15. Linear Regression in scikit-learn
Linear Regression
In this section, you'll use linear regression to predict life expectancy from body mass index (BMI) . Before you do that, let's go over the tools required to build this model.
For your linear regression model, you'll be using scikit-learn's
LinearRegression
class. This class provides the function
fit()
to fit the model to your data.
>>> from sklearn.linear_model import LinearRegression
>>> model = LinearRegression()
>>> model.fit(x_values, y_values)
In the example above, the
model
variable is a linear regression model that has been fitted to the data
x_values
and
y_values
. Fitting the model means finding the best line that fits the training data. Let's make two predictions using the model's
predict()
function.
>>> print(model.predict([ [127], [248] ]))
[[ 438.94308857, 127.14839521]]
The model returned an array of predictions, one prediction for each input array. The first input,
[127]
, got a prediction of
438.94308857
. The second input,
[248]
, got a prediction of
127.14839521
. The reason for predicting on an array like
[127]
and not just
127
, is because you can have a model that makes a prediction using multiple features. We'll go over using multiple variables in linear regression later in this lesson. For now, let's stick to a single value.
Linear Regression Quiz
In this quiz, you'll be working with data on the average life expectancy at birth and the average BMI for males across the world. The data comes from Gapminder .
The data file can be found under the "bmi_and_life_expectancy.csv" tab in the quiz below. It includes three columns, containing the following data:
- Country – The country the person was born in.
- Life expectancy – The average life expectancy at birth for a person in that country.
- BMI – The mean BMI of males in that country.
You'll need to complete each of the following steps:
1. Load the data
- The data is in the file called "bmi_and_life_expectancy.csv".
-
Use pandas
read_csvto load the data into a dataframe (don't forget to import pandas!) -
Assign the dataframe to the variable
bmi_life_data.
2. Build a linear regression model
-
Create a regression model using scikit-learn's
LinearRegressionand assign it tobmi_life_model. - Fit the model to the data.
3. Predict using the model
-
Predict using a BMI of 21.07931 and assign it to the variable
laos_life_exp.
Start Quiz: