ExpectedLoss_assignment.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"91fecf11"},"outputs":[],"source":["#import the basic libraries\n","import numpy as np\n","import pandas as pd"],"id":"91fecf11"},{"cell_type":"code","execution_count":null,"metadata":{"id":"d5092023"},"outputs":[],"source":["pd.options.display.max_rows= None"],"id":"d5092023"},{"cell_type":"code","execution_count":null,"metadata":{"id":"un2Esmri7017"},"outputs":[],"source":["#import warning\n","import warnings\n","warnings.filterwarnings('ignore')"],"id":"un2Esmri7017"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4457,"status":"ok","timestamp":1693058427718,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"},"user_tz":-330},"id":"Nt8dvwqQ7o_E","outputId":"951e0d8f-f709-4f9a-b2b8-62cfae1f800d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","\n","drive.mount('/content/drive/')"],"id":"Nt8dvwqQ7o_E"},{"cell_type":"code","execution_count":null,"metadata":{"id":"FuRqUC0P7y36"},"outputs":[],"source":["#load all the datasets we have created previously\n","\n","load_data_inputs_train=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/ML Projects/Credit risk analysis/loan_data_inputs_train.csv',index_col=0)\n","load_data_targets_train=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/ML Projects/Credit risk analysis/loan_data_targets_train.csv',index_col=0,header=None)\n","load_data_inputs_test=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/ML Projects/Credit risk analysis/loan_data_inputs_test.csv',index_col=0)\n","load_data_targets_test=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/ML Projects/Credit risk analysis/loan_data_targets_test.csv',index_col=0,header=None)"],"id":"FuRqUC0P7y36"},{"cell_type":"code","execution_count":null,"metadata":{"id":"vl1dgag17-JG"},"outputs":[],"source":["#print the head of load_data_targets\n","load_data_targets_train.head()"],"id":"vl1dgag17-JG"},{"cell_type":"code","execution_count":null,"metadata":{"id":"f4022839"},"outputs":[],"source":["#drop the first row as it is string\n","load_data_targets_train=load_data_targets_train.drop(load_data_targets_train.index[0])"],"id":"f4022839"},{"cell_type":"code","execution_count":null,"metadata":{"id":"dae4e358"},"outputs":[],"source":["#view the shape of load_data_inputs_train dataframe\n","load_data_inputs_train.shape"],"id":"dae4e358"},{"cell_type":"code","execution_count":null,"metadata":{"id":"6f5d54ae"},"outputs":[],"source":["#view the shape ofload_data_targets_train dataframe\n","load_data_targets_train.shape"],"id":"6f5d54ae"},{"cell_type":"code","execution_count":null,"metadata":{"id":"c1a35b69"},"outputs":[],"source":["#create a new column outcome(0 or 1) in load_data_inputs_train\n","load_data_inputs_train['outcome'] = load_data_targets_train"],"id":"c1a35b69"},{"cell_type":"code","execution_count":null,"metadata":{"id":"c0f49ca5"},"outputs":[],"source":["#check the value_counts of outcome\n","load_data_inputs_train['outcome'].value_counts()"],"id":"c0f49ca5"},{"cell_type":"code","execution_count":null,"metadata":{"id":"51c0c2c6"},"outputs":[],"source":["#type cast outcome as integer\n","load_data_inputs_train['outcome'] =load_data_inputs_train['outcome'].astype(int)"],"id":"51c0c2c6"},{"cell_type":"code","execution_count":null,"metadata":{"id":"i0HqMn-Y9V7n"},"outputs":[],"source":["#check the value_counts of outcome\n","load_data_inputs_train['outcome'].value_counts()"],"id":"i0HqMn-Y9V7n"},{"cell_type":"code","execution_count":null,"metadata":{"id":"796cb993"},"outputs":[],"source":["#check the head of load_data_inputs_train\n","load_data_inputs_train.head()"],"id":"796cb993"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"d2834da9"},"outputs":[],"source":["#Take only the default cases where outcome = 0\n","load_data_defaults = load_data_inputs_train[load_data_inputs_train['outcome']==0]                                                                             #taking only defaults"],"id":"d2834da9"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"2b7cb80f","outputId":"8f671435-d2cb-4671-f691-95cae0851f30"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-0903e21b-eb84-4637-973e-d4986c1f38cd\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr 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<th>mths_since_last_record:&gt;=127</th>\n","      <th>outcome</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>412305</th>\n","      <td>412305</td>\n","      <td>13827698</td>\n","      <td>15890016</td>\n","      <td>17200</td>\n","      <td>17200</td>\n","      <td>17200.0</td>\n","      <td>36 months</td>\n","      <td>16.59</td>\n","      <td>609.73</td>\n","      <td>D</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>36159</th>\n","      <td>36159</td>\n","      <td>422455</td>\n","      <td>496525</td>\n","      <td>8400</td>\n","      <td>8400</td>\n","      <td>7450.0</td>\n","      <td>36 months</td>\n","      <td>12.84</td>\n","      <td>282.40</td>\n","      <td>C</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>454638</th>\n","      <td>454638</td>\n","      <td>10494891</td>\n","      <td>12367014</td>\n","      <td>20000</td>\n","      <td>20000</td>\n","      <td>20000.0</td>\n","      <td>36 months</td>\n","      <td>11.99</td>\n","      <td>664.20</td>\n","      <td>B</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>222888</th>\n","      <td>222888</td>\n","      <td>1197171</td>\n","      <td>1438165</td>\n","      <td>8500</td>\n","      <td>8500</td>\n","      <td>8500.0</td>\n","      <td>36 months</td>\n","      <td>8.90</td>\n","      <td>269.91</td>\n","      <td>A</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>377139</th>\n","      <td>377139</td>\n","      <td>17523141</td>\n","      <td>19645687</td>\n","      <td>4850</td>\n","      <td>4850</td>\n","      <td>4850.0</td>\n","      <td>36 months</td>\n","      <td>16.99</td>\n","      <td>172.90</td>\n","      <td>D</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 339 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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\n","\n","       dti:25-30 dti:30-35 dti:35-40 dti:>40  mths_since_last_record:Missing  \\\n","412305         0         0         0       0                               1   \n","36159          0         0         0       0                               1   \n","454638         0         0         0       0                               1   \n","222888         0         0         0       0                               1   \n","377139         1         0         0       0                               1   \n","\n","       mths_since_last_record:0-90 mths_since_last_record:90-120  \\\n","412305                           0                             0   \n","36159                            0                             0   \n","454638                           0                             0   \n","222888                           0                             0   \n","377139                           0                             0   \n","\n","       mths_since_last_record:120-127 mths_since_last_record:>=127 outcome  \n","412305                              0                            0       0  \n","36159                               0                            0       0  \n","454638                              0                            0       0  \n","222888                              0                            0       0  \n","377139                              0                            0       0  \n","\n","[5 rows x 339 columns]"]},"execution_count":86,"metadata":{},"output_type":"execute_result"}],"source":["#check the head of load_data_defaults\n","load_data_defaults.head()"],"id":"2b7cb80f"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"33f7e4e2","outputId":"2cb98545-d34e-4e47-d14d-479097570ca3"},"outputs":[{"data":{"text/plain":["Unnamed: 0                                                             0\n","id                                                                     0\n","member_id                                                              0\n","loan_amnt                                                              0\n","funded_amnt                                                            0\n","funded_amnt_inv                                                        0\n","term                                                                   0\n","int_rate                                                               0\n","installment                                                            0\n","grade                                                                  0\n","sub_grade                                                              0\n","emp_title                                                           3046\n","emp_length                                                          2215\n","home_ownership                                                         0\n","annual_inc                                                             0\n","verification_status                                                    0\n","issue_d                                                                0\n","loan_status                                                            0\n","pymnt_plan                                                             0\n","url                                                                    0\n","desc                                                               27179\n","purpose                                                                0\n","title                                                                  2\n","zip_code                                                               0\n","addr_state                                                             0\n","dti                                                                    0\n","delinq_2yrs                                                            0\n","earliest_cr_line                                                       3\n","inq_last_6mths                                                         0\n","mths_since_last_delinq                                             22193\n","mths_since_last_record                                             35408\n","open_acc                                                               0\n","pub_rec                                                                0\n","revol_bal                                                              0\n","revol_util                                                            47\n","total_acc                                                              0\n","initial_list_status                                                    0\n","out_prncp                                                              0\n","out_prncp_inv                                                          0\n","total_pymnt                                                            0\n","total_pymnt_inv                                                        0\n","total_rec_prncp                                                        0\n","total_rec_int                                                          0\n","total_rec_late_fee                                                     0\n","recoveries                                                             0\n","collection_recovery_fee                                                0\n","last_pymnt_d                                                         304\n","last_pymnt_amnt                                                        0\n","next_pymnt_d                                                       34006\n","last_credit_pull_d                                                     6\n","collections_12_mths_ex_med                                            20\n","mths_since_last_major_derog                                        32688\n","policy_code                                                            0\n","application_type                                                       0\n","annual_inc_joint                                                   40778\n","dti_joint                                                          40778\n","verification_status_joint                                          40778\n","acc_now_delinq                                                         0\n","tot_coll_amt                                                        8757\n","tot_cur_bal                                                         8757\n","open_acc_6m                                                        40778\n","open_il_6m                                                         40778\n","open_il_12m                                                        40778\n","open_il_24m                                                        40778\n","mths_since_rcnt_il                                                 40778\n","total_bal_il                                                       40778\n","il_util                                                            40778\n","open_rv_12m                                                        40778\n","open_rv_24m                                                        40778\n","max_bal_bc                                                         40778\n","all_util                                                           