kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
9,356,808 | corr_superior = correlacao.where(np.triu(np.ones(correlacao.shape), k=1 ).astype(np.bool))
drop = [column for column in corr_superior.columns if any(abs(corr_superior[column])> 0.9)]
drop<filter> | appl_counts = application_data.count().sort_values() /len(application_data ) | Home Credit Default Risk |
9,356,808 | correlacao.loc[correlacao['total_bedrooms'].abs() > 0.9, correlacao['total_bedrooms'].abs() > 0.9]<filter> | cols = list(set(appl_counts[(appl_counts < 0.6)].index)- set(['EXT_SOURCE_1', 'OWN_CAR_AGE'])) | Home Credit Default Risk |
9,356,808 | correlacao.loc[correlacao['rooms_not_bedrooms'].abs() > 0.9, correlacao['rooms_not_bedrooms'].abs() > 0.9]<filter> | le = LabelEncoder()
for col in application_data.select_dtypes('object'):
if len(application_data[col].unique())<= 2:
le.fit(application_data[col])
application_data[col] = le.transform(application_data[col] ) | Home Credit Default Risk |
9,356,808 | correlacao.loc[correlacao['rooms_not_bedrooms_per_house'].abs() > 0.9, correlacao['rooms_not_bedrooms_per_house'].abs() > 0.9]<filter> | application_data = pd.get_dummies(application_data, dummy_na=True ) | Home Credit Default Risk |
9,356,808 | correlacao.loc[correlacao['pop_per_bedroom'].abs() > 0.9, correlacao['pop_per_bedroom'].abs() > 0.9]<drop_column> | appl_counts = application_data.count().sort_values() /len(application_data ) | Home Credit Default Risk |
9,356,808 | data.drop(['rooms_not_bedrooms_per_house','pop_per_room','pop_per_house','total_rooms','median_age'], axis = 1, inplace = True )<feature_engineering> | appl_counts[(appl_counts < 1)] | Home Credit Default Risk |
9,356,808 | normcols = list(data.columns)
[normcols.remove(x)for x in ['latitude','longitude','median_house_value']]
fit_norm = data.dropna()
fit_norm = fit_norm[normcols]
means = {}
stds = {}
for c in normcols:
means[c] = fit_norm[c].mean()
stds[c] = fit_norm[c].std()
fit_norm = data.copy()
for c in normcols:
fit_norm.loc[:,c] = fit_norm.loc[:,c].subtract(means[c] ).divide(stds[c])
fit_norm['latitude'] = data['latitude']
fit_norm['longitude'] = data['longitude']
fit_norm['median_house_value'] = data['median_house_value']
fit_norm.head()<compute_test_metric> | train_data = application_data[application_data.IS_TRAIN == 1].merge(train_target, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
9,356,808 | msle = make_scorer(mean_squared_log_error )<train_on_grid> | del(train_data ) | Home Credit Default Risk |
9,356,808 | knn = KNeighborsRegressor(n_neighbors=10)
scores = cross_val_score(knn,
fit_norm.dropna().drop('median_house_value', axis = 1),
fit_norm.dropna().loc[:,'median_house_value'],
cv=10,
scoring = msle)
scores = np.sqrt(scores)
display(scores)
print(f'MSLE = {round(scores.mean() , 4)}, std = {round(scores.std() , 4)}' )<find_best_model_class> | bureau = pd.read_csv('.. /input/home-credit-default-risk/bureau.csv',
na_values=['XNA', 'XAP'], na_filter=True ) | Home Credit Default Risk |
9,356,808 | scores_array = []
scores_var = []
for i in range(21):
knn = KNeighborsRegressor(n_neighbors=10, metric='wminkowski', p=2, metric_params={'w': np.divide([i/20]*2+[1-i/20]*9,15)})
scores = cross_val_score(knn,
fit_norm.dropna().drop('median_house_value', axis = 1),
fit_norm.dropna().loc[:,'median_house_value'],
cv=10,
scoring = msle)
scores = np.sqrt(scores)
scores_array.append(scores.mean())
scores_var.append(scores.std() )<drop_column> | application_data['IS_IN_BUREAU'] = 0 | Home Credit Default Risk |
9,356,808 | test_data = fit_norm.loc[fit_norm['median_house_value'].isnull() ].drop('median_house_value',axis=1 )<save_to_csv> | application_data.loc[application_data.SK_ID_CURR.isin(bureau.SK_ID_CURR.unique()), 'IS_IN_BUREAU'] = 1 | Home Credit Default Risk |
9,356,808 | classifier = KNeighborsRegressor(n_neighbors=8, metric='wminkowski', p=2, metric_params={'w': np.divide([18/20]*2+[1-18/20]*9,15)})
