An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Table 1

Comparison of Machine learning approaches.

No.	Machine learning approaches	Description
1	Supervised learning	It uses previous data as input variable to predict the most probable output value for new data, depending upon those associations learned from the previous data sets.; (i) Regression is a form of predictive modeling technique which examines the relationship between a dependent variable and an independent variable.; (ii) Classification is the technique in which the algorithm learns from the data input given to it and then uses this learning to classify and produce new observation.
2	Unsupervised learning	It uses unlabeled data that have no historical labels to train the algorithm. The purpose is to find some structure within it by exploring the data.; (i) Clustering groups a set of objects in such a way that objects in the same group are more similar to each other in some respect than to those in other groups.; (ii) Dimensionality reduction removes useless data before analysis. This is used to remove redundant data and outliers.
3	Reinforcement learning	(i) With this approach, the algorithm discovers through trial and error which trials produce the best rewards.; (ii) It is often used for gaming, navigation, and robotics.
4	Deep learning	(i) Deep Learning helps in training computers to deal with the problems that are not well defined.; (ii) Deep learning and neural networks are often used in speech and image recognition applications.