Create train.py

train.py

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+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 the 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(f"Best parameters: {rand_search_cv.best_params_}")
+
+print("Serializing the Best Model")
+
+saved_model_path = "model.joblib"
+
+joblib.dump(rand_search_cv.best_estimator_, saved_model_path)