Predictive Analysis of Placement of Students Using Machine Learning Algorithm: Decision Tree

Abstract

Machine learning is one of the emerging trends which have proven to learn automatically from past data. Machine learning using particular algorithm called as classifier. Machine learning uses various algorithms to build mathematical model and making predictions using historical data. Machine learning can be made using supervised, unsupervised or reinforcement learning. To make prediction we can go for supervised learning where output will be in the form either yes or no, 0 or 1, in this case either student placed or not placed. Decision tree is one of the supervised machine learning algorithms in which leaf nodes represents the class label. Supervised learning technique that can be used for both classification and Regression problems, but it is it is preferred for solving Classification problems. Decision tree is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome (here either student get placed or not). In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches. It would really help to student, faculty as well as management to make their placement ratio high.