In this homework, we explore the WEKA machine learning tool and how WEKA can be very helpful in the small to medium size research project. To start, please
In this homework, we explore the WEKA machine learning tool and how WEKA can be very helpful in the small to medium size research project.
To start, please
1. Download and install the WEKA machine learning tool on your machine.
2. Download two datasets (bank, credit-Dataset).
3. Explore the WEKA machine learning tool using the aforementioned datasets and compare at least five different classifiers based on their performance metrics.
4. Submit your homework on D2L.
Deliverable:
· Your report should include screenshots of your implementation using WEKA. Ensure that you capture the entire WEKA environment, not just the results. Please compile the screenshots in a Word document, provide a brief one-sentence explanation for each, and then submit the document.
@relation bank | ||||||||
@attribute age numeric | ||||||||
@attribute sex {MALE | FEMALE} | |||||||
@attribute region {INNER_CITY | RURAL | TOWN | SUBURBAN} | |||||
@attribute income numeric | ||||||||
@attribute married {YES | NO} | |||||||
@attribute children {YES | NO} | |||||||
@attribute car {YES | NO} | |||||||
@attribute mortgage {YES | NO} | |||||||
@attribute pep {YES | NO} | |||||||
@data | ||||||||
48 | FEMALE | INNER_CITY | 17546 | NO | YES | NO | NO | YES |
40 | MALE | TOWN | 30085.1 | YES | YES | YES | YES | NO |
51 | FEMALE | INNER_CITY | 16575.4 | YES | NO | YES | NO | NO |
23 | FEMALE | TOWN | 20375.4 | YES | YES | NO | NO | NO |
57 | FEMALE | RURAL | 50576.3 | YES | NO | NO | NO | NO |
57 | FEMALE | TOWN | 37869.6 | YES | YES | NO | NO | YES |
22 | MALE | RURAL | 8877.07 | NO | NO | NO | NO | YES |
58 | MALE | TOWN | 24946.6 | YES | NO | YES | NO | NO |
37 | FEMALE | SUBURBAN | 25304.3 | YES | YES | YES | NO | NO |
54 | MALE | TOWN | 24212.1 | YES | YES | YES | NO | NO |
66 | FEMALE | TOWN | 59803.9 | YES | NO | NO | NO | NO |
52 | FEMALE | INNER_CITY | 26658.8 | NO | NO | YES | YES | NO |
44 | FEMALE | TOWN | 15735.8 | YES | YES | NO | YES | YES |
66 | FEMALE | TOWN | 55204.7 | YES | YES | YES | YES | YES |
36 | MALE | RURAL | 19474.6 | YES | NO | NO | YES | NO |
38 | FEMALE | INNER_CITY | 22342.1 | YES | NO | YES | YES | NO |
37 | FEMALE | TOWN | 17729.8 | YES | YES | NO | YES | NO |
46 | FEMALE | SUBURBAN | 41016 | YES | NO | NO | YES | NO |
62 | FEMALE | INNER_CITY | 26909.2 | YES | NO | NO | NO | YES |
31 | MALE | TOWN | 22522.8 | YES | NO | YES | NO | NO |
61 | MALE | INNER_CITY | 57880.7 | YES | YES | NO | NO | YES |
50 | MALE | TOWN | 16497.3 | YES | YES | NO | NO | NO |
54 | MALE | INNER_CITY | 38446.6 | YES | NO | NO | NO | NO |
27 | FEMALE | TOWN | 15538.8 | NO | NO | YES | YES | NO |
22 | MALE | INNER_CITY | 12640.3 | NO | YES | YES | NO | NO |
56 | MALE | INNER_CITY | 41034 | YES | NO | YES | YES | NO |
45 | MALE | INNER_CITY | 20809.7 | YES | NO | NO | YES | NO |
39 | FEMALE | TOWN | 20114 | YES | YES | NO | NO | YES |
39 | FEMALE | INNER_CITY | 29359.1 | NO | YES | YES | YES | NO |
61 | MALE | RURAL | 24270.1 | YES | YES | NO | NO | YES |
61 | FEMALE | RURAL | 22942.9 | YES | YES | NO | NO | NO |
20 | FEMALE | TOWN | 16325.8 | YES | YES | NO | NO | NO |
45 | MALE | SUBURBAN | 23443.2 | YES | YES | YES | NO | YES |
33 | FEMALE | INNER_CITY | 29921.3 | NO | YES | YES | NO | NO |
43 | MALE | SUBURBAN | 37521.9 | NO | NO | NO | NO | YES |
27 | FEMALE | INNER_CITY | 19868 | YES | YES | NO | NO | NO |
19 | MALE | RURAL | 10953 | YES | YES | YES | NO | NO |
36 | FEMALE | RURAL | 13381 | NO | NO | YES | NO | YES |
43 | FEMALE | TOWN | 18504.3 | YES | NO | YES | NO | NO |
66 | FEMALE | SUBURBAN | 25391.5 | NO | YES | NO | NO | NO |
55 | MALE | TOWN | 26774.2 | YES | NO | NO | YES | YES |
47 | FEMALE | INNER_CITY | 26952.6 | YES | NO | YES | NO | NO |
67 | MALE | TOWN | 55716.5 | NO | YES | YES | NO | YES |
32 | FEMALE | TOWN | 27571.5 | YES | NO | YES | YES | NO |
20 | MALE | INNER_CITY | 13740 | NO | YES | YES | YES | NO |
64 | MALE | INNER_CITY | 52670.6 | YES | YES | NO | YES | YES |
50 | FEMALE | INNER_CITY | 13283.9 | NO | YES | YES | NO | YES |
29 | MALE | INNER_CITY | 13106.6 | NO | YES | NO | YES | YES |
52 | MALE | INNER_CITY | 39547.8 | NO | YES | YES | NO | YES |
47 | FEMALE | RURAL | 17867.3 | YES | YES | YES | NO | NO |
24 | MALE | TOWN | 14309.7 | NO | YES | YES | NO | NO |
36 | MALE | TOWN | 23894.8 | YES | NO | NO | NO | NO |
43 | MALE | TOWN | 16259.7 | YES | YES | NO | NO | YES |
48 | MALE | SUBURBAN | 29794.1 | NO | YES | NO | NO | YES |
63 | MALE | TOWN | 56842.5 | YES | NO | NO | YES | NO |
52 | FEMALE | RURAL | 47835.8 | NO | YES | NO | NO | YES |
58 | FEMALE | INNER_CITY | 24977.5 | NO | NO | NO | NO | YES |