40778\n","total_rev_hi_lim                                                       0\n","inq_fi                                                             40778\n","total_cu_tl                                                        40778\n","inq_last_12m                                                       40778\n","emp_length_int                                                         0\n","term_int                                                               0\n","earliest_cr_line_date                                                  3\n","mths_since_earliest_cr_line                                            0\n","issue_date                                                             0\n","mths_since_issued_date                                                 0\n","grade:A                                                                0\n","grade:B                                                                0\n","grade:C                                                                0\n","grade:D                                                                0\n","grade:E                                                                0\n","grade:F                                                                0\n","grade:G                                                                0\n","sub_grade:A1                                                           0\n","sub_grade:A2                                                           0\n","sub_grade:A3                                                           0\n","sub_grade:A4                                                           0\n","sub_grade:A5                                                           0\n","sub_grade:B1                                                           0\n","sub_grade:B2                                                           0\n","sub_grade:B3                                                           0\n","sub_grade:B4                                                           0\n","sub_grade:B5                                                           0\n","sub_grade:C1                                                           0\n","sub_grade:C2                                                           0\n","sub_grade:C3                                                           0\n","sub_grade:C4                                                           0\n","sub_grade:C5                                                           0\n","sub_grade:D1                                                           0\n","sub_grade:D2                                                           0\n","sub_grade:D3                                                           0\n","sub_grade:D4                                                           0\n","sub_grade:D5                                                           0\n","sub_grade:E1                                                           0\n","sub_grade:E2                                                           0\n","sub_grade:E3                                                           0\n","sub_grade:E4                                                           0\n","sub_grade:E5                                                           0\n","sub_grade:F1                                                           0\n","sub_grade:F2                                                           0\n","sub_grade:F3                                                           0\n","sub_grade:F4                                                           0\n","sub_grade:F5                                                           0\n","sub_grade:G1                                                           0\n","sub_grade:G2                                                           0\n","sub_grade:G3                                                           0\n","sub_grade:G4                                                           0\n","sub_grade:G5                                                           0\n","home_ownership:ANY                                                     0\n","home_ownership:MORTGAGE                                                0\n","home_ownership:NONE                                                    0\n","home_ownership:OTHER                                                   0\n","home_ownership:OWN                                                     0\n","home_ownership:RENT                                                    0\n","verification_status:Not Verified                                       0\n","verification_status:Source Verified                                    0\n","verification_status:Verified                                           0\n","loan_status:Charged Off                                                0\n","loan_status:Current                                                    0\n","loan_status:Default                                                    0\n","loan_status:Does not meet the credit policy. Status:Charged Off        0\n","loan_status:Does not meet the credit policy. Status:Fully Paid         0\n","loan_status:Fully Paid                                                 0\n","loan_status:In Grace Period                                            0\n","loan_status:Late (16-30 days)                                          0\n","loan_status:Late (31-120 days)                                         0\n","purpose:car                                                            0\n","purpose:credit_card                                                    0\n","purpose:debt_consolidation                                             0\n","purpose:educational                                                    0\n","purpose:home_improvement                                               0\n","purpose:house                                                          0\n","purpose:major_purchase                                                 0\n","purpose:medical                                                        0\n","purpose:moving                                                         0\n","purpose:other                                                          0\n","purpose:renewable_energy                                               0\n","purpose:small_business                                                 0\n","purpose:vacation                                                       0\n","purpose:wedding                                                        0\n","addr_state:AK                                                          0\n","addr_state:AL                                                          0\n","addr_state:AR                                                          0\n","addr_state:AZ                                                          0\n","addr_state:CA                                                          0\n","addr_state:CO                                                          0\n","addr_state:CT                                                          0\n","addr_state:DC                                                          0\n","addr_state:DE                                                          0\n","addr_state:FL                                                          0\n","addr_state:GA                                                          0\n","addr_state:HI                                                          0\n","addr_state:IA                                                          0\n","addr_state:ID                                                          0\n","addr_state:IL                                                          0\n","addr_state:IN                                                          0\n","addr_state:KS                                                          0\n","addr_state:KY                                                          0\n","addr_state:LA                                                          0\n","addr_state:MA                                                          0\n","addr_state:MD                                                          0\n","addr_state:ME                                                          0\n","addr_state:MI                                                          0\n","addr_state:MN                                                          0\n","addr_state:MO                                                          0\n","addr_state:MS                                                          0\n","addr_state:MT                                                          0\n","addr_state:NC                                                          0\n","addr_state:NE                                                          0\n","addr_state:NH                                                          0\n","addr_state:NJ                                                          0\n","addr_state:NM                                                          0\n","addr_state:NV                                                          0\n","addr_state:NY                                                          0\n","addr_state:OH                                                          0\n","addr_state:OK                                                          0\n","addr_state:OR                                                          0\n","addr_state:PA                                                          0\n","addr_state:RI                                                          0\n","addr_state:SC                                                          0\n","addr_state:SD                                                          0\n","addr_state:TN                                                          0\n","addr_state:TX                                                          0\n","addr_state:UT                                                          0\n","addr_state:VA                                                          0\n","addr_state:VT                                                          0\n","addr_state:WA                                                          0\n","addr_state:WI                                                          0\n","addr_state:WV                                                          0\n","addr_state:WY                                                          0\n","initial_list_status:f                                                  0\n","initial_list_status:w                                                  0\n","home_ownership:RENT_OTHER_NONE_ANY                                     0\n","addr_state:ND                                                          0\n","addr_state:ND_NE_IA_NC_FL_HI_AL                                        0\n","addr_state:NM_VA                                                       0\n","addr_state:OK_TN_MO_LA_MD_NC                                           0\n","addr_state:UT_KY_AZ_NJ                                                 0\n","addr_state:AR_MI_PA_OH_MN                                              0\n","addr_state:RI_MA_DE_SD_IN                                              0\n","addr_state:GA_WA_OR                                                    0\n","addr_state:WI_MT                                                       0\n","addr_state:IL_CT                                                       0\n","addr_state:KS_SC_CO_VT_AK_MS                                           0\n","addr_state:WV_NH_WY_DC_ME_ID                                           0\n","purpose:educ_sm_b_wedd_ren_en_mov_house                                0\n","term:36                                                                0\n","term:60                                                                0\n","emp_length:0                                                           0\n","emp_length:1                                                           0\n","emp_length:2-4                                                         0\n","emp_length:5-6                                                         0\n","emp_length:7-9                                                         0\n","emp_length:10                                                          0\n","mths_since_issued_date_factor                                          0\n","mths_since_issued_date:<38                                             0\n","mths_since_issued_date:38-39                                           0\n","mths_since_issued_date:40-41                                           0\n","mths_since_issued_date:42-48                                           0\n","mths_since_issued_date:49-52                                           0\n","mths_since_issued_date:53-64                                           0\n","mths_since_issued_date:65-84                                           0\n","mths_since_issued_date:>84                                             0\n","int_rate_factor                                                        0\n","int_rate:<9.548                                                        0\n","int_rate:9.548-12.025                                                  0\n","int_rate:12.025-15.74                                                  0\n","int_rate:15.74-20.281                                                  0\n","int_rate:20.281                                                        