![03/10/2020 DSN20 - Jupyter Notebook
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In [1]:
In [2]:
In [3]:
train set shape: (56000, 52)
test set shape: (24000, 51)
import pandas as pd
import numpy as np
from pandas import DataFrame
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
import catboost
from rgf.sklearn import RGFClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import VotingClassifier, RandomForestClassifier
from sklearn.model_selection import cross_val_score, StratifiedKFold
train=pd.read_csv("Datasets/2020 Qualifying competition/Train.csv")
test=pd.read_csv("Datasets/2020 Qualifying competition/Test.csv")
print("train set shape:",train.shape)
print("test set shape:",test.shape)](https://image.slidesharecdn.com/dsn20-jupyternotebook-201011054419/85/Loan-defaulters-predictions-Python-codes-1-320.jpg)
![03/10/2020 DSN20 - Jupyter Notebook
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<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56000 entries, 0 to 55999
Data columns (total 52 columns):
Applicant_ID 56000 non-null object
form_field1 53471 non-null float64
form_field2 52156 non-null float64
form_field3 55645 non-null float64
form_field4 55645 non-null float64
form_field5 55645 non-null float64
form_field6 42640 non-null float64
form_field7 50837 non-null float64
form_field8 42640 non-null float64
form_field9 47992 non-null float64
form_field10 55645 non-null float64
form_field11 24579 non-null float64
form_field12 46105 non-null float64
form_field13 50111 non-null float64
form_field14 56000 non-null int64
form_field15 33525 non-null float64
form_field16 42964 non-null float64
form_field17 44849 non-null float64
form_field18 45598 non-null float64
form_field19 55996 non-null float64
form_field20 55645 non-null float64
form_field21 40146 non-null float64
form_field22 35600 non-null float64
form_field23 27877 non-null float64
form_field24 42703 non-null float64
form_field25 50550 non-null float64
form_field26 48562 non-null float64
form_field27 46701 non-null float64
form_field28 55645 non-null float64
form_field29 55645 non-null float64
form_field30 30491 non-null float64
form_field31 16592 non-null float64
form_field32 50550 non-null float64
form_field33 54744 non-null float64
form_field34 55645 non-null float64
form_field35 32852 non-null float64
form_field36 54005 non-null float64
form_field37 50550 non-null float64
form_field38 55645 non-null float64
form_field39 51789 non-null float64
form_field40 12271 non-null float64
form_field41 17771 non-null float64
form_field42 54677 non-null float64
form_field43 55432 non-null float64
form_field44 50617 non-null float64
form_field45 24683 non-null float64
form_field46 40096 non-null float64
form_field47 56000 non-null object
form_field48 35111 non-null float64
form_field49 55645 non-null float64
# Information on each of the columns in the dataset
train.info()](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Loan-defaulters-predictions(Python codes)
- 1. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 1/14 In [1]: In [2]: In [3]: train set shape: (56000, 52) test set shape: (24000, 51) import pandas as pd import numpy as np from pandas import DataFrame from sklearn.metrics import roc_auc_score from sklearn.preprocessing import LabelEncoder, MinMaxScaler import catboost from rgf.sklearn import RGFClassifier from lightgbm import LGBMClassifier from sklearn.ensemble import VotingClassifier, RandomForestClassifier from sklearn.model_selection import cross_val_score, StratifiedKFold train=pd.read_csv("Datasets/2020 Qualifying competition/Train.csv") test=pd.read_csv("Datasets/2020 Qualifying competition/Test.csv") print("train set shape:",train.shape) print("test set shape:",test.shape)
- 2. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 2/14 In [4]: <class 'pandas.core.frame.DataFrame'> RangeIndex: 56000 entries, 0 to 55999 Data columns (total 52 columns): Applicant_ID 56000 non-null object form_field1 53471 non-null float64 form_field2 52156 non-null float64 form_field3 55645 non-null float64 form_field4 55645 non-null float64 form_field5 55645 non-null float64 form_field6 42640 non-null float64 form_field7 50837 non-null float64 form_field8 42640 non-null float64 form_field9 47992 non-null float64 form_field10 55645 non-null float64 form_field11 24579 non-null float64 form_field12 46105 non-null float64 form_field13 50111 non-null float64 form_field14 56000 non-null