Logistic Regression

Logistic regression is a supervised learning algorithm used for binary classification tasks, where the model predicts the probability that a given input belongs to one of two classes, typically represented as 0 or 1, by fitting a logistic curve to the data.

K-Nearest Neighbors

K-Nearest Neighbors (KNN) is a supervised learning algorithm that predicts the class or value of a data point based on the majority class or average value of its k nearest neighbors in the feature space.

Random forest classifier

Random forest classifier is a supervised learning algorithm that builds multiple decision trees and combines their predictions through voting or averaging to classify input data into multiple classes.

Support Vector Classification

Support Vector Classification (SVC) is a supervised learning algorithm that separates data into classes by finding the hyperplane that best separates the classes in a higher-dimensional space. It aims to maximize the margin between classes while minimizing classification errors.

Naive bayes

Naive Bayes is a supervised learning algorithm based on Bayes' theorem, which predicts the class of a given input by calculating the probability of each class based on the input features. It assumes independence among features, hence "naive," and is particularly efficient for text classification tasks like spam detection or sentiment analysis.