Upload train.py

train.py

@@ -0,0 +1,65 @@
+import pandas as pd
+
+import sklearn
+import joblib 
+
+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 
+from math import sqrt
+
+sklearn.set_config(display='diagram')
+
+data = pd.read_csv('insurance.csv')
+df = data.copy(deep=True)
+
+df = df.drop(columns=['index'])
+df.drop_duplicates(inplace=True)
+
+target = 'charges'
+numeric_features = ['age', 'bmi', 'children']
+categorical_features = ['sex', 'smoker', 'region']
+
+print('Creating data subsets')
+
+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 
+)
+
+Xtest = Xtest[['age', 'bmi', 'children', 'sex', 'smoker', 'region']]
+
+preprocessor = make_column_transformer(
+    (StandardScaler(), numeric_features),
+    (OneHotEncoder(handle_unknown='ignore'), categorical_features)
+)
+
+model_linear_regression = LinearRegression(n_jobs=-1)
+
+print('Estimating Best 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)