int64 form_field15 33525 non-null float64 form_field16 42964 non-null float64 form_field17 44849 non-null float64 form_field18 45598 non-null float64 form_field19 55996 non-null float64 form_field20 55645 non-null float64 form_field21 40146 non-null float64 form_field22 35600 non-null float64 form_field23 27877 non-null float64 form_field24 42703 non-null float64 form_field25 50550 non-null float64 form_field26 48562 non-null float64 form_field27 46701 non-null float64 form_field28 55645 non-null float64 form_field29 55645 non-null float64 form_field30 30491 non-null float64 form_field31 16592 non-null float64 form_field32 50550 non-null float64 form_field33 54744 non-null float64 form_field34 55645 non-null float64 form_field35 32852 non-null float64 form_field36 54005 non-null float64 form_field37 50550 non-null float64 form_field38 55645 non-null float64 form_field39 51789 non-null float64 form_field40 12271 non-null float64 form_field41 17771 non-null float64 form_field42 54677 non-null float64 form_field43 55432 non-null float64 form_field44 50617 non-null float64 form_field45 24683 non-null float64 form_field46 40096 non-null float64 form_field47 56000 non-null object form_field48 35111 non-null float64 form_field49 55645 non-null float64 # Information on each of the columns in the dataset train.info()
- 3. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 3/14 form_field50 44944 non-null float64 default_status 56000 non-null object dtypes: float64(48), int64(1), object(3) memory usage: 22.2+ MB
- 4. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 4/14 In [5]: Out[5]: Applicant_ID object form_field1 float64 form_field2 float64 form_field3 float64 form_field4 float64 form_field5 float64 form_field6 float64 form_field7 float64 form_field8 float64 form_field9 float64 form_field10 float64 form_field11 float64 form_field12 float64 form_field13 float64 form_field14 int64 form_field15 float64 form_field16 float64 form_field17 float64 form_field18 float64 form_field19 float64 form_field20 float64 form_field21 float64 form_field22 float64 form_field23 float64 form_field24 float64 form_field25 float64 form_field26 float64 form_field27 float64 form_field28 float64 form_field29 float64 form_field30 float64 form_field31 float64 form_field32 float64 form_field33 float64 form_field34 float64 form_field35 float64 form_field36 float64 form_field37 float64 form_field38 float64 form_field39 float64 form_field40 float64 form_field41 float64 form_field42 float64 form_field43 float64 form_field44 float64 form_field45 float64 form_field46 float64 form_field47 object form_field48 float64 form_field49 float64 form_field50 float64 dtype: object test.dtypes
- 5. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 5/14 In [6]: Out[6]: Applicant_ID form_field1 form_field2 form_field3 form_field4 form_field5 form_field6 form_f 0 Apcnt_1000000 3436.0 0.28505 1.6560 0.0 0.000 0.0 10689 1 Apcnt_1000004 3456.0 0.67400 0.2342 0.0 0.000 0.0 898 2 Apcnt_1000008 3276.0 0.53845 3.1510 0.0 6.282 NaN 956 3 Apcnt_1000012 3372.0 0.17005 0.5050 0.0 0.000 192166.0 3044 4 Apcnt_1000016 3370.0 0.77270 1.1010 0.0 0.000 1556.0 214 5 rows × 52 columns train.head()
- 6. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 6/14 In [7]: Out[7]: Applicant_ID object form_field1 float64 form_field2 float64 form_field3 float64 form_field4 float64 form_field5 float64 form_field6 float64 form_field7 float64 form_field8 float64 form_field9 float64 form_field10 float64 form_field11 float64 form_field12 float64 form_field13 float64 form_field14 int64 form_field15 float64 form_field16 float64 form_field17 float64 form_field18 float64 form_field19 float64 form_field20 float64 form_field21 float64 form_field22 float64 form_field23 float64 form_field24 float64 form_field25 float64 form_field26 float64 form_field27 float64 form_field28 float64 form_field29 float64 form_field30 float64 form_field31 float64 form_field32 float64 form_field33 float64 form_field34 float64 form_field35 float64 form_field36 float64 form_field37 float64 form_field38 float64 form_field39 float64 form_field40 float64 form_field41 float64 form_field42 float64 form_field43 float64 form_field44 float64 form_field45 float64 form_field46 float64 form_field47 object form_field48 float64 form_field49 float64 form_field50 float64 default_status object dtype: object train.dtypes
