Predicting Customer Review Ratings in Fashion E-Commerce: A Machine Learning Approach with Enhanced Feature Engineering

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Jagruti Garg

doi:10.20944/preprints202510.1244.v1

October 16, 2025Open Access

Predicting Customer Review Ratings in Fashion E-Commerce: A Machine Learning Approach with Enhanced Feature Engineering

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Key Points

The LSTM model achieves superior predictive accuracy, significantly enhancing customer review rating forecasts in e-commerce.
Key metrics include an F1 score of 0.7134 for the LSTM, compared to just 0.47 for the Random Forest Classifier.
Advanced feature engineering techniques, particularly sentiment scores and demographic categorization, play a crucial role in the predictive framework.
Utilizing the Women's Clothing E-Commerce Reviews dataset demonstrates the effectiveness of deep learning in capturing nuanced consumer sentiment.

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Implication

This study employs machine learning to enhance prediction accuracy of customer review ratings in e-commerce, highlighting feature engineering and NLP techniques.

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Cite This Study

Jagruti Garg (2025) studied this question.

synapsesocial.com/papers/68f43f09854d1061a58ac94b https://doi.org/10.20944/preprints202510.1244.v1

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Predicting Customer Review Ratings in Fashion E-Commerce: A Machine Learning Approach with Enhanced Feature Engineering

Key Points

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Implication

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Cite This Study

Predicting Customer Review Ratings in Fashion E-Commerce: A Machine Learning Approach with Enhanced Feature Engineering

Key Points

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Implication

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