re·gres·sion
/rəˈɡreSH(ə)n
noun
1. a return to a former or less developed state.
2. STATISTICS a measure of the relation between the mean value of one variable (e.g., output) and corresponding values of other variables (e.g., time and cost).
Regression analysis is commonly cited in the data science community (and science in general), a building block of statistics, and routinely referenced within the rapidly growing machine learning movement. So what is this mysterious math sorcery? Did Isaac Newton use regression analysis? These magical regressions seem important. We shall dig deeper. Here’s what I’ve found, thanks to MIT News, circa 2010:
To grasp the basic concept, take the simplest form of a regression: a linear, bivariate regression, which describes an unchanging relationship between two (and not more) phenomena. Now suppose you are wondering if there is a connection between the time high school students spend doing French homework, and the grades they receive. These types of data can be plotted as points on a graph, where the xaxis is the average number of hours per week a student studies, and the yaxis represents exam scores out of 100. Together, the data points will typically scatter a bit on the graph. The regression analysis creates the single line that best summarizes the distribution of points.
Ok, so it’s correlationary tool. Sounds useful. Additionally, consider the mathematical equation representation of regressions:
Mathematically, the line representing a simple linear regression is expressed through a basic equation: Y = a_{0} + a_{1} X. Here X is hours spent studying per week, the “independent variable.” Y is the exam scores, the “dependent variable,” since — we believe — those scores depend on time spent studying. Additionally, a_{0} is the yintercept (the value of Y when X is zero) and a_{1} is the slope of the line, characterizing the relationship between the two variables. (source: MIT News)
Geesh, that’s kinda dense. But can we do it with Python? Ah, yes we can. Below is the amassed code from Towards Data Science to run a basic regression that generates predictions from a Boston house values dataset within scikit learn.
pip install sklearn and pandas first, by entering in the terminal:

pip install U scikitlearn

python m pip install pandas
Now run copy this code, save as a .py file and run from your terminal or command prompt:
from sklearn import linear_model from sklearn import datasets ## imports datasets from scikitlearn import pandas as pd data = datasets.load_boston() ## loads Boston dataset from datasets library # define the data/predictors as the preset feature names df = pd.DataFrame(data.data, columns=data.feature_names) # Put the target (housing value  MEDV) in another DataFrame target = pd.DataFrame(data.target, columns=["MEDV"]) X = df y = target["MEDV"] lm = linear_model.LinearRegression() model = lm.fit(X,y) predictions = lm.predict(X) print(predictions[0:5]) # print the first 5 predictions for y lm.score(X,y) # This is the R² score of our model. As you probably remember, this the percentage of explained variance of the predictions. lm.coef_ # check coefficients lm.intercept_ # More info: https://towardsdatascience.com/simpleandmultiplelinearregressioninpythonc928425168f9
source: Towards Data Science
The above example is one of many predictive models. Logistic regressions and random forests are examples of other models. This book dives deeper into them.
So there you have it: regressions… part correlation measurement tool between two variables, part fancy mathematical formula, part prediction generator that scikit learn graciously affords us to make predictions of future data. Alright regressions, you can stay. 🙂