Fidelis Egbuna1 and Chukwuemeka Omerenna2, 1IBLT University, Togo, 2MasteryHive, United Kingdom
The aim is to analyze the sales of a supermarket as well as predict the impact of future sales on profit increase and customers satisfaction in the organization. The technique used for value of purchase is Linear Regression Algorithm, a widely acclaimed Algorithm in the field of Machine Learning. Linear Regression was compared with K- Nearest Neighbors Algorithm as well as with Gradient Descent and Random Forest. The actual data of the year, 2019, was compared to the predicted value and the accuracy of prediction calculated. The results testify to the trustworthiness and accuracy of the different prediction algorithms used. The study showed Random Forest as the best model with the prediction's highest accuracy. Random forests accuracy rate based on multiple decision trees and prediction taken from average output from various trees; generalized well and achieved a higher accuracy of 94% than other models.
Data Visualization, Prediction, Machine Learning, Linear Regression and K-Nearest Neighbors , Random forest and Gradient Descent and Radio Frequency Identification Tag.
