Regressionsmodell mit BigQuery DataFrames erstellen

Mit Sammlungen den Überblick behalten Sie können Inhalte basierend auf Ihren Einstellungen speichern und kategorisieren.

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)