Detecting Parkinson Using extreme gradient boosting(XGBOOSTING) Algorithm.
Parkinson’s disease is a progressive disorder of the central nervous system affecting movement and inducing tremors and stiffness. It has 5 stages to it and affects more than 1 million individuals every year in India. This is chronic and has no cure yet. It is a neurodegenerative disorder affecting dopamine-producing neurons in the brain.
XGBoost is a new Machine Learning algorithm designed with speed and performance in mind. XGBoost stands for eXtreme Gradient Boosting and is based on decision trees. In this project, we will import the XGBClassifier from the xgboost library; this is an implementation of the scikit-learn API for XGBoost classification.
The dataset on which we would be working upon consists of 195 rows and 24 features/Columns. One can download it from here -> https://data-flair.training/blogs/python-machine-learning-project-detecting-parkinson-disease/.
With an overall accuracy of around 95% we have made inferences/Predictions about the presence of Parkinson's disease using various factors.