generate yes/no churn

prediction.py

@@ -61,11 +61,13 @@ def run():
         # transfrom data 
         data_inf_transform = final_pipeline.transform(data_inf)
 
-        # Predict using bagging 
+        #modelling data 
         y_pred_inf = model_ann.predict(data_inf_transform)
-        y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
-        y_pred_inf
-
+        
+        if y_pred_inf >= 0.5:
+           st.write('## Customer is Churn : Yes')
+        else :
+           st.write('## Customer is Churn : No')
 
 if __name__=='__main__':
     run()