File size: 1,821 Bytes
69b0062 |
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 |
import math
import pandas as pd
import numpy as np
from datasets import load_dataset
import sklearn
import joblib
from sklearn.datasets import fetch_openml
from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import LinearRegression
data_df = pd.read_csv('insurance.csv')
data_df = data_df.drop(columns=['index'])
categorical_features = ['sex', 'smoker', 'region']
numerical_features = ['age', 'bmi', 'children', ]
target = 'charges'
print("Creating data subsets")
X = data_df[numerical_features + categorical_features]
y = data_df[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(n_jobs=-1)
model_pipeline = make_pipeline(preprocessor, model_linear_regression )
print("Estimating Model Pipeline")
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)
|