diff --git a/is/kaggle/cv.py b/is/kaggle/cv.py
index 57a9417..8411f38 100644
--- a/is/kaggle/cv.py
+++ b/is/kaggle/cv.py
@@ -49,15 +49,34 @@
     return(np.sqrt(error/n))
 
 #1st example
+def interpol(data):
+    return data['Weight'].interpolate()
+
+def calorieBased(data):
+    calMean=data['Calories'].mean()
+    calSTD=data['Calories'].std()
+    #fill with random data for nan-values
+    data.fillna(np.random(loc=calMean,scale=calSTD), axis='Calories')
+    print(data)
+    for i in range(len(data)):
+        if i==0:
+            data['Weight'][0]=data['Weight'].mean()
+        elif np.isnan(data['Weight'][i]):
+            data['Weight',i]=data['Weight'][i-1]+(np.mean(data['Calories'][i-5:i])-calMean)/10
+
+    return(data['Weight'])
+
 rmse=[]
 sum=0
 n=0
 for i in range(10):
-    data_train[i]['WeightInter'] = data_train[i]['Weight'].interpolate()
-    rmse.append(evaluate(data_train[i], data_test[i],'WeightInter'))
+    data_train[i]['predWeight'] = interpol(data_train[i])
+    rmse.append(evaluate(data_train[i], data_test[i]))
     if(~np.isnan(rmse[i])):
         n+=1
         sum+=rmse[i]
     print("RMSE(",i,"):",rmse[i])
 
 print("Mean RSME:",sum/n)
+
+calorieBased(data_train[1])