  Many pulsar folding algorithms are currently deployed to generate strong SNRs
for the total intensity profiles. But they require large observation times to
improve the SNR effectively. New approaches to de-noise the pulsar total
intensity data have sprung up over the years, powered by Machine learning and
Deep learning algorithms. In the current work, efforts are made to implement
the currently proposed supervised machine learning models, such as ensembling
techniques like Decision Tree Regressor, Random Forest Regressor, Adaboost
Regressor, Gradient Boosting Regressor (GBR), K-Nearest Neighbours(KNN), and
Support Vector Regressor (SVR) to find out the best possible algorithm which
can work over a variety of pulsars from the EPN database of pulsars. All the
data used in this work is extracted from the European Pulsar Network (EPN)
database of pulsar profiles. The training dataset is obtained by
post-processing the pulsar profile data from the EPN database hand testing is
performed on a preselected portion of the original data. The results are
obtained by testing the above algorithms for 10 different pulsars, including
some historically significant ones, and the predicted profiles are plotted. We
find that Gradient boosting regressor works the best in denoising pulsar data,
followed closely by KNN regressor. This work also emphasizes that there is a
reduction in the number of periods of folding by 35-40\% when a combination of
machine learning models with the existing pulsar folding techniques like Fast
Folding Algorithm(FFA) is employed, which in turn can further reduce the pulsar
observation times for the telescopes hunting for pulsars today.
