Twitter sentiment analysis has come a long way in recent years, and many experiments have been conducted in this domain. Many machine learning techniques have been used for sentiment analysis in the past and will be utilized in the future to achieve more improved results. People nowadays express their sentimental emotions more openly since they have access to   forums where they can freely express themselves, hence this gives us an opportunity where sentimental analysis could be performed and its results can be used for various different objectives as it could give information about an individual’s emotional state. This work contributes to the sentiment analysis for customer review classification, which is useful for analyzing information in the form of a large number of tweets with highly unstructured thoughts that are either good or bad. Sentiment analysis is usually done in three ways: machine learning-based, sentiment lexicon-based, or a hybrid approach. The proposed approach for sentiment analysis consists of first pre-processing tweets and using a feature extraction method. Further, Logistic regression, SVM, Decision Trees (DTs), Random Forest (RF), and XBG Classifier are used for sentiment analysis classification in the proposed framework and then all the results are compared to find out the best techniques. Out of all the techniques used, SVM is working best with an accuracy of 95.21%.