By Kartik Goel
In the Parkinson Disease classification, we are classifying whether a patient is having a disease or not by using different machine learning algorithm in Python.
Parkinson's Malady is a neurodegenerative medical issue which is common around the world and it results in movement and speech disorders. The clinical diagnosis by specialists involves neurological, psychological, and physical examinations.
For this Project, distinctive Python libraries were used, including, scikit-learn, NumPy, pandas, and xgboost to fabricate a model by utilizing XGBClassifier.
The dataset used for this project is UCI ML Parkinson’s dataset which has 24 columns and 195 records
The initial step of this project was to make every one of the vital imports for the project.Now the next stage was to add the data to a DataFrame .The fit_transform() fits to the information and afterward changes it. Then split the dataset into testing and training and went for twenty percent of the dataset as the testing and the remaining for the initial training. Then initiated a XGBClassifier and prepared the model. The last part create y_pred and to compute the accuracy of the machine learning model.
Submitted by Kartik Goel (6395)
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