ML之FE：基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(手动设计新特征、利用featuretools工具实现自动特征生成)

目录

基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(T1手动设计新特征、T2利用featuretools工具实现自动特征生成)

设计思路

输出结果

T1、手动设计新特征

T2、利用featuretools工具实现自动特征生成

T2.1、79个Feature Primitives

T2.2、深度特征合成：指通过叠加多个基元来得到特征

1)、完整的数据框包含了254-5个新特征，特征工具通过结合与叠加基元特征构造了许多新特征

2)、完整的数据框包含了113-5个新特征

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ML之FE：基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(手动设计新特征、利用featuretools工具实现自动特征生成)
ML之FE：基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(手动设计新特征、利用featuretools工具实现自动特征生成)实现

基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(T1手动设计新特征、T2利用featuretools工具实现自动特征生成)

设计思路

输出结果

clients client_id  age       education  income  credit_score loan_type     joined
0      44966   31  BachelorDegree   58530           822    credit 2019-05-13
1      46602   24    DoctorDegree  176949           583      cash 2020-03-14
2      38435   43    DoctorDegree  183813           596      home 2019-06-26
3      47641   48    DoctorDegree  149357           532    credit 2020-06-25
4      25942   18    DoctorDegree  227219           839      cash 2019-06-09
loans client_id loan_type  loan_amount  repaid  loan_id loan_start   loan_end  \
0      44966    credit         7733       0    11246 2020-09-15 2023-02-22
1      46602      cash         9547       1    10612 2019-01-25 2021-09-22
2      38435      home         6615       0    10339 2019-05-13 2020-12-25
3      47641    credit         5050       1    10442 2020-02-22 2022-10-26
4      25942      cash        14059       0    11871 2019-04-10 2021-03-29   rate
0  0.42
1  4.03
2  5.65
3  7.51
4  3.58
payments loan_id  payment_amount payment_date  missed
0    11246            1136   2020-11-19       0
1    11246             858   2020-11-29       0
2    11246            1245   2020-12-31       0
3    11246            1153   2021-01-17       1
4    11246             838   2021-02-10       1
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

T1、手动设计新特征

loan_type	mean_loan_amount	max_loan_amount	min_loan_amount
cash	12014.5	14884	8458
credit	7329.857143	13457	3795
home	5456.5	10130	1430
other	9025.833333	14108	2466

T2、利用featuretools工具实现自动特征生成

将payments数据框全部加入实体集后，输出信息，Entity: paymentsVariables:payment_id (dtype: index)loan_id (dtype: numeric)payment_amount (dtype: numeric)payment_date (dtype: datetime_time_index)missed (dtype: categorical)Shape:(Rows: 179, Columns: 5)
该实体集现在包括三个实体以及连接这些实体之间的关系，这时候已经做好了构造新特征的准备。 Entityset: clientsEntities:clients [Rows: 25, Columns: 7]payments [Rows: 179, Columns: 5]loans [Rows: 25, Columns: 8]Relationships:loans.client_id -> clients.client_idpayments.loan_id -> loans.loan_id
primitives:  <class 'pandas.core.frame.DataFrame'> 79 

T2.1、79个Feature Primitives

[79 rows x 5 columns]

	name	type	dask_compatible	koalas_compatible	description	描述
0	all	aggregation	TRUE	FALSE	Calculates if all values are 'True' in a list.	计算列表中所有值是否均为“ True”。
1	num_unique	aggregation	TRUE	TRUE	Determines the number of distinct values, ignoring `NaN` values.	确定不同值的数量，忽略“ NaN”值。
2	any	aggregation	TRUE	FALSE	Determines if any value is 'True' in a list.	确定列表中是否有任何值为“ True”。
3	time_since_last	aggregation	FALSE	FALSE	Calculates the time elapsed since the last datetime (default in seconds).	计算自上一个日期时间以来经过的时间（默认值以秒为单位）。
4	n_most_common	aggregation	FALSE	FALSE	Determines the `n` most common elements.	确定n个最常见的元素。
5	last	aggregation	FALSE	FALSE	Determines the last value in a list.	确定列表中的最后一个值。
6	entropy	aggregation	FALSE	FALSE	Calculates the entropy for a categorical variable	计算分类变量的熵
7	num_true	aggregation	TRUE	FALSE	Counts the number of `True` values.	计算“True”值的数量。
8	median	aggregation	FALSE	FALSE	Determines the middlemost number in a list of values.	确定值列表中的最中间数字。
9	skew	aggregation	FALSE	FALSE	Computes the extent to which a distribution differs from a normal distribution.	计算分布与正态分布的差异程度。
10	max	aggregation	TRUE	TRUE	Calculates the highest value, ignoring `NaN` values.	计算最高值，而忽略“ NaN”值。
11	avg_time_between	aggregation	FALSE	FALSE	Computes the average number of seconds between consecutive events.	计算连续事件之间的平均秒数。
12	first	aggregation	FALSE	FALSE	Determines the first value in a list.	确定列表中的第一个值。
13	sum	aggregation	TRUE	TRUE	Calculates the total addition, ignoring `NaN`.	计算总的加法，忽略“ NaN”。
14	min	aggregation	TRUE	TRUE	Calculates the smallest value, ignoring `NaN` values.	计算最小值，忽略“ NaN”值。
15	mean	aggregation	TRUE	TRUE	Computes the average for a list of values.	计算值列表的平均值。
16	std	aggregation	TRUE	TRUE	Computes the dispersion relative to the mean value, ignoring `NaN`.	忽略“ NaN”，计算相对于平均值的离散度。
17	time_since_first	aggregation	FALSE	FALSE	Calculates the time elapsed since the first datetime (in seconds).	计算自第一个日期时间以来经过的时间（以秒为单位）。
18	mode	aggregation	FALSE	FALSE	Determines the most commonly repeated value.	确定最常见的重复值。
19	percent_true	aggregation	TRUE	FALSE	Determines the percent of `True` values.	确定“True”值的百分比。
20	count	aggregation	TRUE	TRUE	Determines the total number of values, excluding `NaN`.	确定值的总数，不包括“ NaN”。
21	trend	aggregation	FALSE	FALSE	Calculates the trend of a variable over time.	计算变量随时间的趋势。
22	multiply_boolean	transform	TRUE	FALSE	Element-wise multiplication of two lists of boolean values.	两个布尔值列表的逐元素相乘。
23	less_than_equal_to	transform	TRUE	TRUE	Determines if values in one list are less than or equal to another list.	确定一个列表中的值是否小于或等于另一个列表。
24	percentile	transform	FALSE	FALSE	Determines the percentile rank for each value in a list.	确定列表中每个值的百分等级。
25	num_characters	transform	TRUE	TRUE	Calculates the number of characters in a string.	计算字符串中的字符数。
26	day	transform	TRUE	TRUE	Determines the day of the month from a datetime.	从日期时间确定一个月中的哪一天。
27	cum_count	transform	FALSE	FALSE	Calculates the cumulative count.	计算累积计数。
28	scalar_subtract_numeric_feature	transform	TRUE	TRUE	Subtract each value in the list from a given scalar.	从给定的标量中减去列表中的每个值。
29	time_since_previous	transform	FALSE	FALSE	Compute the time since the previous entry in a list.	计算自列表中上一个条目以来的时间。
30	is_weekend	transform	TRUE	TRUE	Determines if a date falls on a weekend.	确定日期是否在周末。
31	year	transform	TRUE	TRUE	Determines the year value of a datetime.	确定日期时间的年份值。
32	or	transform	TRUE	TRUE	Element-wise logical OR of two lists.	两个列表的元素级逻辑或。
33	not_equal	transform	TRUE	FALSE	Determines if values in one list are not equal to another list.	确定一个列表中的值是否不等于另一个列表。
34	month	transform	TRUE	TRUE	Determines the month value of a datetime.	确定日期时间的月份值。
35	latitude	transform	FALSE	FALSE	Returns the first tuple value in a list of LatLong tuples.	返回LatLong元组列表中的第一个元组值。
36	modulo_by_feature	transform	TRUE	TRUE	Return the modulo of a scalar by each element in the list.	返回列表中每个元素的标量的模。
37	less_than_scalar	transform	TRUE	TRUE	Determines if values are less than a given scalar.	确定值是否小于给定的标量。
38	less_than_equal_to_scalar	transform	TRUE	TRUE	Determines if values are less than or equal to a given scalar.	确定值是否小于或等于给定的标量。
39	is_null	transform	TRUE	TRUE	Determines if a value is null.	确定值是否为空。
40	modulo_numeric_scalar	transform	TRUE	TRUE	Return the modulo of each element in the list by a scalar.	以标量返回列表中每个元素的模。
41	greater_than_equal_to	transform	TRUE	TRUE	Determines if values in one list are greater than or equal to another list.	确定一个列表中的值是否大于或等于另一个列表。
42	diff	transform	FALSE	FALSE	Compute the difference between the value in a list and the	计算列表中的值与
43	divide_numeric_scalar	transform	TRUE	TRUE	Divide each element in the list by a scalar.	用标量除以列表中的每个元素。
44	modulo_numeric	transform	TRUE	TRUE	Element-wise modulo of two lists.	两个列表的按元素取模。
45	multiply_numeric_scalar	transform	TRUE	TRUE	Multiply each element in the list by a scalar.	将列表中的每个元素乘以标量。
46	divide_numeric	transform	TRUE	TRUE	Element-wise division of two lists.	两个列表的按元素划分。
47	equal_scalar	transform	TRUE	TRUE	Determines if values in a list are equal to a given scalar.	确定列表中的值是否等于给定的标量。
48	age	transform	TRUE	FALSE	Calculates the age in years as a floating point number given a	给定a，以浮点数形式计算年龄（以年为单位）。
49	second	transform	TRUE	TRUE	Determines the seconds value of a datetime.	确定日期时间的秒值。
50	cum_mean	transform	FALSE	FALSE	Calculates the cumulative mean.	计算累积平均值。
51	multiply_numeric	transform	TRUE	TRUE	Element-wise multiplication of two lists.	两个列表的按元素乘法。
52	less_than	transform	TRUE	TRUE	Determines if values in one list are less than another list.	确定一个列表中的值是否小于另一个列表。
53	time_since	transform	TRUE	FALSE	Calculates time from a value to a specified cutoff datetime.	计算从值到指定的截止日期时间的时间。
54	weekday	transform	TRUE	TRUE	Determines the day of the week from a datetime.	从日期时间确定星期几。
55	haversine	transform	FALSE	FALSE	Calculates the approximate haversine distance between two LatLong	计算两个LatLong之间的大致Haversine距离
56	cum_sum	transform	FALSE	FALSE	Calculates the cumulative sum.	计算累计和。
57	add_numeric_scalar	transform	TRUE	TRUE	Add a scalar to each value in the list.	向列表中的每个值添加一个标量。
58	greater_than_scalar	transform	TRUE	TRUE	Determines if values are greater than a given scalar.	确定值是否大于给定的标量。
59	num_words	transform	TRUE	TRUE	Determines the number of words in a string by counting the spaces.	通过计算空格来确定字符串中的单词数。
60	minute	transform	TRUE	TRUE	Determines the minutes value of a datetime.	确定日期时间的分钟值。
61	absolute	transform	TRUE	TRUE	Computes the absolute value of a number.	计算数字的绝对值。
62	and	transform	TRUE	TRUE	Element-wise logical AND of two lists.	两个列表的按元素逻辑与。
63	equal	transform	TRUE	TRUE	Determines if values in one list are equal to another list.	确定一个列表中的值是否等于另一列表。
64	hour	transform	TRUE	TRUE	Determines the hour value of a datetime.	确定日期时间的小时值。
65	isin	transform	TRUE	TRUE	Determines whether a value is present in a provided list.	确定提供的列表中是否存在值。
66	subtract_numeric_scalar	transform	TRUE	TRUE	Subtract a scalar from each element in the list.	从列表中的每个元素中减去一个标量。
67	greater_than_equal_to_scalar	transform	TRUE	TRUE	Determines if values are greater than or equal to a given scalar.	确定值是否大于或等于给定的标量。
68	not	transform	TRUE	TRUE	Negates a boolean value.	取反布尔值。
69	cum_max	transform	FALSE	FALSE	Calculates the cumulative maximum.	计算累计最大值。
70	subtract_numeric	transform	TRUE	FALSE	Element-wise subtraction of two lists.	两个列表的逐元素减法。
71	greater_than	transform	TRUE	FALSE	Determines if values in one list are greater than another list.	确定一个列表中的值是否大于另一个列表。
72	week	transform	TRUE	TRUE	Determines the week of the year from a datetime.	从日期时间确定一年中的星期。
73	add_numeric	transform	TRUE	TRUE	Element-wise addition of two lists.	按元素添加两个列表。
74	divide_by_feature	transform	TRUE	TRUE	Divide a scalar by each value in the list.	将标量除以列表中的每个值。
75	not_equal_scalar	transform	TRUE	TRUE	Determines if values in a list are not equal to a given scalar.	确定列表中的值是否不等于给定的标量。
76	longitude	transform	FALSE	FALSE	Returns the second tuple value in a list of LatLong tuples.	返回LatLong元组列表中的第二个元组值。
77	negate	transform	TRUE	TRUE	Negates a numeric value.	取反数值。
78	cum_min	transform	FALSE	FALSE	Calculates the cumulative minimum.	计算累计最小值。

