if you want the project pls call @8125424511
Detecting Parkinson’s Disease – Python Machine Learning Project
What is Parkinson’s Disease?
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.
What is XGBoost?
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.
Detecting Parkinson’s Disease with XGBoost –
Objective
To build a model to accurately
detect the presence of Parkinson’s disease in an individual.
Detecting Parkinson’s Disease with XGBoost – About
the Python Machine Learning Project
In this Python machine learning project, using the Python libraries scikit-learn,
numpy, pandas, and xgboost, we will build a model using an XGBClassifier. We’ll
load 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.
output :
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