• Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task.
• Regression models a target prediction value based on independent variables.
• It is mostly used for finding out the relationship between variables and forecasting.
• Different regression models differ based on – the kind of relationship between dependent and independent variables,they are considering and the number of independent variables being used.

Following are the type of regressions used in machine learning, which I have implemented.

• Simple Linear Regression
• Multivariate Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• ElasticNet Regression
• Logistic Regression

In machine learning, classification is a supervised learning concept which basically categorizes a set of data into classes.

• Classification is a process of categorizing a given set of data into classes, It can be performed on both structured or unstructured data.

A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes).

K-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new datapoints which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.

I have implemented Decision tree classifier and KNN classifier algorithms