Introduction to Linear Regression Algorithm
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
Linear Regression is a statistical technique used as a machine algorithm to understand the relationship between a dependent and independent variable.
This is a supervised learning algorithm that is used in regression-based problem statements such as predicting future sales, future stock prices, etc.
It is a great algorithm for regression analysis, but it is not as powerful as other regression algorithms because it is affected by outlines as it assumes that the dataset is normally distributed without having any Outliners.
So, whenever you use linear regression, make sure that the dataset is normally distributed without having any outlines.
Assumption of Linear Regression
- Below are the assumptions of the linear regression algorithm that you should know:
There is a linear relationship between dependent and independent features.
All the features are multivariate normally.
There is very little or no multicollinearity in the dataset.
There is very little or no autocorrection in the dataset.
It also assumes that there is homoscedasticity in the dataset.
