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Sentiment analysis of text data has become very much popular in past few years, because of the interesting challenges it can offer. Among these challenges, sarcasm is unique. We can address sarcasm as the 'Achilles heel ' of sentiment analysis. Detection of sarcasm from the given data is same time complicated and interesting. It is interesting because, if researchers can find the optimized solutions for finding sarcastic words in the data, it will enhance the sentiment analysis of that data. Even humans also have difficulties in understanding the actual interpretation of a sentence, if it is presented indirectly. This means, a person stated something, but he meant contradictory to the word meaning of the statement. This makes sarcasm detection an interesting topic for researchers. In this paper, we evaluated the performance of several machine learning models like Support vector machine, Naïve Bayes, Decision tree etc., and different ensemble models like Random Forest, XGBoost and AdaBoost etc., with the collaboration of various feature extraction methods such as Term Frequency-Inverse Document Frequency etc. The main evaluation metrics we used to evaluate the performance are accuracy, precision, f-score and recall. Based on the results, we concluded that the models such as XGBoost, LightGBM and Bagging classifiers provide better results in detecting sarcasm with respect to other machine learning models. [ABSTRACT FROM AUTHOR]