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@article{155006,
        author = {Shantanu Choudhary and Utkarsh Singh and Nikhil Saxena and Sameer Jain},
        title = {Big Mart Sales Prediction and Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {12},
        pages = {1003-1007},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155006},
        abstract = {Machine Learning is a technology that allows machines to become more accurate in predicting outcomes without being explicitly programmed for it. The basic premise of machine learning is to build models and deploy algorithms that can receive input data and use statistical analysis to predict an output while modifying outputs as the new data becomes available. These models can be used in different areas and trained to match the expectations so that accurate steps can be taken to achieve the organization’s target. In this paper, the case of Big Mart Shopping Centre has been discussed to predict the sales of different types of items and for understanding the effects of different factors on the sales of different items. Taking various features of a dataset collected for Big Mart, and the methodology followed for building a predictive model, results with high levels of accuracy are generated, and these observations can be used to take decisions to improve sales.},
        keywords = {Machine Learning, Sales Prediction, Big Mart, Random Forest, Linear Regression},
        month = {},
        }