Predictive Data Analysis Using Linear Regression and Random Forest

Abstract

A statistical technique called predictive analysis (or analytics) makes use of machine learning and computers to find patterns in data and forecasts future actions. It is now preferred to go beyond descriptive analytics in order to learn whether training initiatives are effective and how they may be enhanced. Data from the past as well as the present can be used in predictive analysis to make predictions about what might occur in the future. Businesses can improve upcoming learning projects by taking actionable action after identifying the potential risks or possibilities. This chapter compares two predictive analysis models used in the predictive analysis of data: the Generalized Linear Model with Linear Regression (LR) and the Decision Trees with Random Forest (RF). With an RMSE (Root Mean Square Error) of 0.0264965 and an arithmetic mean for all errors of 0.016056967, Linear Regression did better in this analysis than Random Forest, which had an RMSE of 0.117875 and an arithmetic mean for all errors of 0.07062315. Through the hyper-parameter tuning procedure, these percentage errors can still be decreased. The combined strategy of combining LR and RF predictions, by averaging, nevertheless produced even more accurate predictions and will overcome the danger of over-fitting and producing incorrect predictions by individual algorithms, depending on the quality of data used for the training.