Datasets:

codesignal
/

imbalanced-data-practice

Dataset card Data Studio Files Files and versions Community

Split (2)

train · 382k rows

id int64	Gender string	Age int64	Driving_License int64	Region_Code float64	Previously_Insured int64	Vehicle_Age string	Vehicle_Damage string	Annual_Premium float64	Policy_Sales_Channel float64	Vintage int64	Response int64
388,196	Female	30	1	28	0	1-2 Year	Yes	2,630	124	52	1
192,037	Female	79	1	28	0	1-2 Year	Yes	2,630	26	118	0
161,018	Female	48	1	38	1	1-2 Year	No	43,884	26	269	0
271,463	Female	48	1	28	0	1-2 Year	Yes	45,960	26	33	0
183,632	Male	44	1	41	0	> 2 Years	Yes	48,039	124	163	1
322,398	Male	44	1	28	0	1-2 Year	Yes	2,630	26	240	1
143,075	Female	46	1	8	0	1-2 Year	Yes	68,063	26	100	1
4,564	Female	24	1	13	0	< 1 Year	Yes	35,146	152	39	0
35,522	Male	54	1	47	0	1-2 Year	Yes	23,480	124	34	0
31,934	Female	54	1	28	0	1-2 Year	Yes	31,695	26	37	0
197,459	Male	34	1	36	0	1-2 Year	Yes	28,450	26	91	0
170,901	Female	54	1	47	1	1-2 Year	No	26,510	152	255	0
261,224	Female	51	1	28	0	1-2 Year	Yes	37,492	26	85	0
281,706	Male	39	1	8	0	1-2 Year	No	2,630	157	130	0
189,890	Male	54	1	4	1	< 1 Year	No	2,630	1	207	0
215,772	Female	43	1	33	0	1-2 Year	Yes	2,630	160	255	0
323,079	Female	39	1	28	0	1-2 Year	Yes	27,348	35	142	1
388,019	Female	25	1	10	1	< 1 Year	No	29,904	152	203	0
224,741	Male	48	1	28	0	1-2 Year	Yes	29,822	13	38	0
317,888	Female	38	1	28	1	1-2 Year	No	34,314	124	38	0
201,301	Female	48	1	8	1	1-2 Year	No	67,176	26	286	0
303,483	Female	23	1	2	1	< 1 Year	No	29,317	152	71	0
342,472	Female	30	1	8	0	< 1 Year	Yes	44,898	124	138	0
129,508	Female	26	1	37	1	< 1 Year	No	32,409	152	100	0
336,608	Male	22	1	46	1	< 1 Year	No	35,728	152	224	0
105,708	Female	25	1	14	1	< 1 Year	No	25,755	152	78	0
150,005	Male	49	1	28	0	1-2 Year	Yes	2,630	55	298	0
363,356	Female	61	1	28	1	1-2 Year	No	49,322	26	240	0
366,734	Male	46	1	3	0	1-2 Year	Yes	21,504	26	105	0
129,244	Female	22	1	46	1	< 1 Year	No	26,805	152	161	0
454,830	Male	24	1	10	1	< 1 Year	No	28,777	152	253	0
167,280	Female	25	1	15	0	< 1 Year	No	38,587	152	74	0
74,687	Female	25	1	46	0	< 1 Year	Yes	54,224	152	175	0
472,055	Female	53	1	48	1	1-2 Year	No	2,630	26	122	0
78,286	Female	27	1	30	1	< 1 Year	No	27,424	152	206	0
171,614	Male	35	1	28	1	1-2 Year	No	32,544	152	151	0
62,276	Male	23	1	46	1	< 1 Year	No	26,808	152	199	0
332,530	Male	39	1	46	0	1-2 Year	Yes	23,926	124	97	1
376,915	Male	24	1	15	1	< 1 Year	No	34,721	152	22	0
335,755	Male	26	1	43	1	< 1 Year	No	2,630	152	170	0
21,045	Male	22	1	42	1	< 1 Year	No	2,630	160	147	0
44,989	Female	29	1	30	1	< 1 Year	No	24,132	152	159	0
374,528	Female	21	1	8	0	< 1 Year	No	31,210	160	165	0
447,797	Male	54	1	28	0	1-2 Year	Yes	37,138	26	276	1
340,265	Male	24	1	49	0	< 1 Year	Yes	2,630	160	273	0
213,608	Female	25	1	6	1	< 1 Year	No	22,911	152	131	0
157,507	Male	56	1	17	0	1-2 Year	Yes	45,936	124	244	0
498,091	Female	48	1	28	0	1-2 Year	Yes	42,805	26	80	1
89,317	Male	47	1	43	0	1-2 Year	Yes	32,774	124	105	0
164,549	Male	24	1	15	0	< 1 Year	Yes	23,938	152	105	0
247,064	Male	27	1	28	1	< 1 Year	No	336,395	152	144	0
165,293	Male	45	1	28	0	1-2 Year	Yes	40,443	26	187	0
383,241	Female	28	1	28	1	< 1 Year	No	25,380	152	208	0
401,019	Female	29	1	18	1	< 1 Year	No	30,396	152	104	0

Subsets and Splits

No saved queries yet

Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.