classifier.fit(fit_norm.dropna().drop('median_house_value', axis = 1),fit_norm.dropna().loc[:,'median_house_value'])
testPred = classifier.predict(test_data)
arq = open("prediction_knn.csv", "w")
arq.write("Id,median_house_value
")
for i, j in zip(test_data.index, testPred):
arq.write(str(i)+ "," + str(int(j)) +"
")
arq.close()<save_to_csv> | appl_counts = application_data.count().sort_values() /len(application_data)
appl_counts[(appl_counts < 1)] | Home Credit Default Risk |
9,356,808 | regr = RandomForestRegressor(max_depth=20, random_state=0,n_estimators=400)
regr.fit(fit_norm.dropna().drop('median_house_value', axis = 1),fit_norm.dropna().loc[:,'median_house_value'])
testPred = regr.predict(test_data)
arq = open("prediction_rfr.csv", "w")
arq.write("Id,median_house_value
")
for i, j in zip(test_data.index, testPred):
arq.write(str(i)+ "," + str(int(j)) +"
")
arq.close()<save_to_csv> | application_data['HAS_SOCIAL_CIRCLE'] = 0 | Home Credit Default Risk |
9,356,808 | boost = AdaBoostRegressor(RandomForestRegressor(max_depth=5, random_state=0, n_estimators=300),
n_estimators=100)
boost.fit(fit_norm.dropna().drop('median_house_value', axis = 1),fit_norm.dropna().loc[:,'median_house_value'])
testPred = boost.predict(test_data)
arq = open("prediction_ada_rfr.csv", "w")
arq.write("Id,median_house_value
")
for i, j in zip(test_data.index, testPred):
arq.write(str(i)+ "," + str(int(j)) +"
")
arq.close()<import_modules> | application_data.loc[~application_data.OBS_30_CNT_SOCIAL_CIRCLE.isnull() , 'HAS_SOCIAL_CIRCLE'] = 1 | Home Credit Default Risk |
9,356,808 | import sklearn
from sklearn.metrics import mean_squared_log_error, make_scorer
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import Lasso
import sklearn.naive_bayes as skNB
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor
from sklearn.neighbors import KNeighborsRegressor
from IPython.display import Image
from IPython.core.display import HTML<load_from_csv> | application_data['AMT_CREDIT_FRAC'] = application_data.AMT_CREDIT / application_data.AMT_INCOME_TOTAL | Home Credit Default Risk |
9,356,808 | train_raw = pd.read_csv(".. /input/atividade-3-pmr3508/train.csv", sep = ",")
train = train_raw.copy()
target = train_raw["median_house_value"]
display(train_raw.shape,
train_raw.head() )<load_from_csv> | application_data['AMT_CREDIT_FRAC'] = application_data.AMT_ANNUITY / application_data.AMT_CREDIT | Home Credit Default Risk |
9,356,808 | test_raw = pd.read_csv(".. /input/atividade-3-pmr3508/test.csv", sep = ",",header=0)
test = test_raw.copy()
display(test_raw.shape,test_raw.head())
<feature_engineering> | application_data['AMT_GOODS_FRAC'] = application_data.AMT_GOODS_PRICE / application_data.AMT_CREDIT | Home Credit Default Risk |
9,356,808 | def New_features(data):
data.loc[:,'rooms_per_household'] = data.loc[:,'total_rooms']/data.loc[:,'households']
data.loc[:,'rooms_per_person'] = data.loc[:,'total_rooms']/data.loc[:,'population']
data.loc[:,'bedrooms_per_household'] = data.loc[:,'total_bedrooms']/data.loc[:,'households']
data.loc[:,'bedrooms_per_person'] = data.loc[:,'total_bedrooms']/data.loc[:,'households']
data.loc[:,'persons_per_household'] = data.loc[:,'population']/data.loc[:,'households']
data.loc[:, 'median_income_per_person'] = data.loc[:,'median_income']/data.loc[:,'persons_per_household']
New_features(train_raw)
New_features(test)
New_features(train)
train.head()<load_from_csv> | application_data['AMT_ANNUITY_FRAC'] = application_data.AMT_ANNUITY / application_data.AMT_INCOME_TOTAL | Home Credit Default Risk |
9,356,808 | arq = open(".. /input/us-citiestxt/US_cities.txt","r")
new_table=[]
linhas=arq.readlines()
for i in range(87,len(linhas)) :
palavras=linhas[i].split(",")
if palavras[1] == "5":
new_table.append([palavras[3][1:-1],palavras[5][:-1],palavras[4]])