T2.2、深度特征合成：指通过叠加多个基元来得到特征

1)、完整的数据框包含了254-5个新特征，特征工具通过结合与叠加基元特征构造了许多新特征

feature_names: <class 'list'> 254 [<Feature: age>, <Feature: education>, <Feature: income>, <Feature: credit_score>, <Feature: loan_type>, <Feature: LAST(loans.loan_amount)>, <Feature: LAST(loans.loan_id)>, <Feature: LAST(loans.loan_type)>, <Feature: LAST(loans.rate)>, <Feature: LAST(loans.repaid)>, <Feature: MAX(loans.loan_amount)>, <Feature: MAX(loans.rate)>, <Feature: MEAN(loans.loan_amount)>, <Feature: MEAN(loans.rate)>, <Feature: LAST(payments.missed)>, <Feature: LAST(payments.payment_amount)>, <Feature: LAST(payments.payment_id)>, <Feature: MAX(payments.payment_amount)>, <Feature: MEAN(payments.payment_amount)>, <Feature: age / credit_score>, <Feature: age / income>, <Feature: credit_score / age>, <Feature: credit_score / income>, <Feature: income / age>, <Feature: income / credit_score>, <Feature: MONTH(joined)>, <Feature: age - credit_score>, <Feature: age - income>, <Feature: credit_score - income>, <Feature: YEAR(joined)>, <Feature: LAST(loans.MAX(payments.payment_amount))>, <Feature: LAST(loans.MEAN(payments.payment_amount))>, <Feature: LAST(loans.MONTH(loan_end))>, <Feature: LAST(loans.MONTH(loan_start))>, <Feature: LAST(loans.YEAR(loan_end))>, <Feature: LAST(loans.YEAR(loan_start))>, <Feature: LAST(loans.loan_amount - rate)>, <Feature: LAST(loans.loan_amount / rate)>, <Feature: LAST(loans.rate / loan_amount)>, <Feature: MAX(loans.LAST(payments.payment_amount))>, <Feature: MAX(loans.MEAN(payments.payment_amount))>, <Feature: MAX(loans.loan_amount - rate)>, <Feature: MAX(loans.loan_amount / rate)>, <Feature: MAX(loans.rate / loan_amount)>, <Feature: MEAN(loans.LAST(payments.payment_amount))>, <Feature: MEAN(loans.MAX(payments.payment_amount))>, <Feature: MEAN(loans.MEAN(payments.payment_amount))>, <Feature: MEAN(loans.loan_amount - rate)>, <Feature: MEAN(loans.loan_amount / rate)>, <Feature: MEAN(loans.rate / loan_amount)>, <Feature: LAST(payments.loans.client_id)>, <Feature: LAST(payments.loans.loan_amount)>, <Feature: LAST(payments.loans.loan_type)>, <Feature: LAST(payments.loans.rate)>, <Feature: LAST(payments.loans.repaid)>, <Feature: MAX(payments.loans.loan_amount)>, <Feature: MAX(payments.loans.rate)>, <Feature: MEAN(payments.loans.loan_amount)>, <Feature: MEAN(payments.loans.rate)>, <Feature: LAST(loans.loan_amount) / LAST(loans.rate)>, <Feature: LAST(loans.loan_amount) / LAST(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) / MAX(loans.loan_amount)>, <Feature: LAST(loans.loan_amount) / MAX(loans.rate)>, <Feature: LAST(loans.loan_amount) / MAX(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) / MEAN(loans.loan_amount)>, <Feature: LAST(loans.loan_amount) / MEAN(loans.rate)>, <Feature: LAST(loans.loan_amount) / MEAN(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) / age>, <Feature: LAST(loans.loan_amount) / credit_score>, <Feature: LAST(loans.loan_amount) / income>, <Feature: LAST(loans.rate) / LAST(loans.loan_amount)>, <Feature: LAST(loans.rate) / LAST(payments.payment_amount)>, <Feature: LAST(loans.rate) / MAX(loans.loan_amount)>, <Feature: LAST(loans.rate) / MAX(loans.rate)>, <Feature: LAST(loans.rate) / MAX(payments.payment_amount)>, <Feature: LAST(loans.rate) / MEAN(loans.loan_amount)>, <Feature: LAST(loans.rate) / MEAN(loans.rate)>, <Feature: LAST(loans.rate) / MEAN(payments.payment_amount)>, <Feature: LAST(loans.rate) / age>, <Feature: LAST(loans.rate) / credit_score>, <Feature: LAST(loans.rate) / income>, <Feature: LAST(payments.payment_amount) / LAST(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) / LAST(loans.rate)>, <Feature: LAST(payments.payment_amount) / MAX(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) / MAX(loans.rate)>, <Feature: LAST(payments.payment_amount) / MAX(payments.payment_amount)>, <Feature: LAST(payments.payment_amount) / MEAN(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) / MEAN(loans.rate)>, <Feature: LAST(payments.payment_amount) / MEAN(payments.payment_amount)>, <Feature: LAST(payments.payment_amount) / age>, <Feature: LAST(payments.payment_amount) / credit_score>, <Feature: LAST(payments.payment_amount) / income>, <Feature: MAX(loans.loan_amount) / LAST(loans.loan_amount)>, <Feature: MAX(loans.loan_amount) / LAST(loans.rate)>, <Feature: MAX(loans.loan_amount) / LAST(payments.payment_amount)>, <Feature: MAX(loans.loan_amount) / MAX(loans.rate)>, <Feature: MAX(loans.loan_amount) / MAX(payments.payment_amount)>, <Feature: MAX(loans.loan_amount) / MEAN(loans.loan_amount)>, <Feature: MAX(loans.loan_amount) / MEAN(loans.rate)>, <Feature: MAX(loans.loan_amount) / MEAN(payments.payment_amount)>, <Feature: MAX(loans.loan_amount) / age>, <Feature: MAX(loans.loan_amount) / credit_score>, <Feature: MAX(loans.loan_amount) / income>, <Feature: MAX(loans.rate) / LAST(loans.loan_amount)>, <Feature: MAX(loans.rate) / LAST(loans.rate)>, <Feature: MAX(loans.rate) / LAST(payments.payment_amount)>, <Feature: MAX(loans.rate) / MAX(loans.loan_amount)>, <Feature: MAX(loans.rate) / MAX(payments.payment_amount)>, <Feature: MAX(loans.rate) / MEAN(loans.loan_amount)>, <Feature: MAX(loans.rate) / MEAN(loans.rate)>, <Feature: MAX(loans.rate) / MEAN(payments.payment_amount)>, <Feature: MAX(loans.rate) / age>, <Feature: MAX(loans.rate) / credit_score>, <Feature: MAX(loans.rate) / income>, <Feature: MAX(payments.payment_amount) / LAST(loans.loan_amount)>, <Feature: MAX(payments.payment_amount) / LAST(loans.rate)>, <Feature: MAX(payments.payment_amount) / LAST(payments.payment_amount)>, <Feature: MAX(payments.payment_amount) / MAX(loans.loan_amount)>, <Feature: MAX(payments.payment_amount) / MAX(loans.rate)>, <Feature: MAX(payments.payment_amount) / MEAN(loans.loan_amount)>, <Feature: MAX(payments.payment_amount) / MEAN(loans.rate)>, <Feature: MAX(payments.payment_amount) / MEAN(payments.payment_amount)>, <Feature: MAX(payments.payment_amount) / age>, <Feature: MAX(payments.payment_amount) / credit_score>, <Feature: MAX(payments.payment_amount) / income>, <Feature: MEAN(loans.loan_amount) / LAST(loans.loan_amount)>, <Feature: MEAN(loans.loan_amount) / LAST(loans.rate)>, <Feature: MEAN(loans.loan_amount) / LAST(payments.payment_amount)>, <Feature: MEAN(loans.loan_amount) / MAX(loans.loan_amount)>, <Feature: MEAN(loans.loan_amount) / MAX(loans.rate)>, <Feature: MEAN(loans.loan_amount) / MAX(payments.payment_amount)>, <Feature: MEAN(loans.loan_amount) / MEAN(loans.rate)>, <Feature: MEAN(loans.loan_amount) / MEAN(payments.payment_amount)>, <Feature: MEAN(loans.loan_amount) / age>, <Feature: MEAN(loans.loan_amount) / credit_score>, <Feature: MEAN(loans.loan_amount) / income>, <Feature: MEAN(loans.rate) / LAST(loans.loan_amount)>, <Feature: MEAN(loans.rate) / LAST(loans.rate)>, <Feature: MEAN(loans.rate) / LAST(payments.payment_amount)>, <Feature: MEAN(loans.rate) / MAX(loans.loan_amount)>, <Feature: MEAN(loans.rate) / MAX(loans.rate)>, <Feature: MEAN(loans.rate) / MAX(payments.payment_amount)>, <Feature: MEAN(loans.rate) / MEAN(loans.loan_amount)>, <Feature: MEAN(loans.rate) / MEAN(payments.payment_amount)>, <Feature: MEAN(loans.rate) / age>, <Feature: MEAN(loans.rate) / credit_score>, <Feature: MEAN(loans.rate) / income>, <Feature: MEAN(payments.payment_amount) / LAST(loans.loan_amount)>, <Feature: MEAN(payments.payment_amount) / LAST(loans.rate)>, <Feature: MEAN(payments.payment_amount) / LAST(payments.payment_amount)>, <Feature: MEAN(payments.payment_amount) / MAX(loans.loan_amount)>, <Feature: MEAN(payments.payment_amount) / MAX(loans.rate)>, <Feature: MEAN(payments.payment_amount) / MAX(payments.payment_amount)>, <Feature: MEAN(payments.payment_amount) / MEAN(loans.loan_amount)>, <Feature: MEAN(payments.payment_amount) / MEAN(loans.rate)>, <Feature: MEAN(payments.payment_amount) / age>, <Feature: MEAN(payments.payment_amount) / credit_score>, <Feature: MEAN(payments.payment_amount) / income>, <Feature: age / LAST(loans.loan_amount)>, <Feature: age / LAST(loans.rate)>, <Feature: age / LAST(payments.payment_amount)>, <Feature: age / MAX(loans.loan_amount)>, <Feature: age / MAX(loans.rate)>, <Feature: age / MAX(payments.payment_amount)>, <Feature: age / MEAN(loans.loan_amount)>, <Feature: age / MEAN(loans.rate)>, <Feature: age / MEAN(payments.payment_amount)>, <Feature: credit_score / LAST(loans.loan_amount)>, <Feature: credit_score / LAST(loans.rate)>, <Feature: credit_score / LAST(payments.payment_amount)>, <Feature: credit_score / MAX(loans.loan_amount)>, <Feature: credit_score / MAX(loans.rate)>, <Feature: credit_score / MAX(payments.payment_amount)>, <Feature: credit_score / MEAN(loans.loan_amount)>, <Feature: credit_score / MEAN(loans.rate)>, <Feature: credit_score / MEAN(payments.payment_amount)>, <Feature: income / LAST(loans.loan_amount)>, <Feature: income / LAST(loans.rate)>, <Feature: income / LAST(payments.payment_amount)>, <Feature: income / MAX(loans.loan_amount)>, <Feature: income / MAX(loans.rate)>, <Feature: income / MAX(payments.payment_amount)>, <Feature: income / MEAN(loans.loan_amount)>, <Feature: income / MEAN(loans.rate)>, <Feature: income / MEAN(payments.payment_amount)>, <Feature: MONTH(LAST(loans.loan_end))>, <Feature: MONTH(LAST(loans.loan_start))>, <Feature: MONTH(LAST(payments.payment_date))>, <Feature: LAST(loans.loan_amount) - LAST(loans.rate)>, <Feature: LAST(loans.loan_amount) - LAST(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) - MAX(loans.loan_amount)>, <Feature: LAST(loans.loan_amount) - MAX(loans.rate)>, <Feature: LAST(loans.loan_amount) - MAX(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) - MEAN(loans.loan_amount)>, <Feature: LAST(loans.loan_amount) - MEAN(loans.rate)>, <Feature: LAST(loans.loan_amount) - MEAN(payments.payment_amount)>, <Feature: LAST(loans.rate) - LAST(payments.payment_amount)>, <Feature: LAST(loans.rate) - MAX(loans.loan_amount)>, <Feature: LAST(loans.rate) - MAX(loans.rate)>, <Feature: LAST(loans.rate) - MAX(payments.payment_amount)>, <Feature: LAST(loans.rate) - MEAN(loans.loan_amount)>, <Feature: LAST(loans.rate) - MEAN(loans.rate)>, <Feature: LAST(loans.rate) - MEAN(payments.payment_amount)>, <Feature: LAST(payments.payment_amount) - MAX(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) - MAX(loans.rate)>, <Feature: LAST(payments.payment_amount) - MAX(payments.payment_amount)>, <Feature: LAST(payments.payment_amount) - MEAN(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) - MEAN(loans.rate)>, <Feature: LAST(payments.payment_amount) - MEAN(payments.payment_amount)>, <Feature: MAX(loans.loan_amount) - MAX(loans.rate)>, <Feature: MAX(loans.loan_amount) - MAX(payments.payment_amount)>, <Feature: MAX(loans.loan_amount) - MEAN(loans.loan_amount)>, <Feature: MAX(loans.loan_amount) - MEAN(loans.rate)>, <Feature: MAX(loans.loan_amount) - MEAN(payments.payment_amount)>, <Feature: MAX(loans.rate) - MAX(payments.payment_amount)>, <Feature: MAX(loans.rate) - MEAN(loans.loan_amount)>, <Feature: MAX(loans.rate) - MEAN(loans.rate)>, <Feature: MAX(loans.rate) - MEAN(payments.payment_amount)>, <Feature: MAX(payments.payment_amount) - MEAN(loans.loan_amount)>, <Feature: MAX(payments.payment_amount) - MEAN(loans.rate)>, <Feature: MAX(payments.payment_amount) - MEAN(payments.payment_amount)>, <Feature: MEAN(loans.loan_amount) - MEAN(loans.rate)>, <Feature: MEAN(loans.loan_amount) - MEAN(payments.payment_amount)>, <Feature: MEAN(loans.rate) - MEAN(payments.payment_amount)>, <Feature: age - LAST(loans.loan_amount)>, <Feature: age - LAST(loans.rate)>, <Feature: age - LAST(payments.payment_amount)>, <Feature: age - MAX(loans.loan_amount)>, <Feature: age - MAX(loans.rate)>, <Feature: age - MAX(payments.payment_amount)>, <Feature: age - MEAN(loans.loan_amount)>, <Feature: age - MEAN(loans.rate)>, <Feature: age - MEAN(payments.payment_amount)>, <Feature: credit_score - LAST(loans.loan_amount)>, <Feature: credit_score - LAST(loans.rate)>, <Feature: credit_score - LAST(payments.payment_amount)>, <Feature: credit_score - MAX(loans.loan_amount)>, <Feature: credit_score - MAX(loans.rate)>, <Feature: credit_score - MAX(payments.payment_amount)>, <Feature: credit_score - MEAN(loans.loan_amount)>, <Feature: credit_score - MEAN(loans.rate)>, <Feature: credit_score - MEAN(payments.payment_amount)>, <Feature: income - LAST(loans.loan_amount)>, <Feature: income - LAST(loans.rate)>, <Feature: income - LAST(payments.payment_amount)>, <Feature: income - MAX(loans.loan_amount)>, <Feature: income - MAX(loans.rate)>, <Feature: income - MAX(payments.payment_amount)>, <Feature: income - MEAN(loans.loan_amount)>, <Feature: income - MEAN(loans.rate)>, <Feature: income - MEAN(payments.payment_amount)>, <Feature: YEAR(LAST(loans.loan_end))>, <Feature: YEAR(LAST(loans.loan_start))>, <Feature: YEAR(LAST(payments.payment_date))>]features: <class 'pandas.core.frame.DataFrame'> age       education  income  credit_score loan_type
client_id
33747       44    MasterDegree  138377           683     other
31578       38    DoctorDegree  104028           805     other
27894       47  BachelorDegree  176516           708    credit
44966       31  BachelorDegree   58530           822    credit
29195       46  BachelorDegree   38528           680     other
Show a feature with a depth of 1 MEAN(loans.loan_amount)
client_id
33747                         7855
31578                         7953
27894                        13457
44966                         7733
29195                         2466
25942                        14059
38435                         6615
41646                        14884
31484                         8458
49966                        14108
Show a feature with a depth of 2 LAST(loans.MEAN(payments.payment_amount))
client_id
33747                                    1314.000000
31578                                    1180.714286
27894                                    2118.428571
44966                                    1079.666667
29195                                     368.500000
25942                                    1920.000000
38435                                     960.111111
41646                                    2239.500000
31484                                    1232.750000
49966                                    1671.000000

