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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) |