diff --git a/is/kaggle/cv Max.py b/is/kaggle/cv Max.py
new file mode 100644
index 0000000..1b093d8
--- /dev/null
+++ b/is/kaggle/cv Max.py
@@ -0,0 +1,96 @@
+#including cv
+import pandas as pd
+import numpy as np
+from sklearn.cross_validation import KFold
+
+def read(path="./datasets/train.csv"):
+    return(pd.read_csv(path, index_col='Date', parse_dates='Date'))
+
+data=read()
+
+def cv(data=data, n_folds=10):
+    """split data in n_folds parts for cross validation
+    """
+
+    cleanData=data[pd.notnull(data['Weight'])]
+
+    kf=KFold(len(cleanData), shuffle=True, n_folds=n_folds)
+
+    trainid=[]
+    testid=[]
+    for train, test in kf:
+        trainid.append(train)
+        testid.append(test)
+
+    data_test=[]
+    data_train=[]
+    for i in range(n_folds):
+        data_train.append(data.copy())
+        data_test.append([])
+        for j in testid[i]:
+            data_test[i].append(pd.DataFrame(cleanData.iloc[j]))
+            #crazy hack, necessary ...
+            train=data_train[i][data_train[i]['ID']==cleanData.iloc[j]['ID']]
+            train['Weight']=float('NaN')
+            data_train[i][data_train[i]['ID']==cleanData.iloc[j]['ID']]=train
+    return (data_train,data_test)
+
+data_train, data_test=cv()
+
+def evaluate(predictions, data_test, predictedWeight='predWeight'):
+    """calcs the rmse on the testdata"""
+    n=len(data_test)
+    error=0
+    for i in range(n):
+        test_value=np.float64(data_test[i].loc['Weight'])
+        #no better idea...
+        pred_value=predictions.iloc[int(data_test[i].loc['ID'])-1][predictedWeight]
+        error+= (test_value - pred_value)**2
+    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['Calories']=data['Calories'].fillna(np.random.normal(loc=calMean,scale=calSTD,size=len(data['Calories'])-data['Calories'].count()))
+    nans=len(data['Calories'])-data['Calories'].count()
+    dfrand = calSTD*np.random.randn(nans)+calMean
+    
+    #data['Calories',np.isnan(data['Calories'])]= dfrand[np.isnan(data['Calories'])]; #Erzeuge zufaellige kalorienwerte, ersetze sie durch nan werte.
+    a=[]
+    c=0
+    for i in range(len(data['Calories'])):
+        if np.isnan(data['Calories'][i]):
+        
+            a.append(dfrand[c])
+            c+=1       
+        else:
+            a.append(data['Calories'][i])     
+    data['Calories']=a
+
+    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]['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)
+#print(data_train[1])
+calorieBased(data_train[1])