0\n","funded_amnt_factor                                                     0\n","mths_since_earliest_cr_line_factor                                     0\n","mths_since_earliest_cr_line:<140                                       0\n","mths_since_earliest_cr_line:140-239                                    0\n","mths_since_earliest_cr_line:240-339                                    0\n","mths_since_earliest_cr_line:340-439                                    0\n","mths_since_earliest_cr_line:440-539                                    0\n","mths_since_earliest_cr_line:540-600                                    0\n","mths_since_earliest_cr_line:>600                                       0\n","installment_factor                                                     0\n","delinq_2yrs_factors                                                    0\n","delinq_2yrs:0                                                          0\n","delinq_2yrs:0-3                                                        0\n","delinq_2yrs:3-7                                                        0\n","delinq_2yrs:7-9                                                        0\n","delinq_2yrs:9-11                                                       0\n","delinq_2yrs:11-12                                                      0\n","delinq_2yrs:12-18                                                      0\n","delinq_2yrs:>18                                                        0\n","inq_last_6mths:0                                                       0\n","inq_last_6mths:0-3                                                     0\n","inq_last_6mths:3-7                                                     0\n","inq_last_6mths:7-9                                                     0\n","inq_last_6mths:9-11                                                    0\n","inq_last_6mths:11-12                                                   0\n","inq_last_6mths:12-18                                                   0\n","inq_last_6mths:18                                                      0\n","open_acc:=0                                                            0\n","open_acc:0-1                                                           0\n","open_acc:1-4                                                           0\n","open_acc:4-28                                                          0\n","open_acc:28-35                                                         0\n","open_acc:35-45                                                         0\n","open_acc:>45                                                           0\n","pub_rec:0-3                                                            0\n","pub_rec:3-6                                                            0\n","pub_rec:6-8                                                            0\n","pub_rec:8                                                              0\n","total_acc_fac                                                          0\n","total_acc:<0                                                           0\n","total_acc:0-4                                                          0\n","total_acc:4-62                                                         0\n","total_acc:62-72                                                        0\n","total_acc:72-85                                                        0\n","total_acc:>85                                                          0\n","acc_now_delinq:<0                                                      0\n","acc_now_delinq:0-1                                                     0\n","acc_now_delinq:1-2                                                     0\n","acc_now_delinq:2-3                                                     0\n","acc_now_delinq:3-5                                                     0\n","acc_now_delinq:>5                                                      0\n","total_rev_hi_lim_factor                                              828\n","total_rev_hi_lim:<=5k                                                  0\n","total_rev_hi_lim:5k_10k                                                0\n","total_rev_hi_lim:10k_20k                                               0\n","total_rev_hi_lim:20k_30k                                               0\n","total_rev_hi_lim:30k_40k                                               0\n","total_rev_hi_lim:40k_50k                                               0\n","total_rev_hi_lim:50k_65k                                               0\n","total_rev_hi_lim:65k_80k                                               0\n","total_rev_hi_lim:>80k                                                  0\n","annual_inc_factor                                                      0\n","annual_inc:<20k                                                        0\n","annual_inc:20k-30k                                                     0\n","annual_inc:30k-40k                                                     0\n","annual_inc:40k-50k                                                     0\n","annual_inc:50k-60k                                                     0\n","annual_inc:60k-70k                                                     0\n","annual_inc:70k-80k                                                     0\n","annual_inc:80k-90k                                                     0\n","annual_inc:90k-100k                                                    0\n","annual_inc:100k-120k                                                   0\n","annual_inc:120k-140k                                                   0\n","annual_inc:>140k                                                       0\n","mths_since_last_delinq:Missing                                         0\n","mths_since_last_delinq:0-3                                             0\n","mths_since_last_delinq:4-30                                            0\n","mths_since_last_delinq:31-56                                           0\n","mths_since_last_delinq:>=57                                            0\n","dti_factor                                                             0\n","dti:<5                                                                 0\n","dti:5-10                                                               0\n","dti:10-15                                                              0\n","dti:15-20                                                              0\n","dti:20-25                                                              0\n","dti:25-30                                                              0\n","dti:30-35                                                              0\n","dti:35-40                                                              0\n","dti:>40                                                                0\n","mths_since_last_record:Missing                                         0\n","mths_since_last_record:0-90                                            0\n","mths_since_last_record:90-120                                          0\n","mths_since_last_record:120-127                                         0\n","mths_since_last_record:>=127                                           0\n","outcome                                                                0\n","dtype: int64"]},"execution_count":87,"metadata":{},"output_type":"execute_result"}],"source":["#check for null values in every column\n","load_data_defaults.isnull().sum()"],"id":"33f7e4e2"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"c100fe4c"},"outputs":[],"source":["#select only the important features we have created before in preprocessing steps and create a list of those features\n","\n","feature_selection=[\n","'grade:A',\n","'grade:B',\n","'grade:C',\n","'grade:D',\n","'grade:E',\n","'grade:F',\n","'grade:G',\n","'home_ownership:RENT_OTHER_NONE_ANY',\n","'home_ownership:OWN',\n","'home_ownership:MORTGAGE',\n","'addr_state:ND_NE_IA_NC_FL_HI_AL',\n","'addr_state:NM_VA',\n","'addr_state:OK_TN_MO_LA_MD_NC',\n","'addr_state:UT_KY_AZ_NJ',\n","'addr_state:AR_MI_PA_OH_MN',\n","'addr_state:RI_MA_DE_SD_IN',\n","'addr_state:GA_WA_OR',\n","'addr_state:WI_MT',\n","'addr_state:IL_CT',\n","'addr_state:KS_SC_CO_VT_AK_MS',\n","'addr_state:WV_NH_WY_DC_ME_ID',\n","'verification_status:Verified',\n","'verification_status:Source Verified',\n","'verification_status:Not Verified',\n","    'purpose:educ_sm_b_wedd_ren_en_mov_house',\n","    'purpose:small_business',\n","    'purpose:educational',\n","    'purpose:renewable_energy',\n","    'purpose:moving',\n","    'purpose:house',\n","    'purpose:other',\n","    'purpose:medical',\n","    'purpose:wedding',\n","    'purpose:vacation',\n","    'purpose:debt_consolidation',\n","    'purpose:home_improvement',\n","    'purpose:major_purchase',\n","    'purpose:car',\n","    'purpose:credit_card',\n","    'initial_list_status:f',\n","    'initial_list_status:w',\n","'term:36',\n","'term:60',\n","'emp_length:0',\n","'emp_length:1',\n","'emp_length:2-4',\n","'emp_length:5-6',\n","'emp_length:7-9',\n","'emp_length:10',\n","'mths_since_issued_date:<38',\n","'mths_since_issued_date:38-39',\n","'mths_since_issued_date:40-41',\n","'mths_since_issued_date:42-48',\n","'mths_since_issued_date:49-52',\n","'mths_since_issued_date:53-64',\n","'mths_since_issued_date:65-84',\n","'mths_since_issued_date:>84',\n","'int_rate:<9.548',\n","'int_rate:9.548-12.025',\n","'int_rate:12.025-15.74',\n","'int_rate:15.74-20.281',\n","'int_rate:20.281',\n","    'mths_since_earliest_cr_line:<140',\n","    'mths_since_earliest_cr_line:140-239',\n","    'mths_since_earliest_cr_line:240-339',\n","    'mths_since_earliest_cr_line:340-439',\n","    'mths_since_earliest_cr_line:440-539',\n","    'mths_since_earliest_cr_line:540-600',\n","    'mths_since_earliest_cr_line:>600',\n","    'delinq_2yrs:0',\n","    'delinq_2yrs:0-3',\n","    'delinq_2yrs:3-7',\n","    'delinq_2yrs:7-9',\n","    'delinq_2yrs:9-11',\n","    'delinq_2yrs:11-12',\n","    'delinq_2yrs:12-18',\n","    'delinq_2yrs:>18',\n","    'inq_last_6mths:0',\n","    'inq_last_6mths:0-3',\n","    'inq_last_6mths:3-7',\n","    'inq_last_6mths:7-9',\n","    'inq_last_6mths:9-11',\n","    'inq_last_6mths:11-12',\n","    'inq_last_6mths:12-18',\n","    'inq_last_6mths:18',\n","    'open_acc:=0',\n","    'open_acc:0-1',\n","    'open_acc:1-4',\n","    'open_acc:4-28',\n","    'open_acc:28-35',\n","    'open_acc:35-45',\n","    'open_acc:>45',\n","    'pub_rec:0-3',\n","    'pub_rec:3-6',\n","    'pub_rec:6-8',\n","    'pub_rec:8',\n","    'total_acc:<0',\n","    'total_acc:0-4',\n","    'total_acc:4-62',\n","    'total_acc:62-72',\n","    'total_acc:72-85',\n","    'total_acc:>85',\n","    'acc_now_delinq:<0',\n","    'acc_now_delinq:0-1',\n","    'acc_now_delinq:1-2',\n","    'acc_now_delinq:2-3',\n","    'acc_now_delinq:3-5',\n","    'acc_now_delinq:>5',\n","'annual_inc:<20k',\n","'annual_inc:20k-30k',\n","'annual_inc:30k-40k',\n","'annual_inc:40k-50k',\n","'annual_inc:50k-60k',\n","'annual_inc:60k-70k',\n","'annual_inc:70k-80k',\n","'annual_inc:80k-90k',\n","'annual_inc:90k-100k',\n","'annual_inc:100k-120k',\n","'annual_inc:120k-140k',\n","'annual_inc:>140k',\n","'total_rev_hi_lim:<=5k',\n","'total_rev_hi_lim:5k_10k',\n","'total_rev_hi_lim:10k_20k',\n","'total_rev_hi_lim:20k_30k',\n","'total_rev_hi_lim:30k_40k',\n","'total_rev_hi_lim:40k_50k',\n","'total_rev_hi_lim:50k_65k',\n","'total_rev_hi_lim:65k_80k',\n","'total_rev_hi_lim:>80k',\n","'dti:<5',\n","'dti:5-10',\n","'dti:10-15',\n","'dti:15-20',\n","'dti:20-25',\n","'dti:25-30',\n","'dti:30-35',\n","'dti:35-40',\n","'dti:>40',\n","'mths_since_last_delinq:Missing',\n","'mths_since_last_delinq:0-3',\n","'mths_since_last_delinq:4-30',\n","'mths_since_last_delinq:31-56',\n","'mths_since_last_delinq:>=57',\n","'mths_since_last_record:Missing',\n","'mths_since_last_record:0-90',\n","'mths_since_last_record:90-120',\n","'mths_since_last_record:120-127',\n","'mths_since_last_record:>=127','loan_amnt','funded_amnt','int_rate','installment','recoveries','total_rec_prncp']\n","\n"],"id":"c100fe4c"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"bcb482ca"},"outputs":[],"source":["#assign them into a inputs_train_with_ref_category with only those columns which are in the list(fetaure selection)\n","load_data_defaults = load_data_defaults.reindex(feature_selection, axis='columns')"],"id":"bcb482ca"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"230dd554","outputId":"f8665542-5a3a-4a7d-8437-57e52688e6eb"},"outputs":[{"data":{"text/plain":["grade:A                                    0\n","grade:B                                    0\n","grade:C                                    0\n","grade:D                                    0\n","grade:E                                    0\n","grade:F                                    0\n","grade:G                                    0\n","home_ownership:RENT_OTHER_NONE_ANY         0\n","home_ownership:OWN                         0\n","home_ownership:MORTGAGE                    0\n","addr_state:ND_NE_IA_NC_FL_HI_AL            0\n","addr_state:NM_VA                           0\n","addr_state:OK_TN_MO_LA_MD_NC               0\n","addr_state:UT_KY_AZ_NJ                     0\n","addr_state:AR_MI_PA_OH_MN                  0\n","addr_state:RI_MA_DE_SD_IN                  0\n","addr_state:GA_WA_OR                        0\n","addr_state:WI_MT                           0\n","addr_state:IL_CT                           0\n","addr_state:KS_SC_CO_VT_AK_MS               0\n","addr_state:WV_NH_WY_DC_ME_ID               0\n","verification_status:Verified               0\n","verification_status:Source Verified        0\n","verification_status:Not Verified           0\n","purpose:educ_sm_b_wedd_ren_en_mov_house    0\n","purpose:small_business                     0\n","purpose:educational                        0\n","purpose:renewable_energy                   0\n","purpose:moving                             0\n","purpose:house                              0\n","purpose:other                              0\n","purpose:medical                            0\n","purpose:wedding                            0\n","purpose:vacation                           0\n","purpose:debt_consolidation                 0\n","purpose:home_improvement                   0\n","purpose:major_purchase                     0\n","purpose:car                                0\n","purpose:credit_card                        0\n","initial_list_status:f                      0\n","initial_list_status:w                      0\n","term:36                                    0\n","term:60                                    0\n","emp_length:0                               0\n","emp_length:1                               0\n","emp_length:2-4                             0\n","emp_length:5-6                             0\n","emp_length:7-9                             0\n","emp_length:10                              0\n","mths_since_issued_date:<38                 0\n","mths_since_issued_date:38-39               0\n","mths_since_issued_date:40-41               0\n","mths_since_issued_date:42-48               0\n","mths_since_issued_date:49-52               0\n","mths_since_issued_date:53-64               0\n","mths_since_issued_date:65-84               0\n","mths_since_issued_date:>84                 0\n","int_rate:<9.548                            0\n","int_rate:9.548-12.025                      0\n","int_rate:12.025-15.74                      0\n","int_rate:15.74-20.281                      0\n","int_rate:20.281                            0\n","mths_since_earliest_cr_line:<140           0\n","mths_since_earliest_cr_line:140-239        0\n","mths_since_earliest_cr_line:240-339        0\n","mths_since_earliest_cr_line:340-439        0\n","mths_since_earliest_cr_line:440-539        0\n","mths_since_earliest_cr_line:540-600        0\n","mths_since_earliest_cr_line:>600           0\n","delinq_2yrs:0                              0\n","delinq_2yrs:0-3                            0\n","delinq_2yrs:3-7                            0\n","delinq_2yrs:7-9                            0\n","delinq_2yrs:9-11                           0\n","delinq_2yrs:11-12                          0\n","delinq_2yrs:12-18                          0\n","delinq_2yrs:>18                            0\n","inq_last_6mths:0                           0\n","inq_last_6mths:0-3                         0\n","inq_last_6mths:3-7                         0\n","inq_last_6mths:7-9                         0\n","inq_last_6mths:9-11                        0\n","inq_last_6mths:11-12                       0\n","inq_last_6mths:12-18                       0\n","inq_last_6mths:18                          0\n","open_acc:=0                                0\n","open_acc:0-1                               0\n","open_acc:1-4                               0\n","open_acc:4-28                              0\n","open_acc:28-35                             0\n","open_acc:35-45                             0\n","open_acc:>45                               0\n","pub_rec:0-3                                0\n","pub_rec:3-6                                0\n","pub_rec:6-8                                0\n","pub_rec:8                                  0\n","total_acc:<0                               0\n","total_acc:0-4                              0\n","total_acc:4-62                             0\n","total_acc:62-72                            0\n","total_acc:72-85                            0\n","total_acc:>85                              0\n","acc_now_delinq:<0                          0\n","acc_now_delinq:0-1                         0\n","acc_now_delinq:1-2                         0\n","acc_now_delinq:2-3                         0\n","acc_now_delinq:3-5                         0\n","acc_now_delinq:>5                          0\n","annual_inc:<20k                            0\n","annual_inc:20k-30k                         0\n","annual_inc:30k-40k                         0\n","annual_inc:40k-50k                         0\n","annual_inc:50k-60k                         0\n","annual_inc:60k-70k                         0\n","annual_inc:70k-80k                         0\n","annual_inc:80k-90k                         0\n","annual_inc:90k-100k                        0\n","annual_inc:100k-120k                       0\n","annual_inc:120k-140k                       0\n","annual_inc:>140k                           0\n","total_rev_hi_lim:<=5k                      0\n","total_rev_hi_lim:5k_10k                    0\n","total_rev_hi_lim:10k_20k                   0\n","total_rev_hi_lim:20k_30k                   0\n","total_rev_hi_lim:30k_40k                   0\n","total_rev_hi_lim:40k_50k                   0\n","total_rev_hi_lim:50k_65k                   0\n","total_rev_hi_lim:65k_80k                   0\n","total_rev_hi_lim:>80k                      0\n","dti:<5                                     0\n","dti:5-10                                   0\n","dti:10-15                                  0\n","dti:15-20                                  0\n","dti:20-25                                  0\n","dti:25-30                                  0\n","dti:30-35                                  0\n","dti:35-40                                  0\n","dti:>40                                    0\n","mths_since_last_delinq:Missing             0\n","mths_since_last_delinq:0-3                 0\n","mths_since_last_delinq:4-30                0\n","mths_since_last_delinq:31-56               0\n","mths_since_last_delinq:>=57                0\n","mths_since_last_record:Missing             0\n","mths_since_last_record:0-90                0\n","mths_since_last_record:90-120              0\n","mths_since_last_record:120-127             0\n","mths_since_last_record:>=127               0\n","loan_amnt                                  0\n","funded_amnt                                0\n","int_rate                                   0\n","installment                                0\n","recoveries                                 0\n","total_rec_prncp                            0\n","dtype: int64"]},"execution_count":90,"metadata":{},"output_type":"execute_result"}],"source":["#check for null values in load_data_defaults\n","load_data_defaults.isnull().sum()"],"id":"230dd554"},{"cell_type":"markdown","metadata":{"id":"PdaBg0ScL6Uy"},"source":["### Recovery Rate:\n","It is the recovered amount divided by the funded amount"],"id":"PdaBg0ScL6Uy"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"d69ee3c5"},"outputs":[],"source":["#create a new column recovery_rate i.e 'recoveries'/'funded_amnt'\n","load_data_defaults['recovery_rate'] =load_data_defaults['recoveries']/load_data_defaults['funded_amnt']"],"id":"d69ee3c5"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"421c6e48","outputId":"04cf8eb8-dd8b-4f9f-f0a4-347255cce0a0"},"outputs":[{"data":{"text/plain":["count    40778.000000\n","mean         0.051739\n","std          0.085882\n","min          0.000000\n","25%          0.000000\n","50%          0.000000\n","75%          0.101866\n","max          1.207630\n","Name: recovery_rate, dtype: float64"]},"execution_count":92,"metadata":{},"output_type":"execute_result"}],"source":["#check the statistics of recovery_rate\n","load_data_defaults['recovery_rate'].describe()                                                                                  #recovery rate has to be from 0 to 1"],"id":"421c6e48"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"9ac9ad78"},"outputs":[],"source":["#convert recovery rate in the range of 0 to 1\n","load_data_defaults['recovery_rate'] = np.where(load_data_defaults['recovery_rate'] > 1,1,load_data_defaults['recovery_rate'])\n","load_data_defaults['recovery_rate'] = np.where(load_data_defaults['recovery_rate'] < 0,0,load_data_defaults['recovery_rate'])"],"id":"9ac9ad78"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"0f236f9a","outputId":"0c7e0a8c-2967-4af6-c245-f3f95c33b30b"},"outputs":[{"data":{"text/plain":["count    40778.000000\n","mean         0.051702\n","std          0.085447\n","min          0.000000\n","25%          0.000000\n","50%          0.000000\n","75%          0.101866\n","max          1.000000\n","Name: recovery_rate, dtype: float64"]},"execution_count":94,"metadata":{},"output_type":"execute_result"}],"source":["#recovery rate 0 to 1\n","load_data_defaults['recovery_rate'].describe()"],"id":"0f236f9a"},{"cell_type":"markdown","metadata":{"id":"KFMVMhGGM1a-"},"source":["### The credit conversion factor (CCF) converts the amount of a free credit line and other off-balance-sheet transactions (with the exception of derivatives) to an EAD (exposure at default) amount."],"id":"KFMVMhGGM1a-"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"4fec09b1"},"outputs":[],"source":["#credit conversion factor(CCF)\n","load_data_defaults['CCF'] = (load_data_defaults['funded_amnt'] - load_data_defaults['total_rec_prncp']) / load_data_defaults['funded_amnt']"],"id":"4fec09b1"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"2ed64373","outputId":"6d74a179-2b3c-4344-9e1f-97f1a35540d7"},"outputs":[{"data":{"text/plain":["count    40778.000000\n","mean         0.719456\n","std          0.204450\n","min          0.000000\n","25%          0.608267\n","50%          0.769332\n","75%          0.876847\n","max          1.000000\n","Name: CCF, dtype: float64"]},"execution_count":96,"metadata":{},"output_type":"execute_result"}],"source":["#CCF has to be in the range of 0 to 1\n","load_data_defaults['CCF'].describe()"],"id":"2ed64373"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"b3a9808f"},"outputs":[],"source":["#import matplotlib and seaborn\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","sns.set()"],"id":"b3a9808f"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"75bc11f1","outputId":"bbfb686e-f797-414d-f4e7-d65cb0afaf8d"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#plot a histogram of recovery_rate with bins=50\n","plt.hist(load_data_defaults['recovery_rate'],bins=50)\n","plt.show()"],"id":"75bc11f1"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"7734f21c","outputId":"8eba4109-49b4-43f7-b3c8-4cba2bfaf954"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjcAAAGhCAYAAACd/5VtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAquUlEQVR4nO3df3RUdX7/8de9ExKDySSwh7DsBiWhhwBWSHAlZBOz6vrjEFI5PbvIsQJ2ZUFb5deCheZgike/SCkLLIgiMaxLpbvFtadda6BsWZeUH/2xgPWoPSJMpEiFWJCZsAmSZO73D5qR2YTJr5k7M588H+fk6Nz53Jv3fBwzr/ncz/1cy3EcRwAAAIaw410AAABANBFuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGSYl3AfHiOI6CweivX2jbVkyOi3D0s3voa3fQz+6gn90Rq362bUuWZXXbbsCGm2DQ0YULv43qMVNSbA0ZcqMCgWa1tQWjemx8iX52D33tDvrZHfSzO2LZz0OH3iiPp/tww2kpAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFFS4l0AAAC4Ptu2ZNtWxDbBoKNg0HGposRHuAEAIEHZtqXs7MHyeCKfaGlvD+rixWYCzv8h3AAAkKBs25LHY2vdziP65FxTl21yh2dq2cO3ybYtws3/IdwAAJDgPjnXpJNn/PEuI2kwoRgAABiFkRsAAGKAicDxQ7gBACDKmAgcX4QbAACijInA8UW4AQAgRpgIHB+EGwAA4ijSqavuTmuha4QbAADiIDszTcGgI683Pd6lGIdwAwBAHGSkD5JtWxHn5Uwam6M5FeNdriz5EW4AAIijSPNycnMyXK7GDJzMAwAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADBKr8LN7t279Sd/8icqLy9XYWGhpk+frp///OdyHCes3euvv677779ft956qx544AG9/fbbnY7V1NSkqqoqTZ48WUVFRVq4cKEaGxs7tTt69KhmzpypCRMm6K677tK2bds6/T4AAIAOvQo3r776qtLT07VixQq99NJLKi8v19NPP60tW7aE2rz11lt6+umnNXXqVNXU1KiwsFBPPvmk3nnnnbBjLV68WAcPHtSqVau0bt06NTQ0aN68eWprawu1OXXqlObOnathw4bp5Zdf1iOPPKJNmzZp+/bt/XvVAADAWCm9afzSSy9p6NChocclJSW6ePGifvzjH+tP//RPZdu2Nm3apGnTpmnx4sWSpClTpuj48ePasmWLampqJEnHjh3TgQMHVFtbq7KyMklSXl6eKioqtHfvXlVUVEiSamtrNWTIEK1fv16pqakqKSnRhQsXtHXrVs2ePVupqanR6AMAAGCQXo3cXBtsOowbN06XLl1Sc3OzTp8+rY8//lhTp04Na1NRUaHDhw/rypUrkqT6+np5vV6VlpaG2uTn52vcuHGqr68Pbauvr9e3v/3tsBBTUVGhQCCgY8eO9aZ0AAAwQPRq5KYrR44c0fDhw5WRkaEjR45IujoKc63Ro0ertbVVp0+f1ujRo+Xz+ZSXlyfLssLa5efny+fzSZKam5v16aefKj8/v1Mby7Lk8/lUXFzcr9pTUqI7n9rjscP+idign91DX7uDfnaHm/0cj/+WifL+SYT3c7/CzW9+8xvV1dVp+fLlkiS/3y9J8nq9Ye06Hnc8HwgElJmZ2el4WVlZeu+99yRdnXDc1bFSU1OVnp4eOlZf2balIUNu7NcxrsfrTY/JcRGOfnYPfe0O+tkdpvZzor2ueNbT53Bz9uxZLVmyRMXFxZozZ040a3JFMOgoEGiO6jE9Hlteb7oCgRa1twejemx8iX52D33tDvrZHW72c8fvclOivH9i2c9eb3qPRoT6FG4CgYDmzZun7Oxsbd68WbZ99RdlZWVJujrqMmzYsLD21z7v9Xp19uzZTsf1+/2hNh0jOx0jOB2uXLmilpaWULv+aGuLzZugvT0Ys2PjS/Sze+hrd9DP7ohGP9u2Jdu2um/ookR7/8Sznl6Hm8uXL+uxxx5TU1OT/vZv/zbs9FLH/Bifzxc2V8bn82nQoEEaOXJkqN3hw4flOE7YvJuGhgaNGTNGkjR48GCNGDEiNAfn2jaO43SaiwMAgBts21J29uCEmeOCznoVbtra2rR48WL5fD7t3LlTw4cPD3t+5MiRGjVqlPbs2aN77rkntL2urk4lJSWhq57Ky8v14osv6vDhw/rmN78p6Wpo+eCDD/T9738/tF95ebn27dunp556SoMGDQody+v1qqioqG+vGACACLoblfF4bHk8ttbtPKJPzjV12WbS2BzNqRgfqxLRjV6Fm2eeeUZvv/22VqxYoUuXLoUtzDd+/HilpqZqwYIFWrZsmW666SYVFxerrq5O7777rl577bVQ26KiIpWVlamqqkrLly9XWlqaNmzYoIKCAt13332hdnPnztWbb76ppUuX6qGHHtLx48dVW1urJUuWsMYNACDqejMq88m5Jp080/XFLbk5GdEuDb3Qq