Datasets:

FeatEng
/

Data

Gender stringclasses 2 values	Ever_Married stringclasses 2 values	Age int64 18 89	Graduated stringclasses 2 values	Profession stringclasses 9 values	Work_Experience float64 0 14 ⌀	Spending_Score stringclasses 3 values	Family_Size float64 1 9 ⌀	Var_1 stringclasses 7 values	__FeatEng_target__ stringclasses 4 values	__index_level_0__ int64 1 8.06k
Female	Yes	71	Yes	Lawyer	8	High	2	Cat_6	C	1
Male	Yes	43	Yes	Executive	1	High	4	Cat_6	B	3
Female	No	28	Yes	Engineer	1	Low	6	Cat_2	A	5
Male	Yes	71	Yes	Artist	0	High	2	Cat_6	B	6
Male	null	61	Yes	Artist	1	Average	5	Cat_6	C	8
Male	Yes	51	Yes	Entertainment	0	Low	3	null	D	9
Female	No	26	Yes	Marketing	0	Low	2	Cat_3	A	10
Male	Yes	53	Yes	Artist	6	Average	2	Cat_6	C	12
Male	Yes	67	Yes	Executive	1	Low	2	Cat_6	A	13
Male	Yes	71	Yes	Lawyer	1	High	2	Cat_6	C	14
Male	Yes	76	Yes	Lawyer	0	Low	2	Cat_6	A	15
Female	No	52	Yes	Artist	null	Low	1	Cat_6	C	16
Female	Yes	58	Yes	Artist	0	Average	5	Cat_6	C	17
Female	Yes	31	No	Engineer	8	Low	2	Cat_4	C	19
Female	No	25	Yes	Healthcare	0	Low	1	Cat_6	D	23
Male	Yes	52	Yes	Executive	1	High	4	Cat_6	C	24
Male	No	31	Yes	Healthcare	0	Low	4	Cat_6	D	27
Male	Yes	49	Yes	Artist	1	Low	2	Cat_3	A	28
Male	No	32	Yes	Artist	1	Low	2	Cat_6	A	30
Female	Yes	46	Yes	Marketing	null	High	3	Cat_6	B	31
Male	No	19	No	Healthcare	0	Low	3	Cat_6	D	32
Female	No	29	No	Homemaker	0	Low	4	Cat_4	A	34
Male	Yes	36	Yes	Artist	0	High	3	Cat_6	C	35
Male	Yes	43	Yes	Artist	1	Low	2	Cat_6	B	39
Female	Yes	70	Yes	Engineer	0	High	2	Cat_6	B	40
Male	No	29	Yes	Doctor	1	Low	4	Cat_6	C	41
Male	Yes	40	Yes	Artist	1	Average	2	Cat_4	C	42
Female	No	33	Yes	Engineer	null	Low	5	Cat_7	D	43
Male	Yes	28	Yes	Artist	9	Average	2	Cat_6	A	45
Female	Yes	76	Yes	Marketing	0	Low	2	Cat_6	B	46
Male	Yes	73	Yes	Executive	1	High	2	Cat_6	B	47
Male	Yes	50	Yes	Doctor	1	Low	2	Cat_6	C	48
Female	No	49	Yes	Engineer	9	Low	1	Cat_6	A	49
Male	Yes	65	Yes	Lawyer	11	High	2	Cat_6	C	50
Male	Yes	57	Yes	Artist	1	Average	5	Cat_6	C	51
Male	Yes	59	Yes	Executive	1	High	5	Cat_6	C	52
Female	No	32	Yes	Engineer	0	Low	3	Cat_6	D	55
Female	No	19	No	Healthcare	0	Low	5	Cat_2	D	56
Female	No	86	Yes	Lawyer	1	Low	1	Cat_6	A	58
Male	Yes	52	No	Entertainment	1	Average	4	Cat_3	B	61
Male	Yes	53	Yes	Artist	3	Average	4	Cat_6	C	62
Male	No	25	Yes	Healthcare	0	Low	null	Cat_4	D	63
Male	Yes	61	Yes	Entertainment	11	Average	3	Cat_6	A	64
Female	Yes	45	Yes	Artist	8	Average	2	null	B	65
Female	Yes	52	Yes	Doctor	1	Low	3	Cat_4	B	66
Female	null	33	No	Healthcare	2	Low	3	Cat_2	C	68
Female	No	40	Yes	Artist	null	Low	1	Cat_2	B	69
Male	Yes	25	Yes	null	null	Low	null	Cat_7	A	70
Female	Yes	86	Yes	Lawyer	1	Low	2	Cat_6	C	72