arq=open("US_cities.csv","w")
for i in new_table:
arq.write(i[0]+","+i[1]+","+i[2]+"
" )<load_from_csv> | application_data['AMT_DPD_DEF'] = application_data.DEF_30_CNT_SOCIAL_CIRCLE + application_data.OBS_30_CNT_SOCIAL_CIRCLE | Home Credit Default Risk |
9,356,808 | coords={}
arq=open("US_cities.csv")
for i in arq.readlines() :
lista=i.strip().split(",")
coords[lista[0]]=(float(lista[1]),float(lista[2]))
coords<concatenate> | bureau_balance = pd.read_csv('.. /input/home-credit-default-risk/bureau_balance.csv',
na_values=['XNA', 'XAP'], na_filter=True ) | Home Credit Default Risk |
9,356,808 | dSF_LA=[]
a=np.array(coords["San Francisco"])
b=np.array(coords["Los Angeles"])
for i,c in train.iterrows() :
c=np.array(( c['longitude'],c['latitude']))
dSF_LA.append(min(np.linalg.norm(a-c),np.linalg.norm(b-c)))
train = train.join(pd.Series(dSF_LA,name="dSF_LA"))
dSF_LA=[]
a=np.array(coords["San Francisco"])
b=np.array(coords["Los Angeles"])
for i,c in train.iterrows() :
c=np.array(( c['longitude'],c['latitude']))
dSF_LA.append(min(np.linalg.norm(a-c),np.linalg.norm(b-c)))
test = test.join(pd.Series(dSF_LA,name="dSF_LA"))
<sort_values> | bureau = bureau[bureau.SK_ID_CURR.isin(application_data.SK_ID_CURR.unique())] | Home Credit Default Risk |
9,356,808 | train.corr() ["median_house_value"].abs().sort_values(ascending=False )<compute_train_metric> | bureau_balance = pd.get_dummies(bureau_balance ) | Home Credit Default Risk |
9,356,808 | msle = make_scorer(mean_squared_log_error)
def score(nome,regressor):
pontos = cross_val_score(regressor,trainF,target,cv=10,scoring=msle)
print(nome+ ': '+ str(round(np.sqrt(pontos ).mean() ,4)) + "
" )<choose_model_class> | bureau_balance = bureau_balance.sort_values(['SK_ID_BUREAU', 'MONTHS_BALANCE'] ) | Home Credit Default Risk |
9,356,808 | Lreg = Lasso(positive = True)
score("Regressor Lasso",Lreg)
<choose_model_class> | temp = bureau_balance.groupby('SK_ID_BUREAU' ).size().to_frame()
temp = temp.rename(columns={0: 'COUNT'})
temp.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | RFreg = RandomForestRegressor(max_depth=20, random_state=0,n_estimators=400)
score("Regressor Random Forest",RFreg )<choose_model_class> | bureau_balance = bureau_balance.groupby('SK_ID_BUREAU' ).agg({'last', 'sum', 'mean'} ) | Home Credit Default Risk |
9,356,808 | KNNreg = KNeighborsRegressor(n_neighbors=np.array(pontuacao ).argmin() +1 )<compute_test_metric> | bureau_balance.columns = bureau_balance.columns.map('_'.join ) | Home Credit Default Risk |
9,356,808 | score("Regressor KNN",KNNreg )<choose_model_class> | bureau_balance.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | Adareg = AdaBoostRegressor(RandomForestRegressor(max_depth=15,n_estimators=10),n_estimators=60 )<train_model> | bureau_balance = bureau_balance.merge(temp, how='left', on='SK_ID_BUREAU' ) | Home Credit Default Risk |
9,356,808 | Adareg.fit(trainF,target )<predict_on_test> | bureau_balance.columns = bureau_balance.columns.map(lambda x : 'BLN_' + x if x != 'SK_ID_BUREAU' else x ) | Home Credit Default Risk |
9,356,808 | R = Adareg.predict(testF)
R<save_to_csv> | bureau = bureau.merge(bureau_balance, how='left', on='SK_ID_BUREAU' ) | Home Credit Default Risk |
9,356,808 | arq=open("result1.csv","w")
arq.write("Id,median_house_value
")
for i,j in zip(list(R),list(Id)) :
arq.write(str(j)+","+str(i)+"
")
arq.close()<set_options> | bureau.drop(columns='SK_ID_BUREAU', inplace=True ) | Home Credit Default Risk |
9,356,808 | warnings.filterwarnings('ignore' )<load_from_csv> | del(bureau_balance ) | Home Credit Default Risk |
9,356,808 | df_train = pd.read_csv('.. /input/dataset_treino.csv', sep = ',', encoding = 'utf-8')
df_test = pd.read_csv('.. /input/dataset_teste.csv', sep = ',', encoding = 'utf-8' )<filter> | bureau = bureau.sort_values(['SK_ID_CURR', 'DAYS_CREDIT'] ) | Home Credit Default Risk |
