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Project Activities

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

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


Separate the target and the features into two variables.

Store the features in X and the target y.


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.


Linea Regression

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


Train the linear regression model

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


Make predictions on the test set

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

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Verónica Barraza

This project is part of

Introduction to Supervised Learning with scikit-learn

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