PRICE PREDICTION IN E-COMMERCE USING MACHINE LEARNING MODELS: A COMPARATIVE STUDY

Navneet Kaur

doi:10.29121/shodhkosh.v4.i1.2023.5942

Authors

Dr Navneet Kaur Professor, SGTBIMIT

DOI:

https://doi.org/10.29121/shodhkosh.v4.i1.2023.5942

Keywords:

E-Commerce, Price Prediction, Machine Learning, Random Forest, Regression Models, Dynamic Pricing, Predictive Analytics, Retail Forecasting

Abstract [English]

Accurate product price prediction is essential for inventory control, dynamic pricing strategies, and tailored suggestions in the ever changing world of online retail. A comparison of machine learning regression models for e-commerce final product price prediction is presented in this paper. Attributes at the product and transaction levels, such as category, base price, discount rates, payment methods, and selling price, are included in the dataset. Multilayer Perceptron (MLP) Regressor, Linear Regression, Decision Tree Regressor, and Random Forest Regressor were the four machine learning models that were assessed. R-squared (R²) and Root Mean Squared Error (RMSE) metrics were used to evaluate performance. According to experimental data, the Random Forest Regressor performed better than the other models, obtaining the lowest error and the maximum prediction accuracy. According to the results, ensemble-based methods provide useful insights for demand estimate and pricing automation, making them ideal for price forecasting in e-commerce applications

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