An Efficient Approach of Feature Selection and Metrics for Analyzing the Risk of the Students Using Machine Learning

An eminent fortune in this world today is Data. A million bytes of data can be originated every day in every field of applications such as education, medical, business, government, organizations etc. In educational field the performance of the students is the midway so that it is essential to identify the risk of the students using predictive analytics. If the student risk is identified their performance can be predicted using machine learning algorithms. Machine Learning algorithms are the tools that process the data in an efficient aspect and play a multimodal feature in the field of Data Science, Artificial Intelligence, Predictive analytics etc. For data analytics the machine learning algorithms could not find the text, image or video. Hence it is essential to preprocess the data set which can be then used for analytics for identifying future inferences using Machine learning algorithms. A data set is a collection of samples or objects which can be characterized by the features called variables or attributes. The features are the key elements of the data sets through which the predictions can be obtained by selecting the specified features for correlated prediction. This paper expounds the working methods of data preprocessing and feature selection for predicting the student’s performance and also compares the metrics with its threshold value which would be used for future research work.