  Tropical cyclones are one of the most powerful and destructive natural
phenomena on earth. Tropical storms and heavy rains can cause floods, which
lead to human lives and economic loss. Devastating winds accompanying cyclones
heavily affect not only the coastal regions, even distant areas. Our study
focuses on the intensity estimation, particularly cyclone grade and maximum
sustained surface wind speed (MSWS) of a tropical cyclone over the North Indian
Ocean. We use various machine learning algorithms to estimate cyclone grade and
MSWS. We have used the basin of origin, date, time, latitude, longitude,
estimated central pressure, and pressure drop as attributes of our models. We
use multi-class classification models for the categorical outcome variable,
cyclone grade, and regression models for MSWS as it is a continuous variable.
Using the best track data of 28 years over the North Indian Ocean, we estimate
grade with an accuracy of 88% and MSWS with a root mean square error (RMSE) of
2.3. For higher grade categories (5-7), accuracy improves to an average of
98.84%. We tested our model with two recent tropical cyclones in the North
Indian Ocean, Vayu and Fani. For grade, we obtained an accuracy of 93.22% and
95.23% respectively, while for MSWS, we obtained RMSE of 2.2 and 3.4 and $R^2$
of 0.99 and 0.99, respectively.
