diff --git a/README.md b/README.md
index f5c5e5d..46d2425 100644
--- a/README.md
+++ b/README.md
@@ -31,14 +31,14 @@ The trainer will store the data on disk (for now) in a structured folder that wi
 **Generate a candidate dataset from the learned features**
 
 
-        import pandas as pd
-        import data.maker
+    import pandas as pd
+    import data.maker
 
-        df  = pd.read_csv('sample.csv')
-        id  = 'id'
-        column = 'gender'
-        context = 'demo'
-        data.maker.generate(data=df,id=id,column=column,logs='logs')
+    df  = pd.read_csv('sample.csv')
+    id  = 'id'
+    column = 'gender'
+    context = 'demo'
+    data.maker.generate(data=df,id=id,column=column,logs='logs')
 
 ## Limitations
 
@@ -49,7 +49,7 @@ GANS will generate data assuming the original data has all the value space neede
         Assuming we have a dataset with an gender attribute with values [M,F]. 
             
         The synthetic data will not be able to generate genders outside [M,F]
-        
+
 - Not advised on continuous values
 
         GANS work well on discrete values and thus are not advised to be used.