R code

Python code

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Decision Tree

We have implemented the following algorithms of Decision Trees for comparison of accuracy

• CART
• C 4.5
• C 5.0

Acknowledgement

https://github.com/yoshihiko1218/COVID19ML

The dataset, initially consisting of 1485 instances * 51 variables were reduced down to 13 variables. There are many null values in the dataset which is hard to handle in a dataset where symptoms variables are mostly input as strings. We got rid of most of the string variables with a lot of null values. Even after cutting the dataset down to 13 variables, there were still a lot of null values remaining. We have replaced all the null values

• with "0" in the numeric variables
• with "unknown" in the string variables

We have taken (3/4)th of our Flu Classification Dataset to be used as Training Dataset & (1/3)rd to be used as Testing Dataset

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CART - Classification & Regression Trees

• depth = 8
• leaf nodes = 15

Confusion Matrix: Prediction on Test Dataset

Accuracy = 91.9 %

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Decision Tree (C 4.5)

• depth = 10
• leaf nodes = 24

Confusion Matrix: Prediction on Test Dataset

Accuracy = 92.2 %

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Decision Tree (C 5.0)

• depth = 12
• leaf nodes = 26

Confusion Matrix: Prediction on Test Dataset

Accuracy = 93.8 %

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