Machine learning has secured a significant name in health care sector because of its ability of improving the accuracy
and time for disease prediction. Recently, Parkinson’s is a noteworthy chronic diseases worldwide. It is observed that more than
one million cases are common in India .There is the chances that 1.2 million people will be suffering with this diseases in the US
at the end of 2030. Thus, early stage prediction of people’s parkinson’s severity is important in order to make fast planning of
necessary treatment. In our study, we proposed a framework which will be helpful in real time prediction of parkinsons. We have
use UCI Machine learning repository dataset which contains the acoustic features of voice recordings. Dataset is divided into 8:2
ratio as train and test data respectively. We use four important machine learning classfication technique i.e. SVM, Logistic
regression, Extra tree classifier, Decision tree classifier for predicting parkinsons. 80% of the dataset is used to train the model
