diff --git a/modules/ml-pipeline/src/pipeline/configs/feature_processor_logic.py b/modules/ml-pipeline/src/pipeline/configs/feature_processor_logic.py
index ed2e4a3..d29d7ac 100644
--- a/modules/ml-pipeline/src/pipeline/configs/feature_processor_logic.py
+++ b/modules/ml-pipeline/src/pipeline/configs/feature_processor_logic.py
@@ -192,6 +192,7 @@ def remove_high_ratio_of_area_to_rooms(df):
 
     # Remove top 0.05% of area-to-heated-rooms
     df = df[df['area-to-heated-rooms'] < df['area-to-heated-rooms'].quantile(0.9995)].reset_index(drop=True)
+    df = df.drop(columns=['area-to-heated-rooms'])
     return df
 
 def add_estimate_annual_kwh(df):
diff --git a/modules/ml-pipeline/src/pipeline/configs/settings.yaml b/modules/ml-pipeline/src/pipeline/configs/settings.yaml
index 28b5f2c..b484049 100644
--- a/modules/ml-pipeline/src/pipeline/configs/settings.yaml
+++ b/modules/ml-pipeline/src/pipeline/configs/settings.yaml
@@ -37,7 +37,7 @@ default:
       target: heating_kwh
       identifier_columns: ["uprn"]
       drop_columns: ["hot_water_kwh", 'lodgement-datetime', 'lodgement-date', 'number-habitable-rooms', 'local-authority', 'posttown', 'address', 'inspection-date',
-      "county", "constituency-label", 'address2', 'uprn-source', 'postcode', 'address1',]
+      "county", "constituency-label", 'address2', 'uprn-source', 'postcode', 'address1', 'mainheat-env-eff', 'environment-impact-current', 'hot-water-env-eff', 'floor-env-eff']
       retain_features: null
 
   generate_predictions:
diff --git a/modules/ml-pipeline/src/pipeline/dvc.lock b/modules/ml-pipeline/src/pipeline/dvc.lock
index e1f65f5..536a3bc 100644
--- a/modules/ml-pipeline/src/pipeline/dvc.lock
+++ b/modules/ml-pipeline/src/pipeline/dvc.lock
@@ -35,6 +35,10 @@ stages:
         - uprn-source
         - postcode
         - address1
+        - mainheat-env-eff
+        - environment-impact-current
+        - hot-water-env-eff
+        - floor-env-eff
         default.feature_processor.feature_processor_config.retain_features:
         default.feature_processor.feature_processor_config.subsample_amount:
         default.feature_processor.feature_processor_config.subsample_seed: 0
@@ -50,8 +54,8 @@ stages:
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: f506f1f059945c0f014c3f505a63726c.dir
-      size: 30388447
+      md5: 382d5d02772d4ead3a31fa9420c03417.dir
+      size: 29570807
       nfiles: 2
   build_model:
     cmd: python 2_build_model.py
@@ -62,8 +66,8 @@ stages:
       size: 4820
     - path: data/prepared_data
       hash: md5
-      md5: f506f1f059945c0f014c3f505a63726c.dir
-      size: 30388447
+      md5: 382d5d02772d4ead3a31fa9420c03417.dir
+      size: 29570807
       nfiles: 2
     params:
       configs/build_model.yaml:
@@ -95,18 +99,18 @@ stages:
     outs:
     - path: data/fit_predictions/
       hash: md5
-      md5: 9a2abeada227b8bb4c13d6c745bef581.dir
-      size: 1547064
+      md5: 3ea5e827470bb96408fa70bc45ed6b58.dir
+      size: 1545844
       nfiles: 1
     - path: data/model/
       hash: md5
-      md5: 43b72f9284e92842cbc82bc7cc0950e2.dir
-      size: 506201607
-      nfiles: 36
+      md5: cff0bc3b08dfb48fff929bb4991ea2f5.dir
+      size: 291336375
+      nfiles: 35
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: 4a496483bffad3efe671f29110729e48
-      size: 221
+      md5: 63b660c30b855ee0d86b0c1be4ad537e
+      size: 220
   generate_predictions:
     cmd: python 3_generate_predictions.py
     deps:
@@ -116,13 +120,13 @@ stages:
       size: 2464
     - path: data/model
       hash: md5
-      md5: 43b72f9284e92842cbc82bc7cc0950e2.dir
-      size: 506201607
-      nfiles: 36
+      md5: cff0bc3b08dfb48fff929bb4991ea2f5.dir
+      size: 291336375
+      nfiles: 35
     - path: data/prepared_data
       hash: md5
-      md5: f506f1f059945c0f014c3f505a63726c.dir
-      size: 30388447
+      md5: 382d5d02772d4ead3a31fa9420c03417.dir
+      size: 29570807
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -134,7 +138,7 @@ stages:
     outs:
     - path: data/predictions/
       hash: md5
-      md5: 88832d623c3e437eaec221307ac33aae.dir
+      md5: 88c5ad48fd6035600135a4541f2811a8.dir
       size: 163584
       nfiles: 1
   generate_metrics:
@@ -146,13 +150,13 @@ stages:
       size: 3484
     - path: data/predictions
       hash: md5
-      md5: 88832d623c3e437eaec221307ac33aae.dir
+      md5: 88c5ad48fd6035600135a4541f2811a8.dir
       size: 163584
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: f506f1f059945c0f014c3f505a63726c.dir
-      size: 30388447
+      md5: 382d5d02772d4ead3a31fa9420c03417.dir
+      size: 29570807
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -162,8 +166,8 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: f2783bdec0f0974b6d799609c6189467
-      size: 222
+      md5: f3fd84bd242e9f806aaedbb560f2ac7e
+      size: 219
   generate_scenerio_metrics:
     cmd: python 5_generate_scenarios.py
     deps: