Upload 4 files

insurance.csv

@@ -0,0 +1,1339 @@
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model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49c8d0ba904be2c779b4a3961f29f7c67752a2f81c7c10d43592bca223e6b6e6
+size 4054

requirements.txt

@@ -0,0 +1 @@
+scikit-learn==1.2.2

train.py

@@ -0,0 +1,62 @@
+import joblib
+from sklearn.datasets import fetch_openml
+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
+import numpy as np
+import pandas as pd
+
+# Read data
+df = pd.read_csv("insurance.csv")
+
+# Create the y, X variables by splitting of the target continuous variable 'charges'
+# from the remaining features
+
+print("Creating data subsets")
+
+y = df['charges']
+X = df.drop('charges', axis=1)
+
+print("Data subsets created")
+
+# Extract the names of the numerical and categorical columns
+numeric_features = X.select_dtypes(include=['int64', 'float64']).columns.tolist()
+categorical_features = X.select_dtypes(include=['object']).columns.tolist()
+target = 'charges'
+
+# Split the independent and dependent features into X and y variables with a test size of 20% and random state set to 42
+Xtrain, Xtest, ytrain, ytest = train_test_split(
+    X, y,
+    test_size=0.2,
+    random_state=42
+)
+
+print("Preprocessing Data")
+
+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)