import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
import pickle

url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pd.read_csv(url, names=names)

X = dataframe.iloc[:, :-1].values
Y = dataframe.iloc[:, -1].values

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=7)

# Updated model with StandardScaler and increased max_iter
model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))

model.fit(X_train, Y_train)

# Save the model to disk
filename = 'finalized_model.sav'
pickle.dump(model, open(filename, 'wb'))

# Load the model from disk
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.score(X_test, Y_test)
print(result)