https://github.com/kevin-kip/py-linear-regression

A basic implementation of linear regression in python 3.6
https://github.com/kevin-kip/py-linear-regression

algorithms linear-regression machine-learning python python3-6

Last synced: about 1 year ago
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A basic implementation of linear regression in python 3.6

• Host: GitHub
• URL: https://github.com/kevin-kip/py-linear-regression
• Owner: Kevin-Kip
• Created: 2018-08-18T12:12:33.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-09-01T16:54:20.000Z (almost 8 years ago)
• Last Synced: 2025-01-23T21:18:06.409Z (over 1 year ago)
• Topics: algorithms, linear-regression, machine-learning, python, python3-6
• Language: Python
• Size: 8.79 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Linear Regression (in Python)

This is a basic implementation  of Linear Regression.

The basic formula is `y = mx + b`, where:

1. y is the output,

2. m is the slope of the line,

3. x is the input and

4. b is the y-intercept.

## Easy, Right?

Yes, Linear Regression is the most basic of all.

## Solving mysteries

Since `x` is what the user inputs, and `y` is what the algorithm spits out, "what is m and b" you may ask?

Well:

`m = sum of (eachX - meanOfX)*(eachY - sumOfY) / sum of(square of (eachX - meanOfX))`

`b = meanOfY - (m * meanOfX)`

## You wanna try code?

1. CLone download the repo

2. navigate to the root directory

3. run in terminal `python main.py`

You will be prompted to input a number and you will receive a prediction(float).