UddeshyaSharma21 / repository

Parkinsons-Disease-Detection

In this Python Machine learning project, we will build a model using which we can accurately detect the presence of Parkinson’s disease in one’s body. 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, I have imported the XGBClassifier from the xgboost library; this is an implementation of the scikit-learn API for XGBoost classification. To build a model to accurately detect the presence of Parkinson’s disease in an individual. In this Python machine learning project, using the Python libraries scikit-learn, numpy, pandas, and xgboost, we will build a model using an XGBClassifier. I have loaded the data, get the features and labels, scale the features, then split the dataset, build an XGBClassifier, and then calculate the accuracy of our model.