Update app.py

app.py

@@ -22,14 +22,11 @@ data.head()
 to_use_cols = ['as_of_year', 'county_name', 'applicant_sex_name', 'action_taken_name', 'loan_amount_000s', 'applicant_income_000s', 'state_name', 'property_type_name', 'loan_type_name']
 
 data_reduced = data[to_use_cols]
-data_reduced.head()
 
 data_reduced.shape
 
 data_no_na = data_reduced.dropna()
 
-data_no_na.shape
-
 data_no_na.action_taken_name.value_counts()
 
 succeeded = ['Loan originated', 'Loan purchased by the institution', ]
@@ -103,7 +100,7 @@ tree.fit(X_train, y_train)
 def mapping(**kwargs):
   kwargs['sex'] = 1 if kwargs['sex'] == 'Male' else 0
   kwargs['property_type'] =  1 if kwargs['property_type'] == 'Manufactured housing' else 0
-  kwargs['loan'] = float(kwargs['loan'])
+  kwargs['loan'] = mapped_type[kwargs['loan']]
   kwargs['income'] = float(kwargs['income'])
   kwargs['loan_amount'] = float(kwargs['loan_amount'])
   kwargs['county'] = code[kwargs['county']]
@@ -111,6 +108,8 @@ def mapping(**kwargs):
 
 
 st.title('Loan Approval for Washington House')
+st.dataframe(data_no_na.head())
+
 
 col1, col2 = st.columns([3, 1])
 with col1: