diff --git a/etl/bill_savings/training.py b/etl/bill_savings/training.py
deleted file mode 100644
index df60298b..00000000
--- a/etl/bill_savings/training.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from pprint import pprint
-import msgpack
-from utils.s3 import read_from_s3
-from etl.bill_savings.EnergyConsumptionModel import EnergyConsumptionModel
-
-
-def handler():
-    """
-    This function is used to train the model and store the final models in s3 as pickles
-    :return:
-    """
-
-    dataset_version = "2024-07-08"
-
-    # Usage:
-    cleaned = read_from_s3(
-        s3_file_name="cleaned_epc_data/cleaned.bson",
-        bucket_name="retrofit-data-dev"
-    )
-
-    cleaned = msgpack.unpackb(cleaned, raw=False)
-
-    model = EnergyConsumptionModel(cleaned=cleaned, n_jobs=2)
-    model.read_dataset(f'energy_consumption/{dataset_version}/energy_consumption_dataset.parquet')
-    model.feature_engineering()
-    model.save_dummy_schema(dataset_version=dataset_version)
-
-    # For heating_kwh
-    model.split_dataset(target='heating_kwh')
-    model.fit_model(target='heating_kwh')
-    model.re_train_final_model(target='heating_kwh')
-    evaluation_results = model.evaluate_model(target='heating_kwh')
-
-    pprint(evaluation_results["train"])
-    pprint(evaluation_results["test"])
-
-    model.save_model(target='heating_kwh', dataset_version=dataset_version)
-
-    # importance_df = evaluation_results["train"]["Feature Importance"]
-    # testing_predictions = model.testing_predictions["heating_kwh"]
-    # testing_predictions = testing_predictions.sort_values("residual", ascending=False)
-    # training_predictions = model.training_predictions["heating_kwh"]
-    # training_predictions = training_predictions.sort_values("residual", ascending=False)
-    # # Merge on model.input_data, by the index
-    # merged_data = testing_predictions.merge(model.input_data, left_index=True, right_index=True)
-    # merged_data_train = training_predictions.merge(model.input_data, left_index=True, right_index=True)
-
-    # For hot_water_kwh
-    model.split_dataset(target='hot_water_kwh')
-    model.fit_model(target='hot_water_kwh')
-    model.re_train_final_model(target='hot_water_kwh')
-    evaluation_results = model.evaluate_model(target='hot_water_kwh')
-
-    pprint(evaluation_results["train"])
-    pprint(evaluation_results["test"])
-
-    model.save_model(target='hot_water_kwh', dataset_version=dataset_version)