File size: 1,426 Bytes
9fc9817 9dd7ef5 9fc9817 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | # Import libraries
import pandas as pd
import joblib
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.compose import ColumnTransformer
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 the data
data_df = pd.read_csv("insurance.csv")
# Process the data
data_df = data_df.drop(columns=['index'])
X = data_df.drop(columns='charges')
y = data_df['charges']
categorical_features = X.select_dtypes(include=['object']).columns
numeric_features = X.select_dtypes(include=['number']).columns
print("Creating data subsets")
preprocessor = make_column_transformer(
(StandardScaler(), numeric_features),
(OneHotEncoder(handle_unknown='ignore'), categorical_features)
)
Xtrain, Xtest, ytrain, ytest = train_test_split(
X, y,
test_size=0.2,
random_state=42
)
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)
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