9,356,808 | df_train[df_train.isnull().any(axis=1)]<count_missing_values> | bureau = pd.get_dummies(bureau, dummy_na=True ) | Home Credit Default Risk |
9,356,808 | df_train.isnull().values.any()<count_missing_values> | temp = bureau.groupby('SK_ID_CURR' ).size().to_frame()
temp = temp.rename(columns={0: 'COUNT'})
temp.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | df_train.isnull().any()<count_missing_values> | bureau = bureau.groupby('SK_ID_CURR' ).agg({'sum', 'mean', 'max'} ) | Home Credit Default Risk |
9,356,808 | df_train.isnull().sum()<count_missing_values> | bureau.columns = bureau.columns.map('_'.join ) | Home Credit Default Risk |
9,356,808 | df_test.isnull().sum()<count_missing_values> | bureau = bureau.merge(temp, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
9,356,808 | def info_variavel(df):
print(df.head())
print('')
print('Quantidade de registros:', df.count())
print('Possui valores missing?:', df.isnull().any())
print('Quantidade de valores missing:', df.isnull().sum())
print('')
print('Valores únicos:')
print(df.unique())
print('')
print('Contagem dos elementos:')
print(df.value_counts() )<define_variables> | application_data = application_data.merge(bureau, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
9,356,808 | index = 'Order'<drop_column> | del(bureau ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test['OrderId']<define_variables> | prev_application = pd.read_csv('/kaggle/input/home-credit-default-risk/previous_application.csv',
na_values=['XNA', 'XAP'], na_filter=True ) | Home Credit Default Risk |
9,356,808 | index = 'Property Id'<drop_column> | prev_application = pd.get_dummies(prev_application, dummy_na=True ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
<define_variables> | prev_application.drop(columns='SK_ID_PREV', inplace=True ) | Home Credit Default Risk |
9,356,808 | index = 'Property Name'<drop_column> | prev_application = prev_application.sort_values(['SK_ID_CURR', 'DAYS_DECISION'] ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | temp = prev_application.groupby('SK_ID_CURR' ).size().to_frame()
temp = temp.rename(columns={0: 'COUNT'})
temp.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | index = 'Parent Property Id'<drop_column> | prev_application = prev_application.groupby('SK_ID_CURR' ).agg(['max', 'sum', 'mean'] ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | prev_application.columns = prev_application.columns.map('_'.join ) | Home Credit Default Risk |
9,356,808 | index = 'Parent Property Name'<drop_column> | prev_application = prev_application.merge(temp, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | prev_application.columns = prev_application.columns.map(lambda x : 'PREV_' + x if x != 'SK_ID_CURR' else x ) | Home Credit Default Risk |
9,356,808 | index = 'BBL - 10 digits'<drop_column> | application_data = application_data.merge(prev_application, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | del(prev_application ) | Home Credit Default Risk |
9,356,808 | index = 'NYC Borough, Block and Lot(BBL)self-reported'<drop_column> | pos_cash_balance = pd.read_csv('/kaggle/input/home-credit-default-risk/POS_CASH_balance.csv',
na_values=['XNA', 'XAP'], na_filter=True ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | pos_cash_balance = pos_cash_balance.sort_values(['SK_ID_CURR', 'SK_ID_PREV', 'MONTHS_BALANCE'] ) | Home Credit Default Risk |
9,356,808 | index = 'NYC Building Identification Number(BIN)'<drop_column> | temp = pos_cash_balance.groupby(['SK_ID_CURR', 'SK_ID_PREV'] ).size().to_frame()
temp = temp.rename(columns={0: 'BLN_COUNT'})
temp.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | pos_cash_balance = pd.get_dummies(pos_cash_balance, dummy_na=True ) | Home Credit Default Risk |
