Experimental study of ML techniques on different applications

Abstract

We live in the age of data, where everything around us is connected to a data source and everything in our lives is digitally recorded. Machine Learning has grown rapidly in recent years in the context of data analysis.ML usually provides systems with the ability to learn from experience automatically. In general, the efficiency of a machine learning algorithm depends on the nature and characteristics of the data. We have worked with six machine learning (MultiLinear Regression, Polynomial Regression Decision Tree, SVR, Random Forest, K-NN) and implementing different feature selection methodologies (Correlation coefficients, Mutual Information). We have used RMSE (Root Mean Square Error) and R2_Score to analyze our model’s performance. In two dataset Random Forest has given best result compared to all and Multi-Linear Regression has given the best result.