The most common method for determining positive or negative sentiment within a text is sentiment analysis. It is frequently utilized by businesses to grasp customer’s opinion regarding the products. A sentiment analysis examines message discussions and assesses the tone, expectation, and feeling behind each message. It is turning into a significant tool to notice and figure out the sentiment. It naturally analyzes whether a person is happy/frustrated/sad. It deals with the task of determining the difference during a document or a sentence and has gotten loads of consideration lately for national language. With the ascent of social media, a lot of data is available in provisional language other than English. Telugu is one such language with bountiful information accessible in social media, and it is difficult to look out labeled data of sentences for Telugu sentiment analysis. In this project, we differentiate positive and negative sentiment classes based on the polarity of code-mixed sentences, and the metrics are evaluated using machine learning approaches such as Naive Bayes, support vector machine, and recurrent neural network.