A Predictive Analysis for Sales Forecasting in Imbalanced Grocery Retail Datasets using Various Machine Learning Algorithms


  • K. Prabhakar
  • S. Suresh Kumar
  • P. Prem Delphy


In today’s date data analysis is need for every data analytics to examine the sets of data to extract the useful information from it and to draw conclusion according to the information. Data analytics techniques and algorithms are more used by the commercial industries which enables them to take precise business decisions. It is also used by the analysts and the experts to authenticate or negate experimental layouts, assumptions and conclusions. In this paper, prediction is based on groceries data sets by analyzing or exploring the big mart sales data set. Various machine learning and data extraction models are considered for prediction are linear regression, K-means, Apriori algorithm. Before prediction we have to explore and visualize the data because data exploration and visualization is an important stage of predictive modelling. The outcome of this paper is High quality analysis, more flexibility and power as compared to others.