client_id	age	education	income	credit_score	loan_type	LAST(loans.loan_amount)	LAST(loans.loan_id)	LAST(loans.loan_type)	LAST(loans.rate)	LAST(loans.repaid)	MAX(loans.loan_amount)	MAX(loans.rate)	MEAN(loans.loan_amount)	MEAN(loans.rate)	LAST(payments.missed)	LAST(payments.payment_amount)	LAST(payments.payment_id)	MAX(payments.payment_amount)	MEAN(payments.payment_amount)	age / credit_score	age / income	credit_score / age	credit_score / income	income / age	income / credit_score	MONTH(joined)	age - credit_score	age - income	credit_score - income	YEAR(joined)	LAST(loans.MAX(payments.payment_amount))	LAST(loans.MEAN(payments.payment_amount))	LAST(loans.MONTH(loan_end))	LAST(loans.MONTH(loan_start))	LAST(loans.YEAR(loan_end))	LAST(loans.YEAR(loan_start))	LAST(loans.loan_amount - rate)	LAST(loans.loan_amount / rate)	LAST(loans.rate / loan_amount)	MAX(loans.LAST(payments.payment_amount))	MAX(loans.MEAN(payments.payment_amount))	MAX(loans.loan_amount - rate)	MAX(loans.loan_amount / rate)	MAX(loans.rate / loan_amount)	MEAN(loans.LAST(payments.payment_amount))	MEAN(loans.MAX(payments.payment_amount))	MEAN(loans.MEAN(payments.payment_amount))	MEAN(loans.loan_amount - rate)	MEAN(loans.loan_amount / rate)	MEAN(loans.rate / loan_amount)	LAST(payments.loans.client_id)	LAST(payments.loans.loan_amount)	LAST(payments.loans.loan_type)	LAST(payments.loans.rate)	LAST(payments.loans.repaid)	MAX(payments.loans.loan_amount)	MAX(payments.loans.rate)	MEAN(payments.loans.loan_amount)	MEAN(payments.loans.rate)	LAST(loans.loan_amount) / LAST(loans.rate)	LAST(loans.loan_amount) / LAST(payments.payment_amount)	LAST(loans.loan_amount) / MAX(loans.loan_amount)	LAST(loans.loan_amount) / MAX(loans.rate)	LAST(loans.loan_amount) / MAX(payments.payment_amount)	LAST(loans.loan_amount) / MEAN(loans.loan_amount)	LAST(loans.loan_amount) / MEAN(loans.rate)	LAST(loans.loan_amount) / MEAN(payments.payment_amount)	LAST(loans.loan_amount) / age	LAST(loans.loan_amount) / credit_score	LAST(loans.loan_amount) / income	LAST(loans.rate) / LAST(loans.loan_amount)	LAST(loans.rate) / LAST(payments.payment_amount)	LAST(loans.rate) / MAX(loans.loan_amount)	LAST(loans.rate) / MAX(loans.rate)	LAST(loans.rate) / MAX(payments.payment_amount)	LAST(loans.rate) / MEAN(loans.loan_amount)	LAST(loans.rate) / MEAN(loans.rate)	LAST(loans.rate) / MEAN(payments.payment_amount)	LAST(loans.rate) / age	LAST(loans.rate) / credit_score	LAST(loans.rate) / income	LAST(payments.payment_amount) / LAST(loans.loan_amount)	LAST(payments.payment_amount) / LAST(loans.rate)	LAST(payments.payment_amount) / MAX(loans.loan_amount)	LAST(payments.payment_amount) / MAX(loans.rate)	LAST(payments.payment_amount) / MAX(payments.payment_amount)	LAST(payments.payment_amount) / MEAN(loans.loan_amount)	LAST(payments.payment_amount) / MEAN(loans.rate)	LAST(payments.payment_amount) / MEAN(payments.payment_amount)	LAST(payments.payment_amount) / age	LAST(payments.payment_amount) / credit_score	LAST(payments.payment_amount) / income	MAX(loans.loan_amount) / LAST(loans.loan_amount)	MAX(loans.loan_amount) / LAST(loans.rate)	MAX(loans.loan_amount) / LAST(payments.payment_amount)	MAX(loans.loan_amount) / MAX(loans.rate)	MAX(loans.loan_amount) / MAX(payments.payment_amount)	MAX(loans.loan_amount) / MEAN(loans.loan_amount)	MAX(loans.loan_amount) / MEAN(loans.rate)	MAX(loans.loan_amount) / MEAN(payments.payment_amount)	MAX(loans.loan_amount) / age	MAX(loans.loan_amount) / credit_score	MAX(loans.loan_amount) / income	MAX(loans.rate) / LAST(loans.loan_amount)	MAX(loans.rate) / LAST(loans.rate)	MAX(loans.rate) / LAST(payments.payment_amount)	MAX(loans.rate) / MAX(loans.loan_amount)	MAX(loans.rate) / MAX(payments.payment_amount)	MAX(loans.rate) / MEAN(loans.loan_amount)	MAX(loans.rate) / MEAN(loans.rate)	MAX(loans.rate) / MEAN(payments.payment_amount)	MAX(loans.rate) / age	MAX(loans.rate) / credit_score	MAX(loans.rate) / income	MAX(payments.payment_amount) / LAST(loans.loan_amount)	MAX(payments.payment_amount) / LAST(loans.rate)	MAX(payments.payment_amount) / LAST(payments.payment_amount)	MAX(payments.payment_amount) / MAX(loans.loan_amount)	MAX(payments.payment_amount) / MAX(loans.rate)	MAX(payments.payment_amount) / MEAN(loans.loan_amount)	MAX(payments.payment_amount) / MEAN(loans.rate)	MAX(payments.payment_amount) / MEAN(payments.payment_amount)	MAX(payments.payment_amount) / age	MAX(payments.payment_amount) / credit_score	MAX(payments.payment_amount) / income	MEAN(loans.loan_amount) / LAST(loans.loan_amount)	MEAN(loans.loan_amount) / LAST(loans.rate)	MEAN(loans.loan_amount) / LAST(payments.payment_amount)	MEAN(loans.loan_amount) / MAX(loans.loan_amount)	MEAN(loans.loan_amount) / MAX(loans.rate)	MEAN(loans.loan_amount) / MAX(payments.payment_amount)	MEAN(loans.loan_amount) / MEAN(loans.rate)	MEAN(loans.loan_amount) / MEAN(payments.payment_amount)	MEAN(loans.loan_amount) / age	MEAN(loans.loan_amount) / credit_score	MEAN(loans.loan_amount) / income	MEAN(loans.rate) / LAST(loans.loan_amount)	MEAN(loans.rate) / LAST(loans.rate)	MEAN(loans.rate) / LAST(payments.payment_amount)	MEAN(loans.rate) / MAX(loans.loan_amount)	MEAN(loans.rate) / MAX(loans.rate)	MEAN(loans.rate) / MAX(payments.payment_amount)	MEAN(loans.rate) / MEAN(loans.loan_amount)	MEAN(loans.rate) / MEAN(payments.payment_amount)	MEAN(loans.rate) / age	MEAN(loans.rate) / credit_score	MEAN(loans.rate) / income	MEAN(payments.payment_amount) / LAST(loans.loan_amount)	MEAN(payments.payment_amount) / LAST(loans.rate)	MEAN(payments.payment_amount) / LAST(payments.payment_amount)	MEAN(payments.payment_amount) / MAX(loans.loan_amount)	MEAN(payments.payment_amount) / MAX(loans.rate)	MEAN(payments.payment_amount) / MAX(payments.payment_amount)	MEAN(payments.payment_amount) / MEAN(loans.loan_amount)	MEAN(payments.payment_amount) / MEAN(loans.rate)	MEAN(payments.payment_amount) / age	MEAN(payments.payment_amount) / credit_score	MEAN(payments.payment_amount) / income	age / LAST(loans.loan_amount)	age / LAST(loans.rate)	age / LAST(payments.payment_amount)	age / MAX(loans.loan_amount)	age / MAX(loans.rate)	age / MAX(payments.payment_amount)	age / MEAN(loans.loan_amount)	age / MEAN(loans.rate)	age / MEAN(payments.payment_amount)	credit_score / LAST(loans.loan_amount)	credit_score / LAST(loans.rate)	credit_score / LAST(payments.payment_amount)	credit_score / MAX(loans.loan_amount)	credit_score / MAX(loans.rate)	credit_score / MAX(payments.payment_amount)	credit_score / MEAN(loans.loan_amount)	credit_score / MEAN(loans.rate)	credit_score / MEAN(payments.payment_amount)	income / LAST(loans.loan_amount)	income / LAST(loans.rate)	income / LAST(payments.payment_amount)	income / MAX(loans.loan_amount)	income / MAX(loans.rate)	income / MAX(payments.payment_amount)	income / MEAN(loans.loan_amount)	income / MEAN(loans.rate)	income / MEAN(payments.payment_amount)	MONTH(LAST(loans.loan_end))	MONTH(LAST(loans.loan_start))	MONTH(LAST(payments.payment_date))	LAST(loans.loan_amount) - LAST(loans.rate)	LAST(loans.loan_amount) - LAST(payments.payment_amount)	LAST(loans.loan_amount) - MAX(loans.loan_amount)	LAST(loans.loan_amount) - MAX(loans.rate)	LAST(loans.loan_amount) - MAX(payments.payment_amount)	LAST(loans.loan_amount) - MEAN(loans.loan_amount)	LAST(loans.loan_amount) - MEAN(loans.rate)	LAST(loans.loan_amount) - MEAN(payments.payment_amount)	LAST(loans.rate) - LAST(payments.payment_amount)	LAST(loans.rate) - MAX(loans.loan_amount)	LAST(loans.rate) - MAX(loans.rate)	LAST(loans.rate) - MAX(payments.payment_amount)	LAST(loans.rate) - MEAN(loans.loan_amount)	LAST(loans.rate) - MEAN(loans.rate)	LAST(loans.rate) - MEAN(payments.payment_amount)	LAST(payments.payment_amount) - MAX(loans.loan_amount)	LAST(payments.payment_amount) - MAX(loans.rate)	LAST(payments.payment_amount) - MAX(payments.payment_amount)	LAST(payments.payment_amount) - MEAN(loans.loan_amount)	LAST(payments.payment_amount) - MEAN(loans.rate)	LAST(payments.payment_amount) - MEAN(payments.payment_amount)	MAX(loans.loan_amount) - MAX(loans.rate)	MAX(loans.loan_amount) - MAX(payments.payment_amount)	MAX(loans.loan_amount) - MEAN(loans.loan_amount)	MAX(loans.loan_amount) - MEAN(loans.rate)	MAX(loans.loan_amount) - MEAN(payments.payment_amount)	MAX(loans.rate) - MAX(payments.payment_amount)	MAX(loans.rate) - MEAN(loans.loan_amount)	MAX(loans.rate) - MEAN(loans.rate)	MAX(loans.rate) - MEAN(payments.payment_amount)	MAX(payments.payment_amount) - MEAN(loans.loan_amount)	MAX(payments.payment_amount) - MEAN(loans.rate)	MAX(payments.payment_amount) - MEAN(payments.payment_amount)	MEAN(loans.loan_amount) - MEAN(loans.rate)	MEAN(loans.loan_amount) - MEAN(payments.payment_amount)	MEAN(loans.rate) - MEAN(payments.payment_amount)	age - LAST(loans.loan_amount)	age - LAST(loans.rate)	age - LAST(payments.payment_amount)	age - MAX(loans.loan_amount)	age - MAX(loans.rate)	age - MAX(payments.payment_amount)	age - MEAN(loans.loan_amount)	age - MEAN(loans.rate)	age - MEAN(payments.payment_amount)	credit_score - LAST(loans.loan_amount)	credit_score - LAST(loans.rate)	credit_score - LAST(payments.payment_amount)	credit_score - MAX(loans.loan_amount)	credit_score - MAX(loans.rate)	credit_score - MAX(payments.payment_amount)	credit_score - MEAN(loans.loan_amount)	credit_score - MEAN(loans.rate)	credit_score - MEAN(payments.payment_amount)	income - LAST(loans.loan_amount)	income - LAST(loans.rate)	income - LAST(payments.payment_amount)	income - MAX(loans.loan_amount)	income - MAX(loans.rate)	income - MAX(payments.payment_amount)	income - MEAN(loans.loan_amount)	income - MEAN(loans.rate)	income - MEAN(payments.payment_amount)	YEAR(LAST(loans.loan_end))	YEAR(LAST(loans.loan_start))	YEAR(LAST(payments.payment_date))
33747	44	MasterDegree	138377	683	other	7855	11031	other	4.89	1	7855	4.89	7855	4.89	1	1372	129	1372	1314	0.064421669	0.000317972	15.52272727	0.004935791	3144.931818	202.601757	3	-639	-138333	-137694	2019	1372	1314	3	9	2022	2020	7850.11	1606.339468	0.000622533	1372	1314	7850.11	1606.339468	0.000622533	1372	1372	1314	7850.11	1606.339468	0.000622533	33747	7855	other	4.89	1	7855	4.89	7855	4.89	1606.339468	5.725218659	1	1606.339468	5.725218659	1	1606.339468	5.977929985	178.5227273	11.50073206	0.056765214	0.000622533	0.00356414	0.000622533	1	0.00356414	0.000622533	1	0.003721461	0.111136364	0.00715959	3.53E-05	0.174665818	280.5725971	0.174665818	280.5725971	1	0.174665818	280.5725971	1.04414003	31.18181818	2.008784773	0.009914943	1	1606.339468	5.725218659	1606.339468	5.725218659	1	1606.339468	5.977929985	178.5227273	11.50073206	0.056765214	0.000622533	1	0.00356414	0.000622533	0.00356414	0.000622533	1	0.003721461	0.111136364	0.00715959	3.53E-05	0.174665818	280.5725971	1	0.174665818	280.5725971	0.174665818	280.5725971	1.04414003	31.18181818	2.008784773	0.009914943	1	1606.339468	5.725218659	1	1606.339468	5.725218659	1606.339468	5.977929985	178.5227273	11.50073206	0.056765214	0.000622533	1	0.00356414	0.000622533	1	0.00356414	0.000622533	0.003721461	0.111136364	0.00715959	3.53E-05	0.167281986	268.7116564	0.957725948	0.167281986	268.7116564	0.957725948	0.167281986	268.7116564	29.86363636	1.9238653	0.009495798	0.005601528	8.99795501	0.032069971	0.005601528	8.99795501	0.032069971	0.005601528	8.99795501	0.03348554	0.086950987	139.6728016	0.497813411	0.086950987	139.6728016	0.497813411	0.086950987	139.6728016	0.51978691	17.61642266	28297.95501	100.8578717	17.61642266	28297.95501	100.8578717	17.61642266	28297.95501	105.3097412	3	9	12	7850.11	6483	0	7850.11	6483	0	7850.11	6541	-1367.11	-7850.11	0	-1367.11	-7850.11	0	-1309.11	-6483	1367.11	0	-6483	1367.11	58	7850.11	6483	0	7850.11	6541	-1367.11	-7850.11	0	-1309.11	-6483	1367.11	58	7850.11	6541	-1309.11	-7811	39.11	-1328	-7811	39.11	-1328	-7811	39.11	-1270	-7172	678.11	-689	-7172	678.11	-689	-7172	678.11	-631	130522	138372.11	137005	130522	138372.11	137005	130522	138372.11	137063	2022	2020	2020
31578	38	DoctorDegree	104028	805	other	7953	11439	other	0.13	1	7953	0.13	7953	0.13	1	1557	143	1557	1180.714286	0.047204969	0.000365286	21.18421053	0.007738301	2737.578947	129.2273292	4	-767	-103990	-103223	2019	1557	1180.714286	1	6	2023	2020	7952.87	61176.92308	1.63E-05	1557	1180.714286	7952.87	61176.92308	1.63E-05	1557	1557	1180.714286	7952.87	61176.92308	1.63E-05	31578	7953	other	0.13	1	7953	0.13	7953	0.13	61176.92308	5.107899807	1	61176.92308	5.107899807	1	61176.92308	6.735753176	209.2894737	9.879503106	0.076450571	1.63E-05	8.35E-05	1.63E-05	1	8.35E-05	1.63E-05	1	0.000110103	0.003421053	0.000161491	1.25E-06	0.195775179	11976.92308	0.195775179	11976.92308	1	0.195775179	11976.92308	1.318693285	40.97368421	1.934161491	0.014967124	1	61176.92308	5.107899807	61176.92308	5.107899807	1	61176.92308	6.735753176	209.2894737	9.879503106	0.076450571	1.63E-05	1	8.35E-05	1.63E-05	8.35E-05	1.63E-05	1	0.000110103	0.003421053	0.000161491	1.25E-06	0.195775179	11976.92308	1	0.195775179	11976.92308	0.195775179	11976.92308	1.318693285	40.97368421	1.934161491	0.014967124	1	61176.92308	5.107899807	1	61176.92308	5.107899807	61176.92308	6.735753176	209.2894737	9.879503106	0.076450571	1.63E-05	1	8.35E-05	1.63E-05	1	8.35E-05	1.63E-05	0.000110103	0.003421053	0.000161491	1.25E-06	0.148461497	9082.417582	0.758326452	0.148461497	9082.417582	0.758326452	0.148461497	9082.417582	31.07142857	1.466725821	0.011349966	0.004778071	292.3076923	0.024405909	0.004778071	292.3076923	0.024405909	0.004778071	292.3076923	0.032183908	0.101219666	6192.307692	0.51701991	0.101219666	6192.307692	0.51701991	0.101219666	6192.307692	0.681790684	13.08034704	800215.3846	66.81310212	13.08034704	800215.3846	66.81310212	13.08034704	800215.3846	88.10598911	1	6	12	7952.87	6396	0	7952.87	6396	0	7952.87	6772.285714	-1556.87	-7952.87	0	-1556.87	-7952.87	0	-1180.584286	-6396	1556.87	0	-6396	1556.87	376.2857143	7952.87	6396	0	7952.87	6772.285714	-1556.87	-7952.87	0	-1180.584286	-6396	1556.87	376.2857143	7952.87	6772.285714	-1180.584286	-7915	37.87	-1519	-7915	37.87	-1519	-7915	37.87	-1142.714286	-7148	804.87	-752	-7148	804.87	-752	-7148	804.87	-375.7142857	96075	104027.87	102471	96075	104027.87	102471	96075	104027.87	102847.2857	2023	2020	2020
27894	47	BachelorDegree	176516	708	credit	13457	11225	credit	1.48	1	13457	1.48	13457	1.48	0	2505	42	2505	2118.428571	0.066384181	0.000266265	15.06382979	0.004010968	3755.659574	249.3163842	4	-661	-176469	-175808	2019	2505	2118.428571	10	4	2022	2020	13455.52	9092.567568	0.00010998	2505	2118.428571	13455.52	9092.567568	0.00010998	2505	2505	2118.428571	13455.52	9092.567568	0.00010998	27894	13457	credit	1.48	1	13457	1.48	13457	1.48	9092.567568	5.372055888	1	9092.567568	5.372055888	1	9092.567568	6.352350125	286.3191489	19.00706215	0.076236715	0.00010998	0.000590818	0.00010998	1	0.000590818	0.00010998	1	0.000698631	0.031489362	0.002090395	8.38E-06	0.186148473	1692.567568	0.186148473	1692.567568	1	0.186148473	1692.567568	1.182480275	53.29787234	3.538135593	0.014191348	1	9092.567568	5.372055888	9092.567568	5.372055888	1	9092.567568	6.352350125	286.3191489	19.00706215	0.076236715	0.00010998	1	0.000590818	0.00010998	0.000590818	0.00010998	1	0.000698631	0.031489362	0.002090395	8.38E-06	0.186148473	1692.567568	1	0.186148473	1692.567568	0.186148473	1692.567568	1.182480275	53.29787234	3.538135593	0.014191348	1	9092.567568	5.372055888	1	9092.567568	5.372055888	9092.567568	6.352350125	286.3191489	19.00706215	0.076236715	0.00010998	1	0.000590818	0.00010998	1	0.000590818	0.00010998	0.000698631	0.031489362	0.002090395	8.38E-06	0.157422053	1431.370656	0.845680068	0.157422053	1431.370656	0.845680068	0.157422053	1431.370656	45.07294833	2.992130751	0.01200134	0.003492606	31.75675676	0.018762475	0.003492606	31.75675676	0.018762475	0.003492606	31.75675676	0.022186257	0.052612023	478.3783784	0.282634731	0.052612023	478.3783784	0.282634731	0.052612023	478.3783784	0.334209994	13.11703946	119267.5676	70.46546906	13.11703946	119267.5676	70.46546906	13.11703946	119267.5676	83.32402724	10	4	12	13455.52	10952	0	13455.52	10952	0	13455.52	11338.57143	-2503.52	-13455.52	0	-2503.52	-13455.52	0	-2116.948571	-10952	2503.52	0	-10952	2503.52	386.5714286	13455.52	10952	0	13455.52	11338.57143	-2503.52	-13455.52	0	-2116.948571	-10952	2503.52	386.5714286	13455.52	11338.57143	-2116.948571	-13410	45.52	-2458	-13410	45.52	-2458	-13410	45.52	-2071.428571	-12749	706.52	-1797	-12749	706.52	-1797	-12749	706.52	-1410.428571	163059	176514.52	174011	163059	176514.52	174011	163059	176514.52	174397.5714	2022	2020	2020
