2.py

#preprocessing 
#remove missing values 
loans = loans.dropna() 
#remove outliers 
q_low = loans["annual_inc"].quantile(0.08) 
q_hi = loans["annual_inc"].quantile(0.92) loans = loans[(loans["annual_inc"] < q_hi) & (loans["annual_inc"] > q_low)] 
loans = loans[(loans['dti'] <=45)] 
q_hi = loans['bc_open_to_buy'].quantile(0.95) 
loans = loans[(loans['bc_open_to_buy'] < q_hi)] 
loans = loans[(loans['bc_util'] <=160)] 
loans = loans[(loans['revol_util'] <=150)] 
loans = loans[(loans['num_op_rev_tl'] <=35)] 
#categorical features processing 
cleaner_app_type = {"term": {" 36 months": 1.0, " 60 months": 2.0},
     "sub_grade": {"A1": 1.0, "A2": 2.0, "A3": 3.0, "A4": 4.0,
          "A5": 5.0, "B1": 11.0, "B2": 12.0, "B3": 13.0, "B4": 14.0,
          "B5": 15.0, "C1": 21.0, "C2": 22.0, "C3": 23.0, "C4":
          24.0, "C5": 25.0, "D1": 31.0, "D2": 32.0, "D3": 33.0,
          "D4": 34.0, "D5": 35.0, "E1": 41.0, "E2": 42.0, "E3":
          43.0, "E4": 44.0, "E5": 45.0, "F1": 51.0, "F2": 52.0,
          "F3": 53.0, "F4": 54.0, "F5": 55.0, "G1": 61.0, "G2":
          62.0, "G3": 63.0, "G4": 64.0, "G5": 65.0, }, 
      "emp_length": {"< 1 year": 0.0, '1 year': 1.0, '2 years': 2.0,
          '3 years': 3.0, '4 years': 4.0, '5 years': 5.0, '6 years':
           6.0, '7 years': 7.0, '8 years': 8.0, '9 years': 9.0, '10+
           years': 10.0 } 
       } 
loans = loans.replace(cleaner_app_type)