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2022-020
Commit 8028c92f authored by A.P.R.C. Abeyrathna's avatar A.P.R.C. Abeyrathna
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Merge branch 'master' of http://gitlab.sliit.lk/2022-020/2022-020 into IT19179568

parents 97f3ada7 ab4c046b
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Date,Time,Day,Special,Congestion,Drving Speed,Stops,Weather,Distance,Travel Time
1/1/2019,6,Sunday,No,5,40,14,25,14.7,56
1/2/2019,7,Sunday,No,8,40,14,27,14.7,55
1/3/2019,8,Sunday,No,9,40,14,28,14.7,55
1/4/2019,9,Sunday,No,7,40,14,29,14.7,56
1/5/2019,10,Sunday,No,6,40,14,29,14.7,56
1/6/2019,11,Sunday,No,4,40,14,30,14.7,56
1/7/2019,12,Sunday,No,4,40,14,30,14.7,56
1/8/2019,13,Sunday,No,4,40,14,30,14.7,56
1/9/2019,14,Sunday,No,8,40,14,30,14.7,56
1/10/2019,15,Sunday,No,8,40,14,29,14.7,56
1/11/2019,16,Sunday,Yes,6,40,14,29,14.7,56
1/12/2019,17,Sunday,No,7,40,14,28,14.7,56
1/13/2019,18,Sunday,No,8,40,14,27,14.7,56
1/14/2019,19,Sunday,No,9,40,14,27,14.7,71
1/15/2019,20,Sunday,No,6,40,14,26,14.7,73
1/16/2019,21,Sunday,No,6,40,14,26,14.7,72
1/17/2019,22,Sunday,No,5,40,14,25,14.7,75
1/18/2019,6,Monday,No,5,40,14,25,14.7,56
1/19/2019,7,Monday,No,8,40,14,27,14.7,55
1/20/2019,8,Monday,No,9,40,14,28,14.7,55
1/21/2019,9,Monday,No,7,40,14,29,14.7,56
1/22/2019,10,Monday,No,6,40,14,29,14.7,56
1/23/2019,11,Monday,No,4,40,14,30,14.7,56
1/24/2019,12,Monday,No,4,40,14,30,14.7,56
1/25/2019,13,Monday,No,4,40,14,30,14.7,56
1/26/2019,14,Monday,No,8,40,14,30,14.7,56
1/27/2019,15,Monday,No,8,40,14,29,14.7,56
1/28/2019,16,Monday,Yes,6,40,14,29,14.7,56
1/29/2019,17,Monday,No,7,40,14,28,14.7,56
1/30/2019,18,Monday,No,8,40,14,27,14.7,56
1/31/2019,19,Monday,No,9,40,14,27,14.7,71
2/1/2019,20,Monday,No,6,40,14,26,14.7,73
2/2/2019,21,Monday,No,6,40,14,26,14.7,72
2/3/2019,22,Monday,No,5,40,14,25,14.7,75
2/4/2019,6,Tuesday,No,5,40,14,25,14.7,56
2/5/2019,7,Tuesday,No,8,40,14,27,14.7,55
2/6/2019,8,Tuesday,No,9,40,14,28,14.7,55
2/7/2019,9,Tuesday,No,7,40,14,29,14.7,56
2/8/2019,10,Tuesday,No,6,40,14,29,14.7,56
2/9/2019,11,Tuesday,No,4,40,14,30,14.7,56
2/10/2019,12,Tuesday,No,4,40,14,30,14.7,56
2/11/2019,13,Tuesday,No,4,40,14,30,14.7,56
2/12/2019,14,Tuesday,No,8,40,14,30,14.7,56
2/13/2019,15,Tuesday,No,8,40,14,29,14.7,56
2/14/2019,16,Tuesday,Yes,6,40,14,29,14.7,56
2/15/2019,17,Tuesday,No,7,40,14,28,14.7,56
2/16/2019,18,Tuesday,No,8,40,14,27,14.7,56
2/17/2019,19,Tuesday,No,9,40,14,27,14.7,71
2/18/2019,20,Tuesday,No,6,40,14,26,14.7,73
2/19/2019,21,Tuesday,No,6,40,14,26,14.7,72
2/20/2019,22,Tuesday,No,5,40,14,25,14.7,75
2/21/2019,6,Sunday,No,7,40,0,25,1.2,9
2/22/2019,6,Sunday,No,9,40,0,25,1.2,11
2/23/2019,6,Sunday,No,3,40,1,25,1.2,8
2/24/2019,6,Sunday,No,2,40,0,25,1.2,3
2/25/2019,6,Sunday,No,2,40,1,25,3,3
2/26/2019,17,Sunday,No,9,40,1,25,3,12
2/27/2019,17,Sunday,No,9,40,4,25,4.7,19
2/28/2019,17,Sunday,No,3,40,4,25,4.7,12
3/1/2019,17,Sunday,No,6,40,4,25,4.7,15
3/2/2019,6,Sunday,No,7,40,9,25,7.5,34
3/3/2019,6,Sunday,No,9,40,9,25,7.5,39
3/4/2019,6,Sunday,No,5,40,9,25,7.5,30
3/5/2019,6,Monday,No,5,40,12,25,10.1,36
3/6/2019,6,Monday,No,9,40,12,25,10.1,43
3/7/2019,6,Monday,No,3,40,12,25,10.1,33
3/8/2019,13,Monday,No,5,40,12,25,10.1,36
3/9/2019,13,Monday,No,9,40,12,25,10.1,43
3/10/2019,13,Monday,No,3,40,12,25,10.1,33
3/11/2019,6,Monday,No,5,40,12,25,12.1,43
3/12/2019,6,Monday,No,9,40,12,25,12.1,49
3/13/2019,6,Monday,No,3,40,12,25,12.1,40
3/14/2019,12,Monday,No,5,40,12,25,12.1,43
3/15/2019,12,Monday,No,9,40,12,25,12.1,49
3/16/2019,12,Monday,No,3,40,12,25,12.1,40
3/17/2019,6,Sunday,No,5,40,14,25,14.7,56
3/18/2019,7,Sunday,No,8,40,14,27,14.7,55
3/19/2019,8,Sunday,No,9,40,14,28,14.7,55
3/20/2019,9,Sunday,No,7,40,14,29,14.7,56
3/21/2019,10,Sunday,No,6,40,14,29,14.7,56
3/22/2019,11,Sunday,No,4,40,14,30,14.7,56
3/23/2019,12,Sunday,No,4,40,14,30,14.7,56
3/24/2019,13,Sunday,No,4,40,14,30,14.7,56
3/25/2019,14,Sunday,No,8,40,14,30,14.7,56
3/26/2019,15,Sunday,No,8,40,14,29,14.7,56
3/27/2019,16,Sunday,Yes,6,40,14,29,14.7,56
3/28/2019,17,Sunday,No,7,40,14,28,14.7,56
3/29/2019,18,Sunday,No,8,40,14,27,14.7,56
3/30/2019,19,Sunday,No,9,40,14,27,14.7,71
3/31/2019,20,Sunday,No,6,40,14,26,14.7,73
4/1/2019,21,Sunday,No,6,40,14,26,14.7,72
4/2/2019,22,Sunday,No,5,40,14,25,14.7,75
4/3/2019,6,Monday,No,5,40,14,25,14.7,56
4/4/2019,7,Monday,No,8,40,14,27,14.7,55
4/5/2019,8,Monday,No,9,40,14,28,14.7,55
4/6/2019,9,Monday,No,7,40,14,29,14.7,56
4/7/2019,10,Monday,No,6,40,14,29,14.7,56
4/8/2019,11,Monday,No,4,40,14,30,14.7,56
4/9/2019,12,Monday,No,4,40,14,30,14.7,56
4/10/2019,13,Monday,No,4,40,14,30,14.7,56
4/11/2019,14,Monday,No,8,40,14,30,14.7,56
4/12/2019,15,Monday,No,8,40,14,29,14.7,56
4/13/2019,16,Monday,Yes,6,40,14,29,14.7,56
4/14/2019,17,Monday,No,7,40,14,28,14.7,56
4/15/2019,18,Monday,No,8,40,14,27,14.7,56
4/16/2019,19,Monday,No,9,40,14,27,14.7,71
4/17/2019,20,Monday,No,6,40,14,26,14.7,73
4/18/2019,21,Monday,No,6,40,14,26,14.7,72
4/19/2019,22,Monday,No,5,40,14,25,14.7,75
4/20/2019,6,Tuesday,No,5,40,14,25,14.7,56
4/21/2019,7,Tuesday,No,8,40,14,27,14.7,55
4/22/2019,8,Tuesday,No,9,40,14,28,14.7,55
4/23/2019,9,Tuesday,No,7,40,14,29,14.7,56
4/24/2019,10,Tuesday,No,6,40,14,29,14.7,56
4/25/2019,11,Tuesday,No,4,40,14,30,14.7,56
4/26/2019,12,Tuesday,No,4,40,14,30,14.7,56
4/27/2019,13,Tuesday,No,4,40,14,30,14.7,56
4/28/2019,14,Tuesday,No,8,40,14,30,14.7,56
4/29/2019,15,Tuesday,No,8,40,14,29,14.7,56
4/30/2019,16,Tuesday,Yes,6,40,14,29,14.7,56
5/1/2019,17,Tuesday,No,7,40,14,28,14.7,56
5/2/2019,18,Tuesday,No,8,40,14,27,14.7,56
5/3/2019,19,Tuesday,No,9,40,14,27,14.7,71
5/4/2019,20,Tuesday,No,6,40,14,26,14.7,73
5/5/2019,21,Tuesday,No,6,40,14,26,14.7,72
5/6/2019,22,Tuesday,No,5,40,14,25,14.7,75
5/7/2019,6,Sunday,No,7,40,0,25,1.2,9
5/8/2019,6,Sunday,No,9,40,0,25,1.2,11
5/9/2019,6,Sunday,No,3,40,1,25,1.2,8
5/10/2019,6,Sunday,No,2,40,0,25,1.2,3
5/11/2019,6,Sunday,No,2,40,1,25,3,3
5/12/2019,17,Sunday,No,9,40,1,25,3,12
5/13/2019,17,Sunday,No,9,40,4,25,4.7,19
5/14/2019,17,Sunday,No,3,40,4,25,4.7,12
5/15/2019,17,Sunday,No,6,40,4,25,4.7,15
5/16/2019,6,Sunday,No,7,40,9,25,7.5,34
5/17/2019,6,Sunday,No,9,40,9,25,7.5,39
5/18/2019,6,Sunday,No,5,40,9,25,7.5,30
5/19/2019,6,Monday,No,5,40,12,25,10.1,36
5/20/2019,6,Monday,No,9,40,12,25,10.1,43
5/21/2019,6,Monday,No,3,40,12,25,10.1,33
5/22/2019,13,Monday,No,5,40,12,25,10.1,36
5/23/2019,13,Monday,No,9,40,12,25,10.1,43
5/24/2019,13,Monday,No,3,40,12,25,10.1,33
5/25/2019,6,Monday,No,5,40,12,25,12.1,43
5/26/2019,6,Monday,No,9,40,12,25,12.1,49
5/27/2019,6,Monday,No,3,40,12,25,12.1,40
5/28/2019,12,Monday,No,5,40,12,25,12.1,43
5/29/2019,12,Monday,No,9,40,12,25,12.1,49
5/30/2019,12,Monday,No,3,40,12,25,12.1,40
5/31/2019,6,Sunday,No,5,40,14,25,14.7,56
6/1/2019,7,Sunday,No,8,40,14,27,14.7,55
6/2/2019,8,Sunday,No,9,40,14,28,14.7,55
6/3/2019,9,Sunday,No,7,40,14,29,14.7,56
6/4/2019,10,Sunday,No,6,40,14,29,14.7,56
6/5/2019,11,Sunday,No,4,40,14,30,14.7,56
6/6/2019,12,Sunday,No,4,40,14,30,14.7,56
6/7/2019,13,Sunday,No,4,40,14,30,14.7,56
6/8/2019,14,Sunday,No,8,40,14,30,14.7,56
6/9/2019,15,Sunday,No,8,40,14,29,14.7,56
6/10/2019,16,Sunday,Yes,6,40,14,29,14.7,56
6/11/2019,17,Sunday,No,7,40,14,28,14.7,56
6/12/2019,18,Sunday,No,8,40,14,27,14.7,56
6/13/2019,19,Sunday,No,9,40,14,27,14.7,71
6/14/2019,20,Sunday,No,6,40,14,26,14.7,73
6/15/2019,21,Sunday,No,6,40,14,26,14.7,72
6/16/2019,22,Sunday,No,5,40,14,25,14.7,75
6/17/2019,6,Monday,No,5,40,14,25,14.7,56
6/18/2019,7,Monday,No,8,40,14,27,14.7,55
6/19/2019,8,Monday,No,9,40,14,28,14.7,55
6/20/2019,9,Monday,No,7,40,14,29,14.7,56
6/21/2019,10,Monday,No,6,40,14,29,14.7,56
6/22/2019,11,Monday,No,4,40,14,30,14.7,56
6/23/2019,12,Monday,No,4,40,14,30,14.7,56
6/24/2019,13,Monday,No,4,40,14,30,14.7,56
6/25/2019,14,Monday,No,8,40,14,30,14.7,56
6/26/2019,15,Monday,No,8,40,14,29,14.7,56
6/27/2019,16,Monday,Yes,6,40,14,29,14.7,56
6/28/2019,17,Monday,No,7,40,14,28,14.7,56
6/29/2019,18,Monday,No,8,40,14,27,14.7,56
6/30/2019,19,Monday,No,9,40,14,27,14.7,71
7/1/2019,20,Monday,No,6,40,14,26,14.7,73
7/2/2019,21,Monday,No,6,40,14,26,14.7,72
7/3/2019,22,Monday,No,5,40,14,25,14.7,75
7/4/2019,6,Tuesday,No,5,40,14,25,14.7,56
7/5/2019,7,Tuesday,No,8,40,14,27,14.7,55
7/6/2019,8,Tuesday,No,9,40,14,28,14.7,55
7/7/2019,9,Tuesday,No,7,40,14,29,14.7,56
7/8/2019,10,Tuesday,No,6,40,14,29,14.7,56
7/9/2019,11,Tuesday,No,4,40,14,30,14.7,56
7/10/2019,12,Tuesday,No,4,40,14,30,14.7,56
7/11/2019,13,Tuesday,No,4,40,14,30,14.7,56
7/12/2019,14,Tuesday,No,8,40,14,30,14.7,56
7/13/2019,15,Tuesday,No,8,40,14,29,14.7,56
7/14/2019,16,Tuesday,Yes,6,40,14,29,14.7,56
7/15/2019,17,Tuesday,No,7,40,14,28,14.7,56
7/16/2019,18,Tuesday,No,8,40,14,27,14.7,56
7/17/2019,19,Tuesday,No,9,40,14,27,14.7,71
7/18/2019,20,Tuesday,No,6,40,14,26,14.7,73
7/19/2019,21,Tuesday,No,6,40,14,26,14.7,72
7/20/2019,22,Tuesday,No,5,40,14,25,14.7,75
7/21/2019,6,Sunday,No,7,40,0,25,1.2,9
7/22/2019,6,Sunday,No,9,40,0,25,1.2,11
7/23/2019,6,Sunday,No,3,40,1,25,1.2,8
7/24/2019,6,Sunday,No,2,40,0,25,1.2,3
7/25/2019,6,Sunday,No,2,40,1,25,3,3
7/26/2019,17,Sunday,No,9,40,1,25,3,12
7/27/2019,17,Sunday,No,9,40,4,25,4.7,19
7/28/2019,17,Sunday,No,3,40,4,25,4.7,12
7/29/2019,17,Sunday,No,6,40,4,25,4.7,15
7/30/2019,6,Sunday,No,7,40,9,25,7.5,34
7/31/2019,6,Sunday,No,9,40,9,25,7.5,39
8/1/2019,6,Sunday,No,5,40,9,25,7.5,30
8/2/2019,6,Monday,No,5,40,12,25,10.1,36
8/3/2019,6,Monday,No,9,40,12,25,10.1,43
8/4/2019,6,Monday,No,3,40,12,25,10.1,33
8/5/2019,13,Monday,No,5,40,12,25,10.1,36
8/6/2019,13,Monday,No,9,40,12,25,10.1,43
8/7/2019,13,Monday,No,3,40,12,25,10.1,33
8/8/2019,6,Monday,No,5,40,12,25,12.1,43
8/9/2019,6,Monday,No,9,40,12,25,12.1,49
8/10/2019,6,Monday,No,3,40,12,25,12.1,40
8/11/2019,12,Monday,No,5,40,12,25,12.1,43
8/12/2019,12,Monday,No,9,40,12,25,12.1,49
8/13/2019,12,Monday,No,3,40,12,25,12.1,40
