  We introduce a new machine learning based technique to detect exoplanets
using the transit method. Machine learning and deep learning techniques have
proven to be broadly applicable in various scientific research areas. We aim to
exploit some of these methods to improve the conventional algorithm based
approaches presently used in astrophysics to detect exoplanets. Using the
time-series analysis library TSFresh to analyse light curves, we extracted 789
features from each curve, which capture the information about the
characteristics of a light curve. We then used these features to train a
gradient boosting classifier using the machine learning tool lightgbm. This
approach was tested on simulated data, which showed that is more effective than
the conventional box least squares fitting (BLS) method. We further found that
our method produced comparable results to existing state-of-the-art deep
learning models, while being much more computationally efficient and without
needing folded and secondary views of the light curves. For Kepler data, the
method is able to predict a planet with an AUC of 0.948, so that 94.8 per cent
of the true planet signals are ranked higher than non-planet signals. The
resulting recall is 0.96, so that 96 per cent of real planets are classified as
planets. For the Transiting Exoplanet Survey Satellite (TESS) data, we found
our method can classify light curves with an accuracy of 0.98, and is able to
identify planets with a recall of 0.82 at a precision of 0.63.
