Executive Solution Overview
– ## Module05_Assignment.docx – credit_Dataset.arff.txt **## Module05_Assignment.docx** In this homework, we explore Naïve Bayes, K-Nearest Neighbors, and Support Vector Machine models. 1) (50 points) Use “credit_Dataset.arff” dataset and apply the Naïve Bayes, K-Nearest Neighbors, and Support V…
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– ## Module05_Assignment.docx
– credit_Dataset.arff.txt
**## Module05_Assignment.docx**
In this homework, we explore Naïve Bayes, K-Nearest Neighbors, and Support Vector Machine models.
1) (50 points) Use “credit_Dataset.arff” dataset and apply the Naïve Bayes, K-Nearest Neighbors, and Support Vector Machine technique using the WEKA tool in 2 different settings, including:
a. 10 fold-cross validation.
b. 80% training.
Write a short paragraph about your findings and compare the results (accuracy). Use a table or a bar chart graph (MS Excel) to visualize the results.
2) (25 points) Use “credit_Dataset.arff” dataset and apply the K-Nearest Neighbors with different K values (1, 3, 5, 15). Visualize the results corresponding to different K values using a bar chart graph.
3) (25 points) Use “credit_Dataset.arff” dataset and apply the Support Vector Machine using three different Kernels. Write a short paragraph about your findings and compare the results.
Deliverable:
• Your report including the screenshots of your implementation and the result.
**credit_Dataset.arff.txt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/credit-dataset-arff-txt-10399195)
**credit_Dataset.arff.txt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/credit-dataset-arff-txt-10399195)
**## Module05_Assignment.docx**
In this homework, we explore Naïve Bayes, K-Nearest Neighbors, and Support Vector Machine models.
1) (50 points) Use “credit_Dataset.arff” dataset and apply the Naïve Bayes, K-Nearest Neighbors, and Support Vector Machine technique using the WEKA tool in 2 different settings, including:
a. 10 fold-cross validation.
b. 80% training.
Write a short paragraph about your findings and compare the results (accuracy). Use a table or a bar chart graph (MS Excel) to visualize the results.
2) (25 points) Use “credit_Dataset.arff” dataset and apply the K-Nearest Neighbors with different K values (1, 3, 5, 15). Visualize the results corresponding to different K values using a bar chart graph.
3) (25 points) Use “credit_Dataset.arff” dataset and apply the Support Vector Machine using three different Kernels. Write a short paragraph about your findings and compare the results.
Deliverable:
• Your report including the screenshots of your implementation and the result.
**credit_Dataset.arff.txt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/credit-dataset-arff-txt-10399195)
**## Module05_Assignment.docx**
In this homework, we explore Naïve Bayes, K-Nearest Neighbors, and Support Vector Machine models.
1) (50 points) Use “credit_Dataset.arff” dataset and apply the Naïve Bayes, K-Nearest Neighbors, and Support Vector Machine technique using the WEKA tool in 2 different settings, including:
a. 10 fold-cross validation.
b. 80% training.
Write a short paragraph about your findings and compare the results (accuracy). Use a table or a bar chart graph (MS Excel) to visualize the results.
2) (25 points) Use “credit_Dataset.arff” dataset and apply the K-Nearest Neighbors with different K values (1, 3, 5, 15). Visualize the results corresponding to different K values using a bar chart graph.
3) (25 points) Use “credit_Dataset.arff” dataset and apply the Support Vector Machine using three different Kernels. Write a short paragraph about your findings and compare the results.
Deliverable:
• Your report including the screenshots of your implementation and the result.
**credit_Dataset.arff.txt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/credit-dataset-arff-txt-10399195)
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