  Floods are one of nature's most catastrophic calamities which cause
irreversible and immense damage to human life, agriculture, infrastructure and
socio-economic system. Several studies on flood catastrophe management and
flood forecasting systems have been conducted. The accurate prediction of the
onset and progression of floods in real time is challenging. To estimate water
levels and velocities across a large area, it is necessary to combine data with
computationally demanding flood propagation models. This paper aims to reduce
the extreme risks of this natural disaster and also contributes to policy
suggestions by providing a prediction for floods using different machine
learning models. This research will use Binary Logistic Regression, K-Nearest
Neighbor (KNN), Support Vector Classifier (SVC) and Decision tree Classifier to
provide an accurate prediction. With the outcome, a comparative analysis will
be conducted to understand which model delivers a better accuracy.
