Identify and predict incorrect prices by Machine Learning Model
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Abstract
Electronic commerce (e-commerce) brings huge advantages to businesses for selling products through multiple online shops. However, companies have difficulties in supervising the prices of products set by different retail shops on e-commerce platforms. Addressing these difficulties, we suggest a method to identify and predict products that sell at incorrect prices using a machine learning model combined price analysis. The study uses four machine learning models: K-nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multinomial Naive Bayes (MNB) and two text-based information extraction methods: BoW and TF-IDF to find to the best method. The research results show that the RF model and text-based information extraction method by the BoW provide more average accuracy than other specific models, when experimenting on the filter dataset the average accuracy after 10 runs are RF: 98.06%, SVM: 83.92%, MNB: 92.21%, KNN: 94.06%. Experimental results on the product dataset have an accuracy of RF: 83.02%, SVM: 55%, MNB: 79.33%, KNN: 79.36%.
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