8/14/2019,6,Sunday,No,5,40,14,25,14.7,56
8/15/2019,7,Sunday,No,8,40,14,27,14.7,55
8/16/2019,8,Sunday,No,9,40,14,28,14.7,55
8/17/2019,9,Sunday,No,7,40,14,29,14.7,56
8/18/2019,10,Sunday,No,6,40,14,29,14.7,56
8/19/2019,11,Sunday,No,4,40,14,30,14.7,56
8/20/2019,12,Sunday,No,4,40,14,30,14.7,56
8/21/2019,13,Sunday,No,4,40,14,30,14.7,56
8/22/2019,14,Sunday,No,8,40,14,30,14.7,56
8/23/2019,15,Sunday,No,8,40,14,29,14.7,56
8/24/2019,16,Sunday,Yes,6,40,14,29,14.7,56
8/25/2019,17,Sunday,No,7,40,14,28,14.7,56
8/26/2019,18,Sunday,No,8,40,14,27,14.7,56
8/27/2019,19,Sunday,No,9,40,14,27,14.7,71
8/28/2019,20,Sunday,No,6,40,14,26,14.7,73
8/29/2019,21,Sunday,No,6,40,14,26,14.7,72
8/30/2019,22,Sunday,No,5,40,14,25,14.7,75
8/31/2019,6,Monday,No,5,40,14,25,14.7,56
9/1/2019,7,Monday,No,8,40,14,27,14.7,55
9/2/2019,8,Monday,No,9,40,14,28,14.7,55
9/3/2019,9,Monday,No,7,40,14,29,14.7,56
9/4/2019,10,Monday,No,6,40,14,29,14.7,56
9/5/2019,11,Monday,No,4,40,14,30,14.7,56
9/6/2019,12,Monday,No,4,40,14,30,14.7,56
9/7/2019,13,Monday,No,4,40,14,30,14.7,56
9/8/2019,14,Monday,No,8,40,14,30,14.7,56
9/9/2019,15,Monday,No,8,40,14,29,14.7,56
9/10/2019,16,Monday,Yes,6,40,14,29,14.7,56
9/11/2019,17,Monday,No,7,40,14,28,14.7,56
9/12/2019,18,Monday,No,8,40,14,27,14.7,56
9/13/2019,19,Monday,No,9,40,14,27,14.7,71
9/14/2019,20,Monday,No,6,40,14,26,14.7,73
9/15/2019,21,Monday,No,6,40,14,26,14.7,72
9/16/2019,22,Monday,No,5,40,14,25,14.7,75
9/17/2019,6,Tuesday,No,5,40,14,25,14.7,56
9/18/2019,7,Tuesday,No,8,40,14,27,14.7,55
9/19/2019,8,Tuesday,No,9,40,14,28,14.7,55
9/20/2019,9,Tuesday,No,7,40,14,29,14.7,56
9/21/2019,10,Tuesday,No,6,40,14,29,14.7,56
9/22/2019,11,Tuesday,No,4,40,14,30,14.7,56
9/23/2019,12,Tuesday,No,4,40,14,30,14.7,56
9/24/2019,13,Tuesday,No,4,40,14,30,14.7,56
9/25/2019,14,Tuesday,No,8,40,14,30,14.7,56
9/26/2019,15,Tuesday,No,8,40,14,29,14.7,56
9/27/2019,16,Tuesday,Yes,6,40,14,29,14.7,56
9/28/2019,17,Tuesday,No,7,40,14,28,14.7,56
9/29/2019,18,Tuesday,No,8,40,14,27,14.7,56
9/30/2019,19,Tuesday,No,9,40,14,27,14.7,71
10/1/2019,20,Tuesday,No,6,40,14,26,14.7,73
10/2/2019,21,Tuesday,No,6,40,14,26,14.7,72
10/3/2019,22,Tuesday,No,5,40,14,25,14.7,75
10/4/2019,6,Sunday,No,7,40,0,25,1.2,9
10/5/2019,6,Sunday,No,9,40,0,25,1.2,11
10/6/2019,6,Sunday,No,3,40,1,25,1.2,8
10/7/2019,6,Sunday,No,2,40,0,25,1.2,3
10/8/2019,6,Sunday,No,2,40,1,25,3,3
10/9/2019,17,Sunday,No,9,40,1,25,3,12
10/10/2019,17,Sunday,No,9,40,4,25,4.7,19
10/11/2019,17,Sunday,No,3,40,4,25,4.7,12
10/12/2019,17,Sunday,No,6,40,4,25,4.7,15
10/13/2019,6,Sunday,No,7,40,9,25,7.5,34
10/14/2019,6,Sunday,No,9,40,9,25,7.5,39
10/15/2019,6,Sunday,No,5,40,9,25,7.5,30
10/16/2019,6,Monday,No,5,40,12,25,10.1,36
10/17/2019,6,Monday,No,9,40,12,25,10.1,43
10/18/2019,6,Monday,No,3,40,12,25,10.1,33
10/19/2019,13,Monday,No,5,40,12,25,10.1,36
10/20/2019,13,Monday,No,9,40,12,25,10.1,43
10/21/2019,13,Monday,No,3,40,12,25,10.1,33
10/22/2019,6,Monday,No,5,40,12,25,12.1,43
10/23/2019,6,Monday,No,9,40,12,25,12.1,49
10/24/2019,6,Monday,No,3,40,12,25,12.1,40
10/25/2019,12,Monday,No,5,40,12,25,12.1,43
10/26/2019,12,Monday,No,9,40,12,25,12.1,49
10/27/2019,12,Monday,No,3,40,12,25,12.1,40
10/28/2019,6,Sunday,No,5,40,14,25,14.7,56
10/29/2019,7,Sunday,No,8,40,14,27,14.7,55
10/30/2019,8,Sunday,No,9,40,14,28,14.7,55
10/31/2019,9,Sunday,No,7,40,14,29,14.7,56
11/1/2019,10,Sunday,No,6,40,14,29,14.7,56
11/2/2019,11,Sunday,No,4,40,14,30,14.7,56
11/3/2019,12,Sunday,No,4,40,14,30,14.7,56
11/4/2019,13,Sunday,No,4,40,14,30,14.7,56
11/5/2019,14,Sunday,No,8,40,14,30,14.7,56
11/6/2019,15,Sunday,No,8,40,14,29,14.7,56
11/7/2019,16,Sunday,Yes,6,40,14,29,14.7,56
11/8/2019,17,Sunday,No,7,40,14,28,14.7,56
11/9/2019,18,Sunday,No,8,40,14,27,14.7,56
11/10/2019,19,Sunday,No,9,40,14,27,14.7,71
11/11/2019,20,Sunday,No,6,40,14,26,14.7,73
11/12/2019,21,Sunday,No,6,40,14,26,14.7,72
11/13/2019,22,Sunday,No,5,40,14,25,14.7,75
11/14/2019,6,Monday,No,5,40,14,25,14.7,56
11/15/2019,7,Monday,No,8,40,14,27,14.7,55
11/16/2019,8,Monday,No,9,40,14,28,14.7,55
11/17/2019,9,Monday,No,7,40,14,29,14.7,56
11/18/2019,10,Monday,No,6,40,14,29,14.7,56
11/19/2019,11,Monday,No,4,40,14,30,14.7,56
11/20/2019,12,Monday,No,4,40,14,30,14.7,56
11/21/2019,13,Monday,No,4,40,14,30,14.7,56
11/22/2019,14,Monday,No,8,40,14,30,14.7,56
11/23/2019,15,Monday,No,8,40,14,29,14.7,56
11/24/2019,16,Monday,Yes,6,40,14,29,14.7,56
11/25/2019,17,Monday,No,7,40,14,28,14.7,56
11/26/2019,18,Monday,No,8,40,14,27,14.7,56
11/27/2019,19,Monday,No,9,40,14,27,14.7,71
11/28/2019,20,Monday,No,6,40,14,26,14.7,73
11/29/2019,21,Monday,No,6,40,14,26,14.7,72
11/30/2019,22,Monday,No,5,40,14,25,14.7,75
12/1/2019,6,Tuesday,No,5,40,14,25,14.7,56
12/2/2019,7,Tuesday,No,8,40,14,27,14.7,55
12/3/2019,8,Tuesday,No,9,40,14,28,14.7,55
12/4/2019,9,Tuesday,No,7,40,14,29,14.7,56
12/5/2019,10,Tuesday,No,6,40,14,29,14.7,56
12/6/2019,11,Tuesday,No,4,40,14,30,14.7,56
12/7/2019,12,Tuesday,No,4,40,14,30,14.7,56
