Datasets:

maxfactor71
/

Employee_Performance_Salary

Employee_ID stringlengths 5 5	Age int64 22 59	Gender stringclasses 2 values	Department stringclasses 6 values	Experience_Years float64 0 16.8 ⌀	Performance_Score float64 37.6 97.5 ⌀	Salary float64 5k 400k
E0524	49	Female	Operations	5.6	67.8	33,013
E0603	55	Female	Sales	5.5	null	42,640
E0527	29	Female	Sales	10.7	68.8	72,180
E0032	30	Female	Operations	9.8	null	65,750
E0617	58	Male	Sales	11.5	72.8	74,830
E0586	46	Female	Sales	2.3	73.6	25,481
E0445	56	Male	Sales	9.7	85.5	72,051
E0733	24	Female	IT	6.5	80.1	45,430
E0077	22	Female	Sales	3.3	70.7	37,906
E0412	33	Female	Marketing	6	65.3	45,616
E0569	49	Female	Operations	7.5	null	53,311
E0071	36	Female	HR	4.6	64.4	38,576
E0676	27	Male	Sales	10.6	66.7	58,485
E0102	56	Female	Sales	6.3	74.1	45,674
E0895	54	Male	IT	12.6	83.7	83,020
E0926	59	Female	Marketing	10.4	57.8	63,726
E0630	55	Female	Finance	4.5	63.1	32,165
E0300	48	Male	Sales	null	74.9	23,299
E0925	31	Female	HR	7.3	77.4	47,076
E0425	47	Male	Marketing	9	71.3	62,767
E0097	56	Male	Sales	5.2	63.3	32,226
E0901	31	Female	Finance	8.7	79.4	62,705
E0261	29	Male	Operations	8.7	64.2	49,196
E0351	47	Male	HR	7.1	78.5	46,085
E0529	24	Male	Sales	8.9	61	58,172
E0695	40	Male	IT	3.7	72.7	39,050
E0362	26	Male	IT	7.6	74.4	56,560
E0612	26	Male	Sales	null	37.6	49,109
E0885	50	Female	Operations	9.9	66.8	68,305
E0721	36	Male	Finance	3.4	62.5	22,332
E0487	56	Female	Operations	8.2	54.3	43,800
E0694	53	Male	Marketing	4.3	null	39,064
E0679	32	Female	HR	5.2	71.2	45,765
E0810	35	Female	IT	1	65.1	23,265
E0409	54	Female	Operations	9.9	63	54,525
E0741	49	Male	Marketing	7.2	71	59,270
E0214	22	Female	Finance	7.4	59.6	51,174
E0590	26	Male	IT	9.1	90.8	58,842
E0366	33	Male	Marketing	9.9	59.4	57,743
E0200	54	Female	HR	2.4	68.8	29,528
E0637	33	Female	IT	4.8	74.3	48,502
E0629	53	Female	Sales	4.5	73.7	42,929
E0829	30	Female	Finance	8.9	68.9	56,583
E0353	30	Female	IT	7.7	56.9	60,442
E0330	43	Female	Finance	3.5	76.5	37,819
E0982	50	Female	Finance	null	77.5	106,143
E0838	52	Female	HR	8.7	79.2	67,984
E0245	25	Female	IT	9.8	83.8	73,791
E0287	53	Male	Finance	6.5	85.3	42,026
E0290	24	Male	Marketing	8	60.9	51,536
E0599	43	Female	Operations	2.5	85.4	29,223
E0762	47	Female	Sales	5.5	60.6	32,131
E0060	36	Male	Sales	2.5	57.1	27,381
E0623	35	Female	IT	9.7	81.6	60,697
E0319	33	Female	Finance	5.7	71.2	42,358
E0011	45	Male	Sales	6.2	62.4	49,544
E0238	39	Male	Marketing	8.6	null	57,779
E0332	36	Male	HR	8.7	65.2	66,987
E0706	47	Female	HR	7.4	68.3	53,512
E0087	49	Female	IT	10	59.2	59,270
E0804	41	Female	HR	12.6	81.8	84,888
E0175	32	Male	Sales	2.1	74.9	28,543
E0313	57	Male	Finance	3.8	67.5	43,012
E0463	34	Female	HR	7.2	62.9	49,495
E0219	30	Male	Finance	5.5	88.4	57,081
E0031	42	Male	HR	13.5	85.9	87,004
E0548	36	Male	Finance	6.9	66.9	45,879
E0665	26	Female	IT	5	68.4	39,805
E0055	55	Male	Finance	12.5	71.6	76,748
E0024	48	Male	Operations	5.2	53.9	43,702
E0257	43	Male	Sales	8.4	95.3	66,624
E0451	53	Male	Operations	6.5	74.1	48,250
E0004	42	Male	Marketing	3.7	68.7	25,425
E0834	45	Male	Operations	6	69.8	40,376
E0553	44	Female	IT	9.5	71.4	57,664
E0639	26	Female	Sales	1.9	85.3	22,595
E0478	33	Male	Marketing	10.3	64	66,357
E0957	22	Male	Sales	6.7	66.4	50,223
E0663	51	Female	IT	7.3	67.8	56,146
E0519	31	Male	Sales	7.7	79.4	50,560
E0961	30	Male	Marketing	7.9	59.4	49,518
E0954	47	Female	HR	11.2	59.7	53,259
E0980	45	Male	Operations	5	74	38,899
E0576	54	Female	HR	5.4	82.1	48,402
E0540	32	Female	Marketing	null	70.2	56,189
E0216	26	Female	IT	9.3	66.5	56,536
E0738	36	Male	HR	7.9	72.2	66,434
E0999	40	Female	IT	9.1	73.5	60,994
E0714	51	Male	Marketing	6.1	55	39,742
E0668	31	Male	HR	2.1	67.9	25,855
E0040	41	Male	Operations	6.2	72.9	47,896
E0232	41	Male	HR	null	70.4	63,991
E0495	38	Male	HR	6	75.4	45,017
E0705	33	Male	HR	3.5	71.2	36,536
E0968	59	Male	Operations	null	78.4	66,937
E0067	45	Female	Marketing	5.4	63.6	47,090
E0295	45	Female	HR	11.5	63.5	70,729
E0068	47	Female	IT	5.5	74.6	49,078
E0480	42	Female	Marketing	4.5	48.8	33,091
E0329	43	Male	HR	7.9	67.5	60,666

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