Male	Yes	53	Yes	Artist	1	Average	4	Cat_6	C	77
Female	No	37	Yes	Doctor	1	Low	1	Cat_4	A	79
Male	Yes	58	Yes	Doctor	0	Average	2	Cat_6	C	82
Male	No	28	Yes	Doctor	1	Low	9	Cat_4	B	86
Male	Yes	49	Yes	Artist	1	Average	3	Cat_6	C	87
Male	No	20	No	Healthcare	1	Low	5	Cat_2	D	88
Male	null	47	null	Executive	1	Average	5	Cat_4	B	89
Male	Yes	40	Yes	Executive	null	High	4	Cat_6	A	92
Female	No	30	No	Healthcare	0	Low	9	Cat_6	C	93
Female	No	39	Yes	Entertainment	7	Low	1	Cat_6	A	94
Female	Yes	47	Yes	Artist	0	Low	3	Cat_6	C	95
Male	Yes	53	No	Executive	0	Average	5	Cat_4	B	98
Female	No	22	No	Healthcare	1	Low	9	Cat_4	D	99
Female	Yes	78	No	Lawyer	1	Low	1	Cat_5	A	100
Female	Yes	47	No	Engineer	8	Average	5	Cat_4	B	104
Female	Yes	35	No	Engineer	0	Average	5	Cat_4	D	106
Male	Yes	50	Yes	Artist	0	Low	2	Cat_6	B	107
Male	Yes	47	No	Executive	null	High	4	Cat_4	B	109
Female	No	38	Yes	Healthcare	0	Low	2	Cat_6	C	110
Male	null	81	No	Executive	null	High	2	Cat_4	A	114
Female	Yes	49	No	Engineer	1	Average	4	Cat_6	A	116
Male	Yes	52	Yes	Engineer	1	Average	3	Cat_4	C	117
Male	No	32	No	Healthcare	8	Low	4	null	C	119
Female	Yes	73	Yes	Artist	null	Average	3	Cat_3	C	122
Female	Yes	63	Yes	Artist	0	Average	3	Cat_6	C	123
Male	No	35	Yes	Doctor	0	Low	1	Cat_6	D	124
Male	Yes	46	Yes	Artist	0	Average	2	Cat_6	B	126
Male	Yes	45	Yes	Entertainment	4	Average	3	Cat_2	B	129
Male	Yes	56	Yes	Executive	0	High	1	Cat_6	B	130
Male	Yes	46	No	Entertainment	1	High	3	Cat_6	B	133
Female	No	28	No	Healthcare	0	Low	4	Cat_6	D	134
Female	No	20	No	Doctor	1	Low	6	Cat_6	D	135
Female	No	32	Yes	Engineer	0	Low	2	Cat_6	D	137
Female	No	51	Yes	Artist	0	Low	1	Cat_3	A	138
Female	No	41	No	Engineer	9	Low	1	Cat_6	A	139
Male	Yes	56	No	Lawyer	0	High	3	Cat_3	A	140
Female	Yes	43	Yes	Artist	0	Average	4	Cat_6	C	141
Male	No	27	Yes	Healthcare	1	Low	3	Cat_6	D	144
Female	No	22	No	Marketing	1	Low	2	Cat_1	D	145
Female	No	25	No	Engineer	1	Low	1	Cat_4	A	151
Male	Yes	86	No	Executive	0	High	2	Cat_6	A	152
Female	No	28	Yes	Healthcare	null	Low	5	Cat_6	B	153
Male	Yes	72	Yes	Executive	0	High	2	Cat_6	C	154
Female	Yes	49	Yes	Artist	0	Average	4	Cat_6	C	156
Male	No	53	No	Engineer	1	Low	3	Cat_4	A	157
Female	No	43	Yes	Homemaker	14	Low	1	Cat_6	D	160
Male	No	19	No	Healthcare	1	Low	4	Cat_6	D	161
Female	Yes	36	No	Artist	1	Average	4	Cat_4	B	164
Female	Yes	38	No	Engineer	1	Low	2	Cat_4	A	165
Male	Yes	35	Yes	Artist	1	Average	2	Cat_6	B	170
Male	Yes	36	No	Doctor	0	Average	2	Cat_7	A	171

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Size of downloaded dataset files:

348 MB

Size of the auto-converted Parquet files:

348 MB

Number of rows:

4,594,428

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