- 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