#2020-07-18 11:38:13
Linear Regression has been utilized so heavily for many decades and they continue to find room in the age of modern data science and Machine Learning.
With so many extensions and a majorly fundamental statistical base, Ordinary Least Squares or OLS offers a glance to the world of Linear Models.
Accurate, fast, flexible, statistical and scalable. On the flip side Linear Regression struggles with anything non-linear.
#SUPERVISED
#REGRESSION
Linear Regression
- Dataset: Large and Small
- Speed: Fast
- Ease of Use: Easy
- Normalization: No
- Predictor: Numeric
- Primary Problem: Binary
- Mixed-type: Yes
- Missing Data Handling: Yes
- Popularity: 50%
LINEAR REGRESSION tutorials in python
1- Simple Implementation
A simple implementation of Linear Regression (Ordinary Least Squares) with sklearn library in Python.
2- Step by Step
This Linear Regression tutorial demonstrates every step involved in simple implementation of OLS in Scikit-learn. It can be great if you're new to coding or if you'd like to understand the steps a little better.
3- Optimization
Learn about important parameters when working with Linear Regression Algorithms and how to optimize them.
4- Pros & Cons
Pro and Con list of Linear Regression Algorithms.
5- History
Read about the history of Linear Regression Algorithm.