Parkinsons-Disease-Detection

A ML application to determine the probability of Parkinsons disease in a patient for the given parameters.

Dataset : UCI ML Parkinsons Disease Dataset : https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/

In this application of machine learning, we use different machine learning models like :

1. Logistic Regression
2. Linear Discriminant Analysis
3. K-Neighbours Classifier
4. Decision Tree Classifier
5. Gaussian Naive Bayes
6. Support Vector Machine

This is a classification problem where the algorithm has to decide if the patient has Parkinsons disease (y = 1) or not (y = 0)

We use python with sklearn, pandas and numpy packages in the algorithm

Finally we calculate the accuracy for every model by testing it on the cross-validation set and compare the results.