Parkinson's Disease

The data & attributes information are available at ics.uci.edu

The data-set consists of those diagnosed with Parkinson Disease and those who do not.

Exploratory Data Analysis

Analysis of Parkinson's Disease can be found here

Details of Data Attributes

Name Subject Name
MDVP:Fo (Hz) Average vocal fundamental frequency
MDVP:Fhi (Hz) Maximum vocal fundamental frequency
MDVP:Flo (Hz) Minimum vocal fundamental frequency
MDVP:Jitter (%)
MDVP:Jitter (Abs)
MDVP:RAP Five measures of variation in fundamental frequency
MDVP:PPQ
Jitter:DDP
MDVP:Shimmer
MDVP:Shimmer (dB)
Shimmer:APQ3
Shimmer:APQ5 Six measures of variation in amplitude
MDVP:APQ
Shimmer:DDA
NHR Two measures of ratio of noise to tonal components in the voice
HNR
RPDE Two nonlinear dynamical complexity measures
D2
DFA Signal fractal scaling exponent
Spread1
Spread2 Three nonlinear measures of fundamental frequency variation
PPE
Status Health status of the subject: one, Parkinson’s; zero, healthy

Libraries Used

  • Pandas
    • used for data analysing
    • easy to handle tabular data sets
  • Numpy
    • Numpy arrays are faster compared to Python Lists
  • matplotlib
    • Matplotlib is a Python 2D plotting library
  • seaborn
    • Seaborn is a Python data visualization library based on matplotlib
  • Scikit-Learn
    • provides implementations of a range of machine learning algorithms