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Volume 12, Issue 5 (May 2026)

Buysense: A Dual-model Approach For Purchase Viability Prediction Using Amazon Review Data

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7.883
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Volume 12 Issue 07

July 2026

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Author(s)

Shivaathmajan P Ayswaryaa V Gautham Siddarth Nithya Roopa S

Abstract

This Paper Presents The BuySense, A Machine Learning Application That Predicts Whether A Product Is Worth Purchasing Based On Amazon Review Text Data. The System Builds Two Independent Binary Classifiers—a Viability Model And A Regret Model—trained On The Amazon Review Polarity Dataset. Both Models Use A Text Vectorization Layer Combined With An Embedding And GlobalAveragePooling1D Architecture. The Trained Models Are Deployed In A Streamlit Web Application That Scrapes Amazon Product Pages In Real Time, Extracting Titles, Descriptions, And Structured Content To Generate Buy, Wait, Or Avoid Recommendations. This Dual-model Design Enables Nuanced Purchase Guidance Beyond Simple Positive/negative Polarity Classification.

Keywords

Machine Learning Sentiment Analysis Binary Classification Amazon Reviews Streamlit Natural Language Processing Purchase Recommendation Neural Networks

Paper ID

IJSARTV12I5105491

Publication Date

May 25, 2026

Research Area

IT

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