Beginners Guide To Linear Regression In Python | Developers Corner - Analytics India Magazine

Machine Learning is the scientific process of developing an algorithm that learns the pattern from training data and performs inferences on test data, explains Rajkumar Lakshmanamoorthy, A geek in Machine Learning with a Master's degree in Engineering.

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If a machine learning process is meant to predict some output value, it is called supervised learning. On the other hand, if there is no output value prediction, it is called unsupervised learning. 

Training data in supervised learning contains a set of features and a target. The machine learning algorithm learns from the features to map corresponding targets. Test data contains only features so that the model should predict the targets. Features and targets are also called independent variables and dependent variables, respectively. Training data in unsupervised learning contains only features but not any target. Rather than mapping features and targets as in supervised learning, an unsupervised learning model performs clustering (grouping) the input data based on the patterns among them.

Supervised learning is classified into two categories: 

1. Regression 
2. Classification

Supervised learning is called regression if the dependent variable (aka target) is continuous. Supervised learning is called classification if the dependent variable is discrete. In other words, a regression model outputs a numerical value (a real floating value), but a classification model outputs a class (among two or more classes).

In this article, we discuss linear regression and its implementation with python codes. Regression analysis can be specifically termed linear regression if the dependent variable (target) has a linear relationship with the independent variables (features)...

Wrapping Up

In this article, we have discussed machine learning, its classification, and categorization of supervised learning based on the nature of dependent variables. Further, we explored simple linear regression and multiple linear regression with examples using the SciKit-Learn library. We performed the same task with the statsmodels library and obtained the same results.

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Source: Analytics India Magazine