Datasets:

salmadrigal
/

insurance-charge-mlops-logs

age int64 18 64	bmi float64 17.5 52.6	children int64 0 4	sex stringclasses 2 values	smoker stringclasses 2 values	region stringclasses 4 values	prediction float64 -1,598.4 41k
40	28.5	2	male	no	southeast	8,074.667616
20	33	1	male	no	southwest	3,592.462304
26	27.06	0	male	yes	southeast	26,855.428913
19	30.4	0	male	no	southwest	1,991.122492
35	35.86	2	female	no	southeast	9,367.053995
43	26.7	2	female	yes	southwest	31,845.810289
40	32.775	1	male	yes	northeast	33,544.749694
52	18.335	0	female	no	northwest	7,307.560929
42	30	0	male	yes	southwest	31,596.154283
40	29.3	4	female	no	southwest	9,076.933072
52	26.4	3	male	no	southeast	10,943.09663
43	35.64	1	female	no	southeast	10,872.823771
32	29.8	2	female	no	southwest	6,226.174752
45	25.7	3	female	no	southwest	8,686.751577
29	22.515	3	male	no	northeast	4,440.715978
50	28.12	3	female	no	northwest	11,485.291704
25	22.515	1	female	no	northwest	2,230.295455
57	23.18	0	female	no	northwest	10,205.303594
63	27.74	0	female	yes	northeast	37,439.625986
21	25.7	4	male	yes	southwest	26,680.530717
58	32.395	1	female	no	northeast	14,360.234848
62	38.095	2	female	no	northeast	17,764.406295
49	28.69	3	male	no	northwest	11,286.458025
31	27.645	2	male	no	northeast	6,182.007513
19	24.51	1	female	no	northwest	1,350.044419
18	21.47	0	male	no	northeast	-175.096814
56	35.8	1	female	no	southwest	13,917.139949
33	28.27	1	female	no	southeast	5,848.901148
21	33.63	2	female	no	northwest	5,377.029569
52	27.36	0	male	yes	northwest	33,998.329916
28	23.845	2	female	no	northwest	3,923.847349
59	27.5	1	male	no	southwest	11,797.561496
25	25.74	0	male	no	southeast	2,338.714141
19	22.515	0	female	no	northwest	207.197612
60	18.335	0	female	no	northeast	9,719.505545
34	34.675	0	male	no	northeast	8,333.10098
30	31.57	3	male	no	southeast	7,002.146567
37	30.8	2	female	no	southeast	8,198.825276
54	32.68	0	female	no	northeast	12,946.495643
48	40.15	0	male	no	southeast	13,048.675469
49	29.83	1	male	no	northeast	11,060.489206
43	26.885	0	female	yes	northwest	31,655.877009
20	33	0	female	no	southeast	3,597.843507
23	32.7	3	male	no	southwest	5,223.294276
42	40.37	2	female	yes	southeast	36,488.395938
34	33.7	1	female	no	southwest	7,558.405024
47	26.125	1	female	yes	northeast	33,265.285418
53	36.86	3	female	yes	northwest	38,984.253007
47	38.94	2	male	yes	southeast	37,168.150444
25	32.23	1	female	no	southeast	5,107.574273
63	31.8	0	female	no	southwest	13,908.50655
29	35.5	2	male	yes	southwest	31,039.283825
20	29.6	0	female	no	southwest	2,113.465041
30	19.95	3	female	no	northwest	3,622.432227
19	25.175	0	male	no	northwest	960.723584
36	26.885	0	female	no	northwest	6,034.426362
42	37.18	2	male	no	southeast	11,475.952514
19	17.48	0	male	no	northwest	-1,598.403584
49	42.68	2	female	no	southeast	15,237.215345
54	47.41	0	female	yes	southeast	40,958.40778
36	29.92	0	female	no	southeast	6,690.136697
30	24.4	3	male	yes	southwest	28,084.412825
43	38.06	2	male	yes	southeast	35,846.336428
19	30.59	0	male	no	northwest	2,761.59085
22	32.11	0	male	no	northwest	4,038.961676
23	27.36	1	male	no	northwest	3,195.911016
22	52.58	1	male	yes	southeast	34,792.835782
61	23.655	0	male	no	northeast	11,614.956144
64	40.48	0	male	no	southeast	17,275.03281
42	34.1	0	male	no	southwest	9,139.257703
46	28.05	1	female	no	southeast	9,120.480692
52	37.525	2	female	no	northwest	14,648.320554
58	27.17	0	female	no	northwest	11,789.546497
39	32.34	2	male	no	southeast	9,094.448849
56	25.65	0	female	no	northwest	10,769.463746
61	21.09	0	female	no	northwest	10,539.384318
50	32.11	2	male	no	northeast	12,555.406575
19	28.9	0	female	no	southwest	1,623.377873
31	36.3	2	male	yes	southwest	31,819.91608
52	24.13	1	female	yes	northwest	33,534.608362
26	33.915	1	male	no	northwest	6,147.772457
53	22.88	1	female	yes	southeast	33,022.543755
25	34.485	0	female	no	northwest	5,731.790544
23	37.1	3	male	no	southwest	6,686.602858
26	35.42	0	male	no	southeast	5,815.281096
41	33.06	2	female	no	northwest	10,333.22609
22	23.18	0	female	no	northeast	1,553.860991
28	33	2	female	no	southeast	6,614.886893
19	27.7	0	male	yes	southwest	24,913.617258
32	17.765	2	female	yes	northwest	26,751.407367
44	39.52	0	male	no	northwest	12,163.644003
31	29.1	0	female	no	southwest	4,777.348798
33	27.455	2	male	no	northwest	6,279.755321
59	27.83	3	female	no	southeast	13,350.799023
49	22.515	0	male	no	northeast	8,148.369295
18	41.14	0	male	no	southeast	5,659.277654
22	39.5	0	male	no	southwest	5,789.37492
64	26.885	0	female	yes	northwest	37,058.926581
27	32.585	3	male	no	northeast	7,275.12106
20	26.84	1	female	yes	southeast	25,849.015008

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YAML Metadata Warning:The task_categories "regression" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Insurance Charge MLOps Logs

Dataset Description

This dataset contains inference-time logs generated by a deployed machine learning model that predicts insurance charges based on customer attributes.

The data is produced by a Gradio application running on Hugging Face Spaces as part of an MLOps learning project.