Machine Learning is making the computer learn from studying data and statistics. Machine Learning is a step into the direction of artificial intelligence (AI). Machine Learning is a program that analyses data and learns to predict the outcome.

What is Regression?

The term regression is used when you try to find the relationship between variables. In Machine Learning and statistical modelling, that relationship is used to predict the outcome of future events.

Linear Regression

Linear regression uses the relationship between the data-points to draw a straight line through all them. This line can be used to predict future values.

Getting Started

Python has methods for finding a relationship between data-points and to draw a line of linear regression. We will show you how to use these methods instead of going through the mathematic formula.

An example:

x = [5,7,8,7,2,17,2,9,4,11,12,9,6]
y = [99,86,87,88,111,86,103,87,94,78,77,85,86]

plt.scatter(x, y)
plt.show()

This displays a scatter plot:

Import ‘scipy’ and draw the line of Linear Regression:

import matplotlib.pyplot as plt
from scipy import stats

Create the arrays that represent the values of the x and y-axis:

x = [5,7,8,7,2,17,2,9,4,11,12,9,6]
y = [99,86,87,88,111,86,103,87,94,78,77,85,86]

Execute a method that returns some critical fundamental values of Linear Regression:

slope, intercept, r, p, std_err = stats.linregress(x, y)

Create a function that uses the ‘slope’ and ‘intercept’ values to return a new deal. This new value represents where on the y-axis, the corresponding x value will be placed:

def myfunc(x):
  return slope * x + intercept

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