9,356,808 | index = 'Address 1(self-reported)'<drop_column> | pos_cash_balance = pos_cash_balance.groupby(['SK_ID_PREV', 'SK_ID_CURR'] ).agg(['sum', 'mean', 'max'] ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | pos_cash_balance.columns = pos_cash_balance.columns.map('_'.join ) | Home Credit Default Risk |
9,356,808 | index = 'Address 2'<drop_column> | pos_cash_balance.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | pos_cash_balance = pos_cash_balance.merge(temp, how='left', on=['SK_ID_CURR', 'SK_ID_PREV'] ) | Home Credit Default Risk |
9,356,808 | index = 'Postal Code'<drop_column> | pos_cash_balance.drop(columns='SK_ID_PREV', inplace=True ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | temp = pos_cash_balance.groupby('SK_ID_CURR' ).size().to_frame()
temp = temp.rename(columns={0: 'COUNT'})
temp.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | index = 'Street Number'<drop_column> | pos_cash_balance = pos_cash_balance.groupby(['SK_ID_CURR'] ).agg(['sum', 'mean', 'max'] ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | pos_cash_balance.columns = pos_cash_balance.columns.map('_'.join ) | Home Credit Default Risk |
9,356,808 | index = 'Street Name'<drop_column> | pos_cash_balance.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | pos_cash_balance = pos_cash_balance.merge(temp, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
9,356,808 | index = 'Borough'<data_type_conversions> | pos_cash_balance.columns = pos_cash_balance.columns.map(lambda x : 'CSH_' + x if x != 'SK_ID_CURR' else x ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('category' ).cat.codes
df_test[index] = df_test[index].astype('category' ).cat.codes<filter> | application_data = application_data.merge(pos_cash_balance, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
9,356,808 | df_train = df_train[df_train[index] > -1]<compute_test_metric> | del(pos_cash_balance ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | credit_card_balance = pd.read_csv('/kaggle/input/home-credit-default-risk/credit_card_balance.csv',
na_values=['XNA', 'XAP'], na_filter=True ) | Home Credit Default Risk |
9,356,808 | index = 'DOF Gross Floor Area'<drop_column> | credit_card_balance = pd.get_dummies(credit_card_balance, dummy_na=True ) | Home Credit Default Risk |
9,356,808 | del df_train[index]
del df_test[index]<define_variables> | credit_card_balance = credit_card_balance.sort_values(['SK_ID_CURR', 'SK_ID_PREV', 'MONTHS_BALANCE'] ) | Home Credit Default Risk |
9,356,808 | index = 'Primary Property Type - Self Selected'<data_type_conversions> | temp = credit_card_balance.groupby(['SK_ID_CURR', 'SK_ID_PREV'] ).size().to_frame()
temp = temp.rename(columns={0: 'COUNT'})
temp.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('category' ).cat.codes
df_test[index] = df_test[index].astype('category' ).cat.codes<compute_test_metric> | credit_card_balance = credit_card_balance.groupby(['SK_ID_PREV', 'SK_ID_CURR'] ).agg(['sum', 'mean', 'max'] ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | credit_card_balance.columns = credit_card_balance.columns.map('_'.join ) | Home Credit Default Risk |
9,356,808 | index = 'List of All Property Use Types at Property'<data_type_conversions> | credit_card_balance.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('category' ).cat.codes
df_test[index] = df_test[index].astype('category' ).cat.codes<compute_test_metric> | credit_card_balance = credit_card_balance.merge(temp, how='left', on=['SK_ID_CURR', 'SK_ID_PREV'] ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | credit_card_balance.drop(columns='SK_ID_PREV', inplace=True ) | Home Credit Default Risk |
9,356,808 | index = 'Largest Property Use Type'<data_type_conversions> | credit_card_balance = credit_card_balance.groupby(['SK_ID_CURR'] ).agg(['sum', 'mean'] ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('category' ).cat.codes
