diff --git a/python/crankshaft/crankshaft/segmentation/segmentation.py b/python/crankshaft/crankshaft/segmentation/segmentation.py
index 4f99573..acdae51 100644
--- a/python/crankshaft/crankshaft/segmentation/segmentation.py
+++ b/python/crankshaft/crankshaft/segmentation/segmentation.py
@@ -1,118 +1,118 @@
-# """
-# Segmentation creation and prediction
-# """
-#
-# import sklearn
-# import numpy as np
-# import pandas as pd
-# import pickle
-# import plpy
-# from sklearn.ensemble import ExtraTreesRegressor
-# from sklearn import metrics
-# from sklearn.cross_validation import train_test_split
-#
-# # High level interface ---------------------------------------
-#
-# def cdb_create_segment(segment_name,table_name,column_name,geoid_column,census_table,method):
-#     """
-#     generate a segment with machine learning
-#     Stuart Lynn
-#     """
-#     data     = pd.DataFrame(join_with_census(table_name, column_name,geoid_column, census_table))
-#     features = data[data.columns.difference([column_name, 'geoid'])]
-#     target, mean, std = normalize(data[column_name])
-#     model, accuracy = train_model(target,features, test_split=0.2)
-#     save_model(segment_name, model, accuracy, table_name, column_name, census_table, geoid_column, method)
-#     return accuracy
-#
-# def normalize(target):
-#     mean = np.mean(target)
-#     std  = no.std(target)
-#     return (target - mean)/std, mean, std
-#
-# def denormalize(target, mean ,std):
-#     return target*std + mean
-#
-# def train_model(target,features,test_split):
-#     features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
-#     model = ExtraTreesRegressor(n_estimators = 40, max_features=len(features.columns))
-#     model.fit(features_train, target_train)
-#     accuracy = calculate_model_accuracy(model,features,target)
-#     return model, accuracy
-#
-# def calculate_model_accuracy(model,features,target):
-#     prediction = self.model.predict(features)
-#     return metrics.mean_squared_error(prediction,target)/np.std(target)
-#
-# def join_with_census(table_name, column_name, geoid_column, census_table):
-#     coulmns        = plpy.execute('select {census_table}.* limit 1 ')
-#     feature_names  = ",".join(columns.keys.difference(['the_geom','cartodb_id']))
-#     join_data     = plpy.execute('''
-#         WITH region_extent AS (
-#             SELECT ST_Extent(the_geom) as table_extent FROM {table_name};
-#         )
-#         SELECT {features_names}, {table_name}.{column_name}
-#         FROM   {table_name} ,region_extent
-#         JOIN   {census_table}
-#         ON  {table_name}.{geoid_column} = {census_table}.geoid
-#         WHERE {census_table}.the_geom && region_extent.table_extent
-#     '''.format(**locals()))
-#
-#     if len(join_data) == 0:
-#         plpy.notice('Failed to join with census data')
-#
-#     return join_data
-#
-# def cdb_predict_segment(segment_name,geoid_column,census_table):
-#     """
-#     predict a segment with machine learning
-#     Stuart Lynn
-#     """
-#     data     = fetch_model(segment_name)
-#     model    = data['model']
-#     features = ",".join(data['features'])
-#     targets  = plpy.execute('select {features} from {census_table}')
-#     geo_ids  = plpy.execute('select geoid from {census_table}')
-#     result   = model.predict(targets)
-#     return zip(geo_ids,prediction)
-#
-#
-# def fetch_model(model_name):
-#     """
-#     fetch a model from storage
-#     """
-#     data = plpy.execute('select * from models where name={model_name}')
-#     if len(data)==0:
-#         plpy.notice('model not found')
-#     data = data[0]
-#     data['model'] = pickle.load(data['model'])
-#     return data
-#
-#
-# def create_model_table(model_name):
-#     """
-#     create the model table if requred
-#     """
-#     plpy.execute('''
-#         CREATE table IF NOT EXISTS _cdb_models(
-#             name TEXT,
-#             model BLOB,
-#             features TEXT[],
-#             accuracy NUMERIC,
-#             table_name TEXT,
-#     )''')
-#
-# def save_model(model_name,model,accuracy,table_name, column_name,census_table,geoid_column,method):
-#     """
-#     save a model to the model table for later use