12/8/2019,13,Tuesday,No,4,40,14,30,14.7,56
12/9/2019,14,Tuesday,No,8,40,14,30,14.7,56
12/10/2019,15,Tuesday,No,8,40,14,29,14.7,56
12/11/2019,16,Tuesday,Yes,6,40,14,29,14.7,56
12/12/2019,17,Tuesday,No,7,40,14,28,14.7,56
12/13/2019,18,Tuesday,No,8,40,14,27,14.7,56
12/14/2019,19,Tuesday,No,9,40,14,27,14.7,71
12/15/2019,20,Tuesday,No,6,40,14,26,14.7,73
12/16/2019,21,Tuesday,No,6,40,14,26,14.7,72
12/17/2019,22,Tuesday,No,5,40,14,25,14.7,75
12/18/2019,6,Sunday,No,7,40,0,25,1.2,9
12/19/2019,6,Sunday,No,9,40,0,25,1.2,11
12/20/2019,6,Sunday,No,3,40,1,25,1.2,8
12/21/2019,6,Sunday,No,2,40,0,25,1.2,3
12/22/2019,6,Sunday,No,2,40,1,25,3,3
12/23/2019,17,Sunday,No,9,40,1,25,3,12
12/24/2019,17,Sunday,No,9,40,4,25,4.7,19
12/25/2019,17,Sunday,No,3,40,4,25,4.7,12
12/26/2019,17,Sunday,No,6,40,4,25,4.7,15
12/27/2019,6,Sunday,No,7,40,9,25,7.5,34
12/28/2019,6,Sunday,No,9,40,9,25,7.5,39
12/29/2019,6,Sunday,No,5,40,9,25,7.5,30
12/30/2019,6,Monday,No,5,40,12,25,10.1,36
12/31/2019,6,Monday,No,9,40,12,25,10.1,43
1/1/2020,6,Monday,No,3,40,12,25,10.1,33
1/2/2020,13,Monday,No,5,40,12,25,10.1,36
1/3/2020,13,Monday,No,9,40,12,25,10.1,43
1/4/2020,13,Monday,No,3,40,12,25,10.1,33
1/5/2020,6,Monday,No,5,40,12,25,12.1,43
1/6/2020,6,Monday,No,9,40,12,25,12.1,49
1/7/2020,6,Monday,No,3,40,12,25,12.1,40
1/8/2020,12,Monday,No,5,40,12,25,12.1,43
1/9/2020,12,Monday,No,9,40,12,25,12.1,49
1/10/2020,12,Monday,No,3,40,12,25,12.1,40
1/11/2020,6,Sunday,No,5,40,14,25,14.7,56
1/12/2020,7,Sunday,No,8,40,14,27,14.7,55
1/13/2020,8,Sunday,No,9,40,14,28,14.7,55
1/14/2020,9,Sunday,No,7,40,14,29,14.7,56
1/15/2020,10,Sunday,No,6,40,14,29,14.7,56
1/16/2020,11,Sunday,No,4,40,14,30,14.7,56
1/17/2020,12,Sunday,No,4,40,14,30,14.7,56
1/18/2020,13,Sunday,No,4,40,14,30,14.7,56
1/19/2020,14,Sunday,No,8,40,14,30,14.7,56
1/20/2020,15,Sunday,No,8,40,14,29,14.7,56
1/21/2020,16,Sunday,Yes,6,40,14,29,14.7,56
1/22/2020,17,Sunday,No,7,40,14,28,14.7,56
1/23/2020,18,Sunday,No,8,40,14,27,14.7,56
1/24/2020,19,Sunday,No,9,40,14,27,14.7,71
1/25/2020,20,Sunday,No,6,40,14,26,14.7,73
1/26/2020,21,Sunday,No,6,40,14,26,14.7,72
1/27/2020,22,Sunday,No,5,40,14,25,14.7,75
1/28/2020,6,Monday,No,5,40,14,25,14.7,56
1/29/2020,7,Monday,No,8,40,14,27,14.7,55
1/30/2020,8,Monday,No,9,40,14,28,14.7,55
1/31/2020,9,Monday,No,7,40,14,29,14.7,56
2/1/2020,10,Monday,No,6,40,14,29,14.7,56
2/2/2020,11,Monday,No,4,40,14,30,14.7,56
2/3/2020,12,Monday,No,4,40,14,30,14.7,56
2/4/2020,13,Monday,No,4,40,14,30,14.7,56
2/5/2020,14,Monday,No,8,40,14,30,14.7,56
2/6/2020,15,Monday,No,8,40,14,29,14.7,56
2/7/2020,16,Monday,Yes,6,40,14,29,14.7,56
2/8/2020,17,Monday,No,7,40,14,28,14.7,56
2/9/2020,18,Monday,No,8,40,14,27,14.7,56
2/10/2020,19,Monday,No,9,40,14,27,14.7,71
2/11/2020,20,Monday,No,6,40,14,26,14.7,73
2/12/2020,21,Monday,No,6,40,14,26,14.7,72
2/13/2020,22,Monday,No,5,40,14,25,14.7,75
2/14/2020,6,Tuesday,No,5,40,14,25,14.7,56
2/15/2020,7,Tuesday,No,8,40,14,27,14.7,55
2/16/2020,8,Tuesday,No,9,40,14,28,14.7,55
2/17/2020,9,Tuesday,No,7,40,14,29,14.7,56
2/18/2020,10,Tuesday,No,6,40,14,29,14.7,56
2/19/2020,11,Tuesday,No,4,40,14,30,14.7,56
2/20/2020,12,Tuesday,No,4,40,14,30,14.7,56
2/21/2020,13,Tuesday,No,4,40,14,30,14.7,56
2/22/2020,14,Tuesday,No,8,40,14,30,14.7,56
2/23/2020,15,Tuesday,No,8,40,14,29,14.7,56
2/24/2020,16,Tuesday,Yes,6,40,14,29,14.7,56
2/25/2020,17,Tuesday,No,7,40,14,28,14.7,56
2/26/2020,18,Tuesday,No,8,40,14,27,14.7,56
2/27/2020,19,Tuesday,No,9,40,14,27,14.7,71
2/28/2020,20,Tuesday,No,6,40,14,26,14.7,73
2/29/2020,21,Tuesday,No,6,40,14,26,14.7,72
3/1/2020,22,Tuesday,No,5,40,14,25,14.7,75
3/2/2020,6,Sunday,No,7,40,0,25,1.2,9
3/3/2020,6,Sunday,No,9,40,0,25,1.2,11
3/4/2020,6,Sunday,No,3,40,1,25,1.2,8
3/5/2020,6,Sunday,No,2,40,0,25,1.2,3
3/6/2020,6,Sunday,No,2,40,1,25,3,3
3/7/2020,17,Sunday,No,9,40,1,25,3,12
3/8/2020,17,Sunday,No,9,40,4,25,4.7,19
3/9/2020,17,Sunday,No,3,40,4,25,4.7,12
3/10/2020,17,Sunday,No,6,40,4,25,4.7,15
3/11/2020,6,Sunday,No,7,40,9,25,7.5,34
3/12/2020,6,Sunday,No,9,40,9,25,7.5,39
3/13/2020,6,Sunday,No,5,40,9,25,7.5,30
3/14/2020,6,Monday,No,5,40,12,25,10.1,36
3/15/2020,6,Monday,No,9,40,12,25,10.1,43
3/16/2020,6,Monday,No,3,40,12,25,10.1,33
3/17/2020,13,Monday,No,5,40,12,25,10.1,36
3/18/2020,13,Monday,No,9,40,12,25,10.1,43
3/19/2020,13,Monday,No,3,40,12,25,10.1,33
3/20/2020,6,Monday,No,5,40,12,25,12.1,43
3/21/2020,6,Monday,No,9,40,12,25,12.1,49
3/22/2020,6,Monday,No,3,40,12,25,12.1,40
3/23/2020,12,Monday,No,5,40,12,25,12.1,43
3/24/2020,12,Monday,No,9,40,12,25,12.1,49
3/25/2020,12,Monday,No,3,40,12,25,12.1,40
3/26/2020,6,Sunday,No,5,40,14,25,14.7,56
3/27/2020,7,Sunday,No,8,40,14,27,14.7,55
3/28/2020,8,Sunday,No,9,40,14,28,14.7,55
3/29/2020,9,Sunday,No,7,40,14,29,14.7,56
3/30/2020,10,Sunday,No,6,40,14,29,14.7,56
3/31/2020,11,Sunday,No,4,40,14,30,14.7,56
4/1/2020,12,Sunday,No,4,40,14,30,14.7,56
4/2/2020,13,Sunday,No,4,40,14,30,14.7,56
