Submission for "How to Make a Prediction - Intro to Deep Learning #1' by Siraj Raval on YouTube

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

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

• matplotlib
• pandas
• numpy
• seaborn