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 modeling, 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 of them. This line can be used to predict future values.

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

Run each value of the x array through the function. This will result in a new collection with new values for the y-axis:

mymodel = list(map(myfunc, x))

Draw the original scatter plot:

plt.scatter(x, y)

Draw the line of linear Regression:

plt.plot(x, mymodel)

Display the diagram:

plt.show()

### Multiple Regression

Multiple Regression is like linear Regression, but with more than one independent value, meaning that we try to predict a value based on two or more variables.

We can predict the CO2 emission of a car based on the engine’s size, but with multiple Regression, we can throw in more variables, like the car’s weight, to make the prediction more accurate.

In Python, we have modules that will do the work for us. Start by importing the Pandas module.

import pandas

The Pandas module allows us to read CSV files and return a DataFrame object.

df = pandas.read_csv("cars.csv")

Then make a list of the independent values and call this variable x. Put the dependent values in a variable called y.

X = df[['Weight', 'Volume']]

y = df['CO2']

We will use some methods from the `sklearn`

module, so we will have to import that module as well:

from sklearn import linear_model

From the `sklearn`

module, we will use the ‘LinearRegression’ method to create a linear regression object.

regr = linear_model.LinearRegression()

regr.fit(X, y)

Now we have a regression object that is ready to predict CO2 values based on a car’s weight and volume:

predictedCO2 = regr.predict([[2300, 1300]])

**Full Code Example**

import pandas

from sklearn import linear_model

df = pandas.read_csv("cars.csv")

X = df[['Weight', 'Volume']]

y = df['CO2']

regr = linear_model.LinearRegression()

regr.fit(X, y)

#predict the CO2 emission of a car where the weight is 2300kg, and the volume is 1300ccm:

predictedCO2 = regr.predict([[2300, 1300]])

print(predictedCO2)

### Polynomial Regression

Polynomial Regression, like linear Regression, uses the relationship between the variables x and y to find the best way to draw a line through the data points.

Python has methods for finding a relationship between data-points and to draw a line of polynomial Regression. We will show you how to use these methods instead of going through the mathematic formula. In the example below, we have registered 18 cars as they were passing a certain tollbooth.

The x-axis represents the hours of the day, and the y-axis represents the speed:

import matplotlib.pyplot as plt

x = [1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,21,22]

y = [100,90,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]

plt.scatter(x, y)

plt.show()

**Result**

Import the modules you need:

import numpy

import matplotlib.pyplot as plt

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

x = [1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,21,22]

y = [100,90,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]

NumPy has a method that lets us make a polynomial model:

mymodel = numpy.poly1d(numpy.polyfit(x, y, 3))

Then specify how the line will display; we start at position one and end at position 22:

myline = numpy.linspace(1, 22, 100)

Draw the original scatter plot:

plt.scatter(x, y)

Draw the line of polynomial Regression:

plt.plot(myline, mymodel(myline))

Display the diagram:

plt.show()

It is essential to know how well the relationship between the values of the x- and the y-axis is if there is no relationship, the polynomial Regression can not be used to predict anything.

The relationship is measured with a value called the r-squared. The r-squared value ranges from 0 to 1, where 0 means no relationship, and one means 100% related.

How well does my data fit in a polynomial regression?

import numpy

from sklearn.metrics import r2_score

x = [1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,21,22]

y = [100,90,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]

mymodel = numpy.poly1d(numpy.polyfit(x, y, 3))

print(r2_score(y, mymodel(x)))

The result 0.94 shows that there is a perfect relationship, and we can use polynomial Regression in future predictions.

### Predict Future Values

Now we can use the information we have gathered to predict future values. Predict the speed of a car passing at 5 P.M:

import numpy

from sklearn.metrics import r2_score

y = [100,90,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]

mymodel = numpy.poly1d(numpy.polyfit(x, y, 3))

speed = mymodel(17)

print(speed)

### Conclusion

I hope after this article, you have a basic understanding of Regression and how to use it in Python and that you will be able to run yourself some scripts of Machine Learning now!