4/3/2020,14,Sunday,No,8,40,14,30,14.7,56
4/4/2020,15,Sunday,No,8,40,14,29,14.7,56
4/5/2020,16,Sunday,Yes,6,40,14,29,14.7,56
4/6/2020,17,Sunday,No,7,40,14,28,14.7,56
4/7/2020,18,Sunday,No,8,40,14,27,14.7,56
4/8/2020,19,Sunday,No,9,40,14,27,14.7,71
4/9/2020,20,Sunday,No,6,40,14,26,14.7,73
4/10/2020,21,Sunday,No,6,40,14,26,14.7,72
4/11/2020,22,Sunday,No,5,40,14,25,14.7,75
4/12/2020,6,Monday,No,5,40,14,25,14.7,56
4/13/2020,7,Monday,No,8,40,14,27,14.7,55
4/14/2020,8,Monday,No,9,40,14,28,14.7,55
4/15/2020,9,Monday,No,7,40,14,29,14.7,56
4/16/2020,10,Monday,No,6,40,14,29,14.7,56
4/17/2020,11,Monday,No,4,40,14,30,14.7,56
4/18/2020,12,Monday,No,4,40,14,30,14.7,56
4/19/2020,13,Monday,No,4,40,14,30,14.7,56
4/20/2020,14,Monday,No,8,40,14,30,14.7,56
4/21/2020,15,Monday,No,8,40,14,29,14.7,56
4/22/2020,16,Monday,Yes,6,40,14,29,14.7,56
4/23/2020,17,Monday,No,7,40,14,28,14.7,56
4/24/2020,18,Monday,No,8,40,14,27,14.7,56
4/25/2020,19,Monday,No,9,40,14,27,14.7,71
4/26/2020,20,Monday,No,6,40,14,26,14.7,73
4/27/2020,21,Monday,No,6,40,14,26,14.7,72
4/28/2020,22,Monday,No,5,40,14,25,14.7,75
4/29/2020,6,Tuesday,No,5,40,14,25,14.7,56
4/30/2020,7,Tuesday,No,8,40,14,27,14.7,55
5/1/2020,8,Tuesday,No,9,40,14,28,14.7,55
5/2/2020,9,Tuesday,No,7,40,14,29,14.7,56
5/3/2020,10,Tuesday,No,6,40,14,29,14.7,56
5/4/2020,11,Tuesday,No,4,40,14,30,14.7,56
5/5/2020,12,Tuesday,No,4,40,14,30,14.7,56
5/6/2020,13,Tuesday,No,4,40,14,30,14.7,56
5/7/2020,14,Tuesday,No,8,40,14,30,14.7,56
5/8/2020,15,Tuesday,No,8,40,14,29,14.7,56
5/9/2020,16,Tuesday,Yes,6,40,14,29,14.7,56
5/10/2020,17,Tuesday,No,7,40,14,28,14.7,56
5/11/2020,18,Tuesday,No,8,40,14,27,14.7,56
5/12/2020,19,Tuesday,No,9,40,14,27,14.7,71
5/13/2020,20,Tuesday,No,6,40,14,26,14.7,73
5/14/2020,21,Tuesday,No,6,40,14,26,14.7,72
5/15/2020,22,Tuesday,No,5,40,14,25,14.7,75
5/16/2020,6,Sunday,No,7,40,0,25,1.2,9
5/17/2020,6,Sunday,No,9,40,0,25,1.2,11
5/18/2020,6,Sunday,No,3,40,1,25,1.2,8
5/19/2020,6,Sunday,No,2,40,0,25,1.2,3
5/20/2020,6,Sunday,No,2,40,1,25,3,3
5/21/2020,17,Sunday,No,9,40,1,25,3,12
5/22/2020,17,Sunday,No,9,40,4,25,4.7,19
5/23/2020,17,Sunday,No,3,40,4,25,4.7,12
5/24/2020,17,Sunday,No,6,40,4,25,4.7,15
5/25/2020,6,Sunday,No,7,40,9,25,7.5,34
5/26/2020,6,Sunday,No,9,40,9,25,7.5,39
5/27/2020,6,Sunday,No,5,40,9,25,7.5,30
5/28/2020,6,Monday,No,5,40,12,25,10.1,36
5/29/2020,6,Monday,No,9,40,12,25,10.1,43
5/30/2020,6,Monday,No,3,40,12,25,10.1,33
5/31/2020,13,Monday,No,5,40,12,25,10.1,36
6/1/2020,13,Monday,No,9,40,12,25,10.1,43
6/2/2020,13,Monday,No,3,40,12,25,10.1,33
6/3/2020,6,Monday,No,5,40,12,25,12.1,43
6/4/2020,6,Monday,No,9,40,12,25,12.1,49
6/5/2020,6,Monday,No,3,40,12,25,12.1,40
6/6/2020,12,Monday,No,5,40,12,25,12.1,43
6/7/2020,12,Monday,No,9,40,12,25,12.1,49
6/8/2020,12,Monday,No,3,40,12,25,12.1,40
6/9/2020,6,Sunday,No,5,40,14,25,14.7,56
6/10/2020,7,Sunday,No,8,40,14,27,14.7,55
6/11/2020,8,Sunday,No,9,40,14,28,14.7,55
6/12/2020,9,Sunday,No,7,40,14,29,14.7,56
6/13/2020,10,Sunday,No,6,40,14,29,14.7,56
6/14/2020,11,Sunday,No,4,40,14,30,14.7,56
6/15/2020,12,Sunday,No,4,40,14,30,14.7,56
6/16/2020,13,Sunday,No,4,40,14,30,14.7,56
6/17/2020,14,Sunday,No,8,40,14,30,14.7,56
6/18/2020,15,Sunday,No,8,40,14,29,14.7,56
6/19/2020,16,Sunday,Yes,6,40,14,29,14.7,56
6/20/2020,17,Sunday,No,7,40,14,28,14.7,56
6/21/2020,18,Sunday,No,8,40,14,27,14.7,56
6/22/2020,19,Sunday,No,9,40,14,27,14.7,71
6/23/2020,20,Sunday,No,6,40,14,26,14.7,73
6/24/2020,21,Sunday,No,6,40,14,26,14.7,72
6/25/2020,22,Sunday,No,5,40,14,25,14.7,75
6/26/2020,6,Monday,No,5,40,14,25,14.7,56
6/27/2020,7,Monday,No,8,40,14,27,14.7,55
6/28/2020,8,Monday,No,9,40,14,28,14.7,55
6/29/2020,9,Monday,No,7,40,14,29,14.7,56
6/30/2020,10,Monday,No,6,40,14,29,14.7,56
7/1/2020,11,Monday,No,4,40,14,30,14.7,56
7/2/2020,12,Monday,No,4,40,14,30,14.7,56
7/3/2020,13,Monday,No,4,40,14,30,14.7,56
7/4/2020,14,Monday,No,8,40,14,30,14.7,56
7/5/2020,15,Monday,No,8,40,14,29,14.7,56
7/6/2020,16,Monday,Yes,6,40,14,29,14.7,56
7/7/2020,17,Monday,No,7,40,14,28,14.7,56
7/8/2020,18,Monday,No,8,40,14,27,14.7,56
7/9/2020,19,Monday,No,9,40,14,27,14.7,71
7/10/2020,20,Monday,No,6,40,14,26,14.7,73
7/11/2020,21,Monday,No,6,40,14,26,14.7,72
7/12/2020,22,Monday,No,5,40,14,25,14.7,75
7/13/2020,6,Tuesday,No,5,40,14,25,14.7,56
7/14/2020,7,Tuesday,No,8,40,14,27,14.7,55
7/15/2020,8,Tuesday,No,9,40,14,28,14.7,55
7/16/2020,9,Tuesday,No,7,40,14,29,14.7,56
7/17/2020,10,Tuesday,No,6,40,14,29,14.7,56
7/18/2020,11,Tuesday,No,4,40,14,30,14.7,56
7/19/2020,12,Tuesday,No,4,40,14,30,14.7,56
7/20/2020,13,Tuesday,No,4,40,14,30,14.7,56
7/21/2020,14,Tuesday,No,8,40,14,30,14.7,56
7/22/2020,15,Tuesday,No,8,40,14,29,14.7,56
7/23/2020,16,Tuesday,Yes,6,40,14,29,14.7,56
7/24/2020,17,Tuesday,No,7,40,14,28,14.7,56
7/25/2020,18,Tuesday,No,8,40,14,27,14.7,56
7/26/2020,19,Tuesday,No,9,40,14,27,14.7,71
7/27/2020,20,Tuesday,No,6,40,14,26,14.7,73
7/28/2020,21,Tuesday,No,6,40,14,26,14.7,72
