Classifying Twitter Feeds with Naive Bayes

Machine learning (ML) plays a major part in analyzing large datasets and extracting actionable insights from data. ML algorithms perform tasks such as predicting outcomes, clustering data to extract trends, and building recommendation engines. Knowledge of ML algorithms helps data scientists to understand the nature of data they are dealing with and plan what algorithms should be applied to achieve the desired outcomes from the data. Although there are multiple algorithms that can perform any task, it is important for data scientists to know the pros and cons of different ML algorithms. The decision to apply ML algorithms can be based on various factors, such as the size of the dataset, the budget for the clusters used for the training and deployment of ML models, and the cost of error rates. Although AWS offers a large number of options...