Course Content

Linear Regression with Python

## Linear Regression with Python

# Building Linear Regression Using NumPy

You already know what simple linear regression is and how to find the line that fits the data best. Let's go through all the steps of building a linear regression for a real dataset.

## Loading data

We have a file, `simple_height_data.csv`

, with the data from our examples. Let's load the file and take a look at it.

`import pandas as pd file_link = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/simple_height_data.csv' df = pd.read_csv(file_link) # Read the file print(df.head()) # Print the first 5 instances from a dataset`

So the dataset has two columns, one is 'Height', which is our target, and the second column, 'Father', is the father's height. That is our feature.

Let's assign our target values to the `y`

variable and feature values to `X`

and build a scatterplot.

`import pandas as pd import matplotlib.pyplot as plt file_link = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/simple_height_data.csv' df = pd.read_csv(file_link) # Read the file X = df['Father'] # Assign the feature y = df['Height'] # Assign the target plt.scatter(X,y) # Build scatterplot plt.show()`

## Finding parameters

Now, NumPy has a nice function to find the parameters of linear regression.

Linear Regression is a Polynomial Regression of degree 1(we will talk about Polynomial Regression in later sections). That's why we need to put `deg=1`

to get the parameters for the linear regression.

Here is an example:

`import pandas as pd import numpy as np file_link = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/simple_height_data.csv' df = pd.read_csv(file_link) # Read the files X, y = df['Father'], df['Height'] # Assign the variables beta_1, beta_0 = np.polyfit(X, y, 1) # Get the parameters print('beta_0 is', beta_0) print('beta_1 is', beta_1)`

Note

If you are unfamiliar with the syntax

`beta_1, beta_0 = np.polyfit(X,y,1)`

, that is called unpacking.

If you have an iterator (e.g., list or NumPy array or pandas series) that has two items writingis the same as

And since the return of a

`polyfit()`

function is a NumPy array with two values, we are allowed to do that

## Making the predictions

Now we can plot the line and predict new variables using the parameters.

`import pandas as pd import numpy as np import matplotlib.pyplot as plt file_link = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/simple_height_data.csv' df = pd.read_csv(file_link) # Read the file X, y = df['Father'], df['Height'] # Assign the variables beta_1, beta_0 = np.polyfit(X, y, 1) # Get the parameters plt.scatter(X,y) # Build a scatter plot plt.plot(X, beta_0 + beta_1 * X) # Plot the line`

Now that we have the parameters, we can use the linear regression equation to predict new values.

`import pandas as pd import numpy as np import matplotlib.pyplot as plt file_link = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/simple_height_data.csv' df = pd.read_csv(file_link) # Read the file X, y = df['Father'], df['Height'] # Assign the variables beta_1, beta_0 = np.polyfit(X, y, 1) # Get the parameters X_new = np.array([65, 70, 75]) # Feature values of new instances y_pred = beta_0 + beta_1 * X_new # Predict the target print('Predicted y: ', y_pred)`

So it is pretty easy to get the parameters of the linear regression. But some libraries can also give you some extra information. Let's look at one such library.

Everything was clear?