3Bz8OBBSdKaNWs6Pbdv3z7l5uaqsrJSLS0tqqmp0bZt25SXl6cXXnih00jLxo0b9fzzz6u6ulptbW0qKyvTypUrlZLyZUk333yzamtrtWbNGs2fP19Dhw7VwoUL9eijj/bltQIAEJFtW4zKGKBX4eZXv/pVj9rNmDFDM2bMiNgmMzNTq1ev1urVqyO2mzRpknbt2tXjGgEA6C9GZZIbs6EAAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEbp813BAQBA4uhuVeVg0FEw6LhUTXwRbgAASGLZmWkKBh15vekR27W3B3XxYvOACDiEGwAAklhG+iDZthXxlhG5wzO17OHbZNsW4QYAACSHSLeMGGgINwCAASXS3JSe3A0ciY9wAwAYECzL6tHcFCQ/wg0AYECwbavbuSmTxuZoTsV4lytDtBFuAAADSqS5Kbk5GS5Xg1jg5CIAADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjpMS7AAAA4A6PJ/KYRjDoKBh0XKomdgg3AAAj2LYl27YiPj9QZWemKRh05PWmR2zX3h7UxYvNSR9wCDcAgKRn25ayswd3OzIxUGWkD5JtW1q384g+OdfUZZvc4Zla9vBtsm2LcAMAQLzZtiWPx4744T1pbI7mVIx3ubLE8sm5Jp084493GTFHuAEAGCPSh3duTobL1SBeGL8DAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAKl4IDABJed6sPs3gfrkW4AQAkNFYfRm8RbgAACY3Vh9FbhBsAQFJg9WH0FGN8AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFG2cCAOLKti3ZtnXd5z0evoejdwg3AIC4sW1L2dmDCTCIKsINACBubNuSx2Nr3c4j+uRcU5dtJo3N0ZyK8S5XhmRGuAEAxN0n55p08oy/y+dyczJcrgbJrtfjgKdOnVJ1dbWmT5+u8ePHq7KyslOb2bNnq6CgoNPPyZMnw9o1NTWpqqpKkydPVlFRkRYuXKjGxsZOxzt69KhmzpypCRMm6K677tK2bdvkOE5vSwcAAANAr0duPvroI+3fv18TJ05UMBi8bsiYNGmSli9fHrYtNzc37PHixYt14sQJrVq1Smlpadq4caPmzZunN954QykpV0s7deqU5s6dq9LSUi1evFgffvih1q1bJ4/Ho7lz5/a2fAAAYLheh5u7775b99xzjyRpxYoVeu+997ps5/V6VVhYeN3jHDt2TAcOHFBtba3KysokSXl5eaqoqNDevXtVUVEhSaqtrdWQIUO0fv16paamqqSkRBcuXNDWrVs1e/Zspaam9vYlAAAAg/X6tJRtR2dGe319vbxer0pLS0Pb8vPzNW7cONXX14e1+/a3vx0WYioqKhQIBHTs2LGo1AIAAMwRswnF//7v/67CwkK1t7dr4sSJWrRokW6//fbQ8z6fT3l5ebKs8LUN8vPz5fP5JEnNzc369NNPlZ+f36mNZVny+XwqLi7uc40pKdG99LDjUkYuaYwt+tk99LU7BnI/D8TXnOj6+98kEd7PMQk3t99+u6ZPn65Ro0apsbFRtbW1+t73vqe//uu/VlFRkSQpEAgoMzOz075ZWVmhU11NTVcvC/R6vWFtUlNTlZ6eLr+/65n1PWHbloYMubHP+0fi9abH5LgIRz+7h752B/2MRBCt92E8388xCTcLFy4Me3znnXeqsrJSL774ompqamLxK3stGHQUCDRH9Zgejy2vN12BQIva24NRPTa+RD+7h752x0Du547XjsTR3/dhLN/PXm96j0aEXFnnZvDgwfrWt76lf/qnfwpt83q9Onv2bKe2fr9fWVlZkhQa2ekYwelw5coVtbS0hNr1VVtbbP6ItLcHY3ZsfIl+dg997Q76GYkgWu/DeL6f43ZCLD8/Xw0NDZ0uJW9oaAjNsRk8eLBGjBgRmoNzbRvHcTrNxQEAAHAl3DQ3N+vXv/61br311tC28vJy+f1+HT58OLStoaFBH3zwgcrLy8Pa7du3T62traFtdXV18nq9ofk7AAD32ballBQ74k+kG2ICsdLr01ItLS3av3+/JOnMmTO6dOmS9uzZI0maPHmyfD6fXnnlFd177736+te/rsbGRv34xz/WZ599ph/96Eeh4xQVFamsrExVVVVavny50tLStGHDBhUUFOi+++4LtZs7d67efPNNLV26VA899JCOHz+u2tpaLVmyhDVuACBOenrDy/b2oC5ebFYwyKrycE+vw8358+e1aNGisG0dj3fs2KGvfvWram1t1YYNG3Tx4kWlp6erqKhIzzzzjCZMmBC238aNG/X888+rurpabW1tKisr08qVK0OrE0vSzTffrNraWq1Zs0bz58/X0KFDtXDhQj366KN9eb0AgCjoyQ0vc4dnatnDt8m2LcINXNXrcJObm6sPP/wwYpva2toeHSszM1OrV6/W6tWrI7abNGmSdu3a1eMaAQDuiHTDSyBeWD0JAAAYxZVLwQEAA1ekeTmsUIxYINwAAGIiOzNNwaDDIn1wHeEGABATGemDZNtWxEnHk8bmaE7FeJcrg+kINwCAmIo06Tg3J8PlajAQcLITAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwSkq8CwAAJB7btmTb1nWf93j4bozERbgBAISxbUvZ2YMJMEhahBsAQBjbtuTx2Fq384g+OdfUZZtJY3M0p2K8y5UBPUO4AQB06ZNzTTp5xt/lc7k5GS5XA/QcY44AAMAojNwAAICQ7uZaBYOOgkHHpWr6hnADAACUnZmmYNCR15sesV17e1AXLzYndMAh3AAAAGWkD5JtWxEnkucOz9Syh2+TbVuEGwAAkBwiTSRPFkwoBgAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAo3DgTAAYY27Zk29Z1n/d4+N6L5Ea4AYABxLYtZWcPJsDAaIQbABhAbNuSx2Nr3c4j+uRcU5dtJo3N0ZyK8S5XBkQP4QYABqBPzjXp5Bl/l8/l5mS4XA0QXYxLAgAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADBKr8PNqVOnVF1drenTp2v8+PGqrKzsst3rr7+u+++/X7feeqseeOABvf32253aNDU1qaqqSpMnT1ZRUZEWLlyoxsbGTu2OHj2qmTNnasKECbrrrru0bds2OY7T29IBAMAA0Otw89FHH2n//v26+eabNXr06C7bvPXWW3r66ac1depU1dTUqLCwUE8++aTeeeedsHaLFy/WwYMHtWrVKq1bt04NDQ2aN2+e2traQm1OnTqluXPnatiwYXr55Zf1yCOPaNOmTdq+fXtvSwcA49m2pZQU+7o/Hg8D9jBfSm93uPvuu3XPPfdIklasWKH33nuvU5tNmzZp2rRpWrx4sSRpypQpOn78uLZs2aKamhpJ0rFjx3TgwAHV1taqrKxMkpSXl6eKigrt3btXFRUVkqTa2loNGTJE69evV2pqqkpKSnThwgVt3bpVs2fPVmpqap9eOACYxrYtZWcPJsBgwOt1uLHtyP/TnD59Wh9//LGeeuqpsO0VFRVau3atrly5otTUVNXX18vr9aq0tDTUJj8/X+PGjVN9fX0o3NTX1+vee+8NCzEVFRV6+eWXdezYMRUXF/f2JQCAkWzbksdja93OI/rkXFOXbSaNzdGcivEuVwa4q9fhpjs+n0/S1VGYa40ePVqtra06ffq0Ro8eLZ/Pp7y8PFmWFdYuPz8/dIzm5mZ9+umnys/P79TGsiz5fL5+hZuUlOh+u+n4tsS3ptiin91DX7sjWv3csf8n55p08oy/yza5ORn9+h2AFPm9mgh/N6Iebvz+q/9Deb3esO0djzueDwQCyszM7LR/VlZW6FRXU1NTl8dKTU1Venp66Fh9YduWhgy5sc/7R+L1psfkuAhHP7uHvnYH/Yxk0ZP3ajzfz1EPN8kiGHQUCDRH9Zgejy2vN12BQIva24NRPTa+RD+7h752R0/72bIs2bZ13edt21JGxg2xKBEIE+m9Gsu/G15veo9GhKIebrKysiRdHXUZNmxYaHsgEAh73uv16uzZs5329/v9oTYdIzsdIzgdrly5opaWllC7vmpri80f6/b2YMyOjS/Rz+6hr90RqZ+vThbu2R92INZ68jchnn83oh5uOubH+Hy+sLkyPp9PgwYN0siRI0PtDh8+LMdxwubdNDQ0aMyYMZKkwYMHa8SIEaE5ONe2cRyn01wcADAVk4WBnov6V4CRI0dq1KhR2rNnT9j2uro6lZSUhK56Ki8vl9/v1+HDh0NtGhoa9MEHH6i8vDy0rby8XPv27VNra2vYsbxer4qKiqJdPgAktI7Jwl39NF6I7ql2IFn1euSmpaVF+/fvlySdOXNGly5dCgWZyZMna+jQoVqwYIGWLVumm266ScXFxaqrq9O7776r1157LXScoqIilZWVqaqqSsuXL1daWpo2bNiggoIC3XfffaF2c+fO1ZtvvqmlS5fqoYce0vHjx1VbW6slS5awxg0AAOik1+Hm/PnzWrRoUdi2jsc7duxQcXGxKisr1dLSopqaGm3btk15eXl64YUXOo20bNy4Uc8//7yqq6vV1tamsrIyrVy5UikpX5Z18803q7a2VmvWrNH8+fM1dOhQLVy4UI8++mhfXi8AADBcr8NNbm6uPvzww27bzZgxQzNmzIjYJjMzU6tXr9bq1asjtps0aZJ27drVqzoBAMDAxLR7AABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjDNgbZwJAool03yjuKQX0HOEGAOLMsiwFg4683vR4lwIYgXADAHFm25Zs2+KmmECUEG4AIEF03BSzK7k5GS5XAyQvTuICAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEZhET8AiLGOFYgjPQ8gegg3ABBDtm0pO3swN74EXES4AYAYsm1LHo/NfaMAFxFuAMAF3DcKcA/jpAAAwCiEGwAAYBTCDQAAMApzbgCgH7q7zJurpAD3EW4AoI+4zBtITIQbAOgjLvMGEhPhBgD6icu8gcTCWCoAADAK4QYAABiFcAMAAIzCnBsAuA4u8waSE+EGALrAZd5A8iLcAEAXuMwbSF6EGwCIgMu8geTDeCsAADAK4QYAABiFcAMAAIxCuAEAAEZhQjEAAOiVSEskJMLyCYQbAADQI9mZaQoGHXm96RHbBYOOLOv6C2DGGuEGAAD0SEb6INm2FXH9p9zhmVr28G0RV/eONcINAADolUjrPyWC+J8YAwAAiCLCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAo3ApOIABybatiOtwJMIqqwD6hnADYMCxbUvZ2YMJMIChCDcABhzbtuTx2BFXWZ00NkdzKsa7XBmAaCDcABiwIq2ympuT4XI1AKKFMVkAAGAUwg0AADAKp6UAGIcroYCBjXADIKl0F1wsy1Jm5g0EGGAAI9wASBq9uYSbK6GAgYtwAyBp9OYSbq6EAgYuwg2ApENwARAJJ6UBAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIwSk3Dzd3/3dyooKOj0s27durB2r7/+uu6//37deuuteuCBB/T22293OlZTU5Oqqqo0efJkFRUVaeHChWpsbIxF2QAAwAAxXefmlVdeUWZmZujx8OHDQ//+1ltv6emnn9bjjz+uKVOmqK6uTk8++aR27typwsLCULvFixfrxIkTWrVqldLS0rRx40bNmzdPb7zxhlJSWKYHMAn3hAIQDTFNB7fccouGDh3a5XObNm3StGnTtHjxYknSlClTdPz4cW3ZskU1NTWSpGPHjunAgQOqra1VWVmZJCkvL08VFRXau3evKioqYlk+gCjinlAA3BKXoY/Tp0/r448/1lNPPRW2vaKiQmvXrtWVK1eUmpqq+vp6eb1elZaWhtrk5+dr3Lhxqq+vJ9wASYJ7QgFwU0zDTWVlpT7//HN97Wtf04MPPqjvf//78ng88vl8kq6Owlxr9OjRam1t1enTpzV69Gj5fD7l5eXJssK/7eXn54eOASDxcU8oAG6KSbgZNmyYFixYoIkTJ8qyLP3qV7/Sxo0bde7cOVVXV8vvv/qHy+v1hu3X8bjj+UAgEDZnp0NWVpbee++9fteZkhLd4e+Ob6UMq8cW/eyeaPV1x/4EF2DgsG0r6p+zPRWTcHPHHXfojjvuCD0uKytTWlqafvKTn+jxxx+Pxa/sNdu2NGTIjTE5ttebHpPjIhz97B76GkBvZWTcELff7dqcm6lTp2r79u36r//6L2VlZUm6epn3sGHDQm0CgYAkhZ73er06e/Zsp2P5/f5Qm74KBh0FAs39Osbv8nhseb3pCgRa1N4ejOqx8SX62T3R6uuO4wAYOC5duqzW1vaoHtPrTe/RSHJcJhTn5+dLknw+X+jfOx4PGjRII0eODLU7fPiwHMcJm3fT0NCgMWPG9LuOtrbYfDC2twdjdmx8iX52D30NoLeCQSdufzdcOxlWV1cnj8ej8ePHa+TIkRo1apT27NnTqU1JSYlSU1MlSeXl5fL7/Tp8+HCoTUNDgz744AOVl5e7VToAAEgiMRm5mTt3roqLi1VQUCBJ2rdvn3bt2qU5c+aETkMtWLBAy5Yt00033aTi4mLV1dXp3Xff1WuvvRY6TlFRkcrKylRVVaXly5crLS1NGzZsUEFBge67775YlA4AAJJcTMJNXl6e3njjDZ09e1bBYFCjRo1SVVWVZs+eHWpTWVmplpYW1dTUaNu2bcrLy9MLL7ygoqKisGNt3LhRzz//vKqrq9XW1qaysjKtXLmS1YkBAECXYpIQVq5c2aN2M2bM0IwZMyK2yczM1OrVq7V69epolAYAAAzHQiEAAMAohBsAAGAUwg0AADAKs3IBdKu7RbOCQUfBoONSNQAQGeEGwHVZlqVg0Ol2deH29qAuXmwm4ABICIQbANdl25Zs24p4N+/c4Zla9vBtsm2LcAMgIRBuAHQr0t28ASDREG4AREWkeTk9udEdAEQL4QZAv2RnpvVoXg4AuIVwA6BfMtIHdTsvZ9LYHM2pGO9yZQAGKsINgKiINC8nNyfD5WoADGScCAcAAEYh3AAAAKNwWgoYwDrWsYn0PAAkG8INYKjugotlWcrMvIHLtAEYh3ADGMi2LWVnD+5RcOEqJwCmIdwABrJtSx6P3aPgwlVOAExDuAGSUHennDpGbAguAAYiwg2QZHpzygkABiLCDZBkenPKCQAGIsINkKQ45QQAXWNcGwAAGIVwAwAAjMJpKSDB9PRKKABA1wg3gItYNRgAYo9wA7iEVYMBwB2EG8AlrBoMAO4g3AAuI7gAQGxxYh8AABiFcAMAAIxCuAEAAEYh3AAAAKMwoRjoRndr0/QUa9cAgDsIN0AEPV2bpj3oyBOFAAQA6D/CDRBBb9amidTm2nYAgNgi3AA90JO1aSK1ubYdACC2mAQAAACMwsgNBjTuwA0A5iHcYMDqzY0sAQDJg3ADY/VkVKank4UBAMmDcAMj9WZUhhtZAoBZCDcwUm8u4QYAmIVwg4QTzUm+jMoAwMBDuEFC6enppGDQkWWxIjAAoDPCDRJKT04n5Q7P1LKHb4vK/Z4AAOYh3CAhdbfaLwAA10O4QdKybUspKV2fvmLtGgAYuAg3SDrZmWkKBh1lZNwQ71IAAAmIcANXReNKqIz0QbJti8u8AQBdItygR7oLJdLVK5iCQSfiMaJ5uwMu8wYAdIVwg271NJS0twd18WLzdQMOC+sBANxAuEG3ent5dqTRG4kRFwBAbBFu0GNcng0ASAZcLwsAAIzCyE0Si8YkXwAATEO4SVLRmuQLAIBpCDdJKtqTfAEAMAXhJslFY5JvNBbWAwAgURBuBrhoL6wHAEC8EW7iIJEmAkd7Yb1IIYkABQBwA+HGZfGYCNyTwNHfhfU6bmbp9ab3rUgAAKKEcOMyNycCuxk4uJklACBREG7ixI3VfuMROLi1AgAg3gg3AwCBAwAwkDDDEwAAGIVwAwAAjMJpqRjgcmgAAOKHcBNFlmVF9eokQhIAAL2XFOHm5MmTeu6553Ts2DHdeOONmj59uhYvXqzU1NR4lxamY3G+/l6dxJoxAAD0XcKHG7/fr0ceeUSjRo3S5s2bde7cOa1Zs0aXL19WdXV1vMvrUn+vTmLNGAAA+i7hw83PfvYz/fa3v9ULL7yg7OxsSVJ7e7ueeeYZPfbYYxo+fHh8C4whLuEGAKD3En7iRn19vUpKSkLBRpKmTp2qYDCogwcPxq8wAACQkCzHcWJ/d8Z+KCkp0Xe+8x0tW7YsbPsdd9yh6dOnd9reU44T/RtTWpZk27YuNn2htvZgl23SUj3KHJxKmwHWJhFrog1taEObWLRJ8dj/N3c0qGgnDNu2ZFmRbzwtJUG4ueWWW7Ro0SLNnz8/bHtlZaWKior07LPPxqkyAACQiBL+tBQAAEBvJHy48Xq9amrqfMWQ3+9XVlZWHCoCAACJLOHDTX5+vnw+X9i2pqYmffbZZ8rPz49TVQAAIFElfLgpLy/XoUOHFAgEQtv27Nkj27ZVWloax8oAAEAiSvgJxX6/X9OmTVNeXp4ee+yx0CJ+f/AHf5Cwi/gBAID4SfhwI129/cKzzz4bdvuFJUuWJNztFwAAQPwlRbgBAADoqYSfcwMAANAbhBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3PTQyZMn9b3vfU+FhYUqLS3V2rVrdeXKlW73cxxH27Zt05133qkJEyZo5syZeuedd2JfcJLqSz83NjZq7dq1mj59uoqKilReXq6lS5fqzJkzLlWdnPr6nr7Wq6++qoKCAj322GMxqjL59aefz507p+XLl2vKlCmaMGGCpk6dql/84hcxrjg59bWfP//8c1VXV+vOO+9UYWGhKisr9dOf/tSFipPTqVOnVF1drenTp2v8+PGqrKzs0X5ufxamxOzIBvH7/XrkkUc0atQobd68ObRK8uXLl7tdJbmmpkabNm3SsmXLVFBQoJ07d+rRRx/VP/zDP2jkyJEuvYLk0Nd+fv/99/XLX/5S3/nOdzRx4kR9/vnneumllzRjxgz94z/+o4YOHeriq0gO/XlPd/jss8+0ZcsWfeUrX4lxtcmrP/3c2NiomTNnKi8vT88++6wyMjL00Ucf9TqADgT96edFixbJ5/PpBz/4gUaMGKH6+nqtWrVKHo9HDz74oEuvIHl89NFH2r9/vyZOnKhgMKieLpXn+mehg25t3brVKSwsdD7//PPQtp/97GfOuHHjnLNnz153v8uXLzuTJk1yfvjDH4a2ffHFF85dd93l/MVf/EUMK05Ofe1nv9/vtLa2hm379NNPnYKCAqe2tjZW5Sa1vvb1tZ566innz/7sz5xZs2Y58+fPj1Glya0//bxs2TJn5syZTltbW4yrTH597efGxkZnzJgxzhtvvBG2/eGHH3bmzJkTq3KTWnt7e+jfly9f7kybNq3bfeLxWchpqR6or69XSUmJsrOzQ9umTp2qYDCogwcPXne/o0eP6tKlS5o6dWpoW2pqqu69917V19fHsuSk1Nd+9nq9SkkJH4T86le/qqFDh6qxsTFW5Sa1vvZ1h9/85jf653/+Zy1dujSGVSa/vvbzpUuXtHv3bv3RH/2RPB6PC5Umt772c1tbmyQpMzMzbHtGRkaPRyQGGtvufWyIx2ch4aYHfD6f8vPzw7Z5vV4NGzZMPp8v4n6SOu07evRo/c///I8uX74c/WKTWF/7uSsNDQ06f/68Ro8eHc0SjdGfvm5vb9ezzz6rxx9/XDk5ObEsM+n1tZ/ff/99tba2KiUlRbNmzdItt9yi0tJS/dVf/ZVaW1tjXXbS6Ws/jxgxQmVlZdq6datOnDihS5cuqa6uTgcPHtTDDz8c67IHjHh8FjLnpgcCgYC8Xm+n7VlZWfL7/RH3S01NVVpaWth2r9crx3Hk9/t1ww03RL3eZNXXfv5djuPoueeeU05OjqZNmxbNEo3Rn77+m7/5G7W0tOiP//iPY1SdOfraz//7v/8rSVq5cqUefPBBPfnkk3r33Xe1adMm2bbNiNnv6M/7efPmzVqyZEnob4XH49HKlSt1//33x6TWgSgen4WEGxhn8+bN+td//Ve98sorGjx4cLzLMcr58+e1adMm/eVf/qVSU1PjXY6xgsGgJOmb3/ymVqxYIUmaMmWKfvvb32r79u164okn+GIUBY7j6M///M/18ccf64c//KGGDRumQ4cOafXq1crKyuLLURIj3PSA1+tVU1NTp+1+v19ZWVkR97ty5Yq++OKLsMQaCARkWVbEfQeivvbztXbt2qUtW7bo//2//6eSkpJol2iMvvb1j370IxUUFOgb3/iGAoGApKvzFtra2hQIBDR48OBO858Gsv787ZCuBpprlZSUaOvWrTp16pQKCgqiW2wS62s///rXv9aePXv0i1/8ItSfxcXFOn/+vNasWUO4iZJ4fBYy56YH8vPzO523bWpq0meffdbpHOLv7iddnf9xLZ/Pp6997Wt88/odfe3nDr/85S+1atUqLVy4UN/97ndjVaYR+trXDQ0N+o//+A/dfvvtoZ+jR4/qwIEDuv3223Xo0KFYl55U+trPv/d7vxfxuF988UVU6jNFX/v5xIkT8ng8GjNmTNj2cePGqbGxUS0tLTGpd6CJx2ch4aYHysvLdejQodA3VUnas2ePbNtWaWnpdfebNGmSMjIytHv37tC21tZW7d27V+Xl5TGtORn1tZ8l6d/+7d/0gx/8QDNmzNATTzwR61KTXl/7uqqqSjt27Aj7GTt2rAoLC7Vjxw5NmDDBjfKTRl/7+etf/7rGjBnTKSweOnRIN9xwQ7fhZ6DpTz+3t7frww8/DNv+/vvv6ytf+YrS09NjVvNAEpfPwphcYG6YixcvOqWlpc6sWbOcf/mXf3F+/vOfO9/4xjecZ555JqzdnDlznHvuuSds28svv+z8/u//vvPqq686hw4dchYsWOAUFRU5//3f/+3mS0gKfe3nEydOOLfddptTWVnpHDlyxDl27Fjo59SpU26/jKTQn/f072Kdm+vrTz/v27fPKSgocJ577jnnwIEDzksvveTccsstzvr16918CUmhr/3c1NTk3Hnnnc69997r/P3f/71z6NAhZ+3atc7YsWOdLVu2uP0ykkJzc7Oze/duZ/fu3c6sWbOcb33rW6HH58+fdxwnMT4LOTneA1lZWfrJT36iZ599Vk888YRuvPFGffe739WSJUvC2gWDQbW3t4dtmzdvnhzH0fbt23XhwgWNGzdOtbW1rE7chb7283/+53+qqalJTU1Neuihh8La/uEf/qHWrFnjSv3JpD/vafRcf/r57rvv1vr16/Xiiy/qpz/9qXJycrRgwQLNnz/fzZeQFPrazxkZGXr11Ve1YcMGrVu3Tk1NTcrNzdWKFSs0a9Yst19GUjh//rwWLVoUtq3j8Y4dO1RcXJwQn4WW47BSEQAAMAdzbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABglP8PR/4evUK5qygAAAAASUVORK5CYII=\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#plot a histogram of CCF with 50 bins\n","plt.hist(load_data_defaults['CCF'],bins=50)\n","plt.show()"],"id":"7734f21c"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"5130300e"},"outputs":[],"source":["#create a new column recovery_rate_0_1...if the recovery_rate is 0 then insert 0 and if it is greater than 0 replace it with 1\n","load_data_defaults['recovery_rate_0_1']  = np.where(load_data_defaults['recovery_rate'] == 0,0,1)"],"id":"5130300e"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"99220aa4","outputId":"629b7b3b-d93e-463c-99b1-ebfca05fd89f"},"outputs":[{"data":{"text/plain":["0    21276\n","1    19502\n","Name: recovery_rate_0_1, dtype: int64"]},"execution_count":101,"metadata":{},"output_type":"execute_result"}],"source":["#value_counts of recovery_rate_0_1\n","load_data_defaults['recovery_rate_0_1'].value_counts()"],"id":"99220aa4"},{"cell_type":"markdown","metadata":{"id":"0036c75f"},"source":["# LGD_Model"],"id":"0036c75f"},{"cell_type":"markdown","metadata":{"id":"sZ1H6zADN_t7"},"source":["In LGD model we will basically create 2 models for determining the recovery rate if the recovery rate is 0 or greater than 0:\n","\n","1.we will create a logistic regression model for determining whether the recovery rate is 0 or 1.