# linear-regression-sklearn
**Repository Path**: liweiowl/linear-regression-sklearn
## Basic Information
- **Project Name**: linear-regression-sklearn
- **Description**: Multivariate linear regression with sklearn
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-08-26
- **Last Updated**: 2021-11-02
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# linear-regression-sklearn
2D and 3D multivariate regressing with sklearn applied to cimate change data
Winner of Siraj Ravel's [coding challange](https://youtu.be/vOppzHpvTiQ?t=7m31s)
## Overview
The notebook is split into two sections:
* 2D linear regression on a sample dataset [X, Y]
* 3D multivariate linear regression on a climate change dataset [Year, CO2 emissions, Global temperature].
Because of the small amount of data, and the random 10% of data chosen for testing, the scores have high variance.
2D Linear Regression | 3D Multivariate Linear Regression
:---: | :---:
R2 (Score): 0.651237006724 | R2 (Score): 0.968933216107
 | 
 | 
## Usage
Run the jupyter notebook `linear_regression.ipynb`
##Challenge
> The challenge for this video is to use scikit-learn to create a line of best fit for the included 'challenge_dataset'. Then, make a prediction for an existing data point and see how close it matches up to the actual value. Print out the error you get.
> Bonus points if you perform linear regression on a dataset with 3 different variables
## Dependencies
* matplotlib
* pandas
* numpy
* seaborn