ml-code-generator / datasets /traffic_flow_prediction.csv
Surbhi
Feature extraction and model training
cedd211
hour,road_type,vehicle_count,predicted_traffic
10,98,8096,83
70,92,8041,32
47,30,9556,46
76,9,8276,16
33,10,152,91
85,81,2867,31
11,89,8739,74
56,68,4009,60
41,28,5990,25
14,55,1474,1
49,90,7093,68
70,68,1459,55
89,52,5793,33
13,2,5007,56
11,49,6141,58
39,15,8412,90
82,68,408,66
29,18,4409,47
18,95,8047,56
49,34,3475,91
88,3,2437,40
5,32,550,77
74,20,9491,53
91,57,5825,27
2,2,1166,32
51,60,2189,80
38,55,1805,40
29,41,1273,66
77,40,2853,39
76,30,6629,73
30,23,7933,73
60,42,9705,3
40,43,3303,75
54,7,3790,81
62,15,3608,33
83,85,3653,37
97,32,3653,48
90,39,5925,42
65,73,7474,55
19,94,8245,81
86,97,2983,72
14,92,9678,21
8,15,3342,52
65,17,778,64
22,99,6016,56
62,29,6333,70
98,84,9505,95
80,77,2285,10
36,30,3976,59
67,26,9206,1
23,71,8599,77
73,68,301,87
49,83,1646,58
93,58,9886,90
93,72,2769,54
65,39,5049,44
88,45,8860,17
44,75,9101,42
67,88,8394,76
50,5,3779,17
11,72,1959,90
20,82,4027,39
53,28,6764,60
30,87,1699,4
12,82,7322,37
3,12,6074,30
89,24,7596,82
24,33,968,43
32,29,9256,90
3,16,6929,78
99,41,2975,24
47,22,1187,44
13,78,4522,3
86,33,2686,84
33,61,2346,89
66,41,6627,70
85,61,9907,68
15,90,8237,79
71,18,7380,93
38,43,9697,79
96,33,6127,31
51,61,8693,24
96,9,8035,3
22,26,2880,39
1,36,2187,43
64,97,2084,95
94,4,6106,49
14,9,5775,89
32,99,4837,96
30,77,2561,77
89,83,9732,54
98,27,5377,14
8,80,6647,97
35,60,3717,18
19,95,1963,57
78,50,1372,78
98,93,879,89
87,95,7505,49
75,69,6088,59
71,39,2077,77