44966	31	BachelorDegree	58530	822	credit	7733	11246	credit	0.42	0	7733	0.42	7733	0.42	0	1245	2	1245	1079.666667	0.037712895	0.000529643	26.51612903	0.01404408	1888.064516	71.20437956	5	-791	-58499	-57708	2019	1245	1079.666667	2	9	2023	2020	7732.58	18411.90476	5.43E-05	1245	1079.666667	7732.58	18411.90476	5.43E-05	1245	1245	1079.666667	7732.58	18411.90476	5.43E-05	44966	7733	credit	0.42	0	7733	0.42	7733	0.42	18411.90476	6.21124498	1	18411.90476	6.21124498	1	18411.90476	7.162395801	249.4516129	9.407542579	0.13212028	5.43E-05	0.000337349	5.43E-05	1	0.000337349	5.43E-05	1	0.000389009	0.013548387	0.000510949	7.18E-06	0.160998319	2964.285714	0.160998319	2964.285714	1	0.160998319	2964.285714	1.153133683	40.16129032	1.51459854	0.021271143	1	18411.90476	6.21124498	18411.90476	6.21124498	1	18411.90476	7.162395801	249.4516129	9.407542579	0.13212028	5.43E-05	1	0.000337349	5.43E-05	0.000337349	5.43E-05	1	0.000389009	0.013548387	0.000510949	7.18E-06	0.160998319	2964.285714	1	0.160998319	2964.285714	0.160998319	2964.285714	1.153133683	40.16129032	1.51459854	0.021271143	1	18411.90476	6.21124498	1	18411.90476	6.21124498	18411.90476	7.162395801	249.4516129	9.407542579	0.13212028	5.43E-05	1	0.000337349	5.43E-05	1	0.000337349	5.43E-05	0.000389009	0.013548387	0.000510949	7.18E-06	0.139618087	2570.634921	0.867202142	0.139618087	2570.634921	0.867202142	0.139618087	2570.634921	34.82795699	1.313463098	0.018446381	0.004008793	73.80952381	0.024899598	0.004008793	73.80952381	0.024899598	0.004008793	73.80952381	0.028712566	0.106297685	1957.142857	0.660240964	0.106297685	1957.142857	0.660240964	0.106297685	1957.142857	0.761346094	7.568860727	139357.1429	47.01204819	7.568860727	139357.1429	47.01204819	7.568860727	139357.1429	54.21117629	2	9	12	7732.58	6488	0	7732.58	6488	0	7732.58	6653.333333	-1244.58	-7732.58	0	-1244.58	-7732.58	0	-1079.246667	-6488	1244.58	0	-6488	1244.58	165.3333333	7732.58	6488	0	7732.58	6653.333333	-1244.58	-7732.58	0	-1079.246667	-6488	1244.58	165.3333333	7732.58	6653.333333	-1079.246667	-7702	30.58	-1214	-7702	30.58	-1214	-7702	30.58	-1048.666667	-6911	821.58	-423	-6911	821.58	-423	-6911	821.58	-257.6666667	50797	58529.58	57285	50797	58529.58	57285	50797	58529.58	57450.33333	2023	2020	2020
29195	46	BachelorDegree	38528	680	other	2466	11992	other	0.68	1	2466	0.68	2466	0.68	0	431	73	431	368.5	0.067647059	0.001193937	14.7826087	0.017649502	837.5652174	56.65882353	5	-634	-38482	-37848	2019	431	368.5	4	3	2021	2019	2465.32	3626.470588	0.00027575	431	368.5	2465.32	3626.470588	0.00027575	431	431	368.5	2465.32	3626.470588	0.00027575	29195	2466	other	0.68	1	2466	0.68	2466	0.68	3626.470588	5.721577726	1	3626.470588	5.721577726	1	3626.470588	6.691994573	53.60869565	3.626470588	0.064005399	0.00027575	0.001577726	0.00027575	1	0.001577726	0.00027575	1	0.001845319	0.014782609	0.001	1.76E-05	0.174776967	633.8235294	0.174776967	633.8235294	1	0.174776967	633.8235294	1.169606513	9.369565217	0.633823529	0.011186669	1	3626.470588	5.721577726	3626.470588	5.721577726	1	3626.470588	6.691994573	53.60869565	3.626470588	0.064005399	0.00027575	1	0.001577726	0.00027575	0.001577726	0.00027575	1	0.001845319	0.014782609	0.001	1.76E-05	0.174776967	633.8235294	1	0.174776967	633.8235294	0.174776967	633.8235294	1.169606513	9.369565217	0.633823529	0.011186669	1	3626.470588	5.721577726	1	3626.470588	5.721577726	3626.470588	6.691994573	53.60869565	3.626470588	0.064005399	0.00027575	1	0.001577726	0.00027575	1	0.001577726	0.00027575	0.001845319	0.014782609	0.001	1.76E-05	0.149432279	541.9117647	0.854988399	0.149432279	541.9117647	0.854988399	0.149432279	541.9117647	8.010869565	0.541911765	0.009564473	0.01865369	67.64705882	0.106728538	0.01865369	67.64705882	0.106728538	0.01865369	67.64705882	0.124830393	0.275750203	1000	1.577726218	0.275750203	1000	1.577726218	0.275750203	1000	1.84531886	15.62368208	56658.82353	89.39211137	15.62368208	56658.82353	89.39211137	15.62368208	56658.82353	104.5535957	4	3	10	2465.32	2035	0	2465.32	2035	0	2465.32	2097.5	-430.32	-2465.32	0	-430.32	-2465.32	0	-367.82	-2035	430.32	0	-2035	430.32	62.5	2465.32	2035	0	2465.32	2097.5	-430.32	-2465.32	0	-367.82	-2035	430.32	62.5	2465.32	2097.5	-367.82	-2420	45.32	-385	-2420	45.32	-385	-2420	45.32	-322.5	-1786	679.32	249	-1786	679.32	249	-1786	679.32	311.5	36062	38527.32	38097	36062	38527.32	38097	36062	38527.32	38159.5	2021	2019	2019
25942	18	DoctorDegree	227219	839	cash	14059	11871	cash	3.58	0	14059	3.58	14059	3.58	1	1791	35	2403	1920	0.021454112	7.92E-05	46.61111111	0.003692473	12623.27778	270.8212157	6	-821	-227201	-226380	2019	2403	1920	3	4	2021	2019	14055.42	3927.094972	0.000254641	1791	1920	14055.42	3927.094972	0.000254641	1791	2403	1920	14055.42	3927.094972	0.000254641	25942	14059	cash	3.58	0	14059	3.58	14059	3.58	3927.094972	7.849804578	1	3927.094972	5.850603412	1	3927.094972	7.322395833	781.0555556	16.7568534	0.061874227	0.000254641	0.001998883	0.000254641	1	0.001489804	0.000254641	1	0.001864583	0.198888889	0.004266985	1.58E-05	0.127391706	500.2793296	0.127391706	500.2793296	0.745318352	0.127391706	500.2793296	0.9328125	99.5	2.134684148	0.007882263	1	3927.094972	7.849804578	3927.094972	5.850603412	1	3927.094972	7.322395833	781.0555556	16.7568534	0.061874227	0.000254641	1	0.001998883	0.000254641	0.001489804	0.000254641	1	0.001864583	0.198888889	0.004266985	1.58E-05	0.170922541	671.2290503	1.341708543	0.170922541	671.2290503	0.170922541	671.2290503	1.2515625	133.5	2.864123957	0.0105757	1	3927.094972	7.849804578	1	3927.094972	5.850603412	3927.094972	7.322395833	781.0555556	16.7568534	0.061874227	0.000254641	1	0.001998883	0.000254641	1	0.001489804	0.000254641	0.001864583	0.198888889	0.004266985	1.58E-05	0.136567323	536.3128492	1.072026801	0.136567323	536.3128492	0.799001248	0.136567323	536.3128492	106.6666667	2.288438617	0.008449998	0.001280319	5.027932961	0.010050251	0.001280319	5.027932961	0.007490637	0.001280319	5.027932961	0.009375	0.059677075	234.3575419	0.468453378	0.059677075	234.3575419	0.3491469	0.059677075	234.3575419	0.436979167	16.16181805	63468.99441	126.8671133	16.16181805	63468.99441	94.55638785	16.16181805	63468.99441	118.3432292	3	4	12	14055.42	12268	0	14055.42	11656	0	14055.42	12139	-1787.42	-14055.42	0	-2399.42	-14055.42	0	-1916.42	-12268	1787.42	-612	-12268	1787.42	-129	14055.42	11656	0	14055.42	12139	-2399.42	-14055.42	0	-1916.42	-11656	2399.42	483	14055.42	12139	-1916.42	-14041	14.42	-1773	-14041	14.42	-2385	-14041	14.42	-1902	-13220	835.42	-952	-13220	835.42	-1564	-13220	835.42	-1081	213160	227215.42	225428	213160	227215.42	224816	213160	227215.42	225299	2021	2019	2019
38435	43	DoctorDegree	183813	596	home	6615	10339	home	5.65	0	6615	5.65	6615	5.65	1	1086	21	1177	960.1111111	0.072147651	0.000233933	13.86046512	0.003242426	4274.72093	308.4110738	6	-553	-183770	-183217	2019	1177	960.1111111	12	5	2020	2019	6609.35	1170.79646	0.000854119	1086	960.1111111	6609.35	1170.79646	0.000854119	1086	1177	960.1111111	6609.35	1170.79646	0.000854119	38435	6615	home	5.65	0	6615	5.65	6615	5.65	1170.79646	6.091160221	1	1170.79646	5.620220901	1	1170.79646	6.889827566	153.8372093	11.09899329	0.035987661	0.000854119	0.005202578	0.000854119	1	0.00480034	0.000854119	1	0.005884736	0.131395349	0.009479866	3.07E-05	0.164172336	192.2123894	0.164172336	192.2123894	0.922684792	0.164172336	192.2123894	1.131119083	25.25581395	1.822147651	0.005908178	1	1170.79646	6.091160221	1170.79646	5.620220901	1	1170.79646	6.889827566	153.8372093	11.09899329	0.035987661	0.000854119	1	0.005202578	0.000854119	0.00480034	0.000854119	1	0.005884736	0.131395349	0.009479866	3.07E-05	0.177928949	208.3185841	1.083793738	0.177928949	208.3185841	0.177928949	208.3185841	1.22589978	27.37209302	1.974832215	0.006403247	1	1170.79646	6.091160221	1	1170.79646	5.620220901	1170.79646	6.889827566	153.8372093	11.09899329	0.035987661	0.000854119	1	0.005202578	0.000854119	1	0.00480034	0.000854119	0.005884736	0.131395349	0.009479866	3.07E-05	0.145141513	169.9311701	0.884080213	0.145141513	169.9311701	0.815727367	0.145141513	169.9311701	22.32816537	1.610924683	0.005223304	0.006500378	7.610619469	0.039594843	0.006500378	7.610619469	0.03653356	0.006500378	7.610619469	0.044786483	0.090098262	105.4867257	0.548802947	0.090098262	105.4867257	0.506372133	0.090098262	105.4867257	0.620761486	27.78730159	32533.27434	169.2569061	27.78730159	32533.27434	156.1707732	27.78730159	32533.27434	191.4497165	12	5	3	6609.35	5529	0	6609.35	5438	0	6609.35	5654.888889	-1080.35	-6609.35	0	-1171.35	-6609.35	0	-954.4611111	-5529	1080.35	-91	-5529	1080.35	125.8888889	6609.35	5438	0	6609.35	5654.888889	-1171.35	-6609.35	0	-954.4611111	-5438	1171.35	216.8888889	6609.35	5654.888889	-954.4611111	-6572	37.35	-1043	-6572	37.35	-1134	-6572	37.35	-917.1111111	-6019	590.35	-490	-6019	590.35	-581	-6019	590.35	-364.1111111	177198	183807.35	182727	177198	183807.35	182636	177198	183807.35	182852.8889	2020	2019	2020
41646	27	AssociateDegree	47995	514	cash	14884	10334	cash	0.04	0	14884	0.04	14884	0.04	0	1978	174	2501	2239.5	0.052529183	0.000562559	19.03703704	0.010709449	1777.592593	93.37548638	7	-487	-47968	-47481	2019	2501	2239.5	10	10	2022	2020	14883.96	372100	2.69E-06	1978	2239.5	14883.96	372100	2.69E-06	1978	2501	2239.5	14883.96	372100	2.69E-06	41646	14884	cash	0.04	0	14884	0.04	14884	0.04	372100	7.524772497	1	372100	5.951219512	1	372100	6.646126367	551.2592593	28.95719844	0.310115637	2.69E-06	2.02E-05	2.69E-06	1	1.60E-05	2.69E-06	1	1.79E-05	0.001481481	7.78E-05	8.33E-07	0.132894383	49450	0.132894383	49450	0.790883647	0.132894383	49450	0.883232864	73.25925926	3.848249027	0.041212626	1	372100	7.524772497	372100	5.951219512	1	372100	6.646126367	551.2592593	28.95719844	0.310115637	2.69E-06	1	2.02E-05	2.69E-06	1.60E-05	2.69E-06	1	1.79E-05	0.001481481	7.78E-05	8.33E-07	0.168032787	62525	1.264408493	0.168032787	62525	0.168032787	62525	1.116767136	92.62962963	4.865758755	0.052109595	1	372100	7.524772497	1	372100	5.951219512	372100	6.646126367	551.2592593	28.95719844	0.310115637	2.69E-06	1	2.02E-05	2.69E-06	1	1.60E-05	2.69E-06	1.79E-05	0.001481481	7.78E-05	8.33E-07	0.150463585	55987.5	1.132204247	0.150463585	55987.5	0.895441823	0.150463585	55987.5	82.94444444	4.357003891	0.046661111	0.001814028	675	0.013650152	0.001814028	675	0.010795682	0.001814028	675	0.012056263	0.034533727	12850	0.259858443	0.034533727	12850	0.205517793	0.034533727	12850	0.229515517	3.224603601	1199875	24.26440849	3.224603601	1199875	19.19032387	3.224603601	1199875	21.43112302	10	10	12	14883.96	12906	0	14883.96	12383	0	14883.96	12644.5	-1977.96	-14883.96	0	-2500.96	-14883.96	0	-2239.46	-12906	1977.96	-523	-12906	1977.96	-261.5	14883.96	12383	0	14883.96	12644.5	-2500.96	-14883.96	0	-2239.46	-12383	2500.96	261.5	14883.96	12644.5	-2239.46	-14857	26.96	-1951	-14857	26.96	-2474	-14857	26.96	-2212.5	-14370	513.96	-1464	-14370	513.96	-1987	-14370	513.96	-1725.5	33111	47994.96	46017	33111	47994.96	45494	33111	47994.96	45755.5	2022	2020	2020
31484	25	AssociateDegree	151928	547	cash	8458	10288	cash	4.04	0	8458	4.04	8458	4.04	0	1429	61	1491	1232.75	0.045703839	0.000164552	21.88	0.00360039	6077.12	277.7477148	7	-522	-151903	-151381	2019	1491	1232.75	7	6	2021	2019	8453.96	2093.564356	0.000477654	1429	1232.75	8453.96	2093.564356	0.000477654	1429	1491	1232.75	8453.96	2093.564356	0.000477654	31484	8458	cash	4.04	0	8458	4.04	8458	4.04	2093.564356	5.918824353	1	2093.564356	5.672702884	1	2093.564356	6.861082945	338.32	15.46252285	0.055671107	0.000477654	0.002827152	0.000477654	1	0.002709591	0.000477654	1	0.003277226	0.1616	0.00738574	2.66E-05	0.168952471	353.7128713	0.168952471	353.7128713	0.95841717	0.168952471	353.7128713	1.159196917	57.16	2.612431444	0.009405771	1	2093.564356	5.918824353	2093.564356	5.672702884	1	2093.564356	6.861082945	338.32	15.46252285	0.055671107	0.000477654	1	0.002827152	0.000477654	0.002709591	0.000477654	1	0.003277226	0.1616	0.00738574	2.66E-05	0.176282809	369.0594059	1.043386984	0.176282809	369.0594059	0.176282809	369.0594059	1.209490975	59.64	2.725776965	0.009813859	1	2093.564356	5.918824353	1	2093.564356	5.672702884	2093.564356	6.861082945	338.32	15.46252285	0.055671107	0.000477654	1	0.002827152	0.000477654	1	0.002709591	0.000477654	0.003277226	0.1616	0.00738574	2.66E-05	0.145749586	305.1361386	0.8626662	0.145749586	305.1361386	0.826794098	0.145749586	305.1361386	49.31	2.253656307	0.008114041	0.002955782	6.188118812	0.017494752	0.002955782	6.188118812	0.01676727	0.002955782	6.188118812	0.020279862	0.064672499	135.3960396	0.382785164	0.064672499	135.3960396	0.366867874	0.064672499	135.3960396	0.443723383	17.96263892	37605.94059	106.3177047	17.96263892	37605.94059	101.8967136	17.96263892	37605.94059	123.2431555	7	6	5	8453.96	7029	0	8453.96	6967	0	8453.96	7225.25	-1424.96	-8453.96	0	-1486.96	-8453.96	0	-1228.71	-7029	1424.96	-62	-7029	1424.96	196.25	8453.96	6967	0	8453.96	7225.25	-1486.96	-8453.96	0	-1228.71	-6967	1486.96	258.25	8453.96	7225.25	-1228.71	-8433	20.96	-1404	-8433	20.96	-1466	-8433	20.96	-1207.75	-7911	542.96	-882	-7911	542.96	-944	-7911	542.96	-685.75	143470	151923.96	150499	143470	151923.96	150437	143470	151923.96	150695.25	2021	2019	2020
49966	32	AssociateDegree	164888	629	other	14108	10299	other	4.69	1	14108	4.69	14108	4.69	1	1671	119	1671	1671	0.050874404	0.000194071	19.65625	0.003814711	5152.75	262.1430843	10	-597	-164856	-164259	2019	1671	1671	12	9	2022	2020	14103.31	3008.102345	0.000332435	1671	1671	14103.31	3008.102345	0.000332435	1671	1671	1671	14103.31	3008.102345	0.000332435	49966	14108	other	4.69	1	14108	4.69	14108	4.69	3008.102345	8.442848594	1	3008.102345	8.442848594	1	3008.102345	8.442848594	440.875	22.42925278	0.085561108	0.000332435	0.002806703	0.000332435	1	0.002806703	0.000332435	1	0.002806703	0.1465625	0.00745628	2.84E-05	0.118443436	356.2899787	0.118443436	356.2899787	1	0.118443436	356.2899787	1	52.21875	2.656597774	0.010134152	1	3008.102345	8.442848594	3008.102345	8.442848594	1	3008.102345	8.442848594	440.875	22.42925278	0.085561108	0.000332435	1	0.002806703	0.000332435	0.002806703	0.000332435	1	0.002806703	0.1465625	0.00745628	2.84E-05	0.118443436	356.2899787	1	0.118443436	356.2899787	0.118443436	356.2899787	1	52.21875	2.656597774	0.010134152	1	3008.102345	8.442848594	1	3008.102345	8.442848594	3008.102345	8.442848594	440.875	22.42925278	0.085561108	0.000332435	1	0.002806703	0.000332435	1	0.002806703	0.000332435	0.002806703	0.1465625	0.00745628	2.84E-05	0.118443436	356.2899787	1	0.118443436	356.2899787	1	0.118443436	356.2899787	52.21875	2.656597774	0.010134152	0.002268217	6.823027719	0.019150209	0.002268217	6.823027719	0.019150209	0.002268217	6.823027719	0.019150209	0.044584633	134.1151386	0.376421305	0.044584633	134.1151386	0.376421305	0.044584633	134.1151386	0.376421305	11.68755316	35157.35608	98.67624177	11.68755316	35157.35608	98.67624177	11.68755316	35157.35608	98.67624177	12	9	12	14103.31	12437	0	14103.31	12437	0	14103.31	12437	-1666.31	-14103.31	0	-1666.31	-14103.31	0	-1666.31	-12437	1666.31	0	-12437	1666.31	0	14103.31	12437	0	14103.31	12437	-1666.31	-14103.31	0	-1666.31	-12437	1666.31	0	14103.31	12437	-1666.31	-14076	27.31	-1639	-14076	27.31	-1639	-14076	27.31	-1639	-13479	624.31	-1042	-13479	624.31	-1042	-13479	624.31	-1042	150780	164883.31	163217	150780	164883.31	163217	150780	164883.31	163217	2022	2020	2020
34610	40	BachelorDegree	53512	771	credit	4497	11322	credit	2.3	1	4497	2.3	4497	2.3	1	503	97	679	601.6666667	0.051880674	0.000747496	19.275	0.014407983	1337.8	69.40596628	10	-731	-53472	-52741	2019	679	601.6666667	1	8	2022	2020	4494.7	1955.217391	0.000511452	503	601.6666667	4494.7	1955.217391	0.000511452	503	679	601.6666667	4494.7	1955.217391	0.000511452	34610	4497	credit	2.3	1	4497	2.3	4497	2.3	1955.217391	8.940357853	1	1955.217391	6.622974963	1	1955.217391	7.474238227	112.425	5.832684825	0.084037225	0.000511452	0.004572565	0.000511452	1	0.003387334	0.000511452	1	0.003822715	0.0575	0.002983139	4.30E-05	0.111852346	218.6956522	0.111852346	218.6956522	0.740795287	0.111852346	218.6956522	0.83601108	12.575	0.652399481	0.009399761	1	1955.217391	8.940357853	1955.217391	6.622974963	1	1955.217391	7.474238227	112.425	5.832684825	0.084037225	0.000511452	1	0.004572565	0.000511452	0.003387334	0.000511452	1	0.003822715	0.0575	0.002983139	4.30E-05	0.150989549	295.2173913	1.349900596	0.150989549	295.2173913	0.150989549	295.2173913	1.128531856	16.975	0.880674449	0.012688743	1	1955.217391	8.940357853	1	1955.217391	6.622974963	1955.217391	7.474238227	112.425	5.832684825	0.084037225	0.000511452	1	0.004572565	0.000511452	1	0.003387334	0.000511452	0.003822715	0.0575	0.002983139	4.30E-05	0.133792899	261.5942029	1.196156395	0.133792899	261.5942029	0.88610702	0.133792899	261.5942029	15.04166667	0.780371812	0.011243584	0.008894819	17.39130435	0.079522863	0.008894819	17.39130435	0.058910162	0.008894819	17.39130435	0.066481994	0.171447632	335.2173913	1.532803181	0.171447632	335.2173913	1.135493373	0.171447632	335.2173913	1.281440443	11.89948855	23266.08696	106.3856859	11.89948855	23266.08696	78.81001473	11.89948855	23266.08696	88.93961219	1	8	12	4494.7	3994	0	4494.7	3818	0	4494.7	3895.333333	-500.7	-4494.7	0	-676.7	-4494.7	0	-599.3666667	-3994	500.7	-176	-3994	500.7	-98.66666667	4494.7	3818	0	4494.7	3895.333333	-676.7	-4494.7	0	-599.3666667	-3818	676.7	77.33333333	4494.7	3895.333333	-599.3666667	-4457	37.7	-463	-4457	37.7	-639	-4457	37.7	-561.6666667	-3726	768.7	268	-3726	768.7	92	-3726	768.7	169.3333333	49015	53509.7	53009	49015	53509.7	52833	49015	53509.7	52910.33333	2022	2020	2020
49134	31	MasterDegree	74686	708	home	2101	10293	home	0.95	1	2101	0.95	2101	0.95	1	396	114	398	315.5	0.043785311	0.000415071	22.83870968	0.009479688	2409.225806	105.4887006	11	-677	-74655	-73978	2019	398	315.5	6	7	2022	2020	2100.05	2211.578947	0.000452166	396	315.5	2100.05	2211.578947	0.000452166	396	398	315.5	2100.05	2211.578947	0.000452166	49134	2101	home	0.95	1	2101	0.95	2101	0.95	2211.578947	5.305555556	1	2211.578947	5.278894472	1	2211.578947	6.659270998	67.77419355	2.967514124	0.028131109	0.000452166	0.00239899	0.000452166	1	0.002386935	0.000452166	1	0.003011094	0.030645161	0.001341808	1.27E-05	0.188481675	416.8421053	0.188481675	416.8421053	0.994974874	0.188481675	416.8421053	1.255150555	12.77419355	0.559322034	0.005302199	1	2211.578947	5.305555556	2211.578947	5.278894472	1	2211.578947	6.659270998	67.77419355	2.967514124	0.028131109	0.000452166	1	0.00239899	0.000452166	0.002386935	0.000452166	1	0.003011094	0.030645161	0.001341808	1.27E-05	0.189433603	418.9473684	1.005050505	0.189433603	418.9473684	0.189433603	418.9473684	1.261489699	12.83870968	0.562146893	0.005328977	1	2211.578947	5.305555556	1	2211.578947	5.278894472	2211.578947	6.659270998	67.77419355	2.967514124	0.028131109	0.000452166	1	0.00239899	0.000452166	1	0.002386935	0.000452166	0.003011094	0.030645161	0.001341808	1.27E-05	0.150166587	332.1052632	0.796717172	0.150166587	332.1052632	0.792713568	0.150166587	332.1052632	10.17741935	0.445621469	0.004224353	0.014754879	32.63157895	0.078282828	0.014754879	32.63157895	0.077889447	0.014754879	32.63157895	0.098256735	0.336982389	745.2631579	1.787878788	0.336982389	745.2631579	1.778894472	0.336982389	745.2631579	2.244057052	35.54783436	78616.84211	188.6010101	35.54783436	78616.84211	187.6532663	35.54783436	78616.84211	236.7226624	6	7	12	2100.05	1705	0	2100.05	1703	0	2100.05	1785.5	-395.05	-2100.05	0	-397.05	-2100.05	0	-314.55	-1705	395.05	-2	-1705	395.05	80.5	2100.05	1703	0	2100.05	1785.5	-397.05	-2100.05	0	-314.55	-1703	397.05	82.5	2100.05	1785.5	-314.55	-2070	30.05	-365	-2070	30.05	-367	-2070	30.05	-284.5	-1393	707.05	312	-1393	707.05	310	-1393	707.05	392.5	72585	74685.05	74290	72585	74685.05	74288	72585	74685.05	74370.5	2022	2020	2020
42221	18	AssociateDegree	150127	814	credit	3795	11465	credit	2.26	1	3795	2.26	3795	2.26	1	381	94	712	535.8571429	0.022113022	0.000119898	45.22222222	0.005422076	8340.388889	184.4312039	11	-796	-150109	-149313	2019	712	535.8571429	2	5	2022	2020	3792.74	1679.20354	0.00059552	381	535.8571429	3792.74	1679.20354	0.00059552	381	712	535.8571429	3792.74	1679.20354	0.00059552	42221	3795	credit	2.26	1	3795	2.26	3795	2.26	1679.20354	9.960629921	1	1679.20354	5.33005618	1	1679.20354	7.082111437	210.8333333	4.662162162	0.025278597	0.00059552	0.005931759	0.00059552	1	0.003174157	0.00059552	1	0.004217542	0.125555556	0.002776413	1.51E-05	0.100395257	168.5840708	0.100395257	168.5840708	0.53511236	0.100395257	168.5840708	0.711010397	21.16666667	0.468058968	0.002537851	1	1679.20354	9.960629921	1679.20354	5.33005618	1	1679.20354	7.082111437	210.8333333	4.662162162	0.025278597	0.00059552	1	0.005931759	0.00059552	0.003174157	0.00059552	1	0.004217542	0.125555556	0.002776413	1.51E-05	0.187615283	315.0442478	1.868766404	0.187615283	315.0442478	0.187615283	315.0442478	1.328712343	39.55555556	0.874692875	0.004742651	1	1679.20354	9.960629921	1	1679.20354	5.33005618	1679.20354	7.082111437	210.8333333	4.662162162	0.025278597	0.00059552	1	0.005931759	0.00059552	1	0.003174157	0.00059552	0.004217542	0.125555556	0.002776413	1.51E-05	0.141200828	237.1049305	1.406449194	0.141200828	237.1049305	0.752608347	0.141200828	237.1049305	29.76984127	0.658301158	0.003569359	0.004743083	7.96460177	0.047244094	0.004743083	7.96460177	0.025280899	0.004743083	7.96460177	0.033591042	0.214492754	360.1769912	2.13648294	0.214492754	360.1769912	1.143258427	0.214492754	360.1769912	1.519061584	39.55915679	66427.87611	394.0341207	39.55915679	66427.87611	210.8525281	39.55915679	66427.87611	280.1623567	2	5	11	3792.74	3414	0	3792.74	3083	0	3792.74	3259.142857	-378.74	-3792.74	0	-709.74	-3792.74	0	-533.5971429	-3414	378.74	-331	-3414	378.74	-154.8571429	3792.74	3083	0	3792.74	3259.142857	-709.74	-3792.74	0	-533.5971429	-3083	709.74	176.1428571	3792.74	3259.142857	-533.5971429	-3777	15.74	-363	-3777	15.74	-694	-3777	15.74	-517.8571429	-2981	811.74	433	-2981	811.74	102	-2981	811.74	278.1428571	146332	150124.74	149746	146332	150124.74	149415	146332	150124.74	149591.1429	2022	2020	2020
48531	45	MasterDegree	106017	658	home	1430	11232	home	6.81	1	1430	6.81	1430	6.81	0	258	110	266	216.3333333	0.068389058	0.00042446	14.62222222	0.006206552	2355.933333	161.1200608	12	-613	-105972	-105359	2019	266	216.3333333	4	11	2022	2019	1423.19	209.9853157	0.004762238	258	216.3333333	1423.19	209.9853157	0.004762238	258	266	216.3333333	1423.19	209.9853157	0.004762238	48531	1430	home	6.81	1	1430	6.81	1430	6.81	209.9853157	5.542635659	1	209.9853157	5.37593985	1	209.9853157	6.610169492	31.77777778	2.17325228	0.013488403	0.004762238	0.026395349	0.004762238	1	0.025601504	0.004762238	1	0.031479199	0.151333333	0.010349544	6.42E-05	0.18041958	37.88546256	0.18041958	37.88546256	0.969924812	0.18041958	37.88546256	1.192604006	5.733333333	0.392097264	0.002433572	1	209.9853157	5.542635659	209.9853157	5.37593985	1	209.9853157	6.610169492	31.77777778	2.17325228	0.013488403	0.004762238	1	0.026395349	0.004762238	0.025601504	0.004762238	1	0.031479199	0.151333333	0.010349544	6.42E-05	0.186013986	39.06020558	1.031007752	0.186013986	39.06020558	0.186013986	39.06020558	1.229583975	5.911111111	0.404255319	0.002509032	1	209.9853157	5.542635659	1	209.9853157	5.37593985	209.9853157	6.610169492	31.77777778	2.17325228	0.013488403	0.004762238	1	0.026395349	0.004762238	1	0.025601504	0.004762238	0.031479199	0.151333333	0.010349544	6.42E-05	0.151282051	31.7670093	0.838501292	0.151282051	31.7670093	0.813283208	0.151282051	31.7670093	4.807407407	0.328774063	0.002040553	0.031468531	6.607929515	0.174418605	0.031468531	6.607929515	0.169172932	0.031468531	6.607929515	0.208012327	0.46013986	96.6226138	2.550387597	0.46013986	96.6226138	2.473684211	0.46013986	96.6226138	3.041602465	74.13776224	15567.84141	410.9186047	74.13776224	15567.84141	398.5601504	74.13776224	15567.84141	490.0631741	4	11	9	1423.19	1172	0	1423.19	1164	0	1423.19	1213.666667	-251.19	-1423.19	0	-259.19	-1423.19	0	-209.5233333	-1172	251.19	-8	-1172	251.19	41.66666667	1423.19	1164	0	1423.19	1213.666667	-259.19	-1423.19	0	-209.5233333	-1164	259.19	49.66666667	1423.19	1213.666667	-209.5233333	-1385	38.19	-213	-1385	38.19	-221	-1385	38.19	-171.3333333	-772	651.19	400	-772	651.19	392	-772	651.19	441.6666667	104587	106010.19	105759	104587	106010.19	105751	104587	106010.19	105800.6667	2022	2019	2020
30036	39	MasterDegree	217948	842	other	13279	11264	other	2.2	1	13279	2.2	13279	2.2	0	2452	154	2452	1814.833333	0.04631829	0.000178942	21.58974359	0.003863307	5588.410256	258.8456057	12	-803	-217909	-217106	2019	2452	1814.833333	6	9	2022	2019	13276.8	6035.909091	0.000165675	2452	1814.833333	13276.8	6035.909091	0.000165675	2452	2452	1814.833333	13276.8	6035.909091	0.000165675	30036	13279	other	2.2	1	13279	2.2	13279	2.2	6035.909091	5.415579119	1	6035.909091	5.415579119	1	6035.909091	7.316925337	340.4871795	15.77078385	0.060927377	0.000165675	0.000897227	0.000165675	1	0.000897227	0.000165675	1	0.001212233	0.056410256	0.002612827	1.01E-05	0.184652459	1114.545455	0.184652459	1114.545455	1	0.184652459	1114.545455	1.351088254	62.87179487	2.912114014	0.01125039	1	6035.909091	5.415579119	6035.909091	5.415579119	1	6035.909091	7.316925337	340.4871795	15.77078385	0.060927377	0.000165675	1	0.000897227	0.000165675	0.000897227	0.000165675	1	0.001212233	0.056410256	0.002612827	1.01E-05	0.184652459	1114.545455	1	0.184652459	1114.545455	0.184652459	1114.545455	1.351088254	62.87179487	2.912114014	0.01125039	1	6035.909091	5.415579119	1	6035.909091	5.415579119	6035.909091	7.316925337	340.4871795	15.77078385	0.060927377	0.000165675	1	0.000897227	0.000165675	1	0.000897227	0.000165675	0.001212233	0.056410256	0.002612827	1.01E-05	0.136669428	824.9242424	0.7401441	0.136669428	824.9242424	0.7401441	0.136669428	824.9242424	46.53418803	2.155384006	0.00832691	0.002936968	17.72727273	0.015905383	0.002936968	17.72727273	0.015905383	0.002936968	17.72727273	0.021489577	0.063408389	382.7272727	0.343393148	0.063408389	382.7272727	0.343393148	0.063408389	382.7272727	0.463954449	16.41298291	99067.27273	88.8858075	16.41298291	99067.27273	88.8858075	16.41298291	99067.27273	120.0925705	6	9	4	13276.8	10827	0	13276.8	10827	0	13276.8	11464.16667	-2449.8	-13276.8	0	-2449.8	-13276.8	0	-1812.633333	-10827	2449.8	0	-10827	2449.8	637.1666667	13276.8	10827	0	13276.8	11464.16667	-2449.8	-13276.8	0	-1812.633333	-10827	2449.8	637.1666667	13276.8	11464.16667	-1812.633333	-13240	36.8	-2413	-13240	36.8	-2413	-13240	36.8	-1775.833333	-12437	839.8	-1610	-12437	839.8	-1610	-12437	839.8	-972.8333333	204669	217945.8	215496	204669	217945.8	215496	204669	217945.8	216133.1667	2022	2019	2020
39851	21	AssociateDegree	157615	649	credit	9646	10759	credit	2.41	1	9646	2.41	9646	2.41	0	995	162	1746	1391.625	0.032357473	0.000133236	30.9047619	0.004117628	7505.47619	242.8582435	1	-628	-157594	-156966	2020	1746	1391.625	5	1	2022	2020	9643.59	4002.489627	0.000249844	995	1391.625	9643.59	4002.489627	0.000249844	995	1746	1391.625	9643.59	4002.489627	0.000249844	39851	9646	credit	2.41	1	9646	2.41	9646	2.41	4002.489627	9.694472362	1	4002.489627	5.524627721	1	4002.489627	6.931465014	459.3333333	14.86286595	0.061199759	0.000249844	0.002422111	0.000249844	1	0.001380298	0.000249844	1	0.001731788	0.114761905	0.003713405	1.53E-05	0.103151565	412.8630705	0.103151565	412.8630705	0.569873998	0.103151565	412.8630705	0.714991467	47.38095238	1.533127889	0.006312851	1	4002.489627	9.694472362	4002.489627	5.524627721	1	4002.489627	6.931465014	459.3333333	14.86286595	0.061199759	0.000249844	1	0.002422111	0.000249844	0.001380298	0.000249844	1	0.001731788	0.114761905	0.003713405	1.53E-05	0.181007672	724.4813278	1.754773869	0.181007672	724.4813278	0.181007672	724.4813278	1.254648343	83.14285714	2.690292758	0.011077626	1	4002.489627	9.694472362	1	4002.489627	5.524627721	4002.489627	6.931465014	459.3333333	14.86286595	0.061199759	0.000249844	1	0.002422111	0.000249844	1	0.001380298	0.000249844	0.001731788	0.114761905	0.003713405	1.53E-05	0.144269645	577.4377593	1.39861809	0.144269645	577.4377593	0.797036082	0.144269645	577.4377593	66.26785714	2.144260401	0.008829268	0.002177068	8.713692946	0.021105528	0.002177068	8.713692946	0.012027491	0.002177068	8.713692946	0.015090272	0.067281775	269.2946058	0.652261307	0.067281775	269.2946058	0.371706758	0.067281775	269.2946058	0.466361268	16.33993365	65400.41494	158.4070352	16.33993365	65400.41494	90.2720504	16.33993365	65400.41494	113.2596784	5	1	9	9643.59	8651	0	9643.59	7900	0	9643.59	8254.375	-992.59	-9643.59	0	-1743.59	-9643.59	0	-1389.215	-8651	992.59	-751	-8651	992.59	-396.625	9643.59	7900	0	9643.59	8254.375	-1743.59	-9643.59	0	-1389.215	-7900	1743.59	354.375	9643.59	8254.375	-1389.215	-9625	18.59	-974	-9625	18.59	-1725	-9625	18.59	-1370.625	-8997	646.59	-346	-8997	646.59	-1097	-8997	646.59	-742.625	147969	157612.59	156620	147969	157612.59	155869	147969	157612.59	156223.375	2022	2020	2020
27521	25	MasterDegree	40205	503	other	8494	11082	other	0.54	1	8494	0.54	8494	0.54	0	1061	167	1311	1110.4	0.049701789	0.000621813	20.12	0.012510882	1608.2	79.9304175	2	-478	-40180	-39702	2020	1311	1110.4	4	9	2021	2019	8493.46	15729.62963	6.36E-05	1061	1110.4	8493.46	15729.62963	6.36E-05	1061	1311	1110.4	8493.46	15729.62963	6.36E-05	27521	8494	other	0.54	1	8494	0.54	8494	0.54	15729.62963	8.005655042	1	15729.62963	6.479023646	1	15729.62963	7.649495677	339.76	16.88667992	0.211267255	6.36E-05	0.000508954	6.36E-05	1	0.000411899	6.36E-05	1	0.000486311	0.0216	0.001073559	1.34E-05	0.124911702	1964.814815	0.124911702	1964.814815	0.809305873	0.124911702	1964.814815	0.955511527	42.44	2.109343936	0.026389753	1	15729.62963	8.005655042	15729.62963	6.479023646	1	15729.62963	7.649495677	339.76	16.88667992	0.211267255	6.36E-05	1	0.000508954	6.36E-05	0.000411899	6.36E-05	1	0.000486311	0.0216	0.001073559	1.34E-05	0.154344243	2427.777778	1.235626767	0.154344243	2427.777778	0.154344243	2427.777778	1.18065562	52.44	2.606361829	0.032607885	1	15729.62963	8.005655042	1	15729.62963	6.479023646	15729.62963	7.649495677	339.76	16.88667992	0.211267255	6.36E-05	1	0.000508954	6.36E-05	1	0.000411899	6.36E-05	0.000486311	0.0216	0.001073559	1.34E-05	0.130727572	2056.296296	1.046559849	0.130727572	2056.296296	0.846987033	0.130727572	2056.296296	44.416	2.207554672	0.027618455	0.002943254	46.2962963	0.023562677	0.002943254	46.2962963	0.019069413	0.002943254	46.2962963	0.022514409	0.059218272	931.4814815	0.474081056	0.059218272	931.4814815	0.383676583	0.059218272	931.4814815	0.452989914	4.733341182	74453.7037	37.8934967	4.733341182	74453.7037	30.66742944	4.733341182	74453.7037	36.20767291	4	9	4	8493.46	7433	0	8493.46	7183	0	8493.46	7383.6	-1060.46	-8493.46	0	-1310.46	-8493.46	0	-1109.86	-7433	1060.46	-250	-7433	1060.46	-49.4	8493.46	7183	0	8493.46	7383.6	-1310.46	-8493.46	0	-1109.86	-7183	1310.46	200.6	8493.46	7383.6	-1109.86	-8469	24.46	-1036	-8469	24.46	-1286	-8469	24.46	-1085.4	-7991	502.46	-558	-7991	502.46	-808	-7991	502.46	-607.4	31711	40204.46	39144	31711	40204.46	38894	31711	40204.46	39094.6	2021	2019	2020
46602	24	DoctorDegree	176949	583	cash	9547	10612	cash	4.03	1	9547	4.03	9547	4.03	0	1078	12	1895	1319.714286	0.041166381	0.000135632	24.29166667	0.003294735	7372.875	303.5145798	3	-559	-176925	-176366	2020	1895	1319.714286	9	1	2021	2019	9542.97	2368.98263	0.000422122	1078	1319.714286	9542.97	2368.98263	0.000422122	1078	1895	1319.714286	9542.97	2368.98263	0.000422122	46602	9547	cash	4.03	1	9547	4.03	9547	4.03	2368.98263	8.856215213	1	2368.98263	5.037994723	1	2368.98263	7.234141589	397.7916667	16.37564322	0.053953399	0.000422122	0.003738404	0.000422122	1	0.002126649	0.000422122	1	0.003053691	0.167916667	0.006912521	2.28E-05	0.112915052	267.4937965	0.112915052	267.4937965	0.568865435	0.112915052	267.4937965	0.816843473	44.91666667	1.849056604	0.006092151	1	2368.98263	8.856215213	2368.98263	5.037994723	1	2368.98263	7.234141589	397.7916667	16.37564322	0.053953399	0.000422122	1	0.003738404	0.000422122	0.002126649	0.000422122	1	0.003053691	0.167916667	0.006912521	2.28E-05	0.198491673	470.2233251	1.757884972	0.198491673	470.2233251	0.198491673	470.2233251	1.435916865	78.95833333	3.250428816	0.0107093	1	2368.98263	8.856215213	1	2368.98263	5.037994723	2368.98263	7.234141589	397.7916667	16.37564322	0.053953399	0.000422122	1	0.003738404	0.000422122	1	0.002126649	0.000422122	0.003053691	0.167916667	0.006912521	2.28E-05	0.138233402	327.4725275	1.224224755	0.138233402	327.4725275	0.696419148	0.138233402	327.4725275	54.98809524	2.263660867	0.007458162	0.002513879	5.955334988	0.022263451	0.002513879	5.955334988	0.012664908	0.002513879	5.955334988	0.018185754	0.061066304	144.6650124	0.540816327	0.061066304	144.6650124	0.307651715	0.061066304	144.6650124	0.441762286	18.53451346	43907.94045	164.1456401	18.53451346	43907.94045	93.376781	18.53451346	43907.94045	134.0812947	9	1	10	9542.97	8469	0	9542.97	7652	0	9542.97	8227.285714	-1073.97	-9542.97	0	-1890.97	-9542.97	0	-1315.684286	-8469	1073.97	-817	-8469	1073.97	-241.7142857	9542.97	7652	0	9542.97	8227.285714	-1890.97	-9542.97	0	-1315.684286	-7652	1890.97	575.2857143	9542.97	8227.285714	-1315.684286	-9523	19.97	-1054	-9523	19.97	-1871	-9523	19.97	-1295.714286	-8964	578.97	-495	-8964	578.97	-1312	-8964	578.97	-736.7142857	167402	176944.97	175871	167402	176944.97	175054	167402	176944.97	175629.2857	2021	2019	2019
