Description
1. Build a decision tree by taking as input a maximum depth and by randomly splitting the
dataset as 80/20 split i.e., 80% for training and 20% for testing. Provide the accuracy by
averaging over 10 random 80/20 splits. Consider that particular tree which provides the
best test accuracy as the desired one. 30 marks
2. What is the best possible depth limit to be used for your dataset. Provide a plot
explaining the same. 20 marks
3. Perform the pruning operation over the tree obtained in question 2 using a valid
statistical test for comparison. 30 marks
4. Print the final decision tree obtained from question 3 following the hierarchical levels of
data attributes as nodes of the tree. 10 marks
5. A brief report explaining the procedure and the results 10 marks
Dataset:
1. COVID-19 percentage rate (aggregated):
It contains the time-series percentage increase in COVID-19 cases worldwide. The
attributes are date, confirmed cases, recovered cases, number of deaths, and increase
rate of confirmed cases. Target attribute is the rate of increase in confirmed cases.
Filename: PercentageIncreaseCOVIDWorldwide.csv
Submission instructions:
1. Submit your codes by implementing them only in PYTHON. No other programming
language is allowed. Do not use any library functions for building the decision tree.
2. Submit a README file which will contain the instructions on how to execute your code.
3. Submit a report briefly explaining the procedure and the results.
The source code, README file, and the report must be uploaded as a single compressed file
(.tar.gz or .zip). The compressed file should be named as: {Group_Number}_ML_A1.zip or
{Group_NUMBER}_ML_A1.tar.gz. Do not submit the data file within the compressed file.
