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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frombigframes.ml.linear_modelimport LinearRegression
importbigframes.pandasasbpd
# 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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