diff --git a/src/features/standardization/main.py b/src/features/standardization/main.py
deleted file mode 100644
index d91fca6d..00000000
--- a/src/features/standardization/main.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-
-import sys
-
-sensor_data_files = dict(snakemake.input)
-
-provider = snakemake.params["provider"]
-provider_key = snakemake.params["provider_key"]
-sensor_key = snakemake.params["sensor_key"]
-
-pd.set_option('display.max_columns', None)
-
-if provider_key == "cr":
-    sys.path.append('/rapids/src/features/')
-    from cr_features_helper_methods import extract_second_order_features
-
-    provider_main = snakemake.params["provider_main"]
-    prefix = sensor_key + "_" + provider_key + "_"
-
-    windows_features_data = pd.read_csv(sensor_data_files["windows_features_data"])
-    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', prefix + "level_1"]
-    
-    if windows_features_data.empty:
-        windows_features_data.to_csv(snakemake.output[1], index=False)
-        windows_features_data.to_csv(snakemake.output[0], index=False)
-    else:
-        windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)] = StandardScaler().fit_transform(windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)])
-
-        windows_features_data.to_csv(snakemake.output[1], index=False)
-
-        if provider_main["WINDOWS"]["COMPUTE"] and "SECOND_ORDER_FEATURES" in provider_main["WINDOWS"]:
-            so_features_names = provider_main["WINDOWS"]["SECOND_ORDER_FEATURES"]
-            windows_so_features_data = extract_second_order_features(windows_features_data, so_features_names, prefix)
-            windows_so_features_data.to_csv(snakemake.output[0], index=False)
-        else:
-            pd.DataFrame().to_csv(snakemake.output[0], index=False)
-
-else: 
-    for sensor_features in sensor_data_files["sensor_features"]:
-        if "/" + sensor_key + ".csv" in sensor_features:
-            sensor_data = pd.read_csv(sensor_features)
-            excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
-
-            if not sensor_data.empty:
-                sensor_data.loc[:, ~sensor_data.columns.isin(excluded_columns)] = StandardScaler().fit_transform(sensor_data.loc[:, ~sensor_data.columns.isin(excluded_columns)])
-            
-            sensor_data.to_csv(snakemake.output[0], index=False)
-            break
\ No newline at end of file