**Simple linear regression** is a statistical method that is used to analyze the relationship between two continuous variables: an independent variable and a dependent variable. Simple linear regression can be used in various practical applications. For example, analysis of the relationship between a person's weight and their height, the price of a product and the number of units sold. This tutorial shows how to perform simple linear regression using scikit-learn.

## Prepare environment

- Install the following package using
`pip`

:

`pip install scikit-learn`

## Code

The following code shows how to use a simple linear regression model that allows to predict the value of `y`

for the given value of `x`

. Relationship between `x`

and `y`

variables are described by formula `y = 2 * x + 1`

.

The code defines the data points that will be used to train the model. The `xs`

array contains the independent variable values, and the `ys`

array contains the dependent variable values. In this case, each independent variable has one associated dependent variable. After the data points are defined, we create a linear regression model which is trained using the `xs`

and `ys`

arrays.

```
import numpy as np
from sklearn.linear_model import LinearRegression
xs = np.array([[-2.0], [-1.0], [0.0], [1.0], [2.0], [3.0], [4.0]])
ys = np.array([[-3.0], [-1.0], [1.0], [3.0], [5.0], [7.0], [9.0]])
model = LinearRegression()
model.fit(xs, ys)
x = 15.0
y = model.predict([[x]])
print(y[0])
```

Once the model has been trained, we try to predict a value of `y`

for a previously unknown value of `x`

. In our case, if `x`

is 15.0, then the trained model returns that `y`

is 31. It can be verified:

`y = 2 * x + 1 = 2 * 15 + 1 = 31`

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