Create a regression model with BigQuery DataFrames

Create a linear regression model on the body mass of penguins using the BigQuery DataFrames API.

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Code sample

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

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from bigframes.ml.linear_model import LinearRegression import bigframes.pandas as bpd # Load data from BigQuery query_or_table = "bigquery-public-data.ml_datasets.penguins" bq_df = bpd.read_gbq(query_or_table) # Filter down to the data to the Adelie Penguin species adelie_data = bq_df[bq_df.species == "Adelie Penguin (Pygoscelis adeliae)"] # Drop the species column adelie_data = adelie_data.drop(columns=["species"]) # Drop rows with nulls to get training data training_data = adelie_data.dropna() # Specify your feature (or input) columns and the label (or output) column: feature_columns = training_data[ ["island", "culmen_length_mm", "culmen_depth_mm", "flipper_length_mm", "sex"] ] label_columns = training_data[["body_mass_g"]] test_data = adelie_data[adelie_data.body_mass_g.isnull()] # Create the linear model model = LinearRegression() model.fit(feature_columns, label_columns) # Score the model score = model.score(feature_columns, label_columns) # Predict using the model result = model.predict(test_data)

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