Business Development by Using Prediction Model

Abstract

In this research, we investigated business development by using the performance of regression algorithms namely Simple Linear Regression, Multiple Linear Regression and Polynomial Regression for predicting the number of Investment growth in business. We utilized the information of two years of speculation of foundation to anticipate the quantity of legitimate interest in every voting public. Foreseeing pace of venture from the aggregate sum of speculation is extremely vital for foundation Data Analytics. From past venture of foundation and year esteems, we can foresee speculation development of benefit threw and from those qualities, we can without much of a stretch infer speculation turnout, which is important for establishment partners. A high or low contribute turnout means that how much development benefit and stream a sum in an establishment. We used excel to carry out the all the research work such as data preprocessing, feature selection, data analysis and evaluation. We found that Multiple Linear Regression is the best performer among Simple Linear Regression and Polynomial Regression both in terms of In-Sample and Out-of-Sample evaluation. We reached to the conclusion that multiple attributes can contribute to less error prone prediction.