42766	19	AssociateDegree	173466	790	credit	7131	11776	credit	1.17	0	7131	1.17	7131	1.17	0	1015	82	1199	1092.444444	0.024050633	0.000109532	41.57894737	0.004554207	9129.789474	219.5772152	3	-771	-173447	-172676	2020	1199	1092.444444	6	2	2021	2019	7129.83	6094.871795	0.000164072	1015	1092.444444	7129.83	6094.871795	0.000164072	1015	1199	1092.444444	7129.83	6094.871795	0.000164072	42766	7131	credit	1.17	0	7131	1.17	7131	1.17	6094.871795	7.025615764	1	6094.871795	5.947456214	1	6094.871795	6.527563059	375.3157895	9.026582278	0.04110892	0.000164072	0.001152709	0.000164072	1	0.000975813	0.000164072	1	0.001070993	0.061578947	0.001481013	6.74E-06	0.142336278	867.5213675	0.142336278	867.5213675	0.846538782	0.142336278	867.5213675	0.929109032	53.42105263	1.284810127	0.005851291	1	6094.871795	7.025615764	6094.871795	5.947456214	1	6094.871795	6.527563059	375.3157895	9.026582278	0.04110892	0.000164072	1	0.001152709	0.000164072	0.000975813	0.000164072	1	0.001070993	0.061578947	0.001481013	6.74E-06	0.168139111	1024.786325	1.181280788	0.168139111	1024.786325	0.168139111	1024.786325	1.097538649	63.10526316	1.517721519	0.006912017	1	6094.871795	7.025615764	1	6094.871795	5.947456214	6094.871795	6.527563059	375.3157895	9.026582278	0.04110892	0.000164072	1	0.001152709	0.000164072	1	0.000975813	0.000164072	0.001070993	0.061578947	0.001481013	6.74E-06	0.153196528	933.7132004	1.076299945	0.153196528	933.7132004	0.911129645	0.153196528	933.7132004	57.49707602	1.382841069	0.006297744	0.002664423	16.23931624	0.018719212	0.002664423	16.23931624	0.015846539	0.002664423	16.23931624	0.017392189	0.110783901	675.2136752	0.778325123	0.110783901	675.2136752	0.658882402	0.110783901	675.2136752	0.723148902	24.32562053	148261.5385	170.9024631	24.32562053	148261.5385	144.675563	24.32562053	148261.5385	158.787022	6	2	12	7129.83	6116	0	7129.83	5932	0	7129.83	6038.555556	-1013.83	-7129.83	0	-1197.83	-7129.83	0	-1091.274444	-6116	1013.83	-184	-6116	1013.83	-77.44444444	7129.83	5932	0	7129.83	6038.555556	-1197.83	-7129.83	0	-1091.274444	-5932	1197.83	106.5555556	7129.83	6038.555556	-1091.274444	-7112	17.83	-996	-7112	17.83	-1180	-7112	17.83	-1073.444444	-6341	788.83	-225	-6341	788.83	-409	-6341	788.83	-302.4444444	166335	173464.83	172451	166335	173464.83	172267	166335	173464.83	172373.5556	2021	2019	2019
31648	47	DoctorDegree	54228	635	home	10130	11861	home	5.81	1	10130	5.81	10130	5.81	0	1878	64	1878	1365.666667	0.074015748	0.000866711	13.5106383	0.011709818	1153.787234	85.3984252	3	-588	-54181	-53593	2020	1878	1365.666667	6	8	2022	2020	10124.19	1743.545611	0.000573544	1878	1365.666667	10124.19	1743.545611	0.000573544	1878	1878	1365.666667	10124.19	1743.545611	0.000573544	31648	10130	home	5.81	1	10130	5.81	10130	5.81	1743.545611	5.394036209	1	1743.545611	5.394036209	1	1743.545611	7.417622651	215.5319149	15.95275591	0.186803865	0.000573544	0.003093717	0.000573544	1	0.003093717	0.000573544	1	0.004254332	0.123617021	0.009149606	0.00010714	0.185389931	323.2358003	0.185389931	323.2358003	1	0.185389931	323.2358003	1.375152551	39.95744681	2.957480315	0.034631556	1	1743.545611	5.394036209	1743.545611	5.394036209	1	1743.545611	7.417622651	215.5319149	15.95275591	0.186803865	0.000573544	1	0.003093717	0.000573544	0.003093717	0.000573544	1	0.004254332	0.123617021	0.009149606	0.00010714	0.185389931	323.2358003	1	0.185389931	323.2358003	0.185389931	323.2358003	1.375152551	39.95744681	2.957480315	0.034631556	1	1743.545611	5.394036209	1	1743.545611	5.394036209	1743.545611	7.417622651	215.5319149	15.95275591	0.186803865	0.000573544	1	0.003093717	0.000573544	1	0.003093717	0.000573544	0.004254332	0.123617021	0.009149606	0.00010714	0.134814084	235.0545037	0.727192048	0.134814084	235.0545037	0.727192048	0.134814084	235.0545037	29.05673759	2.150656168	0.025183792	0.004639684	8.089500861	0.025026624	0.004639684	8.089500861	0.025026624	0.004639684	8.089500861	0.034415426	0.062685094	109.2943201	0.338125666	0.062685094	109.2943201	0.338125666	0.062685094	109.2943201	0.464974371	5.353208292	9333.562823	28.87539936	5.353208292	9333.562823	28.87539936	5.353208292	9333.562823	39.70807908	6	8	12	10124.19	8252	0	10124.19	8252	0	10124.19	8764.333333	-1872.19	-10124.19	0	-1872.19	-10124.19	0	-1359.856667	-8252	1872.19	0	-8252	1872.19	512.3333333	10124.19	8252	0	10124.19	8764.333333	-1872.19	-10124.19	0	-1359.856667	-8252	1872.19	512.3333333	10124.19	8764.333333	-1359.856667	-10083	41.19	-1831	-10083	41.19	-1831	-10083	41.19	-1318.666667	-9495	629.19	-1243	-9495	629.19	-1243	-9495	629.19	-730.6666667	44098	54222.19	52350	44098	54222.19	52350	44098	54222.19	52862.33333	2022	2020	2020
41140	36	BachelorDegree	171301	551	home	4297	10896	home	3.78	0	4297	3.78	4297	3.78	0	642	145	788	715	0.065335753	0.000210156	15.30555556	0.00321656	4758.361111	310.8911071	5	-515	-171265	-170750	2020	788	715	10	9	2022	2020	4293.22	1136.772487	0.000879684	642	715	4293.22	1136.772487	0.000879684	642	788	715	4293.22	1136.772487	0.000879684	41140	4297	home	3.78	0	4297	3.78	4297	3.78	1136.772487	6.693146417	1	1136.772487	5.453045685	1	1136.772487	6.00979021	119.3611111	7.798548094	0.0250845	0.000879684	0.00588785	0.000879684	1	0.004796954	0.000879684	1	0.005286713	0.105	0.006860254	2.21E-05	0.149406563	169.8412698	0.149406563	169.8412698	0.814720812	0.149406563	169.8412698	0.897902098	17.83333333	1.165154265	0.003747789	1	1136.772487	6.693146417	1136.772487	5.453045685	1	1136.772487	6.00979021	119.3611111	7.798548094	0.0250845	0.000879684	1	0.00588785	0.000879684	0.004796954	0.000879684	1	0.005286713	0.105	0.006860254	2.21E-05	0.183383756	208.4656085	1.22741433	0.183383756	208.4656085	0.183383756	208.4656085	1.102097902	21.88888889	1.430127042	0.00460009	1	1136.772487	6.693146417	1	1136.772487	5.453045685	1136.772487	6.00979021	119.3611111	7.798548094	0.0250845	0.000879684	1	0.00588785	0.000879684	1	0.004796954	0.000879684	0.005286713	0.105	0.006860254	2.21E-05	0.166395159	189.1534392	1.113707165	0.166395159	189.1534392	0.907360406	0.166395159	189.1534392	19.86111111	1.297640653	0.004173939	0.008377938	9.523809524	0.056074766	0.008377938	9.523809524	0.045685279	0.008377938	9.523809524	0.05034965	0.128228997	145.7671958	0.858255452	0.128228997	145.7671958	0.699238579	0.128228997	145.7671958	0.770629371	39.86525483	45317.72487	266.8239875	39.86525483	45317.72487	217.3870558	39.86525483	45317.72487	239.5818182	10	9	12	4293.22	3655	0	4293.22	3509	0	4293.22	3582	-638.22	-4293.22	0	-784.22	-4293.22	0	-711.22	-3655	638.22	-146	-3655	638.22	-73	4293.22	3509	0	4293.22	3582	-784.22	-4293.22	0	-711.22	-3509	784.22	73	4293.22	3582	-711.22	-4261	32.22	-606	-4261	32.22	-752	-4261	32.22	-679	-3746	547.22	-91	-3746	547.22	-237	-3746	547.22	-164	167004	171297.22	170659	167004	171297.22	170513	167004	171297.22	170586	2022	2020	2020
47641	48	DoctorDegree	149357	532	credit	5050	10442	credit	7.51	1	5050	7.51	5050	7.51	1	874	27	876	749.1666667	0.090225564	0.000321378	11.08333333	0.003561935	3111.604167	280.7462406	6	-484	-149309	-148825	2020	876	749.1666667	10	2	2022	2020	5042.49	672.436751	0.001487129	874	749.1666667	5042.49	672.436751	0.001487129	874	876	749.1666667	5042.49	672.436751	0.001487129	47641	5050	credit	7.51	1	5050	7.51	5050	7.51	672.436751	5.778032037	1	672.436751	5.764840183	1	672.436751	6.740823137	105.2083333	9.492481203	0.033811606	0.001487129	0.008592677	0.001487129	1	0.008573059	0.001487129	1	0.010024472	0.156458333	0.014116541	5.03E-05	0.173069307	116.3781625	0.173069307	116.3781625	0.997716895	0.173069307	116.3781625	1.166629588	18.20833333	1.642857143	0.005851751	1	672.436751	5.778032037	672.436751	5.764840183	1	672.436751	6.740823137	105.2083333	9.492481203	0.033811606	0.001487129	1	0.008592677	0.001487129	0.008573059	0.001487129	1	0.010024472	0.156458333	0.014116541	5.03E-05	0.173465347	116.644474	1.00228833	0.173465347	116.644474	0.173465347	116.644474	1.169299221	18.25	1.646616541	0.005865142	1	672.436751	5.778032037	1	672.436751	5.764840183	672.436751	6.740823137	105.2083333	9.492481203	0.033811606	0.001487129	1	0.008592677	0.001487129	1	0.008573059	0.001487129	0.010024472	0.156458333	0.014116541	5.03E-05	0.148349835	99.75588105	0.857170099	0.148349835	99.75588105	0.85521309	0.148349835	99.75588105	15.60763889	1.40820802	0.005015946	0.00950495	6.391478029	0.054919908	0.00950495	6.391478029	0.054794521	0.00950495	6.391478029	0.06407119	0.105346535	70.83888149	0.608695652	0.105346535	70.83888149	0.607305936	0.105346535	70.83888149	0.710122358	29.57564356	19887.74967	170.889016	29.57564356	19887.74967	170.4988584	29.57564356	19887.74967	199.3641824	10	2	10	5042.49	4176	0	5042.49	4174	0	5042.49	4300.833333	-866.49	-5042.49	0	-868.49	-5042.49	0	-741.6566667	-4176	866.49	-2	-4176	866.49	124.8333333	5042.49	4174	0	5042.49	4300.833333	-868.49	-5042.49	0	-741.6566667	-4174	868.49	126.8333333	5042.49	4300.833333	-741.6566667	-5002	40.49	-826	-5002	40.49	-828	-5002	40.49	-701.1666667	-4518	524.49	-342	-4518	524.49	-344	-4518	524.49	-217.1666667	144307	149349.49	148483	144307	149349.49	148481	144307	149349.49	148607.8333	2022	2020	2020
31027	21	AssociateDegree	59927	722	cash	12791	10644	cash	4.07	1	12791	4.07	12791	4.07	1	2051	170	2305	2140.666667	0.029085873	0.000350426	34.38095238	0.012047992	2853.666667	83.00138504	8	-701	-59906	-59205	2020	2305	2140.666667	6	8	2022	2020	12786.93	3142.751843	0.000318192	2051	2140.666667	12786.93	3142.751843	0.000318192	2051	2305	2140.666667	12786.93	3142.751843	0.000318192	31027	12791	cash	4.07	1	12791	4.07	12791	4.07	3142.751843	6.236470015	1	3142.751843	5.549240781	1	3142.751843	5.975241358	609.0952381	17.71606648	0.213443022	0.000318192	0.001984398	0.000318192	1	0.001765727	0.000318192	1	0.001901277	0.193809524	0.005637119	6.79E-05	0.160347119	503.9312039	0.160347119	503.9312039	0.889804772	0.160347119	503.9312039	0.958112737	97.66666667	2.840720222	0.034224974	1	3142.751843	6.236470015	3142.751843	5.549240781	1	3142.751843	5.975241358	609.0952381	17.71606648	0.213443022	0.000318192	1	0.001984398	0.000318192	0.001765727	0.000318192	1	0.001901277	0.193809524	0.005637119	6.79E-05	0.180204832	566.3390663	1.123842028	0.180204832	566.3390663	0.180204832	566.3390663	1.076767362	109.7619048	3.192520776	0.038463464	1	3142.751843	6.236470015	1	3142.751843	5.549240781	3142.751843	5.975241358	609.0952381	17.71606648	0.213443022	0.000318192	1	0.001984398	0.000318192	1	0.001765727	0.000318192	0.001901277	0.193809524	0.005637119	6.79E-05	0.167357256	525.962326	1.043718511	0.167357256	525.962326	0.928705712	0.167357256	525.962326	101.9365079	2.964912281	0.035721239	0.001641779	5.15970516	0.010238908	0.001641779	5.15970516	0.009110629	0.001641779	5.15970516	0.009810028	0.056445939	177.3955774	0.352023403	0.056445939	177.3955774	0.313232104	0.056445939	177.3955774	0.337278107	4.68509108	14724.07862	29.21843003	4.68509108	14724.07862	25.99869848	4.68509108	14724.07862	27.99454998	6	8	12	12786.93	10740	0	12786.93	10486	0	12786.93	10650.33333	-2046.93	-12786.93	0	-2300.93	-12786.93	0	-2136.596667	-10740	2046.93	-254	-10740	2046.93	-89.66666667	12786.93	10486	0	12786.93	10650.33333	-2300.93	-12786.93	0	-2136.596667	-10486	2300.93	164.3333333	12786.93	10650.33333	-2136.596667	-12770	16.93	-2030	-12770	16.93	-2284	-12770	16.93	-2119.666667	-12069	717.93	-1329	-12069	717.93	-1583	-12069	717.93	-1418.666667	47136	59922.93	57876	47136	59922.93	57622	47136	59922.93	57786.33333	2022	2020	2020
42476	35	AssociateDegree	76007	502	cash	12348	11024	cash	1.79	1	12348	1.79	12348	1.79	1	1395	45	1395	1395	0.069721116	0.000460484	14.34285714	0.006604655	2171.628571	151.4083665	9	-467	-75972	-75505	2020	1395	1395	6	10	2022	2020	12346.21	6898.324022	0.000144963	1395	1395	12346.21	6898.324022	0.000144963	1395	1395	1395	12346.21	6898.324022	0.000144963	42476	12348	cash	1.79	1	12348	1.79	12348	1.79	6898.324022	8.851612903	1	6898.324022	8.851612903	1	6898.324022	8.851612903	352.8	24.59760956	0.162458721	0.000144963	0.001283154	0.000144963	1	0.001283154	0.000144963	1	0.001283154	0.051142857	0.003565737	2.36E-05	0.112973761	779.3296089	0.112973761	779.3296089	1	0.112973761	779.3296089	1	39.85714286	2.778884462	0.018353573	1	6898.324022	8.851612903	6898.324022	8.851612903	1	6898.324022	8.851612903	352.8	24.59760956	0.162458721	0.000144963	1	0.001283154	0.000144963	0.001283154	0.000144963	1	0.001283154	0.051142857	0.003565737	2.36E-05	0.112973761	779.3296089	1	0.112973761	779.3296089	0.112973761	779.3296089	1	39.85714286	2.778884462	0.018353573	1	6898.324022	8.851612903	1	6898.324022	8.851612903	6898.324022	8.851612903	352.8	24.59760956	0.162458721	0.000144963	1	0.001283154	0.000144963	1	0.001283154	0.000144963	0.001283154	0.051142857	0.003565737	2.36E-05	0.112973761	779.3296089	1	0.112973761	779.3296089	1	0.112973761	779.3296089	39.85714286	2.778884462	0.018353573	0.002834467	19.55307263	0.025089606	0.002834467	19.55307263	0.025089606	0.002834467	19.55307263	0.025089606	0.040654357	280.4469274	0.359856631	0.040654357	280.4469274	0.359856631	0.040654357	280.4469274	0.359856631	6.155409783	42462.01117	54.48530466	6.155409783	42462.01117	54.48530466	6.155409783	42462.01117	54.48530466	6	10	12	12346.21	10953	0	12346.21	10953	0	12346.21	10953	-1393.21	-12346.21	0	-1393.21	-12346.21	0	-1393.21	-10953	1393.21	0	-10953	1393.21	0	12346.21	10953	0	12346.21	10953	-1393.21	-12346.21	0	-1393.21	-10953	1393.21	0	12346.21	10953	-1393.21	-12313	33.21	-1360	-12313	33.21	-1360	-12313	33.21	-1360	-11846	500.21	-893	-11846	500.21	-893	-11846	500.21	-893	63659	76005.21	74612	63659	76005.21	74612	63659	76005.21	74612	2022	2020	2020
47497	30	BachelorDegree	50855	606	home	8166	11735	home	0.98	1	8166	0.98	8166	0.98	0	1160	87	1468	1193.4	0.04950495	0.000589912	20.2	0.011916232	1695.166667	83.91914191	9	-576	-50825	-50249	2020	1468	1193.4	8	2	2022	2020	8165.02	8332.653061	0.00012001	1160	1193.4	8165.02	8332.653061	0.00012001	1160	1468	1193.4	8165.02	8332.653061	0.00012001	47497	8166	home	0.98	1	8166	0.98	8166	0.98	8332.653061	7.039655172	1	8332.653061	5.5626703	1	8332.653061	6.84263449	272.2	13.47524752	0.160574181	0.00012001	0.000844828	0.00012001	1	0.000667575	0.00012001	1	0.000821183	0.032666667	0.001617162	1.93E-05	0.142052412	1183.673469	0.142052412	1183.673469	0.790190736	0.142052412	1183.673469	0.972012737	38.66666667	1.914191419	0.02280995	1	8332.653061	7.039655172	8332.653061	5.5626703	1	8332.653061	6.84263449	272.2	13.47524752	0.160574181	0.00012001	1	0.000844828	0.00012001	0.000667575	0.00012001	1	0.000821183	0.032666667	0.001617162	1.93E-05	0.179769777	1497.959184	1.265517241	0.179769777	1497.959184	0.179769777	1497.959184	1.230098877	48.93333333	2.422442244	0.028866385	1	8332.653061	7.039655172	1	8332.653061	5.5626703	8332.653061	6.84263449	272.2	13.47524752	0.160574181	0.00012001	1	0.000844828	0.00012001	1	0.000667575	0.00012001	0.000821183	0.032666667	0.001617162	1.93E-05	0.146142542	1217.755102	1.028793103	0.146142542	1217.755102	0.812942779	0.146142542	1217.755102	39.78	1.969306931	0.023466719	0.003673769	30.6122449	0.025862069	0.003673769	30.6122449	0.020435967	0.003673769	30.6122449	0.02513826	0.07421014	618.3673469	0.522413793	0.07421014	618.3673469	0.41280654	0.07421014	618.3673469	0.507792861	6.227651237	51892.85714	43.84051724	6.227651237	51892.85714	34.64237057	6.227651237	51892.85714	42.61354114	8	2	8	8165.02	7006	0	8165.02	6698	0	8165.02	6972.6	-1159.02	-8165.02	0	-1467.02	-8165.02	0	-1192.42	-7006	1159.02	-308	-7006	1159.02	-33.4	8165.02	6698	0	8165.02	6972.6	-1467.02	-8165.02	0	-1192.42	-6698	1467.02	274.6	8165.02	6972.6	-1192.42	-8136	29.02	-1130	-8136	29.02	-1438	-8136	29.02	-1163.4	-7560	605.02	-554	-7560	605.02	-862	-7560	605.02	-587.4	42689	50854.02	49695	42689	50854.02	49387	42689	50854.02	49661.6	2022	2020	2020

