app.py

app.py

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+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)