\n","\n","2.For all the recovery rate greater than 0 we will create a linear regression model to determine how much greater than 0 the value of recovery rate is."],"id":"sZ1H6zADN_t7"},{"cell_type":"markdown","metadata":{"id":"Z4Fbg_saRdNx"},"source":["#### Doing The First part: Stage_1"],"id":"Z4Fbg_saRdNx"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"89d0127a","outputId":"96c52949-a5cb-45b4-90b2-518bb1c0fad3"},"outputs":[{"data":{"text/plain":["Index(['grade:A', 'grade:B', 'grade:C', 'grade:D', 'grade:E', 'grade:F',\n","       'grade:G', 'home_ownership:RENT_OTHER_NONE_ANY', 'home_ownership:OWN',\n","       'home_ownership:MORTGAGE',\n","       ...\n","       'mths_since_last_record:>=127', 'loan_amnt', 'funded_amnt', 'int_rate',\n","       'installment', 'recoveries', 'total_rec_prncp', 'recovery_rate', 'CCF',\n","       'recovery_rate_0_1'],\n","      dtype='object', length=157)"]},"execution_count":102,"metadata":{},"output_type":"execute_result"}],"source":["#check the column name of load_data_defaults\n","load_data_defaults.columns"],"id":"89d0127a"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"f8786dad"},"outputs":[],"source":["#Train Test split of load_data_defaults\n","from sklearn.model_selection import train_test_split\n","\n","lgd_input_train,lgd_input_test,lgd_target_train,lgd_target_test = train_test_split(load_data_defaults.drop(['recoveries','recovery_rate','recovery_rate_0_1','CCF'],axis=1),load_data_defaults['recovery_rate_0_1'],test_size=0.2,random_state=42)"],"id":"f8786dad"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"07bf4ca4"},"outputs":[],"source":["#create a list of reference categories selecting only one column from each parent column\n","ref_categories=['grade:G',\n","               'home_ownership:RENT_OTHER_NONE_ANY',\n","               'addr_state:ND_NE_IA_NC_FL_HI_AL',\n","               'verification_status:Verified',\n","                'purpose:educ_sm_b_wedd_ren_en_mov_house',\n","                'initial_list_status:f',\n","                'term:60',\n","                'emp_length:0',\n","                'mths_since_issued_date:>84',\n","                'int_rate:20.281',\n","                'mths_since_earliest_cr_line:<140',\n","                'delinq_2yrs:>18',\n","                'inq_last_6mths:7-9',\n","                'open_acc:=0',\n","                'pub_rec:0-3',\n","                'total_acc:>85',\n","                'acc_now_delinq:<0',\n","                'total_rev_hi_lim:<=5k',\n","                'annual_inc:<20k',\n","                'dti:>40',\n","                'mths_since_last_delinq:0-3',\n","                'mths_since_last_record:Missing'\n","               ]"],"id":"07bf4ca4"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"bc31ae31"},"outputs":[],"source":["#drop the reference categories from lgd_input_train\n","lgd_input_train =lgd_input_train.drop(ref_categories,axis=1)"],"id":"bc31ae31"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"625508d5"},"outputs":[],"source":["#import logistic regression and metrics\n","from sklearn.linear_model import LogisticRegression\n","from sklearn import metrics"],"id":"625508d5"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"31821abb"},"outputs":[],"source":["#create an object of logistic regression with max_iter=200\n","reg =LogisticRegression(max_iter=200)                                                          #for prediction 0 or 1 via logistic and then how much by linear reg"],"id":"31821abb"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"6d251d95","outputId":"50a9ff39-f7e9-4306-ec83-6a48c614b2a8"},"outputs":[{"data":{"text/plain":["(32622, 131)"]},"execution_count":108,"metadata":{},"output_type":"execute_result"}],"source":["#check the shape of lgd_inputs_train\n","lgd_input_train.shape"],"id":"6d251d95"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"be265679","outputId":"4b5e5def-16ef-448f-9f3f-07633c9ead22"},"outputs":[{"data":{"text/plain":["135503    1\n","228193    1\n","311282    0\n","47643     1\n","200256    1\n","182968    1\n","133165    1\n","162554    0\n","433743    0\n","42363     1\n","442555    0\n","174583    1\n","457134    0\n","141290    0\n","351658    0\n","193928    1\n","21480     1\n","278159    0\n","205190    1\n","172743    0\n","389683    0\n","241619    1\n","369679    0\n","54840     1\n","225938    0\n","3101      1\n","153164    0\n","392106    1\n","122683    1\n","395564    1\n","332620    1\n","395391    0\n","145074    1\n","453768    1\n","212249    1\n","74640     0\n","21609     1\n","459690    0\n","88660     1\n","385939    0\n","456753    0\n","464593    0\n","25894     1\n","200669    1\n","166324    1\n","332443    0\n","382766    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1\n","191089    1\n","66423     1\n","390453    1\n","322002    1\n","90634     1\n","211918    1\n","409939    1\n","387802    0\n","51064     0\n","370927    0\n","245602    0\n","417887    1\n","38331     1\n","78159     1\n","361161    0\n","139825    0\n","103807    1\n","200833    1\n","180166    1\n","40331     0\n","228919    1\n","441464    0\n","417362    0\n","250102    0\n","13912     1\n","Name: recovery_rate_0_1, dtype: int64"]},"execution_count":109,"metadata":{},"output_type":"execute_result"}],"source":["#check the shape of lgd_target_train\n","lgd_target_train"],"id":"be265679"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"bf47d93e","outputId":"88d043d8-ff4e-44e6-a80f-75d60c5da839"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=200)</pre></div></div></div></div></div>"],"text/plain":["LogisticRegression(max_iter=200)"]},"execution_count":110,"metadata":{},"output_type":"execute_result"}],"source":["#fit the logistic reg model on lgd_inputs_train and lgd_target_train\n","reg.fit(lgd_input_train,lgd_target_train)"],"id":"bf47d93e"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"bff0e0a3"},"outputs":[],"source":["#testing"],"id":"bff0e0a3"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"bf0c2e70","outputId":"4aca92f2-a55a-48b2-9d4d-5b74409cf0c6"},"outputs":[{"data":{"text/plain":["(8156, 153)"]},"execution_count":112,"metadata":{},"output_type":"execute_result"}],"source":["#check the shape of lgd_input_test\n","lgd_input_test.shape"],"id":"bf0c2e70"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"bdf550d7"},"outputs":[],"source":["#drop the reference categories from lgd_input_test\n","lgd_input_test =lgd_input_test.drop(ref_categories,axis=1)"],"id":"bdf550d7"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"f2e6640a","outputId":"75a9d133-de36-46b0-bf92-9c5fee64a1bb"},"outputs":[{"data":{"text/plain":["(8156, 131)"]},"execution_count":114,"metadata":{},"output_type":"execute_result"}],"source":["#check the shape of lgd_input_test ---- make sure that the number of columns used in traing is same as the number of columns in testing\n","lgd_input_test.shape"],"id":"f2e6640a"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"c8b296d0"},"outputs":[],"source":["#predict the results of lgd_input_test\n","pred =reg.predict(lgd_input_test)"],"id":"c8b296d0"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"ca35ce9e"},"outputs":[],"source":["#predict the result using predict_proba function\n","prob_pred = reg.predict_proba(lgd_input_test)[:][:,1]"],"id":"ca35ce9e"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"5b0edaeb"},"outputs":[],"source":["#create an empty dataframe that will store the final results\n","df_actual_predicted_probs =pd.DataFrame()"],"id":"5b0edaeb"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"a97dc7e2"},"outputs":[],"source":["#create 2 columns one will store the pred and other will store the prob_pred\n","df_actual_predicted_probs['lgd_test_outcome'] =pred\n","df_actual_predicted_probs['lgd_test_proba'] =prob_pred"],"id":"a97dc7e2"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"6707a986","outputId":"1bb3c280-9ef4-4d1c-b14a-c0bb66b88ad5"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-c7364ec9-7699-4bec-837f-ad1b5b762e52\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>lgd_test_outcome</th>\n","      <th>lgd_test_proba</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.734807</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0.554669</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>0.571423</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>0.547397</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>0.448636</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7364ec9-7699-4bec-837f-ad1b5b762e52')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 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tr,1,0)"],"id":"80491959"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"78839971","outputId":"c4663c2b-6849-4736-9f2e-6f145574ba53"},"outputs":[{"data":{"text/plain":["(8156, 2)"]},"execution_count":121,"metadata":{},"output_type":"execute_result"}],"source":["#check the shape of df_actual_predicted_probs\n","df_actual_predicted_probs.shape"],"id":"78839971"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"28fe5212","outputId":"1a2bb68a-28e8-4c7a-ed29-0eaf7e49deee"},"outputs":[{"data":{"text/plain":["array([[2981, 1250],\n","       [1528, 2397]])"]},"execution_count":122,"metadata":{},"output_type":"execute_result"}],"source":["#print the confusion matrix using lgd_target_test and pred\n","metrics.confusion_matrix(lgd_target_test,pred)\n"],"id":"28fe5212"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"fc1ce297","outputId":"2acff2d6-7f7e-41e2-aeef-1eef3ab9fd81"},"outputs":[{"data":{"text/plain":["0.6593918587542913"]},"execution_count":123,"metadata":{},"output_type":"execute_result"}],"source":["#accuracy of model\n","metrics.accuracy_score(lgd_target_test,pred)"],"id":"fc1ce297"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"576facf3"},"outputs":[],"source":["#import pickle\n","import pickle"],"id":"576facf3"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"8782af36"},"outputs":[],"source":["#save the reg model\n","pickle.dump(reg,open('lgd_stage_1.sav','wb'))"],"id":"8782af36"},{"cell_type":"markdown","metadata":{"id":"592bb8b9"},"source":["stage-2...where >0 linear reg to determine how much >0   for recovery rate"],"id":"592bb8b9"},{"cell_type":"markdown","metadata":{"id":"TIETFj0vRqMX"},"source":["Stage_2:Take those records where recovery_rate_0_1 is 1 and determine the recovery_rate by Linear REG"],"id":"TIETFj0vRqMX"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"aec5e471"},"outputs":[],"source":["#take all records from load_data_defaults hwere recovery_rate_0_1 is 1\n","lgd_loan_data_2 = load_data_defaults[load_data_defaults['recovery_rate_0_1'] == 1]"],"id":"aec5e471"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"output_embedded_package_id":"1kCnkQmAH5gVSZUUTwvIzFSTQUh-eIxwK"},"id":"428d49ae","outputId":"49be86b6-750d-490b-990c-d24b63a0a035"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["#check the shape of lgd_loan_data_2\n","lgd_loan_data_2"],"id":"428d49ae"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"3f2ed807"},"outputs":[],"source":["#train test split\n","lgd_input_train_2,lgd_input_test_2,lgd_target_train_2,lgd_target_test_2 = train_test_split(lgd_loan_data_2.drop(['recoveries','recovery_rate','recovery_rate_0_1','CCF'],axis=1),lgd_loan_data_2['recovery_rate'],test_size=0.2,random_state=42)"],"id":"3f2ed807"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"580c8473"},"outputs":[],"source":["#drop the refrence categories from lgd_input_train_2\n","lgd_input_train_2 =lgd_input_train_2.drop(ref_categories,axis=1)"],"id":"580c8473"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"dc6f02ae"},"outputs":[],"source":["#import linear regression\n","from sklearn.linear_model import LinearRegression"],"id":"dc6f02ae"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"4e22535d"},"outputs":[],"source":["#create an object  of linear regression\n","lin_reg =LinearRegression()"],"id":"4e22535d"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"a9b26776","outputId":"1d2b831b-1290-440c-e149-147a6f6f9c3a"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"],"text/plain":["LinearRegression()"]},"execution_count":132,"metadata":{},"output_type":"execute_result"}],"source":["#fit the lin_reg on lgd_input_train_2,lgd_target_train_2\n","lin_reg.fit(lgd_input_train_2,lgd_target_train_2)"],"id":"a9b26776"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"f7e025d7"},"outputs":[],"source":["#drop the refrence categories from lgd_input_test\n","lgd_input_test_2 =lgd_input_test_2.drop(ref_categories,axis=1)"],"id":"f7e025d7"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"1c075689","outputId":"306d29c6-f42a-4af0-9c7d-b8ccfe6d499c"},"outputs":[{"data":{"text/plain":["(3901, 131)"]},"execution_count":134,"metadata":{},"output_type":"execute_result"}],"source":["#check the shape of lgd_input_test_2...make sure the number of columns are same as that of lgd_input_train_2\n","lgd_input_test_2.shape"],"id":"1c075689"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"9cafc941"},"outputs":[],"source":["#predict the results\n","pred_lin =lin_reg.predict(lgd_input_test_2)"],"id":"9cafc941"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"b84a4458","outputId":"3df76fbf-7605-4e56-dd68-cf7d0f86768a"},"outputs":[{"data":{"text/plain":["array([0.13567379, 0.06718956, 0.09462008, ..., 0.12465915, 0.07248101,\n","       0.08528765])"]},"execution_count":136,"metadata":{},"output_type":"execute_result"}],"source":["#print the predicted