Due to the drastic increase in usage of Internet and e-commerce, online shopping becomes very common in day to day lives. One of the popular e-commerce websites is Amazon, which allows users to post Reviews and Ratings regarding the purchased products. It finds useful for other customers and companies to improve the product quality. Since the manual extraction of sentiments from User Product Reviews is a tedious process, accurate and Automated Sentiment Analysis (SA) tools are essential. This research work aims to examine the performance of different Machine Learning (ML) models in analyzing the sentiments of Amazon Product Reviews. Initially, the Product Reviews are pre-processed in different ways to transform the data into a useful format. Besides, Term Frequency -Inverse Document Frequency (TF-IDF) vectorizer is used to derive Feature Embeddings. Finally, three Machine Learning (ML) models namely Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Support Vector Machines (SVM) are used for Sentiment Analysis (SA). The Performance Validation of the Machine Learning (ML) models is performed using Benchmark Dataset from Kaggle Repository. The Experimental Results reported that the Support Vector Machine (SVM) model has resulted in better Performance over the other Machine Learning (ML) models interms of Different Measures.