To perform sentiment analysis, a number of machine learning and deep learning approaches were utilised in this paper to address the challenge of sentiment categorization on the Twitter dataset. Finally, on the Kaggle's public leaderboard, a majority vote ensemble approach has been used using 5 of our best models to get a classification accuracy of 83.58 percent. Various strategies for analysing sentiment in tweets (a binary classification problem) have been compared. The training dataset should be a CSV file with the following columns: tweet_id, sentiment, tweet, where tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed within "". For library requirements particular to some methods, such as Keras with TensorFlow backend for Logistic Regression, MLP, RNN (LSTM), and CNN for XGBoost, we used the Anaconda Python distribution. Preprocessing, baselines, Naive Bayes, Maximum Entropy, Decision Trees, Random Forests, Multi-Layer Perception, and other techniques that are implemented.