A machine learning driven approach to improve efficiency of classification algorithm using prediction of affliction

Abstract

Machine learning plays a key role in a wide range of applications such as data mining, natural language processing and expert systems. It provides a solution in all domains for further development when large data is applied. Supervised learning is consist of mathematical algorithm to optimize the functions with given inputs. Machine learning solves problems that cannot be solved by numerical values. In this research paper, a model is developed to improve classification algorithm using anxiety of juvenile. Prediction and classification are made using data. A machine learning tool is used for pre-processing and first level of model is data preparation and ranking prototype used for filtration of data . Then Probabilistic estimation hypothesis is to find the hypothesis value based on statistical functions and classification of anxiety predictor model is used for prediction and classification. Comparison of Algorithm and experimental are done using machine learning software. According to the experiment, the model is more efficient and accurate compared with other classification algorithm as results shown.