Application of xgboost algorithm for sales forecasting using walmart dataset

Jun 26, 2022 · This study explores how extreme gradient boosting (XGBoost) can be used to forecast sales.

call_split. Dec 5, 2021 · Next revised the Model using Akaike information criterion (AIC) Statistical algorithm. Experimental results show our method achieves superior performance over the other machine learning approaches. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised. To optimize the manufacturing process and therefore. Description: One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. . Next we have analysed the models for multicollinearity between the variables using VIF.

Modeling and comparing models are used by looking into the mean absolute error (MAE) and root mean squared error (RMSE) which are used in comparing model performance. auto_awesome_motion. Now we got a daily Sold Units table. Star 1. For modern retail corporations operating a huge chain of businesses, exact sales predication is the key in driving corporations development, even success or failure. Using this we obtained a model with adjusted R-squared of 0. Regression, Random Forest, Decision Tree, XGBoost these algorithms are suitable for sales forecast.

But even this is very low to be considered for deployment by bussiness for the forecasting sales.

The business is facing a challenge due to unforeseen demands and runs out of stock some times, due. Forecasts are at the heart of supply chain decisions, and they have never. This model performs well on sales prediction by utilising.

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