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In The Era Of E-commerce, Vast Amounts Of User-generated Product Reviews Provide Valuable Insights Into Customer Satisfaction And Product Quality. However, Extracting Meaningful Information From Such Unstructured Textual Data Remains A Major Challenge. This Study Focuses On Amazon Product Review Classification Using Natural Language Processing (NLP) Techniques And Logistic Regression As The Classification Model. The Proposed System Preprocesses Raw Textual Reviews Through Tokenization, Stop-word Removal, Stemming/lemmatization, And Feature Extraction Methods Such As Bag-of-Words And TF-IDF. Logistic Regression Is Then Employed To Classify Reviews Into Sentiment Categories (positive Or Negative), Enabling Automated Opinion Mining With High Interpretability. Furthermore, This Work Provides A Review Of Machine Learning-based Performance Prediction Systems, Highlighting The Role Of Various Algorithms In Improving Sentiment Analysis Tasks. By Comparing Logistic Regression With Other ML Models, The Study Emphasizes Its Simplicity, Efficiency, And Robustness In Handling Large-scale Review Data. The Findings Contribute To The Development Of More Reliable Recommendation Systems, Assisting Businesses In Decision-making While Enhancing Customer Experience.