# How to do linear regression in Python

• 1 minThere are multiple ways to perform linear regression in Python, here are a few examples:

- Using
`scipy.stats.linregress()`

: This function calculates a linear least-squares regression for two sets of measurements.

```
from scipy.stats import linregress
slope, intercept, r_value, p_value, std_err = linregress(x,y)
```

- Using
`numpy.polyfit()`

: This function returns the coefficients of a polynomial of a specified order that is the best fit for the data using a least-squares approach. To perform linear regression, set the order to 1.

```
import numpy as np
coefficients = np.polyfit(x, y, 1)
```

- Using
`scikit-learn`

: This library provides a`LinearRegression`

class in the`linear_model`

module for performing linear regression.

```
from sklearn.linear_model import LinearRegression
reg = LinearRegression().fit(X, y)
```

- Using
`statsmodels`

: This library provides a`OLS`

class in the`stats`

module for performing ordinary least squares linear regression.

```
import statsmodels.api as sm
model = sm.OLS(y, X).fit()
```

In all the above examples, x and y should be **numpy **arrays or pandas **dataframe **or **series **containing the **independent **and **dependent **variables respectively. Linear regression can be used to model the relationship between a scalar dependent variable y and one or more explanatory variables denoted X.

You can also use the coefficients obtained to predict the y values for a given x values by using the equation **y = slope*x + intercept.**