Python Machine Learning Project – Detecting Parkinson’s Disease with XGBoost

Objective and Information about this Python Machine Learning Project

To build a model which is to accurately identify whether a person is suffered from Parkinson’s disease or not. In this Python machine learning repository, a model is built by utilizing an XGBClassifier, the Python libraries scikit-learn, numpy, pandas, and xgboost. The steps are loading the data, getting the features and labels, scaling the features, splitting the dataset, building an XGBClassifier, and calculating the accuracy of the model.

Dataset for Python Machine Learning Project

This is the UCI ML Parkinsons dataset, and the dataset has 24 columns and 195 records (and it is only 39.7 KB.)

What is XGBoost?

XGBoost is a Machine Learning algorithm designed with both speed and performance. XGBoost stands for eXtreme Gradient Boosting and is based on decision trees. In this repository, the XGBClassifier is imported from the xgboost library, and the scikit-learn API for XGBoost classification is implemented.