Walmart Sales Prediction Based on Decision Tree, Random Forest, and K Neighbors Regressor

Abstract

Sales forecasting is a very important research direction in the business and academic fields, and sales forecasting methods are also in full bloom, such as time series model, machine learning model and deep neural network model. This paper will use three machine learning models: Decision Tree Regressor, Random Forest Regressor, and K Neighbors Regressor to predict Walmart Recruiting - Store Sales data. Using correlation, mean absolute error, and mean square error to evaluate the prediction results of these three models, it is found that the prediction effect of Random Forest Registrar performs the best of these three models. The R2 value between the predicted sales volume of Random Forest Regressor and the sales volume of the test set is 0.937, the average absolute error is 1937.810, and the mean square error is 32993323.634. Therefore, Walmart can use Random Forest Regressor when forecasting the weekly sales of its own stores. At the same time, this paper provides a good model reference value (especially Random Forest Regressor) for other industries when researching the sales forecast, as well as methods for evaluating different model predictions. Overall, these results shed light on guiding further exploration of Sales forecasts for supermarkets.

Analyzing the Temporal Interplay and Contribution of Socioeconomic, CO₂ Related Industry, and Education to the Year-on-Year Change in CO₂ Emissions: An In-Depth Analysis Using Machine Learning Approach

“…For the regressions approach, the performance of following regressors was compared: Linear Regression [10], Ridge Regression [11], Bagging Regressor [12], Random Forest Regressor [13], Gradient Boosting Regressor [14], XGBoost Regressor [15], AdaBoost Regressor [16] and KNeighbors Regressor [17] . Concerning classification, the performance of the following classifiers was compared: Logistic Regression (LogReg) [18], Decision Tree (DT) [19], Random Forest Classifier (RF) [20], XGBoost classifier (XGB) [21], Multi-Layer Perceptron classifier (MLP) [13], Bagging (BC) [22], AdaBoost (ABC) [23], Gradient Boosting (GB) [24], Support Vector (SVC) [25], Gaussian Naïve Bayes (GNB) [26].…”

Determinants of Yearly CO2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics

“…For the regressions approach, the performance of the following regressors was compared: Linear Regression [10], Ridge Regression [11], Bagging Regressor [12], Random Forest Regressor [13], Gradient Boosting Regressor [14], XGBoost Regressor [15], AdaBoost Regressor [16] and KNeighbors Regressor [17]. Concerning classification, the performance of the following classifiers was compared: Logistic Regression (LogReg) [18], Decision Tree (DT) [19], Random Forest Classifier (RF) [20], XGBoost classifier (XGB) [21], Multi-Layer Perceptron classifier (MLP) [13], Bagging (BC) [22], AdaBoost (ABC) [23], Gradient Boosting (GB) [24], Support Vector (SVC) [25], and Gaussian Naïve Bayes (GNB) [26].…”