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The most serious issue with code-mixing is that people switch between languages (for example, Malayalam and English) and type in English instead of writing Malayalam words. Traditional NLP models can’t handle data code-mixing data. Sentiment Analysis on Kannada-English, Malayalam-English, or Tamil-English code-mixed datasets based on five labels is the Dravidian Code-Mixed FIRE 2021 challenge. The classification is to be done based on the following labels’ Not-Malayalam," Neutral state,’Positive,’Mixed feelings,’Negative’. This paper focuses on Malayalam-English code-mixed data sentiment analysis based on the Ensemble voting model with machine learning models-Support Vector machine (SVM) and Logistic Regression and Bagging. The Hard Voting classifier model provided an accuracy: 67.78% and F1-score: 67.53%.