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Predicting Parkinson's disease using various machine learning models

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Parkinson_disease_detection.ipynb		Parkinson_disease_detection.ipynb
README.md		README.md

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Objective

To train and test various supervised and unsupervised machine learning models and compare the models

Models

Supervised Learning

Random Forest
Logistic Regression
Decision Tree
K-Nearest Neighbour
Support Vector Machine

Unsupervised Learning

K-Means Clustering
LGBoost
Perceptron
Gausian Naive Bayes
XGBoost

Dataset

Dataset has 195 rows and 24 columns
The status attribute is the dependent variable

Column
name
MDVP:Fo(Hz)
MDVP:Fhi(Hz)
MDVP:Flo(Hz)
MDVP:Jitter(%)
MDVP:Jitter(Abs)
MDVP:RAP
MDVP:PPQ
Jitter:DDP
MDVP:Shimmer
MDVP:Shimmer(dB)
Shimmer:APQ3
Shimmer:APQ5
MDVP:APQ
Shimmer:DDA
NHR
HNR
status
RPDE
DFA
spread1
spread2
D2
PPE