Linear Regression Fram scratch
Let's start with simplest case of linear regression which is one dependent variable and one independent variable, simple linear regression. The equation of simple linear regression is given by:
\[
y(x) = a + bx
\]
Dataset
| index | feature | target |
|---|---|---|
| 0 | -0.469474 | -9.23956 |
| 1 | -0.234153 | -3.44105 |
| 2 | -0.234137 | -5.78107 |
| 3 | -0.138264 | -3.43807 |
| 4 | 0.496714 | 8.46012 |
| 5 | 0.54256 | 7.72548 |
| 6 | 0.647689 | 9.16405 |
| 7 | 0.767435 | 13.2115 |
| 8 | 1.52303 | 28.6429 |
| 9 | 1.57921 | 28.286 |
Since in the dataset we have y: target and x: feature, the goal is to find the best values for a and b.