7/29/2020,22,Tuesday,No,5,40,14,25,14.7,75
7/30/2020,6,Sunday,No,7,40,0,25,1.2,9
7/31/2020,6,Sunday,No,9,40,0,25,1.2,11
8/1/2020,6,Sunday,No,3,40,1,25,1.2,8
8/2/2020,6,Sunday,No,2,40,0,25,1.2,3
8/3/2020,6,Sunday,No,2,40,1,25,3,3
8/4/2020,17,Sunday,No,9,40,1,25,3,12
8/5/2020,17,Sunday,No,9,40,4,25,4.7,19
8/6/2020,17,Sunday,No,3,40,4,25,4.7,12
8/7/2020,17,Sunday,No,6,40,4,25,4.7,15
8/8/2020,6,Sunday,No,7,40,9,25,7.5,34
8/9/2020,6,Sunday,No,9,40,9,25,7.5,39
8/10/2020,6,Sunday,No,5,40,9,25,7.5,30
8/11/2020,6,Monday,No,5,40,12,25,10.1,36
8/12/2020,6,Monday,No,9,40,12,25,10.1,43
8/13/2020,6,Monday,No,3,40,12,25,10.1,33
8/14/2020,13,Monday,No,5,40,12,25,10.1,36
8/15/2020,13,Monday,No,9,40,12,25,10.1,43
8/16/2020,13,Monday,No,3,40,12,25,10.1,33
8/17/2020,6,Monday,No,5,40,12,25,12.1,43
8/18/2020,6,Monday,No,9,40,12,25,12.1,49
8/19/2020,6,Monday,No,3,40,12,25,12.1,40
8/20/2020,12,Monday,No,5,40,12,25,12.1,43
8/21/2020,12,Monday,No,9,40,12,25,12.1,49
8/22/2020,12,Monday,No,3,40,12,25,12.1,40
TravelTimePrediction.ipynb 0 → 100644
View file @ 8028c92f
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('BusTravelData.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" <th>Travel Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1/1/2019</td>\n",
" <td>6</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>25</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1/2/2019</td>\n",
" <td>7</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>8</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>27</td>\n",
" <td>14.7</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1/3/2019</td>\n",
" <td>8</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>28</td>\n",
" <td>14.7</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1/4/2019</td>\n",
" <td>9</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>7</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>29</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1/5/2019</td>\n",
" <td>10</td>\n",
" <td>Sunday</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>29</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Time Day Special Congestion Drving Speed Stops Weather \\\n",
"0 1/1/2019 6 Sunday No 5 40 14 25 \n",
"1 1/2/2019 7 Sunday No 8 40 14 27 \n",
"2 1/3/2019 8 Sunday No 9 40 14 28 \n",
"3 1/4/2019 9 Sunday No 7 40 14 29 \n",
"4 1/5/2019 10 Sunday No 6 40 14 29 \n",
"\n",
" Distance Travel Time \n",
"0 14.7 56 \n",
"1 14.7 55 \n",
"2 14.7 55 \n",
"3 14.7 56 \n",
"4 14.7 56 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" <th>Travel Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>595</th>\n",
" <td>8/18/2020</td>\n",
" <td>6</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>596</th>\n",
" <td>8/19/2020</td>\n",
" <td>6</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>597</th>\n",
" <td>8/20/2020</td>\n",
" <td>12</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>598</th>\n",
" <td>8/21/2020</td>\n",
" <td>12</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>599</th>\n",
" <td>8/22/2020</td>\n",
" <td>12</td>\n",
" <td>Monday</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Time Day Special Congestion Drving Speed Stops \\\n",
"595 8/18/2020 6 Monday No 9 40 12 \n",
"596 8/19/2020 6 Monday No 3 40 12 \n",
"597 8/20/2020 12 Monday No 5 40 12 \n",
"598 8/21/2020 12 Monday No 9 40 12 \n",
"599 8/22/2020 12 Monday No 3 40 12 \n",
"\n",
" Weather Distance Travel Time \n",
"595 25 12.1 49 \n",
"596 25 12.1 40 \n",
"597 25 12.1 43 \n",
"598 25 12.1 49 \n",
"599 25 12.1 40 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(600, 10)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of rows 600\n",
"Number of columns 10\n"
]
}
],
"source": [
"print(\"Number of rows\",data.shape[0])\n",
"print(\"Number of columns\",data.shape[1])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 600 entries, 0 to 599\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Date 600 non-null object \n",
" 1 Time 600 non-null int64 \n",
" 2 Day 600 non-null object \n",
" 3 Special 600 non-null object \n",
" 4 Congestion 600 non-null int64 \n",
" 5 Drving Speed 600 non-null int64 \n",
" 6 Stops 600 non-null int64 \n",
" 7 Weather 600 non-null int64 \n",
" 8 Distance 600 non-null float64\n",
" 9 Travel Time 600 non-null int64 \n",
"dtypes: float64(1), int64(6), object(3)\n",
"memory usage: 47.0+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" <th>Travel Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>600.000000</td>\n",
" <td>600.000000</td>\n",
" <td>600.0</td>\n",
" <td>600.000000</td>\n",
" <td>600.000000</td>\n",
" <td>600.00000</td>\n",
" <td>600.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>12.546667</td>\n",
" <td>6.253333</td>\n",
" <td>40.0</td>\n",
" <td>12.000000</td>\n",
" <td>27.000000</td>\n",
" <td>12.40400</td>\n",
