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@article{162572,
        author = {M. Bharath and P. Vanathi and V. Balaji and Harsh Kumar Pandey and M. Jeeva},
        title = {ELEVATE SALES PROFIT INSIGHT ANALYSIS USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {345-351},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162572},
        abstract = {The sales forecast is based on BigMart sales for various outlets to adjust the business model to expected outcomes. The resulting data can then be used to prediction potential sales volumes for retailers such as BigMart through various machine learning methods. The estimate of the system proposed should take account of price tag, outlet and outlet location. A number of networks use the various machine- learning algorithms, such as linear regression and decision tree algorithms, and an XGBoost regressor, which offers an efficient prevision of BigMart sales based on gradient. At last, hyperparameter tuning is used to help you to choose relevant hyperparameters that make the algorithm shine and produce the highest accuracy. Furthermore, leveraging advanced machine learning techniques enables the creation of robust models capable of capturing intricate patterns within BigMart's sales data. Through meticulous feature engineering, including variables like price, outlet size, location, and historical sales trends, these models can offer precise sales forecasts essential for informed decision-making. The incorporation of diverse algorithms, ranging from traditional linear regression to sophisticated ensemble methods like XGBoost, enhances the predictive prowess of the system. By harnessing the power of gradient boosting, the XGBoost regressor elevates forecasting accuracy by effectively capturing nonlinear relationships and interactions among variables.},
        keywords = {Machine Learning Algorithms, Prediction, Reliability, Sales forecasting, Prediction model, Regression.},
        month = {},
        }