df_test[index] = df_test[index].astype('category' ).cat.codes<compute_test_metric> | credit_card_balance.columns = credit_card_balance.columns.map('_'.join ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | credit_card_balance.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | index = 'Largest Property Use Type - Gross Floor Area(ft²)'<compute_test_metric> | credit_card_balance.columns = credit_card_balance.columns.map(lambda x : 'CRD_' + x if x != 'SK_ID_CURR' else x ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | application_data = application_data.merge(credit_card_balance, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
9,356,808 | index = '2nd Largest Property Use Type'<data_type_conversions> | del(credit_card_balance ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('category' ).cat.codes
df_test[index] = df_test[index].astype('category' ).cat.codes<compute_test_metric> | installments_payments = pd.read_csv('/kaggle/input/home-credit-default-risk/installments_payments.csv',
na_values=['XNA', 'XAP'], na_filter=True ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | installments_payments = installments_payments.sort_values(['SK_ID_CURR', 'SK_ID_PREV', 'DAYS_ENTRY_PAYMENT'] ) | Home Credit Default Risk |
9,356,808 | index = '2nd Largest Property Use - Gross Floor Area(ft²)'<feature_engineering> | temp = installments_payments.groupby(['SK_ID_CURR', 'SK_ID_PREV'] ).size().to_frame()
temp = temp.rename(columns={0: 'COUNT'})
temp.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | installments_payments.fillna(0, inplace=True ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | installments_payments = installments_payments.groupby(['SK_ID_PREV', 'SK_ID_CURR'] ).agg(['sum', 'mean', 'max', 'min'] ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | installments_payments.columns = installments_payments.columns.map('_'.join ) | Home Credit Default Risk |
9,356,808 | index = '3rd Largest Property Use Type'<data_type_conversions> | installments_payments.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('category' ).cat.codes
df_test[index] = df_test[index].astype('category' ).cat.codes<compute_test_metric> | installments_payments = installments_payments.merge(temp, how='left', on=['SK_ID_CURR', 'SK_ID_PREV'] ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | installments_payments.drop(columns='SK_ID_PREV', inplace=True ) | Home Credit Default Risk |
9,356,808 | index = '3rd Largest Property Use Type - Gross Floor Area(ft²)'<feature_engineering> | installments_payments = installments_payments.groupby(['SK_ID_CURR'] ).agg(['sum', 'mean', 'max', 'min'] ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].str.replace('Not Available', '0')
df_test[index] = df_test[index].str.replace('Not Available', '0' )<data_type_conversions> | installments_payments.columns = installments_payments.columns.map('_'.join ) | Home Credit Default Risk |
9,356,808 | df_train[index] = df_train[index].astype('float64')
df_test[index] = df_test[index].astype('float64' )<compute_test_metric> | installments_payments.reset_index(inplace=True ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | installments_payments.columns = installments_payments.columns.map(lambda x : 'INS_' + x if x != 'SK_ID_CURR' else x ) | Home Credit Default Risk |
9,356,808 | index = 'Year Built'<compute_test_metric> | application_data = application_data.merge(installments_payments, how='left', on='SK_ID_CURR' ) | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | del(installments_payments ) | Home Credit Default Risk |
9,356,808 | index = 'Number of Buildings - Self-reported'<compute_test_metric> | application_data.columns = ["".join(c if c.isalnum() else "_" for c in str(x)) for x in application_data.columns] | Home Credit Default Risk |
9,356,808 | intervalo_valores(df_train[index] )<define_variables> | model = lgb.LGBMClassifier() | Home Credit Default Risk |
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