Abstract:
In this research, we have investigated Cholera disease and its fatality rate from previous years in
terms of various countries. This disease is not new, yet researchers are currently utilising several
approaches to detect the difficulties and extract hidden information from previous records. This
study proposed an alternative solution in terms of Cholera disease. Several traditional machine
learning algorithms are experimented with to analyse and predict the disorder from the existing
dataset. The proposed research methodology has consisted of four phases, for instance, Research
Dataset, Data Preprocessing and Algorithm Selection. Our investigation shows that the Gradient
Boosting algorithm performs well in this type of dataset with an accuracy of 93%. We have
discovered the R2 score, Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean
Absolute Error (MAE) are 0.932841%, 398.1827%, 158549.4724%, and 71.6621098%,
respectively.
We have carried out an exploratory data analysis where Cholera case data analysis of Bangladesh
has been done from 1996 to 2000. In addition to the disease prediction, data analysis of different
countries has been carried out, and correlations have been made through which the
interrelationships of each indicator can be found very quickly.
