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] =... | 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)}' )... | 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,
s... | 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.... | 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(tes... | 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("I... | 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 RandomForestReg... | 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'... | 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... | 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... | 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... | 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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