results\n","pred_lin"],"id":"b84a4458"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"83ad20ad","outputId":"10d38930-819c-41c5-fb7b-7115a2be1d41"},"outputs":[{"data":{"text/plain":["0.12478323688471715"]},"execution_count":137,"metadata":{},"output_type":"execute_result"}],"source":["#check the r2_score\n","metrics.r2_score(lgd_target_test_2,pred_lin)"],"id":"83ad20ad"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"78337475","outputId":"41a6a1ce-d45d-42e8-a328-4e8687dede4f"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#plot the distribution plot for lgd_target_test_2-pred_lin\n","sns.distplot(lgd_target_test_2-pred_lin)\n","\n","plt.show()"],"id":"78337475"},{"cell_type":"code","execution_count":144,"metadata":{"id":"da0f73e3","executionInfo":{"status":"ok","timestamp":1693059496874,"user_tz":-330,"elapsed":507,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#save the model using pickle\n","pickle.dump(lin_reg,open('lgd_stage_2.sav','wb'))"],"id":"da0f73e3"},{"cell_type":"markdown","metadata":{"id":"b61e1b61"},"source":["# combination of stage1 and stage2"],"id":"b61e1b61"},{"cell_type":"markdown","metadata":{"id":"KqeA0iaXI5DV"},"source":["we will multiply the results of stage_1 and stage_2"],"id":"KqeA0iaXI5DV"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"52b38a12"},"outputs":[],"source":["#using the ln_reg method predict the results of stage_1 test i.e lgd_input_test\n","pred_stage_2_all = lin_reg.predict(lgd_input_test)"],"id":"52b38a12"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"7734605c","outputId":"98f2842b-f54c-42b4-e189-69277c29fb22"},"outputs":[{"data":{"text/plain":["array([0.06618586, 0.04787434, 0.15914674, ..., 0.07241948, 0.10081735,\n","       0.14634088])"]},"execution_count":141,"metadata":{},"output_type":"execute_result"}],"source":["#print the pred_stage_2_all\n","pred_stage_2_all"],"id":"7734605c"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"bd7f8ff4","outputId":"c37cd174-2592-4ac9-d2c5-8b56dfd8c227"},"outputs":[{"name":"stdout","output_type":"stream","text":["8156 8156\n"]}],"source":["#make sure that the length of pred and pred_stage_2_all is same becoz we will combine the results by multiplying them\n","print(len(pred),len(pred_stage_2_all))"],"id":"bd7f8ff4"},{"cell_type":"code","execution_count":145,"metadata":{"id":"6e76d999","executionInfo":{"status":"ok","timestamp":1693059617569,"user_tz":-330,"elapsed":477,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#multiply the stage_1 and stage_2 results\n","pred_lgd_final =pred*pred_stage_2_all"],"id":"6e76d999"},{"cell_type":"code","execution_count":146,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8bb80168","executionInfo":{"status":"ok","timestamp":1693059628416,"user_tz":-330,"elapsed":1902,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"07e485d3-20e2-4ace-b249-b2601da8ce1f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.06618586, 0.04787434, 0.15914674, ..., 0.07241948, 0.10081735,\n","       0.        ])"]},"metadata":{},"execution_count":146}],"source":["#print the pred_lgd_final\n","pred_lgd_final"],"id":"8bb80168"},{"cell_type":"code","execution_count":147,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"7544f2ad","executionInfo":{"status":"ok","timestamp":1693059914465,"user_tz":-330,"elapsed":479,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"87fbd9c0-2eba-41f3-a1ec-0cdf0cff2319"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                 0\n","count  8156.000000\n","mean      0.046905\n","std       0.056774\n","min      -0.000001\n","25%       0.000000\n","50%       0.000000\n","75%       0.097783\n","max       0.294530"],"text/html":["\n","  <div id=\"df-2ee5d768-34dd-4054-ab25-b4f8aa9482e3\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead 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'funded_amnt', 'int_rate',\n","       'installment', 'recoveries', 'total_rec_prncp', 'recovery_rate', 'CCF',\n","       'recovery_rate_0_1'],\n","      dtype='object', length=157)"]},"metadata":{},"execution_count":149}],"source":["#check the columns of load_data_defaults\n","load_data_defaults.columns"],"id":"4d988b97"},{"cell_type":"code","execution_count":150,"metadata":{"id":"31bf23a7","executionInfo":{"status":"ok","timestamp":1693060050575,"user_tz":-330,"elapsed":3737,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#Train test split using the CCF as output feature\n","ead_inputs_train, ead_inputs_test, ead_targets_train, ead_targets_test = train_test_split(load_data_defaults.drop(['recoveries','recovery_rate','recovery_rate_0_1','CCF'],axis=1),load_data_defaults['CCF'],test_size=0.2,random_state=42)"],"id":"31bf23a7"},{"cell_type":"code","execution_count":151,"metadata":{"id":"a9434967","executionInfo":{"status":"ok","timestamp":1693060084384,"user_tz":-330,"elapsed":13,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#drop the refrence categories\n","ead_inputs_train =ead_inputs_train.drop(ref_categories,axis=1)"],"id":"a9434967"},{"cell_type":"code","execution_count":152,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"767026e5","executionInfo":{"status":"ok","timestamp":1693060244802,"user_tz":-330,"elapsed":719,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"63b063ae-070a-4110-a33b-952e7017dad8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression()"],"text/html":["<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":152}],"source":["#create an object of linear regression and fit it on ead_inputs_train,ead_targets_train\n","reg_ead =LinearRegression()\n","reg_ead.fit(ead_inputs_train, ead_targets_train)"],"id":"767026e5"},{"cell_type":"code","execution_count":154,"metadata":{"id":"6a49000d","executionInfo":{"status":"ok","timestamp":1693060302620,"user_tz":-330,"elapsed":18,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#drop the refrence categories from ead_inputs_test\n","ead_inputs_test =ead_inputs_test.drop(ref_categories,axis=1)"],"id":"6a49000d"},{"cell_type":"code","execution_count":155,"metadata":{"id":"d42c0adb","executionInfo":{"status":"ok","timestamp":1693060346660,"user_tz":-330,"elapsed":1885,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#predict the results of ead_inputs_test\n","y_pred_test_ead =reg_ead.predict(ead_inputs_test)"],"id":"d42c0adb"},{"cell_type":"code","execution_count":156,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d55201f0","executionInfo":{"status":"ok","timestamp":1693060360441,"user_tz":-330,"elapsed":487,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"bf702d24-2941-46f2-a5fc-9295f88a7985"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.55958399, 0.37705677, 0.90225855, ..., 0.66296642, 0.89692882,\n","       0.89477416])"]},"metadata":{},"execution_count":156}],"source":["#print the results\n","y_pred_test_ead"],"id":"d55201f0"},{"cell_type":"code","execution_count":157,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"id":"8932d0a5","executionInfo":{"status":"ok","timestamp":1693060433418,"user_tz":-330,"elapsed":1273,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"7170a56a-8715-44ac-9265-ed2c06a4512a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='CCF', ylabel='Density'>"]},"metadata":{},"execution_count":157},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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the distribution plot of ead_targets_test-y_pred_test_ead\n","sns.distplot(ead_targets_test-y_pred_test_ead)"],"id":"8932d0a5"},{"cell_type":"code","execution_count":158,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"33ec4798","executionInfo":{"status":"ok","timestamp":1693060438522,"user_tz":-330,"elapsed":420,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"7fa1b6a4-4281-4a76-de4d-4accc451d327"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                 0\n","count  8156.000000\n","mean      0.720407\n","std       0.174406\n","min      -0.602701\n","25%       0.634794\n","50%       0.731027\n","75%       0.832212\n","max       1.286389"],"text/html":["\n","  <div id=\"df-5bcd5978-968f-492a-bb3f-46a615b50873\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th 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np.where(y_pred_test_ead>1,1,y_pred_test_ead)"],"id":"43d3d0d2"},{"cell_type":"code","execution_count":160,"metadata":{"id":"ae19a353","executionInfo":{"status":"ok","timestamp":1693060536002,"user_tz":-330,"elapsed":438,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#save the EAD model\n","pickle.dump(reg_ead,open('ead_model.sav','wb'))"],"id":"ae19a353"},{"cell_type":"code","execution_count":161,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"22d7e0e7","executionInfo":{"status":"ok","timestamp":1693060537815,"user_tz":-330,"elapsed":49,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"8177f1e9-1eb0-4a91-c2eb-70ed9ee29eb5"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                 0\n","count  8156.000000\n","mean      0.719043\n","std       0.166765\n","min       0.000000\n","25%       0.634794\n","50%       0.731027\n","75%       0.832212\n","max       1.000000"],"text/html":["\n","  <div id=\"df-2e4e783f-e164-4823-b3fc-c8dd0e3b0880\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>8156.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.719043</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.166765</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.634794</td>\n","    </tr>\n","   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lgd_input_test\n","lgd_input_test.shape"],"id":"db6169a9"},{"cell_type":"code","execution_count":163,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"50c6418a","executionInfo":{"status":"ok","timestamp":1693061044150,"user_tz":-330,"elapsed":468,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"33621594-2b4a-4ac2-bb90-ae85ae007dfa"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(8156,)"]},"metadata":{},"execution_count":163}],"source":["#check the shape of lgd_target_test\n","lgd_target_test.shape"],"id":"50c6418a"},{"cell_type":"code","execution_count":164,"metadata":{"id":"d36d1077","executionInfo":{"status":"ok","timestamp":1693061055830,"user_tz":-330,"elapsed":472,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#Take a testing sample as lgd_input_test and predict the EXPECTED LOSS\n","testing_sample = lgd_input_test"],"id":"d36d1077"},{"cell_type":"code","execution_count":166,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2e3671ed","executionInfo":{"status":"ok","timestamp":1693061102526,"user_tz":-330,"elapsed":2219,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"cff66e7b-4d30-44bc-8891-a83a3764e887"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(8156, 131)"]},"metadata":{},"execution_count":166}],"source":["#check the shape of testing_sample\n","testing_sample.shape"],"id":"2e3671ed"},{"cell_type":"code","execution_count":167,"metadata":{"id":"c864e211","executionInfo":{"status":"ok","timestamp":1693061102529,"user_tz":-330,"elapsed":16,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#create an empty dataframe that will store the final results\n","testing_outcome = pd.DataFrame()"],"id":"c864e211"},{"cell_type":"code","execution_count":168,"metadata":{"id":"2d89dcb2","executionInfo":{"status":"ok","timestamp":1693061483386,"user_tz":-330,"elapsed":548,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#create 2 columns for prediction of LGD_stage_1 and LGD_stage_2\n","testing_outcome['recovery_rate_stage_1'] =reg.predict(testing_sample)\n","testing_outcome['recovery_rate_stage_2'] =lin_reg.predict(testing_sample)"],"id":"2d89dcb2"},{"cell_type":"code","execution_count":169,"metadata":{"id":"2424425a","executionInfo":{"status":"ok","timestamp":1693061591439,"user_tz":-330,"elapsed":1816,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#create a new column recovery_rate that will have the fianl results of LGD_Model i.e the combition of stage_1 and stage_2\n","testing_outcome['recovery_rate'] =testing_outcome['recovery_rate_stage_1']*testing_outcome['recovery_rate_stage_2']"],"id":"2424425a"},{"cell_type":"code","execution_count":170,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"3a325855","executionInfo":{"status":"ok","timestamp":1693061623613,"user_tz":-330,"elapsed":1231,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"db312e0f-b9e0-4745-f84d-e6aeefec1d36"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   recovery_rate_stage_1  recovery_rate_stage_2  recovery_rate\n","0                      1               0.066186       0.066186\n","1                      1               0.047874       0.047874\n","2                      1               0.159147       0.159147\n","3                      1               0.057790       0.057790\n","4                      0               0.094851       0.000000"],"text/html":["\n","  <div id=\"df-a71bfaed-7014-4cd9-9abb-f42af6ba8bf3\" 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=np.where(testing_outcome['recovery_rate']>1,1,testing_outcome['recovery_rate'])"],"id":"7e6d622c"},{"cell_type":"code","execution_count":172,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"92e71838","executionInfo":{"status":"ok","timestamp":1693061849390,"user_tz":-330,"elapsed":496,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"702d05f6-1313-48bb-97bd-3065a07b0346"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   recovery_rate_stage_1  recovery_rate_stage_2  recovery_rate       LGD\n","0                      1               0.066186       0.066186  0.933814\n","1                      1               0.047874       0.047874  0.952126\n","2                      1               0.159147       0.159147  0.840853\n","3                      1               0.057790       0.057790  0.942210\n","4                      0               0.094851       0.000000  1.000000"],"text/html":["\n","  <div 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  <th>2</th>\n","      <td>1</td>\n","      <td>0.159147</td>\n","      <td>0.159147</td>\n","      <td>0.840853</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>0.057790</td>\n","      <td>0.057790</td>\n","      <td>0.942210</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>0.094851</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c154779-7c0a-45f3-ad16-c078fd72dd5b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-4e974d00-5ad7-4105-881e-a16846b51486\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4e974d00-5ad7-4105-881e-a16846b51486')\"\n","            title=\"Suggest charts.