-#     """
-#
-#     plpy.execute('''
-#         DELETE FROM _cdb_models WHERE model_name = {model_name}
-#     '''.format(**locals()))
-#
-#     plpy.execute("""
-#         INSERT INTO _cdb_models ({model_name},{model_pickle},{accuracy})
-#     """)
-#
-# def
+"""
+Segmentation creation and prediction
+"""
+
+import sklearn
+import numpy as np
+import pandas as pd
+import pickle
+import plpy
+from sklearn.ensemble import ExtraTreesRegressor
+from sklearn import metrics
+from sklearn.cross_validation import train_test_split
+
+# High level interface ---------------------------------------
+
+def cdb_create_segment(segment_name,table_name,column_name,geoid_column,census_table,method):
+    """
+    generate a segment with machine learning
+    Stuart Lynn
+    """
+    data     = pd.DataFrame(join_with_census(table_name, column_name,geoid_column, census_table,))
+    features = data[data.columns.difference([column_name, 'geoid'])]
+    target, mean, std = normalize(data[column_name])
+    model, accuracy = train_model(target,features, test_split=0.2)
+    save_model(segment_name, model, accuracy, table_name, column_name, census_table, geoid_column, method)
+    return accuracy
+
+def normalize(target):
+    mean = np.mean(target)
+    std  = no.std(target)
+    return (target - mean)/std, mean, std
+
+def denormalize(target, mean ,std):
+    return target*std + mean
+
+def train_model(target,features,test_split):
+    features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
+    model = ExtraTreesRegressor(n_estimators = 40, max_features=len(features.columns))
+    model.fit(features_train, target_train)
+    accuracy = calculate_model_accuracy(model,features,target)
+    return model, accuracy
+
+def calculate_model_accuracy(model,features,target):
+    prediction = self.model.predict(features)
+    return metrics.mean_squared_error(prediction,target)/np.std(target)
+
+def join_with_census(table_name, column_name, geoid_column, census_table):
+    coulmns        = plpy.execute('select {census_table}.* limit 1 ')
+    feature_names  = ",".join(columns.keys.difference(['the_geom','cartodb_id']))
+    join_data     = plpy.execute('''
+        WITH region_extent AS (
+            SELECT ST_Extent(the_geom) as table_extent FROM {table_name};
+        )
+        SELECT {features_names}, {table_name}.{column_name}
+        FROM   {table_name} ,region_extent
+        JOIN   {census_table}
+        ON  {table_name}.{geoid_column} = {census_table}.geoid
+        WHERE {census_table}.the_geom && region_extent.table_extent
+    '''.format(**locals()))
+
+    if len(join_data) == 0:
+        plpy.notice('Failed to join with census data')
+
+    return join_data
+
+def cdb_predict_segment(segment_name,geoid_column,census_table):
+    """
+    predict a segment with machine learning
+    Stuart Lynn
+    """
+    data     = fetch_model(segment_name)
+    model    = data['model']
+    features = ",".join(data['features'])
+    targets  = plpy.execute('select {features} from {census_table}')
+    geo_ids  = plpy.execute('select geoid from {census_table}')
+    result   = model.predict(targets)
+    return zip(geo_ids,prediction)
+
+
+def fetch_model(model_name):
+    """
+    fetch a model from storage
+    """
+    data = plpy.execute('select * from models where name={model_name}')
+    if len(data)==0:
+        plpy.notice('model not found')
+    data = data[0]
+    data['model'] = pickle.load(data['model'])
+    return data
+
+
+def create_model_table(model_name):
+    """
+    create the model table if requred
+    """
+    plpy.execute('''
+        CREATE table IF NOT EXISTS _cdb_models(
+            name TEXT,
+            model BLOB,
+            features TEXT[],
+            accuracy NUMERIC,
+            table_name TEXT,
+    )''')
+
+def save_model(model_name,model,accuracy,table_name, column_name,census_table,geoid_column,method):
+    """
+    save a model to the model table for later use
+    """
+
+    plpy.execute('''
+        DELETE FROM _cdb_models WHERE model_name = {model_name}
+    '''.format(**locals()))
+
+    plpy.execute("""
+        INSERT INTO _cdb_models ({model_name},{model_pickle},{accuracy})
+    """)
+
+def