text: # Importing the libraries
  from pathlib import Path
  import failure_prediction_model
  
  # Project Directories
  PACKAGE_ROOT = Path(failure_prediction_model.__file__).resolve().parent
  ROOT = PACKAGE_ROOT.parent
  CONFIG_FILE_PATH = PACKAGE_ROOT / "config.yml"
  DATASET_DIR = PACKAGE_ROOT / "data"
  RAW_DATA_DIR = DATASET_DIR / "raw"
  INTERIM_DATA_DIR = DATASET_DIR / "interim"
  TRAINED_MODEL_DIR = PACKAGE_ROOT / "models"
  OUTPUT_DIR = PACKAGE_ROOT / "outputs"
  
  # Training dataset path
  TRAIN_DATA = "predict_failure.csv"
  INTERIM_TRAIN_DATA = "train.csv"
  INTERIM_TEST_DATA = "test.csv"
  
  # Input parameters for data preprocessing
  TARGET = "failure"
  TEST_SIZE = 0.2
  SEED = 42
  N_SPLITS = 10
  
  # Input parameters for data samplking techniques
  SMOTE_SAMPLING_STRATERGY = 0.1
  UNDER_SAMPLING_STRATERGY = 0.75
  
  # Features to be dropped
  DROP_ATTRIBUTES = ["date", "device", "attribute7"]
  
  # Train and Test features to select
  TRAIN_FEATURES = ["attribute1", "attribute2", "attribute3",
                    "attribute4", "attribute5", "attribute6", "attribute8", "attribute9", "failure"]
  TEST_FEATURES = ["attribute1", "attribute2", "attribute3",
                   "attribute4", "attribute5",  "attribute6", "attribute8", "attribute9"]
  
  # List of features to be scaled based on Standardization/Normalization
  STANDARD_SCALER_FEATURES = ["attribute2", "attribute3",
                              "attribute4", "attribute5", "attribute8", "attribute9"]
  MIN_MAX__SCALER_FEATURES = ["attribute1", "attribute6"]
  
  # Package name
  PACKAGE_NAME = "failure_detction_model"
  
  # Pipeline name
  PIPELINE_NAME = "failure_detction_model"
  PIPELINE_SAVE_FILE = "failure_detction_model_v"
  