The digitalization era witnesses Teach driving platforms like social media, Ott, shopping, & food delivery. Yet, ensuring seamless functionality poses challenges. Customer reviews express opinions. This study explores machine learning for sentiment analysis, particularly on Twitter. It scrutinizes models, methods, & algorithms, emphasizing a robust sentiment analysis framework for automatic tweet classification. The study delves into Natural Language Processing terms like stop-word removal, tokenization, lemmatization, stemming, Part of Speech tagging & casing. ML algorithms enable comprehensive sentiment classifier comparisons. Results offer insights into suitable algorithms for Twitter sentiment analysis, empowering data-driven decisions to enhance customer satisfaction. Ongoing sentiment analysis research in the evolving landscape of customer reviews on social media is crucial, contributing to Data Science knowledge.