" <td>49.786667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5.184524</td>\n",
" <td>2.035369</td>\n",
" <td>0.0</td>\n",
" <td>4.033251</td>\n",
" <td>1.961227</td>\n",
" <td>4.05381</td>\n",
" <td>17.968470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>6.000000</td>\n",
" <td>2.000000</td>\n",
" <td>40.0</td>\n",
" <td>0.000000</td>\n",
" <td>25.000000</td>\n",
" <td>1.20000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>7.000000</td>\n",
" <td>5.000000</td>\n",
" <td>40.0</td>\n",
" <td>12.000000</td>\n",
" <td>25.000000</td>\n",
" <td>12.10000</td>\n",
" <td>43.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>12.000000</td>\n",
" <td>6.000000</td>\n",
" <td>40.0</td>\n",
" <td>14.000000</td>\n",
" <td>27.000000</td>\n",
" <td>14.70000</td>\n",
" <td>56.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>17.000000</td>\n",
" <td>8.000000</td>\n",
" <td>40.0</td>\n",
" <td>14.000000</td>\n",
" <td>29.000000</td>\n",
" <td>14.70000</td>\n",
" <td>56.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>22.000000</td>\n",
" <td>9.000000</td>\n",
" <td>40.0</td>\n",
" <td>14.000000</td>\n",
" <td>30.000000</td>\n",
" <td>14.70000</td>\n",
" <td>75.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Congestion Drving Speed Stops Weather \\\n",
"count 600.000000 600.000000 600.0 600.000000 600.000000 \n",
"mean 12.546667 6.253333 40.0 12.000000 27.000000 \n",
"std 5.184524 2.035369 0.0 4.033251 1.961227 \n",
"min 6.000000 2.000000 40.0 0.000000 25.000000 \n",
"25% 7.000000 5.000000 40.0 12.000000 25.000000 \n",
"50% 12.000000 6.000000 40.0 14.000000 27.000000 \n",
"75% 17.000000 8.000000 40.0 14.000000 29.000000 \n",
"max 22.000000 9.000000 40.0 14.000000 30.000000 \n",
"\n",
" Distance Travel Time \n",
"count 600.00000 600.000000 \n",
"mean 12.40400 49.786667 \n",
"std 4.05381 17.968470 \n",
"min 1.20000 3.000000 \n",
"25% 12.10000 43.000000 \n",
"50% 14.70000 56.000000 \n",
"75% 14.70000 56.000000 \n",
"max 14.70000 75.000000 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Sunday', 'Monday', 'Tuesday'], dtype=object)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Day'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
" ..\n",
"595 1\n",
"596 1\n",
"597 1\n",
"598 1\n",
"599 1\n",
"Name: Day, Length: 600, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Day'].map({'Sunday':0,'Monday':1,'Tuesday':2})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data['Day'] = data['Day'].map({'Sunday':0,'Monday':1,'Tuesday':2})"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
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" <th>Travel Time</th>\n",
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" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1/2/2019</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>8</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>27</td>\n",
" <td>14.7</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1/3/2019</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>28</td>\n",
" <td>14.7</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1/4/2019</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>7</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>29</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1/5/2019</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>29</td>\n",
" <td>14.7</td>\n",
" <td>56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Time Day Special Congestion Drving Speed Stops Weather \\\n",
"0 1/1/2019 6 0 No 5 40 14 25 \n",
"1 1/2/2019 7 0 No 8 40 14 27 \n",
"2 1/3/2019 8 0 No 9 40 14 28 \n",
"3 1/4/2019 9 0 No 7 40 14 29 \n",
"4 1/5/2019 10 0 No 6 40 14 29 \n",
"\n",
" Distance Travel Time \n",
"0 14.7 56 \n",
"1 14.7 55 \n",
"2 14.7 55 \n",
"3 14.7 56 \n",
"4 14.7 56 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"data['Special'] = data['Special'].map({'No':0,'Yes':1})"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
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" <th>Travel Time</th>\n",
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" <td>8/18/2020</td>\n",
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" <td>49</td>\n",
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" <tr>\n",
" <th>596</th>\n",
" <td>8/19/2020</td>\n",
" <td>6</td>\n",
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" <td>40</td>\n",
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" <tr>\n",
" <th>597</th>\n",
" <td>8/20/2020</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>598</th>\n",
" <td>8/21/2020</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>599</th>\n",
" <td>8/22/2020</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>40</td>\n",
" <td>12</td>\n",
" <td>25</td>\n",
" <td>12.1</td>\n",
" <td>40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Time Day Special Congestion Drving Speed Stops Weather \\\n",
"595 8/18/2020 6 1 0 9 40 12 25 \n",