\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const charts = await google.colab.kernel.invokeFunction(\n","          'suggestCharts', [key], {});\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-4e974d00-5ad7-4105-881e-a16846b51486 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":172}],"source":["#create a new column LGD in testing_outcome i.e (1-recovery_rate)\n","testing_outcome['LGD'] =1-testing_outcome['recovery_rate']\n","testing_outcome.head()"],"id":"92e71838"},{"cell_type":"code","execution_count":174,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f0319147","executionInfo":{"status":"ok","timestamp":1693061930632,"user_tz":-330,"elapsed":395,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"a3fcdfdf-3db7-446b-c268-fd3cbbeb2300"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    8156.000000\n","mean        0.953095\n","std         0.056774\n","min         0.705470\n","25%         0.902217\n","50%         1.000000\n","75%         1.000000\n","max         1.000000\n","Name: LGD, dtype: float64"]},"metadata":{},"execution_count":174}],"source":["#check the basic stats of LGD\n","testing_outcome['LGD'].describe()"],"id":"f0319147"},{"cell_type":"code","execution_count":175,"metadata":{"id":"7b6171b3","executionInfo":{"status":"ok","timestamp":1693061961809,"user_tz":-330,"elapsed":9,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#create a new column CCF that will store the predicted results of EAD_Model\n","testing_outcome['CCF'] =reg_ead.predict(testing_sample)"],"id":"7b6171b3"},{"cell_type":"code","execution_count":176,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d19056ff","executionInfo":{"status":"ok","timestamp":1693061985757,"user_tz":-330,"elapsed":412,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"46546cf8-676f-4806-ce08-61cbfc59853c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    8156.000000\n","mean        0.720407\n","std         0.174406\n","min        -0.602701\n","25%         0.634794\n","50%         0.731027\n","75%         0.832212\n","max         1.286389\n","Name: CCF, dtype: float64"]},"metadata":{},"execution_count":176}],"source":["#check the basic stats of CCF\n","testing_outcome['CCF'].describe()"],"id":"d19056ff"},{"cell_type":"code","execution_count":177,"metadata":{"id":"4dcffce6","executionInfo":{"status":"ok","timestamp":1693062100284,"user_tz":-330,"elapsed":1914,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#replace all the negitive values in CCF with 0 and all values which are greater than 1 with 1\n","testing_outcome['CCF'] =np.where(testing_outcome['CCF']<0,0,testing_outcome['CCF'])\n","testing_outcome['CCF'] =np.where(testing_outcome['CCF']>1,1,testing_outcome['CCF'])"],"id":"4dcffce6"},{"cell_type":"code","execution_count":178,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b42014dc","executionInfo":{"status":"ok","timestamp":1693062114555,"user_tz":-330,"elapsed":642,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"d42f634a-e9de-44bd-d2d5-eb7b1523a658"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    8156.000000\n","mean        0.719043\n","std         0.166765\n","min         0.000000\n","25%         0.634794\n","50%         0.731027\n","75%         0.832212\n","max         1.000000\n","Name: CCF, dtype: float64"]},"metadata":{},"execution_count":178}],"source":["#check the basic stats of CCF\n","testing_outcome['CCF'].describe()"],"id":"b42014dc"},{"cell_type":"code","execution_count":179,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"463e86b8","executionInfo":{"status":"ok","timestamp":1693062127733,"user_tz":-330,"elapsed":676,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"359f634f-d17b-4406-d649-f75efc1b7b05"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(8156, 5)"]},"metadata":{},"execution_count":179}],"source":["#check the shape of testing_outcome\n","testing_outcome.shape"],"id":"463e86b8"},{"cell_type":"code","execution_count":180,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e35fa424","executionInfo":{"status":"ok","timestamp":1693062151980,"user_tz":-330,"elapsed":18,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"35d81a13-d7bf-4293-a04e-633aac04b3c6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(8156, 131)"]},"metadata":{},"execution_count":180}],"source":["#check the shape of testing_sample\n","testing_sample.shape"],"id":"e35fa424"},{"cell_type":"code","execution_count":181,"metadata":{"id":"dcba970c","executionInfo":{"status":"ok","timestamp":1693062161749,"user_tz":-330,"elapsed":1787,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#create a new column EAD i.e (CCF * funded_amt)\n","testing_outcome['EAD'] = testing_outcome['CCF'] * testing_sample['funded_amnt'].values"],"id":"dcba970c"},{"cell_type":"code","execution_count":182,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"84ef9ef3","executionInfo":{"status":"ok","timestamp":1693062176450,"user_tz":-330,"elapsed":445,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"166ff0ec-455e-4d0a-8e53-8ac2dd210840"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   recovery_rate_stage_1  recovery_rate_stage_2  recovery_rate       LGD  \\\n","0                      1               0.066186       0.066186  0.933814   \n","1                      1               0.047874       0.047874  0.952126   \n","2                      1               0.159147       0.159147  0.840853   \n","3                      1               0.057790       0.057790  0.942210   \n","4                      0               0.094851       0.000000  1.000000   \n","\n","        CCF           EAD  \n","0  0.559584   8393.759820  \n","1  0.377057   4524.681272  \n","2  0.902259  15631.629299  \n","3  0.513916   2569.582343  \n","4  0.621515   4739.051819  "],"text/html":["\n","  <div id=\"df-8bda4a8d-0b91-40c2-ab49-70d6ae3e5b9d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: 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<td>15631.629299</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>0.057790</td>\n","      <td>0.057790</td>\n","      <td>0.942210</td>\n","      <td>0.513916</td>\n","      <td>2569.582343</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>0.094851</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.621515</td>\n","      <td>4739.051819</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8bda4a8d-0b91-40c2-ab49-70d6ae3e5b9d')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 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EAD\n","testing_outcome['EAD'].describe()"],"id":"f7dc78e2"},{"cell_type":"markdown","metadata":{"id":"Pt4fzbYRNcJt"},"source":["## Calculate results of PD_Model"],"id":"Pt4fzbYRNcJt"},{"cell_type":"code","execution_count":184,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d88ef8e0","executionInfo":{"status":"ok","timestamp":1693062245815,"user_tz":-330,"elapsed":11,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"49808898-7042-4d5a-b528-a6d71204307a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['grade:A', 'grade:B', 'grade:C', 'grade:D', 'grade:E', 'grade:F',\n","       'home_ownership:OWN', 'home_ownership:MORTGAGE', 'addr_state:NM_VA',\n","       'addr_state:OK_TN_MO_LA_MD_NC',\n","       ...\n","       'mths_since_last_delinq:>=57', 'mths_since_last_record:0-90',\n","       'mths_since_last_record:90-120', 'mths_since_last_record:120-127',\n","       'mths_since_last_record:>=127', 'loan_amnt', 'funded_amnt', 'int_rate',\n","       'installment', 'total_rec_prncp'],\n","      dtype='object', length=131)"]},"metadata":{},"execution_count":184}],"source":["#check the columns of testing_sample\n","testing_sample.columns"],"id":"d88ef8e0"},{"cell_type":"markdown","metadata":{"id":"kKSu0aDWNrye"},"source":["### Make sure you pass only those columns to the PD_Model which you have passed in PD_Model while model training"],"id":"kKSu0aDWNrye"},{"cell_type":"code","execution_count":185,"metadata":{"id":"7b1f621d","executionInfo":{"status":"ok","timestamp":1693062284850,"user_tz":-330,"elapsed":417,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}}},"outputs":[],"source":["#Drop total_rec_prncp column as we didn't pass this in PD_Model while model training\n","testing_sample.drop('total_rec_prncp',axis=1,inplace=True)"],"id":"7b1f621d"},{"cell_type":"code","execution_count":186,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":193},"id":"b6f61857","executionInfo":{"status":"error","timestamp":1693062286939,"user_tz":-330,"elapsed":22,"user":{"displayName":"SOUMYA NAYAK","userId":"05998776148464585121"}},"outputId":"e372d77a-b8b0-4362-e5e1-94d9d7df11fe"},"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-186-7c6bfd5451c2>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#load the PD_model which we have created Earlier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mreg_pd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pd_model_gradient.sav'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'pd_model_gradient.sav'"]}],"source":["#load the PD_model which we have created Earlier\n","reg_pd = pickle.load(open('pd_model_gradient.sav','rb'))"],"id":"b6f61857"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"e28b11da"},"outputs":[],"source":["#predict the results of testing_sample using the PD_model\n","reg_pd.predict_proba(testing_sample)[:][:,0]"],"id":"e28b11da"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"c9a924bf"},"outputs":[],"source":["#predict the predict_proba results of testing_sample using PD_Model and store it in 'PD'\n","testing_outcome['PD'] ="],"id":"c9a924bf"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"6ab149a8"},"outputs":[],"source":["#check the hed of testing_outcome\n"],"id":"6ab149a8"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"2e236d74"},"outputs":[],"source":["#check the basic stats of PD\n"],"id":"2e236d74"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"05ded910"},"outputs":[],"source":["#create a new column 'EL' i.e Expected_Loss by ('PD' * 'LGD' * 'EAD')\n","testing_outcome['EL'] ="],"id":"05ded910"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"94f3aace"},"outputs":[],"source":["#check the head of testing_outcome\n"],"id":"94f3aace"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"c9d1355f"},"outputs":[],"source":["#Add the column 'funded_amnt' from testing_sample to testing_outcome\n","testing_outcome['Funded_amt'] = testing_sample['funded_amnt'].values"],"id":"c9d1355f"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"740b5d63"},"outputs":[],"source":["#check the head of testing_outcome\n"],"id":"740b5d63"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"2d857f8d"},"outputs":[],"source":["#Finally, create a new_dataframe testing_outcome_final which will have only 'Funded_amt','PD','LGD','EAD','EL'\n","testing_outcome_final =\n"],"id":"2d857f8d"},{"cell_type":"markdown","metadata":{"id":"mqy-1fCpQMrG"},"source":["record:0---When the Funded amount is 15000 the bank suffers a loss of 2117.6 from that customer..\n","\n","record:1---When the Funded amount is 12000 the bank suffers a loss of 1036.1 from that customer..\n","\n","record:3---When the Funded amount is 17325 the bank suffers a loss of 4657.0 from that customer..\n","\n","\n","and so on......\n","\n"],"id":"mqy-1fCpQMrG"},{"cell_type":"markdown","metadata":{"id":"3OnE1O1cQsd4"},"source":["## We will get the overall expected_loss suffered by the bank"],"id":"3OnE1O1cQsd4"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"c361d9e2"},"outputs":[],"source":["#Divide the  expected_loss sum by funded_amnt sum\n","testing_outcome_final['EL'].sum()/testing_outcome_final['Funded_amt'].sum()"],"id":"c361d9e2"},{"cell_type":"markdown","metadata":{"id":"ZyjniZwPPTC1"},"source":["We observe that the Bank suffers an Expected loss of 10.8% while giving loans to it's customers"],"id":"ZyjniZwPPTC1"},{"cell_type":"markdown","metadata":{"id":"LXcfIpY0PMav"},"source":["#observed EL is between 2% to 15% based on this banks can be aggressive or lenient while giving loans"],"id":"LXcfIpY0PMav"},{"cell_type":"markdown","metadata":{"id":"uRqUv4LVC5Ad"},"source":["Thank you!!!!"],"id":"uRqUv4LVC5Ad"}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"}},"nbformat":4,"nbformat_minor":5}