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A huge amount of textual data is generated due to the boom of microblogging. Microblogging sites such as Facebook, Twitter and Google+ are used by millions of people to express their views and emotions on different subjects. In this paper, we discuss sentiment analysis on a Twitter dataset having various tweets from different users. Sentiment analysis is useful for gaining the opinion of people using large volumes of text data where texts are highly unstructured and heterogeneous. In this paper, different classification techniques like Support Vector Machine, Logistic Regression, Logistic Regression with Stochastic Gradient Descent optimizer, Decision Tree Classification, Naive Bayes, Bidirectional LSTM and Random Forest Classification have been applied to analyze the sentiment of people, ie, whether their tweets are positive or negative. The corpus has been analyzed by plotting descriptive insights such as the word cloud and frequency of positive and negative tweets. The best classifier was selected by comparing the results of accuracy, recall, precision, F1 score, AUC score and ROC curve.