Delete train.py

train.py

@@ -1,76 +0,0 @@
-
-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)