1. Logistic Regression: It is a very common machine learning algorithm used in binary classification. It is a simple algorithm which can be used as a performance baseline.
2. Linear Regression: Linear Regression is one of the easiest algorithms in machine learning and data analysis. This algorithm methodology is used mostly for forecasting and finding out cause and effect relationship between data variables.
3. K-Nearest Neighbor Algorithm (KNN): K-Nearest Neighbor Algorithm is one of the most used learning algorithms for classification and regression. Its purpose from a database is to read the data points which are separated into several classes and then predict the new sample point classification.
4. NaÃ¯ve Bayes Classifier Algorithm: Naive Bayes classifier is a very straight and powerful algorithm for the classification task. It gives great results when used for textual data analysis. (natural language processing)
5. K-Means Clustering Algorithm: K-Means is one of the most popular “clustering” algorithms. It is an unsupervised learning used in unlabelled data sources.
6. Support Vector Algorithm: Support Vector Algorithm is a supervised machine learning algorithm which can be used for both classification or regression problems. However, it is mostly used in classification cases.
7. Decision Trees: Decision Tree belongs to the family supervised learning algorithms. The main function of Decision Tree is to create a training model which can be used to predict class or value of target variables by learning decision rules inferred from given data.