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

• Author(s):

  Shivaathmajan P | Ayswaryaa V | Gautham Siddarth | Nithya Roopa S

• Keywords:

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

• 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.

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Other Details

• Paper id:

  IJSARTV12I5105491

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-25

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