| Linear Regression | A basic algorithm predicting quantitative outcomes based on independent variables. |
| Logistic Regression | It’s used for binary classification problems, predicting a probability between 0 and 1. |
| K-Nearest Neighbours (KNN) | A multi-use algorithm that can perform both classification and regression. |
| Support Vector Machine (SVM) | An algorithm used typically for binary classification problems. |
| Decision Trees | An algorithm used for solving both regression and classification problems. |
| Random Forests | It uses ensemble learning method for classification, regression and other tasks. |
| Naive Bayes | Based on Bayes’ Theorem, this algorithm is particularly suited when the dimensionality of the inputs is high. |
| K-means Clustering | An iterative algorithm that divides a group of n datasets into k subgroups/clusters. |
| Gradient Boosting Algorithms (GBM) | A machine learning technique for regression and classification problems. |
| Principal Component Analysis (PCA) | A technique used to emphasize variation and bring out strong patterns in a dataset. |
| Artificial Neural Networks (ANN) | A computing system inspired by the biological neural networks that constitute animal brains. |
| Deep Learning | A class of machine learning algorithms that use artificial neural networks with multiple layers. |
| Time Series Algorithms | A family of algorithms designed specifically for use in time series data. |
| Hierarchical Clustering | A method of clustering where you build nested clusters by merging or splitting them successively. |
| Association Rule Learning | A rule-based machine learning method for discovering relationships between variables in large databases. |
| Ridge Regression | A technique for analyzing multiple regression data that suffer from multicollinearity. |
| Lasso Regression | A regression analysis method that performs both variable selection and regularization. |
| Elastic Net | A regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods. |
| Factor Analysis | A statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables. |
| Discriminant Analysis | It is used when the dependent variable is categorical and the independent variable is interval in nature. |
