 6. 9. 10 10 writing skills team player 10 6. 6. 8. 7 4 10 9. 2. leadership enthusiasm ready to start flexible in hours able to relocate references 4 10 4 8 7 7 9. 6. 9 10 6. 7. 6. 6. 6 10 4 10 9. 4. • Apply PCA analysis to identify top 2 PCs. Eigen No. 0 Eigen value- (33.50+0j) Contribution % (40.87+0j) Points on new seale [5 5-8 30-5] Eigen No. 1 Eigen value (22.65+0j) Contribution % (27.63+0j) Points on new scale [0-4107-5] • Calculate the scores on top 2 PCs: PCI [5 5-8 3 0-5] PC2 | 0 -4 107-5j • Apply K-Means cluster algorithm to classify candidates into 2 clusters. Plot the results as shown (see below plot) • Repeat clustering and plot for K-3 (see below plot) • Finally, present discussion paragraph (about % page) to answer the following point: "If you are a hiring manger, how the this analysis help you to choose/narrow candidates"
Write a python program to cluster candidates into 2/3 groups. The program performs dimensionality reduction, then clustering of candidates based on top 2 PCs. The results are plotted to visually show the performance of candidates. The program performs the following tasks: • Read input "pca cluster.csv" file. Use Panda library. Example of the file: candl cand2 cand3 cand4 cand5 cad6 9. 3 experience education health interview 10 10 8. 10 8 10 4 6. 8. 9. 9. 9 6. 6. 8. test 4 6. 10 9. communication skills 6. 9. 10 10 writing skills team player 10 6. 6. 8. 7 4 10 9. 2. leadership enthusiasm ready to start flexible in hours able to relocate references 4 10 4 8 7 7 9. 6. 9 10 6. 7. 6. 6. 6 10 4 10 9. 4. • Apply PCA analysis to identify top 2 PCs. Eigen No. 0 Eigen value- (33.50+0j) Contribution % (40.87+0j) Points on new seale [5 5-8 30-5] Eigen No. 1 Eigen value (22.65+0j) Contribution % (27.63+0j) Points on new scale [0-4107-5] • Calculate the scores on top 2 PCs: PCI [5 5-8 3 0-5] PC2 | 0 -4 107-5j • Apply K-Means cluster algorithm to classify candidates into 2 clusters. Plot the results as shown (see below plot) • Repeat clustering and plot for K-3 (see below plot) • Finally, present discussion paragraph (about % page) to answer the following point: "If you are a hiring manger, how the this analysis help you to choose/narrow candidates"
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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