text: """"
  date: 06-10-2020
  author: Poulomi Chatterjee
  """"
  import pandas as pd
  import numpy as np
  from sklearn import preprocessing
  import matplotlib.pyplot as plt 
  plt.rc("font", size=14)
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  import seaborn as sns
  sns.set(style="white")
  sns.set(style="whitegrid", color_codes=True)
  
  data = pd.read_csv(r"banking.csv")
  data = data.dropna() #to remove null rows
  
  #reduce number of categories
  data['education']=np.where(data['education'] =='basic.9y', 'Basic', data['education'])
  data['education']=np.where(data['education'] =='basic.6y', 'Basic', data['education'])
  data['education']=np.where(data['education'] =='basic.4y', 'Basic', data['education'])
  
  #create dummy variables
  cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']
  for var in cat_vars:
      cat_list='var'+'_'+var
      cat_list = pd.get_dummies(data[var], prefix=var)
      data1=data.join(cat_list)
      data=data1
  cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']
  data_vars=data.columns.values.tolist()
  to_keep=[i for i in data_vars if i not in cat_vars]
  data_final=data[to_keep] #final coulumns 
  
  #SMOTE
  X = data_final.loc[:, data_final.columns != 'y']
  y = data_final.loc[:, data_final.columns == 'y']
  from imblearn.over_sampling import SMOTE
  os = SMOTE(random_state=0)
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
  columns = X_train.columns
  os_data_X,os_data_y=os.fit_sample(X_train, y_train)
  os_data_X = pd.DataFrame(data=os_data_X,columns=columns )
  os_data_y= pd.DataFrame(data=os_data_y,columns=['y'])
  
  #Recursive model fitting to determine the best or worst performing feature
  data_final_vars=data_final.columns.values.tolist()
  y=['y']
  X=[i for i in data_final_vars if i not in y]
  from sklearn.feature_selection import RFE
  from sklearn.linear_model import LogisticRegression
  logreg = LogisticRegression()
  rfe = RFE(logreg, 20)
  rfe = rfe.fit(os_data_X, os_data_y.values.ravel())
  
  cols=['euribor3m', 'job_blue-collar', 'job_housemaid', 'marital_unknown', 'education_illiterate', 
        'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar', 
        'month_may', 'month_nov', 'month_oct', "poutcome_failure", "poutcome_success"] 
  X=os_data_X[cols]
  y=os_data_y['y']
  
  #implementation of the model
  import statsmodels.api as sm
  logit_model=sm.Logit(y,X)
  result=logit_model.fit()
  
  #Logistic Regression model fitting
  from sklearn.linear_model import LogisticRegression
  from sklearn import metrics
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
  logreg = LogisticRegression()
  logreg.fit(X_train, y_train)
  
  #predicting test results and finding accuracy
  y_pred = logreg.predict(X_test)
  print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))
  
  #Confusion matrix
  from sklearn.metrics import confusion_matrix
  confusion_matrix = confusion_matrix(y_test, y_pred)
  print(confusion_matrix)
  
  #ROC curve
  from sklearn.metrics import roc_auc_score
  from sklearn.metrics import roc_curve
  logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))
  fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])
  plt.figure()
  plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)
  plt.plot([0, 1], [0, 1],'r--')
  plt.xlim([0.0, 1.0])
  plt.ylim([0.0, 1.05])
  plt.xlabel('False Positive Rate')
  plt.ylabel('True Positive Rate')
  plt.title('Receiver operating characteristic')
  plt.legend(loc="lower right")
  plt.savefig('Log_ROC')
  plt.show()
  
  #end
  
  
  
  
  
  