- 7. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 7/14 In [8]: Out[8]: Applicant_ID 0 form_field1 2529 form_field2 3844 form_field3 355 form_field4 355 form_field5 355 form_field6 13360 form_field7 5163 form_field8 13360 form_field9 8008 form_field10 355 form_field11 31421 form_field12 9895 form_field13 5889 form_field14 0 form_field15 22475 form_field16 13036 form_field17 11151 form_field18 10402 form_field19 4 form_field20 355 form_field21 15854 form_field22 20400 form_field23 28123 form_field24 13297 form_field25 5450 form_field26 7438 form_field27 9299 form_field28 355 form_field29 355 form_field30 25509 form_field31 39408 form_field32 5450 form_field33 1256 form_field34 355 form_field35 23148 form_field36 1995 form_field37 5450 form_field38 355 form_field39 4211 form_field40 43729 form_field41 38229 form_field42 1323 form_field43 568 form_field44 5383 form_field45 31317 form_field46 15904 form_field47 0 form_field48 20889 form_field49 355 form_field50 11056 default_status 0 dtype: int64 # Exploring the number of missing values(NaN) in each column of the dataset train.isnull().sum()
- 8. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 8/14 In [9]: In [10]: Out[10]: Applicant_ID 0 form_field1 0 form_field2 0 form_field3 0 form_field4 0 form_field5 0 form_field6 0 form_field7 0 form_field8 0 form_field9 0 form_field10 0 form_field11 0 form_field12 0 form_field13 0 form_field14 0 form_field15 0 form_field16 0 form_field17 0 form_field18 0 f fi ld19 0 #fill empty cell(NaN data)with -999 train=train.fillna(-999) test= test.fillna(-999) test.isnull().sum()
- 9. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 9/14 In [11]: Out[11]: Applicant_ID 0 form_field1 0 form_field2 0 form_field3 0 form_field4 0 form_field5 0 form_field6 0 form_field7 0 form_field8 0 form_field9 0 form_field10 0 form_field11 0 form_field12 0 form_field13 0 form_field14 0 form_field15 0 form_field16 0 form_field17 0 form_field18 0 form_field19 0 form_field20 0 form_field21 0 form_field22 0 form_field23 0 form_field24 0 form_field25 0 form_field26 0 form_field27 0 form_field28 0 form_field29 0 form_field30 0 form_field31 0 form_field32 0 form_field33 0 form_field34 0 form_field35 0 form_field36 0 form_field37 0 form_field38 0 form_field39 0 form_field40 0 form_field41 0 form_field42 0 form_field43 0 form_field44 0 form_field45 0 form_field46 0 form_field47 0 form_field48 0 form_field49 0 form_field50 0 default_status 0 dtype: int64 train.isnull().sum()
- 10. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 10/14 In [12]: In [13]: In [14]: In [15]: Out[12]: Applicant_ID form_field1 form_field2 form_field3 form_field4 form_field5 form_field6 form_f 0 Apcnt_1000000 3436.0 0.28505 1.6560 0.0 0.000 0.0 10689 1 Apcnt_1000004 3456.0 0.67400 0.2342 0.0 0.000 0.0 898 2 Apcnt_1000008 3276.0 0.53845 3.1510 0.0 6.282 -9999.0 956 3 Apcnt_1000012 3372.0 0.17005 0.5050 0.0 0.000 192166.0 3044 4 Apcnt_1000016 3370.0 0.77270 1.1010 0.0 0.000 1556.0 214 5 rows × 52 columns Out[15]: 0 0 1 0 2 1 3 0 4 0 .. 55995 0 55996 1 55997 0 55998 0 55999 0 Name: default_status, Length: 56000, dtype: int32 train.head() # Encoding Categorical features encoder= ('form_field47', 'default_status') for x in encoder: le= LabelEncoder() train[x] =le.fit_transform(train[x].values) test['form_field47']=le.fit_transform(test['form_field47'].values) y= train["default_status"] y
- 11. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 11/14 In [16]: In [17]: In [18]: In [19]: In [20]: In [21]: In [23]: In [24]: Out[16]: form_field1 form_field2 form_field3 form_field4 form_field5 form_field6 form_field7 form_field 0 3436.0 0.28505 1.6560 0.0 0.000 0.0 10689720.0 252072 1 3456.0 0.67400 0.2342 0.0 0.000 0.0 898979.0 497531 2 3276.0 0.53845 3.1510 0.0 6.282 -9999.0 956940.0 -9999 3 3372.0 0.17005 0.5050 0.0 0.000 192166.0 3044703.0 385499 4 3370.0 0.77270 1.1010 0.0 0.000 1556.0 214728.0 214728 5 rows × 50 columns # drop appropriate column train= train.drop(["Applicant_ID","default_status"], axis=1) test= test.drop(["Applicant_ID"], axis=1) train.head() robust= MinMaxScaler() scaledtrain= robust.fit_transform(train) scaledtrain= DataFrame(scaledtrain) scaledtest= robust.transform(test) scaledtest= DataFrame(scaledtest) # split input(x) and output(y) data #from sklearn.model_selection import train_test_split #X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.3, #stratify=y,random_state=42) params= {'n_estimators': 2500,'learning_rate':0.01,'objective': 'CrossEntropy','e 'random_seed': 3500,'verbose': False} fold= StratifiedKFold(n_splits= 15, shuffle= True,random_state= 999) from catboost import CatBoostClassifier model1= CatBoostClassifier(random_state= 499, verbose= False) model2= RGFClassifier(max_leaf= 1500) model3= LGBMClassifier(n_estimators= 900, random_state= 499) localscore, testscore= [], [] model= VotingClassifier(estimators= [('model1', model1), ('model2', model2), ('mo
