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This paper gives a performance comparison in terms of Root mean square error (RMSE) of the six regression techniques used to predict the Parkinson disease severity score. People affected by Parkinson disease suffer various muscular impairments like gait, speech etc. The severity of the disease is generally assessed by the clinicians by observing the different muscular functions of the affected people or by performing scans of the brain. This paper focusses on predicting the disease severity using features of speech signal and performing regression on these features. The features used in the prediction are the phonation features extracted from voice samples of both Parkinson disease affected people and healthy people. The 14 phonation features extracted include the frequency variability features jitter and its other variants, the energy variability features shimmer and its other variants, the mean auto correlation of the pitch frequencies, harmonicity features harmonic to noise ratio and noise to harmonic ratio. The six regression techniques used to predict the severity score are the Linear, Stepwise, Lasso, Ridge regression, prediction using Neural network model and Classification and Regression trees (CART). The trained regression model is validated using the $k$-fold cross-validation method with $k$ values three, five, seven and ten and also using the hold out validation model in which the hold out value is taken to be 0.3. The results obtained from the six regression techniques is then compared and it shows that the severity score prediction using Neural network model provides the least RMSE of 1.5 followed by 1.8 using the CART regression technique.