Notes

Logistic regression
- supervised machine learning algorithm
- used for classification tasks
- goal is to predict the probability that instance belongs to a given class.
- from sklearn.linear_model import LogisticRegression

Decision tree
- one of the most powerful tools of supervised learning algorithms
- used for both classification and regression tasks
- It builds a flowchart-like tree structure where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label
- from sklearn.tree import DecisionTreeClassifier

Random Decision Forest
-is supervised Machine learning algorithm
- used for classification, regression, and other tasks using decision trees
- particularly well-suited for handling large and complex datasets
- from sklearn.ensemble import RandomForestClassifier

K-Nearest Neighbors (KNN) algorithm
- is a simple, easy-to-implement supervised machine learning algorithm
- used to solve both classification and regression problems
- The KNN algorithm assumes that similar things exist in close proximity. In other words, similar things are near to each other
- from sklearn.neighbors import KNeighborsClassifier

Support Vector Machine (SVM)
- a powerful machine learning algorithm
- used for linear or nonlinear classification, regression, and even outlier detection tasks.
- SVMs can be used for a variety of tasks, such as text classification, image classification, spam detection, handwriting identification, gene expression analysis, face detection, and anomaly detection
- from sklearn.svm import SVC

Clustering
- It is basically a type of unsupervised learning method
- is the task of dividing the population or data points into a number of groups
- The goal is to divide the population or set of data points into a number of groups so that the data points within each group are more comparable to one another and different from the data points within the other group
- from sklearn.cluster import KMeans