train.py

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

#Read csv
df = pd.read_csv("insurance.csv")

# split data in to numerical and categorical
target = 'charges'
numeric_features = ['age', 'bmi', 'children']
categorical_features = ['sex','smoker','region']

print("Creating Data Subsets")

#Model Estimation
X = df[numeric_features + categorical_features]
y = 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)