Validated case study · Data Science

Humach Mobility: Trip Demand

Engineer temporal/weather features and reason about demand patterns without leaking future trip counts.

Download mini_mobility.csvUse Python, SQL, Excel or your preferred workflow unless a task specifies otherwise.

Project dataset

Inspect the CSV before you answer the tasks

Scroll horizontally or vertically to examine the exact miniature data used by the validator.

record_id	hour	temperature_c	rain	weekday	trips
1	0	15	0	1	49
2	1	18	0	2	46
3	2	21	0	3	43
4	3	24	0	4	40
5	4	27	0	5	37
6	5	12	0	6	35
7	6	15	0	0	33
8	7	18	0	1	32
9	8	21	0	2	32
10	9	24	1	3	25
11	10	27	0	4	34
12	11	12	0	5	37
13	12	15	0	6	40
14	13	18	0	0	43
15	14	21	0	1	46
16	15	24	0	2	49
17	16	27	0	3	52
18	17	12	0	4	55
19	18	15	0	5	56
20	19	18	1	6	49
21	20	21	0	0	57
22	21	24	0	1	56
23	22	27	0	2	55
24	23	12	0	3	52
25	0	15	0	4	49
26	1	18	0	5	46
27	2	21	0	6	43
28	3	24	0	0	40
29	4	27	0	1	37
30	5	12	1	2	27
31	6	15	0	3	33
32	7	18	0	4	32
33	8	21	0	5	32
34	9	24	0	6	33
35	10	27	0	0	34
36	11	12	0	1	37
37	12	15	0	2	40
38	13	18	0	3	43
39	14	21	0	4	46
40	15	24	1	5	41
41	16	27	0	6	52
42	17	12	0	0	55
43	18	15	0	1	56
44	19	18	0	2	57
45	20	21	0	3	57
46	21	24	0	4	56
47	22	27	0	5	55
48	23	12	0	6	52

48 rows6 columnsmini_mobility.csv

Project workspace

Analyse → validate → submit.

Numeric and decision tasks are auto-checked. Written reasoning is checked for completeness and key ideas, then included in your local submission report.

0/5 validated

01

How many hourly records are provided?

02

How many records have rain=1?

03

What is mean trip demand?

04

Which feature is legal for forecasting the next hour if known in advance?

Future observed trip count Forecast/known temperature Next hour realised target copied from data

05

Propose one cyclical time feature for hour-of-day.

Submit your project locally

HumachLearn stores no server-side data. Submission creates a portable JSON report containing your answers, validation state and timestamp.

record_id	hour	temperature_c	rain	weekday	trips
1	0	15	0	1	49
2	1	18	0	2	46
3	2	21	0	3	43
4	3	24	0	4	40
5	4	27	0	5	37
6	5	12	0	6	35
7	6	15	0	0	33
8	7	18	0	1	32
9	8	21	0	2	32
10	9	24	1	3	25
11	10	27	0	4	34
12	11	12	0	5	37
13	12	15	0	6	40
14	13	18	0	0	43
15	14	21	0	1	46
16	15	24	0	2	49
17	16	27	0	3	52
18	17	12	0	4	55
19	18	15	0	5	56
20	19	18	1	6	49
21	20	21	0	0	57
22	21	24	0	1	56
23	22	27	0	2	55
24	23	12	0	3	52
25	0	15	0	4	49
26	1	18	0	5	46
27	2	21	0	6	43
28	3	24	0	0	40
29	4	27	0	1	37
30	5	12	1	2	27
31	6	15	0	3	33
32	7	18	0	4	32
33	8	21	0	5	32
34	9	24	0	6	33
35	10	27	0	0	34
36	11	12	0	1	37
37	12	15	0	2	40
38	13	18	0	3	43
39	14	21	0	4	46
40	15	24	1	5	41
41	16	27	0	6	52
42	17	12	0	0	55
43	18	15	0	1	56
44	19	18	0	2	57
45	20	21	0	3	57
46	21	24	0	4	56
47	22	27	0	5	55
48	23	12	0	6	52

record_id	hour	temperature_c	rain	weekday	trips
1	0	15	0	1	49
2	1	18	0	2	46
3	2	21	0	3	43
4	3	24	0	4	40
5	4	27	0	5	37
6	5	12	0	6	35
7	6	15	0	0	33
8	7	18	0	1	32
9	8	21	0	2	32
10	9	24	1	3	25
11	10	27	0	4	34
12	11	12	0	5	37
13	12	15	0	6	40
14	13	18	0	0	43
15	14	21	0	1	46
16	15	24	0	2	49
17	16	27	0	3	52
18	17	12	0	4	55
19	18	15	0	5	56
20	19	18	1	6	49
21	20	21	0	0	57
22	21	24	0	1	56
23	22	27	0	2	55
24	23	12	0	3	52
25	0	15	0	4	49
26	1	18	0	5	46
27	2	21	0	6	43
28	3	24	0	0	40
29	4	27	0	1	37
30	5	12	1	2	27
31	6	15	0	3	33
32	7	18	0	4	32
33	8	21	0	5	32
34	9	24	0	6	33
35	10	27	0	0	34
36	11	12	0	1	37
37	12	15	0	2	40
38	13	18	0	3	43
39	14	21	0	4	46
40	15	24	1	5	41
41	16	27	0	6	52
42	17	12	0	0	55
43	18	15	0	1	56
44	19	18	0	2	57
45	20	21	0	3	57
46	21	24	0	4	56
47	22	27	0	5	55
48	23	12	0	6	52

record_id	hour	temperature_c	rain	weekday	trips
1	0	15	0	1	49
2	1	18	0	2	46
3	2	21	0	3	43
4	3	24	0	4	40
5	4	27	0	5	37
6	5	12	0	6	35
7	6	15	0	0	33
8	7	18	0	1	32
9	8	21	0	2	32
10	9	24	1	3	25
11	10	27	0	4	34
12	11	12	0	5	37
13	12	15	0	6	40
14	13	18	0	0	43
15	14	21	0	1	46
16	15	24	0	2	49
17	16	27	0	3	52
18	17	12	0	4	55
19	18	15	0	5	56
20	19	18	1	6	49
21	20	21	0	0	57
22	21	24	0	1	56
23	22	27	0	2	55
24	23	12	0	3	52
25	0	15	0	4	49
26	1	18	0	5	46
27	2	21	0	6	43
28	3	24	0	0	40
29	4	27	0	1	37
30	5	12	1	2	27
31	6	15	0	3	33
32	7	18	0	4	32
33	8	21	0	5	32
34	9	24	0	6	33
35	10	27	0	0	34
36	11	12	0	1	37
37	12	15	0	2	40
38	13	18	0	3	43
39	14	21	0	4	46
40	15	24	1	5	41
41	16	27	0	6	52
42	17	12	0	0	55
43	18	15	0	1	56
44	19	18	0	2	57
45	20	21	0	3	57
46	21	24	0	4	56
47	22	27	0	5	55
48	23	12	0	6	52