Data Analytics Made Accessible

by Anil Maheshwari

Here are brief descriptions of some of the most important data mining techniques used to generate insights from data. Decision Trees: They help classify populations into classes. It is said that 70% of all data mining work is about classification solutions; and that 70% of all classification work uses decision trees. Thus, decision trees are the most popular and important data mining technique. There are many popular algorithms to make decision trees. They differ in terms of their mechanisms and each technique work well for different situations. It is possible to try multiple decision-tree algorithms on a data set and compare the predictive accuracy of each tree. Regression: This is a well-understood technique from the field of statistics. The goal is to find a best fitting curve through the many data points. The best fitting curve is that which minimizes the (error) distance between the actual data points and the values predicted by the curve. Regression models can be projected into the future for prediction and forecasting purposes. Artificial Neural Networks: Originating in the field of artificial intelligence and machine learning, ANNs are multi-layer non-linear information processing models that learn from past data and predict future values. These models predict well, leading to their popularity. The model’s parameters may not be very intuitive. Thus, neural networks are opaque like a black-box. These systems also require a large amount of past data to adequate tra...