Main Article Content
Machine learning is booming field which is growing rapidly in every discipline but developing countries are still spinning wheels in this sector. When we talk about India there are many supermarkets and marts emerging day by day but due to lack of exposure to technology and advancement it has been observed that there is no proper algorithm or technique used to visualize and manage sales and thus many supermarkets end up being in loss. This paper addresses the issue of the highly un-optimized and unpredictable market of groceries. The paper discusses of attempts to predict the sales of Chain of a Mega Supermarkets based on the historical sales data using Machine Learning, it attempts at finding hidden relations between different factors that can and might drive the sales of the super market up. This problem can be resolved my making a machine learning model that can explore, visualize and manipulate data and hence provide us with the requirements and statistics of the mart. Section 1 focusses on data mining and data pre-processing. The performance evaluations of various prediction algorithms using machine learning approaches are stated. Data visualization and patterns visualization is stated in section 2. Section 3 focuses on the different algorithms like KNN (K-nearest neighbor), Random Forest regression, Gradient Boost algorithm etc. and thus find the best fit for the data. Finally, the result is analyzed and concluded by research findings and future scope. paper addresses the issue of the highly un-optimized and unpredictable market of groceries.
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
CC Attribution-NoDerivatives 4.0