#dataframe = read_csv(url, names=names)
array = df_test.values
X_FS = array[:,0:25]
Y_FS = array[:,25]
# feature extraction
model = LogisticRegression(solver='lbfgs')
rfe = RFE(model, 15)
fit = rfe.fit(X_FS,Y_FS)
print("Num Features: %d" % fit.n_features_)
print("Selected Features: %s" % fit.support_)
print("Feature Ranking: %s" % fit.ranking_)
#Creating a dataframe for selected 15 columns
rfe=pd.DataFrame(df_test)
rfe.drop(['Hospital Id','ccs_diagnosis_code','ccs_procedure_code','Tot_charg', 'Tot_cost','Area_Service', 'Hospital County', 'Hospital Name', 'apr_drg_description','Abortion',],inplace=True,axis=1)
rfe.columns
rfe.shape
X_rfe=rfe.iloc[:,0:15]
Y_rfe= rfe.iloc[:,15]
#################################################################################
#Function used to plot confusion matrix
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
