In this slide, A Linear Regression model is implemented without relying on Python’s easy-to-use sklearn library. This post aims to discuss the fundamental mathematics and statistics behind a Linear Regression model. I hope this will help us fully understand how Linear Regression works in the background.

1. https://www.youtube.com/watch?v=EN8u8KtYWtM
Linear Regression From Scratch
Linear
Regression

2. Linear Regression Algorithm
Relationship between two or more variables
Independent Variable
Dependent Variable
Algorithm

3. Why Linear Regression from Scratch
Understanding the fundamentals
Math behind the algorithm
No dependency
Introduction

4. Statistical concepts used
Mean
Average of a data set
Add all numbers
Divide by the number of values

5. Statistical concepts used
Variance
Measure of the spread
Distance from Mean
Divide by the number of values

6. Statistical concepts used
Covariance
Two random variables
How variables vary together
Measure of the directional relationship

7. Linear Regression
Salary data with years of experience

8. Linear Regression
Equation of a line is Y = m * X + c

9. Linear Regression
Implementation Steps
Import the required libraries
Load the data
Visualize the data
Calculate the Mean
Calculate the variance
Calculate the covariance
Calculate the coefficients
Predict using the model

10. https://www.youtube.com/watch?v=EN8u8KtYWtM
https://medium.com/@dhiraj8899
Happy Learning
Thank You