import joblib from sklearn.datasets import fetch_openml from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.compose import make_column_transformer from sklearn.pipeline import make_pipeline from sklearn.model_selection import train_test_split, RandomizedSearchCV from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, classification_report dataset = fetch_openml(data_id=42890, as_frame=True, parser="auto") data_df = dataset.data target = 'Machine failure' numeric_features = [ 'Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]' ] categorical_features = ['Type'] print("Creating data subsets") X = data_df[numeric_features + categorical_features] y = data_df[target] Xtrain, Xtest, ytrain, ytest = train_test_split( X, y, test_size=0.2, random_state=42 ) preprocessor = make_column_transformer( (StandardScaler(), numeric_features), (OneHotEncoder(handle_unknown='ignore'), categorical_features) ) model_logistic_regression = LogisticRegression(n_jobs=-1) print("Estimating Best Model Pipeline") model_pipeline = make_pipeline( preprocessor, model_logistic_regression ) param_distribution = { "logisticregression__C": [0.001, 0.01, 0.1, 0.5, 1, 5, 10] } rand_search_cv = RandomizedSearchCV( model_pipeline, param_distribution, n_iter=3, cv=3, random_state=42 ) rand_search_cv.fit(Xtrain, ytrain) print("Logging Metrics") print(f"Accuracy: {rand_search_cv.best_score_}") print("Serializing Model") saved_model_path = "model.joblib" joblib.dump(rand_search_cv.best_estimator_, saved_model_path)