train.py

import numpy as np
import pandas as pd
import seaborn as sns
import joblib
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.metrics import mean_squared_error, r2_score

data = pd.read_csv("/Users/debjanighosh/insurance.csv")

target = 'charges'

numerical_features = ['age', 'bmi','children']
categorical_features = ['sex','smoker','region']

print("Creating data subsets")

X = data[numerical_features + categorical_features]
y = data[target]

Xtrain, Xtest, ytrain, ytest = train_test_split(
    X,y,
    test_size=0.2,
    random_state=42
)

numerical_pipeline = Pipeline([
    ('imputer',SimpleImputer(strategy='median')),
    ('scaler',StandardScaler())
])  

categorical_pipeline = Pipeline([
    ('imputer',SimpleImputer(strategy='most_frequent')),
    ('onehot',OneHotEncoder(handle_unknown='ignore'))
])  

preprocessor = make_column_transformer(
    (numerical_pipeline, numerical_features),
    (categorical_pipeline, categorical_features)
)

model_linear_regression = LinearRegression()

print ("Estimating Best Model Pipeline")

model_pipeline = make_pipeline(
    preprocessor,
    model_linear_regression
)

model_pipeline.fit(Xtrain, ytrain)
print("Logging Metrics")
print(f"R2 Score:{r2_score(ytest, model_pipeline.predict(Xtest))}")
print("Serializing Model")
saved_model_path = "model.joblib"

joblib.dump(model_pipeline, saved_model_path)