Tidal Wave Prediction Using Machine Learning

  • Mahika Godbole, Rucha Devshatwar, Pallavi Malekar, G.V. Raman Rao, Vivek Saxena


Machine Learning is an application of artificial intelligence where a computer/machine learns from the past experiences (input data) and makes future predictions. The performance of such a system should be at least human level. Machine Learning is generally categorized into three types: Supervised Learning, Unsupervised Learning, Reinforcement learning.

Supervised Learning: In supervised learning the machine experiences the examples along with the labels or targets for each example. The labels in the data help the algorithm to correlate the features. Two of the most common supervised machine learning tasks are classification and regression. Unsupervised Learning: When we have unclassified and unlabelled data, the system attempts to uncover patterns from the data. There is no label or target given for the examples.

Reinforcement Learning: Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps. As machine learning technologies improve, they can be adapted to compile a continuous stream of real-time data collected locally with available forecasts into ever evolving and improving machine learning model parameters.