Linear regression

Finally! We will explore our first true machine learning model. Linear regressions are a form of regression, which means that it is a machine learning model that attempts to find a relationship between predictors and a response variable and that response variable is, you guessed it, continuous! This notion is synonymous with making a line of best fit.

In the case of linear regression, we will attempt to find a linear relationship between our predictors and our response variable. Formally, we wish to solve for a formula of the following format:

• y is our response variable

• x_i is our ith variable (i^th column or i^th predictor)

• B₀ is the intercept

• B_i is the coefficient for the xi term

Let's take a look at some data before we go in-depth. This dataset is publically available and attempts to predict the number of bikes needed on a particular day for a bike sharing program:

# read the data and set the datetime as the index
# taken from Kaggle: https://www.kaggle.com/c/bike-sharing-demand...