Save a model
Save a model Data Science Project
Introduction to Supervised Learning with scikit-learn

Save a model

In this lab, you will practice how to train a machine learning model, save it to a file, and then use the saved model to make predictions on new data. We will use a simple example with the dummy dataset and a simple model.
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Save a modelSave a model
Project Created by

Verónica Barraza

Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

multiplechoice

Select whether this problem is a regression or a classification problem.

codevalidated

Separate the target and the features into two variables.

Store the features in X and the target y.

codevalidated

Use train_test_split to split the data into training and testing sets. Split the dataset in 80% training, 20% testing, and random_state=0.

Set the random_state parameter to a desired integer value for reproducibility. Store this variable in random_state and then used in the function.

Store the values in the variables in X_train,X_test,y_train and y_test.

codevalidated

Linea Regression

Create an instance of the LinearRegression and store the model in lr.

codevalidated

Train the linear regression model

It's time to train the linear regression model using the training dataset.

codevalidated

Make predictions on the test set

Use the trained model to make predictions on the test data. Store the prediction in y_pred.

Save a modelSave a model
Project Created by

Verónica Barraza

This project is part of

Introduction to Supervised Learning with scikit-learn

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