| import joblib | |
| import pandas as pd | |
| from sklearn.preprocessing import StandardScaler, OneHotEncoder | |
| from sklearn.compose import make_column_transformer | |
| from sklearn.pipeline import make_pipeline | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.metrics import mean_squared_error, r2_score | |
| data_df = pd.read_csv("insurance.csv") | |
| target = 'charges' | |
| numeric_features = ['age', 'bmi', 'children'] | |
| categorical_features = ['sex', 'smoker', 'region'] | |
| print("Creating data subsets") | |
| X = data_df[numeric_features + categorical_features] | |
| y = data_df[target] | |
| Xtrain, Xtest, ytrain, ytest = train_test_split( | |
| X, y, | |
| test_size=0.2, | |
| random_state=42 | |
| ) | |
| preprocessor = make_column_transformer( | |
| (StandardScaler(), numeric_features), | |
| (OneHotEncoder(handle_unknown='ignore'), categorical_features) | |
| ) | |
| model_linear_regression = LinearRegression(n_jobs=-1) | |
| print("Estimating Model Pipeline") | |
| model_pipeline = make_pipeline( | |
| preprocessor, | |
| model_linear_regression | |
| ) | |
| model_pipeline.fit(Xtrain, ytrain) | |
| print("Logging Metrics") | |
| print(f"R-squared: {r2_score(ytest, model_pipeline.predict(Xtest))}") | |
| print("Serializing Model") | |
| saved_model_path = "model.joblib" | |
| joblib.dump(model_pipeline, saved_model_path) | |