2)、完整的数据框包含了113-5个新特征

auto_feature_names: 113 [<Feature: age>, <Feature: education>, <Feature: income>, <Feature: credit_score>, <Feature: loan_type>, <Feature: COUNT(loans)>, <Feature: MAX(loans.loan_amount)>, <Feature: MAX(loans.rate)>, <Feature: MEAN(loans.loan_amount)>, <Feature: MEAN(loans.rate)>, <Feature: MIN(loans.loan_amount)>, <Feature: MIN(loans.rate)>, <Feature: MODE(loans.loan_type)>, <Feature: MODE(loans.repaid)>, <Feature: NUM_UNIQUE(loans.loan_type)>, <Feature: NUM_UNIQUE(loans.repaid)>, <Feature: SKEW(loans.loan_amount)>, <Feature: SKEW(loans.rate)>, <Feature: STD(loans.loan_amount)>, <Feature: STD(loans.rate)>, <Feature: SUM(loans.loan_amount)>, <Feature: SUM(loans.rate)>, <Feature: COUNT(payments)>, <Feature: MAX(payments.payment_amount)>, <Feature: MEAN(payments.payment_amount)>, <Feature: MIN(payments.payment_amount)>, <Feature: MODE(payments.missed)>, <Feature: NUM_UNIQUE(payments.missed)>, <Feature: SKEW(payments.payment_amount)>, <Feature: STD(payments.payment_amount)>, <Feature: SUM(payments.payment_amount)>, <Feature: DAY(joined)>, <Feature: MONTH(joined)>, <Feature: WEEKDAY(joined)>, <Feature: YEAR(joined)>, <Feature: MAX(loans.COUNT(payments))>, <Feature: MAX(loans.MEAN(payments.payment_amount))>, <Feature: MAX(loans.MIN(payments.payment_amount))>, <Feature: MAX(loans.NUM_UNIQUE(payments.missed))>, <Feature: MAX(loans.SKEW(payments.payment_amount))>, <Feature: MAX(loans.STD(payments.payment_amount))>, <Feature: MAX(loans.SUM(payments.payment_amount))>, <Feature: MEAN(loans.COUNT(payments))>, <Feature: MEAN(loans.MAX(payments.payment_amount))>, <Feature: MEAN(loans.MEAN(payments.payment_amount))>, <Feature: MEAN(loans.MIN(payments.payment_amount))>, <Feature: MEAN(loans.NUM_UNIQUE(payments.missed))>, <Feature: MEAN(loans.SKEW(payments.payment_amount))>, <Feature: MEAN(loans.STD(payments.payment_amount))>, <Feature: MEAN(loans.SUM(payments.payment_amount))>, <Feature: MIN(loans.COUNT(payments))>, <Feature: MIN(loans.MAX(payments.payment_amount))>, <Feature: MIN(loans.MEAN(payments.payment_amount))>, <Feature: MIN(loans.NUM_UNIQUE(payments.missed))>, <Feature: MIN(loans.SKEW(payments.payment_amount))>, <Feature: MIN(loans.STD(payments.payment_amount))>, <Feature: MIN(loans.SUM(payments.payment_amount))>, <Feature: MODE(loans.DAY(loan_end))>, <Feature: MODE(loans.DAY(loan_start))>, <Feature: MODE(loans.MODE(payments.missed))>, <Feature: MODE(loans.MONTH(loan_end))>, <Feature: MODE(loans.MONTH(loan_start))>, <Feature: MODE(loans.WEEKDAY(loan_end))>, <Feature: MODE(loans.WEEKDAY(loan_start))>, <Feature: MODE(loans.YEAR(loan_end))>, <Feature: MODE(loans.YEAR(loan_start))>, <Feature: NUM_UNIQUE(loans.DAY(loan_end))>, <Feature: NUM_UNIQUE(loans.DAY(loan_start))>, <Feature: NUM_UNIQUE(loans.MODE(payments.missed))>, <Feature: NUM_UNIQUE(loans.MONTH(loan_end))>, <Feature: NUM_UNIQUE(loans.MONTH(loan_start))>, <Feature: NUM_UNIQUE(loans.WEEKDAY(loan_end))>, <Feature: NUM_UNIQUE(loans.WEEKDAY(loan_start))>, <Feature: NUM_UNIQUE(loans.YEAR(loan_end))>, <Feature: NUM_UNIQUE(loans.YEAR(loan_start))>, <Feature: SKEW(loans.COUNT(payments))>, <Feature: SKEW(loans.MAX(payments.payment_amount))>, <Feature: SKEW(loans.MEAN(payments.payment_amount))>, <Feature: SKEW(loans.MIN(payments.payment_amount))>, <Feature: SKEW(loans.NUM_UNIQUE(payments.missed))>, <Feature: SKEW(loans.STD(payments.payment_amount))>, <Feature: SKEW(loans.SUM(payments.payment_amount))>, <Feature: STD(loans.COUNT(payments))>, <Feature: STD(loans.MAX(payments.payment_amount))>, <Feature: STD(loans.MEAN(payments.payment_amount))>, <Feature: STD(loans.MIN(payments.payment_amount))>, <Feature: STD(loans.NUM_UNIQUE(payments.missed))>, <Feature: STD(loans.SKEW(payments.payment_amount))>, <Feature: STD(loans.SUM(payments.payment_amount))>, <Feature: SUM(loans.MAX(payments.payment_amount))>, <Feature: SUM(loans.MEAN(payments.payment_amount))>, <Feature: SUM(loans.MIN(payments.payment_amount))>, <Feature: SUM(loans.NUM_UNIQUE(payments.missed))>, <Feature: SUM(loans.SKEW(payments.payment_amount))>, <Feature: SUM(loans.STD(payments.payment_amount))>, <Feature: MAX(payments.loans.loan_amount)>, <Feature: MAX(payments.loans.rate)>, <Feature: MEAN(payments.loans.loan_amount)>, <Feature: MEAN(payments.loans.rate)>, <Feature: MIN(payments.loans.loan_amount)>, <Feature: MIN(payments.loans.rate)>, <Feature: MODE(payments.loans.client_id)>, <Feature: MODE(payments.loans.loan_type)>, <Feature: MODE(payments.loans.repaid)>, <Feature: NUM_UNIQUE(payments.loans.client_id)>, <Feature: NUM_UNIQUE(payments.loans.loan_type)>, <Feature: NUM_UNIQUE(payments.loans.repaid)>, <Feature: SKEW(payments.loans.loan_amount)>, <Feature: SKEW(payments.loans.rate)>, <Feature: STD(payments.loans.loan_amount)>, <Feature: STD(payments.loans.rate)>, <Feature: SUM(payments.loans.loan_amount)>, <Feature: SUM(payments.loans.rate)>]
auto_features: <class 'pandas.core.frame.DataFrame'> 