- 12. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 12/14 In [25]: #########################fold0 [Voting] ................... (1 of 3) Processing model1, total= 36.0s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.1s #########################fold1 [Voting] ................... (1 of 3) Processing model1, total= 37.4s [Voting] ................... (2 of 3) Processing model2, total= 1.8min [Voting] ................... (3 of 3) Processing model3, total= 10.2s #########################fold2 [Voting] ................... (1 of 3) Processing model1, total= 36.4s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.2s #########################fold3 [Voting] ................... (1 of 3) Processing model1, total= 38.4s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.2s #########################fold4 [Voting] ................... (1 of 3) Processing model1, total= 37.6s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 12.2s #########################fold5 [Voting] ................... (1 of 3) Processing model1, total= 35.5s [Voting] ................... (2 of 3) Processing model2, total= 1.8min [Voting] ................... (3 of 3) Processing model3, total= 10.1s #########################fold6 [Voting] ................... (1 of 3) Processing model1, total= 37.6s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.2s #########################fold7 [Voting] ................... (1 of 3) Processing model1, total= 35.9s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.3s #########################fold8 [Voting] ................... (1 of 3) Processing model1, total= 37.4s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.2s #########################fold9 [Voting] ................... (1 of 3) Processing model1, total= 37.4s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.1s #########################fold10 for i, (trainval, valvalue) in enumerate(fold.split(scaledtrain, y)): print( '#' * 25+ 'fold'+ str(i)) train_x, val_x= scaledtrain.iloc[trainval], scaledtrain.iloc[valvalue] train_y, val_y= y.iloc[trainval], y.iloc[valvalue] model.fit(train_x, train_y) pred= model.predict_proba(val_x)[:,-1] result= roc_auc_score(val_y, pred) localscore.append(result) testpred= model.predict_proba(scaledtest)[:,-1] testscore.append(testpred)
- 13. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 13/14 In [26]: In [28]: [Voting] ................... (1 of 3) Processing model1, total= 35.9s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.3s #########################fold11 [Voting] ................... (1 of 3) Processing model1, total= 37.7s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 12.6s #########################fold12 [Voting] ................... (1 of 3) Processing model1, total= 35.8s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.4s #########################fold13 [Voting] ................... (1 of 3) Processing model1, total= 37.1s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.0s #########################fold14 [Voting] ................... (1 of 3) Processing model1, total= 36.2s [Voting] ................... (2 of 3) Processing model2, total= 1.7min [Voting] ................... (3 of 3) Processing model3, total= 10.6s 0.84036499050012 Out[28]: 0 1 2 3 4 5 6 7 8 0 0.837618 0.835608 0.837453 0.856249 0.829797 0.84765 0.836926 0.84438 0.823843 0.85009 print(np.mean(localscore)) output= pd.DataFrame(localscore).T output.head()
- 14. 03/10/2020 DSN20 - Jupyter Notebook localhost:8888/notebooks/Desktop/DSN20.ipynb 14/14 In [29]: In [33]: In [34]: In [ ]: Out[29]: Applicant_ID default_status 0 Apcnt_1000032 1 1 Apcnt_1000048 1 2 Apcnt_1000052 1 3 Apcnt_1000076 1 4 Apcnt_1000080 1 ... ... ... 23995 Apcnt_999940 1 23996 Apcnt_999956 1 23997 Apcnt_999976 1 23998 Apcnt_999984 1 23999 Apcnt_999992 1 24000 rows × 2 columns submission= pd.read_csv('Datasets/2020 Qualifying Competition/SampleSubmission.cs submission test_pred= np.mean(testscore, axis=0) submission['default_status']= test_pred submission.to_csv('grace.csv', index= False)