Data Analytics Made Accessible

by Anil Maheshwari

There are many benefits of using ANN. ANNs impose very little restrictions on their use. ANN can deal with (identify/model) highly nonlinear relationships on their own, without much work from the user or analyst. They help find practical data-driven solutions where algorithmic solutions are non-existent or too complicated. There is no need to program neural networks, as they learn from examples. They get better with use, without much programming effort. They can handle a variety of problem types, including classification, clustering, associations, etc. ANN are tolerant of data quality issues and they do not restrict the data to follow strict normality and/or independence assumptions. They can handle both numerical and categorical variables. ANNs can be much faster than other techniques. Most importantly, they usually provide better results (prediction and/or clustering) compared to statistical counterparts, once they have been trained enough.