client_id	age	education	income	credit_score	loan_type	COUNT(loans)	MAX(loans.loan_amount)	MAX(loans.rate)	MEAN(loans.loan_amount)	MEAN(loans.rate)	MIN(loans.loan_amount)	MIN(loans.rate)	MODE(loans.loan_type)	MODE(loans.repaid)	NUM_UNIQUE(loans.loan_type)	NUM_UNIQUE(loans.repaid)	SKEW(loans.loan_amount)	SKEW(loans.rate)	STD(loans.loan_amount)	STD(loans.rate)	SUM(loans.loan_amount)	SUM(loans.rate)	COUNT(payments)	MAX(payments.payment_amount)	MEAN(payments.payment_amount)	MIN(payments.payment_amount)	MODE(payments.missed)	NUM_UNIQUE(payments.missed)	SKEW(payments.payment_amount)	STD(payments.payment_amount)	SUM(payments.payment_amount)	DAY(joined)	MONTH(joined)	WEEKDAY(joined)	YEAR(joined)	MAX(loans.COUNT(payments))	MAX(loans.MEAN(payments.payment_amount))	MAX(loans.MIN(payments.payment_amount))	MAX(loans.NUM_UNIQUE(payments.missed))	MAX(loans.SKEW(payments.payment_amount))	MAX(loans.STD(payments.payment_amount))	MAX(loans.SUM(payments.payment_amount))	MEAN(loans.COUNT(payments))	MEAN(loans.MAX(payments.payment_amount))	MEAN(loans.MEAN(payments.payment_amount))	MEAN(loans.MIN(payments.payment_amount))	MEAN(loans.NUM_UNIQUE(payments.missed))	MEAN(loans.SKEW(payments.payment_amount))	MEAN(loans.STD(payments.payment_amount))	MEAN(loans.SUM(payments.payment_amount))	MIN(loans.COUNT(payments))	MIN(loans.MAX(payments.payment_amount))	MIN(loans.MEAN(payments.payment_amount))	MIN(loans.NUM_UNIQUE(payments.missed))	MIN(loans.SKEW(payments.payment_amount))	MIN(loans.STD(payments.payment_amount))	MIN(loans.SUM(payments.payment_amount))	MODE(loans.DAY(loan_end))	MODE(loans.DAY(loan_start))	MODE(loans.MODE(payments.missed))	MODE(loans.MONTH(loan_end))	MODE(loans.MONTH(loan_start))	MODE(loans.WEEKDAY(loan_end))	MODE(loans.WEEKDAY(loan_start))	MODE(loans.YEAR(loan_end))	MODE(loans.YEAR(loan_start))	NUM_UNIQUE(loans.DAY(loan_end))	NUM_UNIQUE(loans.DAY(loan_start))	NUM_UNIQUE(loans.MODE(payments.missed))	NUM_UNIQUE(loans.MONTH(loan_end))	NUM_UNIQUE(loans.MONTH(loan_start))	NUM_UNIQUE(loans.WEEKDAY(loan_end))	NUM_UNIQUE(loans.WEEKDAY(loan_start))	NUM_UNIQUE(loans.YEAR(loan_end))	NUM_UNIQUE(loans.YEAR(loan_start))	SKEW(loans.COUNT(payments))	SKEW(loans.MAX(payments.payment_amount))	SKEW(loans.MEAN(payments.payment_amount))	SKEW(loans.MIN(payments.payment_amount))	SKEW(loans.NUM_UNIQUE(payments.missed))	SKEW(loans.STD(payments.payment_amount))	SKEW(loans.SUM(payments.payment_amount))	STD(loans.COUNT(payments))	STD(loans.MAX(payments.payment_amount))	STD(loans.MEAN(payments.payment_amount))	STD(loans.MIN(payments.payment_amount))	STD(loans.NUM_UNIQUE(payments.missed))	STD(loans.SKEW(payments.payment_amount))	STD(loans.SUM(payments.payment_amount))	SUM(loans.MAX(payments.payment_amount))	SUM(loans.MEAN(payments.payment_amount))	SUM(loans.MIN(payments.payment_amount))	SUM(loans.NUM_UNIQUE(payments.missed))	SUM(loans.SKEW(payments.payment_amount))	SUM(loans.STD(payments.payment_amount))	MAX(payments.loans.loan_amount)	MAX(payments.loans.rate)	MEAN(payments.loans.loan_amount)	MEAN(payments.loans.rate)	MIN(payments.loans.loan_amount)	MIN(payments.loans.rate)	MODE(payments.loans.client_id)	MODE(payments.loans.loan_type)	MODE(payments.loans.repaid)	NUM_UNIQUE(payments.loans.client_id)	NUM_UNIQUE(payments.loans.loan_type)	NUM_UNIQUE(payments.loans.repaid)	SKEW(payments.loans.loan_amount)	SKEW(payments.loans.rate)	STD(payments.loans.loan_amount)	STD(payments.loans.rate)	SUM(payments.loans.loan_amount)	SUM(payments.loans.rate)
33747	44	MasterDegree	138377	683	other	1	7855	4.89	7855	4.89	7855	4.89	other	1	1	1					7855	4.89	2	1372	1314	1256	1	1		82.02438662	2628	27	3	2	2019	2	1314	1256	1		82.02438662	2628	2	1372	1314	1256	1		82.02438662	2628	2	1372	1314	1		82.02438662	2628	1	20	1	3	9	1	6	2022	2020	1	1	1	1	1	1	1	1	1															1372	1314	1256	1	0	82.02438662	7855	4.89	7855	4.89	7855	4.89	33747	other	1	1	1	1			0	0	15710	9.78
31578	38	DoctorDegree	104028	805	other	1	7953	0.13	7953	0.13	7953	0.13	other	1	1	1					7953	0.13	7	1557	1180.714286	854	1	2	0.327985609	225.5117989	8265	5	4	4	2019	7	1180.714286	854	2	0.327985609	225.5117989	8265	7	1557	1180.714286	854	2	0.327985609	225.5117989	8265	7	1557	1180.714286	2	0.327985609	225.5117989	8265	4	20	1	1	6	2	5	2023	2020	1	1	1	1	1	1	1	1	1															1557	1180.714286	854	2	0.327985609	225.5117989	7953	0.13	7953	0.13	7953	0.13	31578	other	1	1	1	1	0	0	0	0	55671	0.91
27894	47	BachelorDegree	176516	708	credit	1	13457	1.48	13457	1.48	13457	1.48	credit	1	1	1					13457	1.48	7	2505	2118.428571	1692	0	2	0.104454852	296.4820721	14829	7	4	6	2019	7	2118.428571	1692	2	0.104454852	296.4820721	14829	7	2505	2118.428571	1692	2	0.104454852	296.4820721	14829	7	2505	2118.428571	2	0.104454852	296.4820721	14829	8	18	0	10	4	5	5	2022	2020	1	1	1	1	1	1	1	1	1															2505	2118.428571	1692	2	0.104454852	296.4820721	13457	1.48	13457	1.48	13457	1.48	27894	credit	1	1	1	1	0	0	0	0	94199	10.36
44966	31	BachelorDegree	58530	822	credit	1	7733	0.42	7733	0.42	7733	0.42	credit	0	1	1					7733	0.42	3	1245	1079.666667	858	0	1	-1.169092087	199.555339	3239	13	5	0	2019	3	1079.666667	858	1	-1.169092087	199.555339	3239	3	1245	1079.666667	858	1	-1.169092087	199.555339	3239	3	1245	1079.666667	1	-1.169092087	199.555339	3239	22	15	0	2	9	2	1	2023	2020	1	1	1	1	1	1	1	1	1															1245	1079.666667	858	1	-1.169092087	199.555339	7733	0.42	7733	0.42	7733	0.42	44966	credit	0	1	1	1	0	0	0	0	23199	1.26
29195	46	BachelorDegree	38528	680	other	1	2466	0.68	2466	0.68	2466	0.68	other	1	1	1					2466	0.68	6	431	368.5	289	0	2	-0.359126877	60.32163791	2211	22	5	2	2019	6	368.5	289	2	-0.359126877	60.32163791	2211	6	431	368.5	289	2	-0.359126877	60.32163791	2211	6	431	368.5	2	-0.359126877	60.32163791	2211	27	6	0	4	3	1	2	2021	2019	1	1	1	1	1	1	1	1	1															431	368.5	289	2	-0.359126877	60.32163791	2466	0.68	2466	0.68	2466	0.68	29195	other	1	1	1	1	0	0	0	0	14796	4.08
25942	18	DoctorDegree	227219	839	cash	1	14059	3.58	14059	3.58	14059	3.58	cash	0	1	1					14059	3.58	8	2403	1920	1441	0	2	0.19589847	336.7555281	15360	9	6	6	2019	8	1920	1441	2	0.19589847	336.7555281	15360	8	2403	1920	1441	2	0.19589847	336.7555281	15360	8	2403	1920	2	0.19589847	336.7555281	15360	29	10	0	3	4	0	2	2021	2019	1	1	1	1	1	1	1	1	1															2403	1920	1441	2	0.19589847	336.7555281	14059	3.58	14059	3.58	14059	3.58	25942	cash	0	1	1	1	0	0	0	0	112472	28.64
38435	43	DoctorDegree	183813	596	home	1	6615	5.65	6615	5.65	6615	5.65	home	0	1	1					6615	5.65	9	1177	960.1111111	730	0	2	-0.110570695	156.9844295	8641	26	6	2	2019	9	960.1111111	730	2	-0.110570695	156.9844295	8641	9	1177	960.1111111	730	2	-0.110570695	156.9844295	8641	9	1177	960.1111111	2	-0.110570695	156.9844295	8641	25	13	0	12	5	4	0	2020	2019	1	1	1	1	1	1	1	1	1															1177	960.1111111	730	2	-0.110570695	156.9844295	6615	5.65	6615	5.65	6615	5.65	38435	home	0	1	1	1	0	0	0	0	59535	50.85
41646	27	AssociateDegree	47995	514	cash	1	14884	0.04	14884	0.04	14884	0.04	cash	0	1	1					14884	0.04	2	2501	2239.5	1978	0	2		369.8168466	4479	6	7	5	2019	2	2239.5	1978	2		369.8168466	4479	2	2501	2239.5	1978	2		369.8168466	4479	2	2501	2239.5	2		369.8168466	4479	22	22	0	10	10	5	3	2022	2020	1	1	1	1	1	1	1	1	1															2501	2239.5	1978	2	0	369.8168466	14884	0.04	14884	0.04	14884	0.04	41646	cash	0	1	1	1			0	0	29768	0.08
31484	25	AssociateDegree	151928	547	cash	1	8458	4.04	8458	4.04	8458	4.04	cash	0	1	1					8458	4.04	8	1491	1232.75	883	1	2	-0.485776673	202.5901916	9862	16	7	1	2019	8	1232.75	883	2	-0.485776673	202.5901916	9862	8	1491	1232.75	883	2	-0.485776673	202.5901916	9862	8	1491	1232.75	2	-0.485776673	202.5901916	9862	13	18	1	7	6	1	1	2021	2019	1	1	1	1	1	1	1	1	1															1491	1232.75	883	2	-0.485776673	202.5901916	8458	4.04	8458	4.04	8458	4.04	31484	cash	0	1	1	1	0	0	0	0	67664	32.32
49966	32	AssociateDegree	164888	629	other	1	14108	4.69	14108	4.69	14108	4.69	other	1	1	1					14108	4.69	1	1671	1671	1671	1	1			1671	7	10	0	2019	1	1671	1671	1			1671	1	1671	1671	1671	1			1671	1	1671	1671	1			1671	8	16	1	12	9	3	2	2022	2020	1	1	1	1	1	1	1	1	1															1671	1671	1671	1	0	0	14108	4.69	14108	4.69	14108	4.69	49966	other	1	1	1	1					14108	4.69
34610	40	BachelorDegree	53512	771	credit	1	4497	2.3	4497	2.3	4497	2.3	credit	1	1	1					4497	2.3	3	679	601.6666667	503	1	1	-1.007538211	89.9184816	1805	24	10	3	2019	3	601.6666667	503	1	-1.007538211	89.9184816	1805	3	679	601.6666667	503	1	-1.007538211	89.9184816	1805	3	679	601.6666667	1	-1.007538211	89.9184816	1805	14	28	1	1	8	4	4	2022	2020	1	1	1	1	1	1	1	1	1															679	601.6666667	503	1	-1.007538211	89.9184816	4497	2.3	4497	2.3	4497	2.3	34610	credit	1	1	1	1	0	0	0	0	13491	6.9
49134	31	MasterDegree	74686	708	home	1	2101	0.95	2101	0.95	2101	0.95	home	1	1	1					2101	0.95	4	398	315.5	224	0	2	-0.038285805	94.46516113	1262	19	11	1	2019	4	315.5	224	2	-0.038285805	94.46516113	1262	4	398	315.5	224	2	-0.038285805	94.46516113	1262	4	398	315.5	2	-0.038285805	94.46516113	1262	11	21	0	6	7	5	1	2022	2020	1	1	1	1	1	1	1	1	1															398	315.5	224	2	-0.038285805	94.46516113	2101	0.95	2101	0.95	2101	0.95	49134	home	1	1	1	1	0	0	0	0	8404	3.8
42221	18	AssociateDegree	150127	814	credit	1	3795	2.26	3795	2.26	3795	2.26	credit	1	1	1					3795	2.26	7	712	535.8571429	381	1	2	0.404340426	100.3783319	3751	21	11	3	2019	7	535.8571429	381	2	0.404340426	100.3783319	3751	7	712	535.8571429	381	2	0.404340426	100.3783319	3751	7	712	535.8571429	2	0.404340426	100.3783319	3751	28	9	1	2	5	0	5	2022	2020	1	1	1	1	1	1	1	1	1															712	535.8571429	381	2	0.404340426	100.3783319	3795	2.26	3795	2.26	3795	2.26	42221	credit	1	1	1	1	0	0	0	0	26565	15.82
48531	45	MasterDegree	106017	658	home	1	1430	6.81	1430	6.81	1430	6.81	home	1	1	1					1430	6.81	9	266	216.3333333	162	0	2	-0.086442644	42.65852787	1947	2	12	0	2019	9	216.3333333	162	2	-0.086442644	42.65852787	1947	9	266	216.3333333	162	2	-0.086442644	42.65852787	1947	9	266	216.3333333	2	-0.086442644	42.65852787	1947	2	19	0	4	11	5	1	2022	2019	1	1	1	1	1	1	1	1	1															266	216.3333333	162	2	-0.086442644	42.65852787	1430	6.81	1430	6.81	1430	6.81	48531	home	1	1	1	1	0	0	0	0	12870	61.29
30036	39	MasterDegree	217948	842	other	1	13279	2.2	13279	2.2	13279	2.2	other	1	1	1					13279	2.2	6	2452	1814.833333	1485	0	2	1.132810699	365.5163015	10889	3	12	1	2019	6	1814.833333	1485	2	1.132810699	365.5163015	10889	6	2452	1814.833333	1485	2	1.132810699	365.5163015	10889	6	2452	1814.833333	2	1.132810699	365.5163015	10889	11	24	0	6	9	5	1	2022	2019	1	1	1	1	1	1	1	1	1															2452	1814.833333	1485	2	1.132810699	365.5163015	13279	2.2	13279	2.2	13279	2.2	30036	other	1	1	1	1	0	0	0	0	79674	13.2
39851	21	AssociateDegree	157615	649	credit	1	9646	2.41	9646	2.41	9646	2.41	credit	1	1	1					9646	2.41	8	1746	1391.625	970	0	2	-0.305586974	305.6557062	11133	16	1	3	2020	8	1391.625	970	2	-0.305586974	305.6557062	11133	8	1746	1391.625	970	2	-0.305586974	305.6557062	11133	8	1746	1391.625	2	-0.305586974	305.6557062	11133	4	25	0	5	1	2	5	2022	2020	1	1	1	1	1	1	1	1	1															1746	1391.625	970	2	-0.305586974	305.6557062	9646	2.41	9646	2.41	9646	2.41	39851	credit	1	1	1	1	0	0	0	0	77168	19.28
27521	25	MasterDegree	40205	503	other	1	8494	0.54	8494	0.54	8494	0.54	other	1	1	1					8494	0.54	5	1311	1110.4	1003	0	2	1.509440198	121.2303592	5552	22	2	5	2020	5	1110.4	1003	2	1.509440198	121.2303592	5552	5	1311	1110.4	1003	2	1.509440198	121.2303592	5552	5	1311	1110.4	2	1.509440198	121.2303592	5552	23	27	0	4	9	4	4	2021	2019	1	1	1	1	1	1	1	1	1															1311	1110.4	1003	2	1.509440198	121.2303592	8494	0.54	8494	0.54	8494	0.54	27521	other	1	1	1	1	0	0	0	0	42470	2.7
46602	24	DoctorDegree	176949	583	cash	1	9547	4.03	9547	4.03	9547	4.03	cash	1	1	1					9547	4.03	7	1895	1319.714286	1078	0	2	1.343759834	314.342549	9238	14	3	5	2020	7	1319.714286	1078	2	1.343759834	314.342549	9238	7	1895	1319.714286	1078	2	1.343759834	314.342549	9238	7	1895	1319.714286	2	1.343759834	314.342549	9238	22	25	0	9	1	2	4	2021	2019	1	1	1	1	1	1	1	1	1															1895	1319.714286	1078	2	1.343759834	314.342549	9547	4.03	9547	4.03	9547	4.03	46602	cash	1	1	1	1	0	0	0	0	66829	28.21
42766	19	AssociateDegree	173466	790	credit	1	7131	1.17	7131	1.17	7131	1.17	credit	0	1	1					7131	1.17	9	1199	1092.444444	730	0	2	-2.200999473	148.2684315	9832	15	3	6	2020	9	1092.444444	730	2	-2.200999473	148.2684315	9832	9	1199	1092.444444	730	2	-2.200999473	148.2684315	9832	9	1199	1092.444444	2	-2.200999473	148.2684315	9832	13	6	0	6	2	6	2	2021	2019	1	1	1	1	1	1	1	1	1															1199	1092.444444	730	2	-2.200999473	148.2684315	7131	1.17	7131	1.17	7131	1.17	42766	credit	0	1	1	1	0	0	0	0	64179	10.53
31648	47	DoctorDegree	54228	635	home	1	10130	5.81	10130	5.81	10130	5.81	home	1	1	1					10130	5.81	3	1878	1365.666667	1081	0	2	1.700079287	444.6080671	4097	17	3	1	2020	3	1365.666667	1081	2	1.700079287	444.6080671	4097	3	1878	1365.666667	1081	2	1.700079287	444.6080671	4097	3	1878	1365.666667	2	1.700079287	444.6080671	4097	13	19	0	6	8	0	2	2022	2020	1	1	1	1	1	1	1	1	1															1878	1365.666667	1081	2	1.700079287	444.6080671	10130	5.81	10130	5.81	10130	5.81	31648	home	1	1	1	1	0	0	0	0	30390	17.43
41140	36	BachelorDegree	171301	551	home	1	4297	3.78	4297	3.78	4297	3.78	home	0	1	1					4297	3.78	2	788	715	642	0	2		103.2375901	1430	14	5	3	2020	2	715	642	2		103.2375901	1430	2	788	715	642	2		103.2375901	1430	2	788	715	2		103.2375901	1430	12	28	0	10	9	2	0	2022	2020	1	1	1	1	1	1	1	1	1															788	715	642	2	0	103.2375901	4297	3.78	4297	3.78	4297	3.78	41140	home	0	1	1	1			0	0	8594	7.56
47641	48	DoctorDegree	149357	532	credit	1	5050	7.51	5050	7.51	5050	7.51	credit	1	1	1					5050	7.51	6	876	749.1666667	623	1	2	0.199406377	108.7463409	4495	25	6	3	2020	6	749.1666667	623	2	0.199406377	108.7463409	4495	6	876	749.1666667	623	2	0.199406377	108.7463409	4495	6	876	749.1666667	2	0.199406377	108.7463409	4495	26	22	1	10	2	2	5	2022	2020	1	1	1	1	1	1	1	1	1															876	749.1666667	623	2	0.199406377	108.7463409	5050	7.51	5050	7.51	5050	7.51	47641	credit	1	1	1	1	0	0	0	0	30300	45.06
31027	21	AssociateDegree	59927	722	cash	1	12791	4.07	12791	4.07	12791	4.07	cash	1	1	1					12791	4.07	3	2305	2140.666667	2051	1	2	1.710489413	142.5143268	6422	25	8	1	2020	3	2140.666667	2051	2	1.710489413	142.5143268	6422	3	2305	2140.666667	2051	2	1.710489413	142.5143268	6422	3	2305	2140.666667	2	1.710489413	142.5143268	6422	21	12	1	6	8	1	2	2022	2020	1	1	1	1	1	1	1	1	1															2305	2140.666667	2051	2	1.710489413	142.5143268	12791	4.07	12791	4.07	12791	4.07	31027	cash	1	1	1	1	0	0	0	0	38373	12.21
42476	35	AssociateDegree	76007	502	cash	1	12348	1.79	12348	1.79	12348	1.79	cash	1	1	1					12348	1.79	1	1395	1395	1395	1	1			1395	12	9	5	2020	1	1395	1395	1			1395	1	1395	1395	1395	1			1395	1	1395	1395	1			1395	29	24	1	6	10	2	5	2022	2020	1	1	1	1	1	1	1	1	1															1395	1395	1395	1	0	0	12348	1.79	12348	1.79	12348	1.79	42476	cash	1	1	1	1					12348	1.79
47497	30	BachelorDegree	50855	606	home	1	8166	0.98	8166	0.98	8166	0.98	home	1	1	1					8166	0.98	5	1468	1193.4	956	1	2	0.274517917	219.316666	5967	19	9	5	2020	5	1193.4	956	2	0.274517917	219.316666	5967	5	1468	1193.4	956	2	0.274517917	219.316666	5967	5	1468	1193.4	2	0.274517917	219.316666	5967	9	14	1	8	2	1	4	2022	2020	1	1	1	1	1	1	1	1	1															1468	1193.4	956	2	0.274517917	219.316666	8166	0.98	8166	0.98	8166	0.98	47497	home	1	1	1	1	0	0	0	0	40830	4.9

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