import pandas as pd
from sklearn.feature_selection import RFE
        import statsmodels.api as sm
                                                def runLogit():

                                                       df = pd.read_excel('InputFile.xlsx', sheetname='InputToCode')

                                                       field1    = df['field1']
                                                       field2    = df['field2']
                                                       field3    = df['field3']
                                                       field4    = df['field4']
                                                       field5      = df['field5']
                                                       field6     = df['field6']
                                                       field7    = df['field7']
                                                       field8    = df['field8']
                                                       field9    = df['field9']

                                                       field10    = df['field10']
                                                       field11e  = df['field11']
                                                       field12  = df['field12']
                                                       field13 = df['field13']

                                                       df = pd.DataFrame(
                                                                          {
                                                                           'field1': field1,
                                                                           'field2': field2,
                                                                           'field3': field3,
                                                                           'field4': field4,
                                                                           'field5': field5,
                                                                           'field6': field6,
                                                                           'field7': feild7,
                                                                           'field8': field8,
                                                                           'field9': field9,
                                                                           'field10': field10,
                                                                           'field11': field11,
                                                                           'field12': field12,
                                                                           'field13':   field13
                                                                           }
                                                                        )

                                              # Field1 is an Actual list of 1's and 0's in the input data set (which we are trying to predict through the Logit)
                                                       y = df['field1'].values

                                                       print (len(y))
                                                       print (df.shape)
                                                       logit_model = sm.Logit(y.astype(float), df.astype(float))
                                                       result = logit_model.fit()
                                                       print (result.summary())




                                                #==============================================================================
                                                # Initial call
                                                #==============================================================================

                                                runLogit()


            The warning I am getting is:

           Warning: Maximum number of iterations has been exceeded.
             Current function value: 0.296861
             Iterations: 35
    Traceback (most recent call last):

      File "<ipython-input-53-4d118e1bf22e>", line 1, in <module>
        runfile('C:/Users/xxxx/Desktop/proj/proj.py', wdir='C:/Users/xxxx/Desktop/KYV')

      File "C:UsersxxxxAppDataLocalContinuumanaconda3libsite-packagesspyderutilssitesitecustomize.py", line 710, in runfile
        execfile(filename, namespace)

      File "C:UsersxxxxAppDataLocalContinuumanaconda3libsite-packagesspyderutilssitesitecustomize.py", line 101, in execfile
        exec(compile(f.read(), filename, 'exec'), namespace)

      File "C:/Users/xxxx/Desktop/KYV/KYV.py", line 135, in <module>
        runLogit()

      File "C:/Users/xxxx/Desktop/KYV/KYV.py", line 125, in runLogit
        result = logit_model.fit()

      File "C:UsersxxxxAppDataLocalContinuumanaconda3libsite-packagesstatsmodelsdiscretediscrete_model.py", line 1377, in fit
        disp=disp, callback=callback, **kwargs)

      File "C:UsersxxxxAppDataLocalContinuumanaconda3libsite-packagesstatsmodelsdiscretediscrete_model.py", line 204, in fit
        disp=disp, callback=callback, **kwargs)

      File "C:UsersxxxxAppDataLocalContinuumanaconda3libsite-packagesstatsmodelsbasemodel.py", line 458, in fit
        Hinv = np.linalg.inv(-retvals['Hessian']) / nobs

      File "C:UsersxxxxAppDataLocalContinuumanaconda3libsite-packagesnumpylinalglinalg.py", line 513, in inv
        ainv = _umath_linalg.inv(a, signature=signature, extobj=extobj)

      File "C:UsersxxxxAppDataLocalContinuumanaconda3libsite-packagesnumpylinalglinalg.py", line 90, in _raise_linalgerror_singular
        raise LinAlgError("Singular matrix")

    LinAlgError: Singular matrix                End of Stack Trace.

y = ['y']
   X = [i for i in data_final if i not in y]

   #Feature selection
   logistic = LogisticRegression()
   rfe = RFE(logistic, *number of features*)
   rfe = rfe.fit(***df[X], data_final[y]***)
   print(rfe.support_)
   print(rfe.ranking_)