In[73]: data.head(20)
 Out[73]:

   admit  gre   gpa  rank_2  rank_3  rank_4
0      0  380  3.61     0.0     1.0     0.0
1      1  660  3.67     0.0     1.0     0.0
2      1  800  4.00     0.0     0.0     0.0
3      1  640  3.19     0.0     0.0     1.0
4      0  520  2.93     0.0     0.0     1.0
5      1  760  3.00     1.0     0.0     0.0
6      1  560  2.98     0.0     0.0     0.0

y = data['admit']
x = data[data.columns[1:]]

from sklearn.cross_validation import  train_test_split
xtrain,xtest,ytrain,ytest  = train_test_split(x,y,random_state=2)

ytrain=np.ravel(ytrain)

#modelling
clf = LogisticRegression(penalty='l2')
clf.fit(xtrain,ytrain)
ypred = clf.predict(xtest)

In[77]: #checking the classification accuracy
accuracy_score(ytest,ypred)
Out[77]: 0.66000000000000003

In[78]: #confusion metrix...
from sklearn.metrics import confusion_matrix
confusion_matrix(ytest,ypred)

Out[78]:
array([[62,  1],
       [33,  4]])