"596 8/19/2020 6 1 0 3 40 12 25 \n",
"597 8/20/2020 12 1 0 5 40 12 25 \n",
"598 8/21/2020 12 1 0 9 40 12 25 \n",
"599 8/22/2020 12 1 0 3 40 12 25 \n",
"\n",
" Distance Travel Time \n",
"595 12.1 49 \n",
"596 12.1 40 \n",
"597 12.1 43 \n",
"598 12.1 49 \n",
"599 12.1 40 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([25, 27, 28, 29, 30, 26], dtype=int64)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Weather'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'Time', 'Day', 'Special', 'Congestion', 'Drving Speed', 'Stops',\n",
" 'Weather', 'Distance', 'Travel Time'],\n",
" dtype='object')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"X = data.drop(['Date','Travel Time'],axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"y = data['Travel Time']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Train/test split\n",
"\n",
"1. split data into two parts \n",
"\n",
" training data set\n",
" testing data set\n",
"\n",
"2. Train the models on training set\n",
"\n",
"3. Test the models on testing data set"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"145 49\n",
"9 56\n",
"375 56\n",
"523 49\n",
"188 56\n",
" ..\n",
"71 40\n",
"106 73\n",
"270 56\n",
"435 34\n",
"102 56\n",
"Name: Travel Time, Length: 480, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.svm import SVR\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.neural_network import MLPClassifier\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Shiv\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier()</pre></div></div></div></div></div>"
],
"text/plain": [
"MLPClassifier()"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr = LinearRegression()\n",
"NN = MLPClassifier()\n",
"lr.fit(X_train,y_train)\n",
"NN.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"y_pred1 = lr.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.DataFrame({'Actual':y_test, 'Lr Results':y_pred1})"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Actual</th>\n",
" <th>Lr Results</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>55</td>\n",
" <td>56.187653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>419</th>\n",
" <td>56</td>\n",
" <td>55.732541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>56</td>\n",
" <td>53.591764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>55</td>\n",
" <td>56.690085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>73</td>\n",
" <td>66.133304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>56</td>\n",
" <td>54.599261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>340</th>\n",
" <td>56</td>\n",
" <td>53.591764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>49</td>\n",
" <td>52.322530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>494</th>\n",
" <td>56</td>\n",
" <td>55.732541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>439</th>\n",
" <td>43</td>\n",
" <td>38.662629</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>120 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Actual Lr Results\n",
"110 55 56.187653\n",
"419 56 55.732541\n",
"565 56 53.591764\n",
"77 55 56.690085\n",
"181 73 66.133304\n",
".. ... ...\n",
"399 56 54.599261\n",
"340 56 53.591764\n",
"148 49 52.322530\n",
"494 56 55.732541\n",
"439 43 38.662629\n",
"\n",
"[120 rows x 2 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Actual</th>\n",
" <th>NN Results</th>\n",
" <th>LR Results</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>56.187653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>419</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>55.732541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>53.591764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>56.690085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" <td>66.133304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>54.599261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>340</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>53.591764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>49</td>\n",
" <td>49</td>\n",
" <td>52.322530</td>\n",
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" <tr>\n",
" <th>494</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>55.732541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>439</th>\n",
" <td>43</td>\n",
" <td>43</td>\n",
" <td>38.662629</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>120 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Actual NN Results LR Results\n",
"110 55 55 56.187653\n",
"419 56 56 55.732541\n",
"565 56 56 53.591764\n",
"77 55 55 56.690085\n",
"181 73 73 66.133304\n",
".. ... ... ...\n",
"399 56 56 54.599261\n",
"340 56 56 53.591764\n",
"148 49 49 52.322530\n",
"494 56 56 55.732541\n",
"439 43 43 38.662629\n",
"\n",
"[120 rows x 3 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred2 = NN.predict(X_test)\n",
"df2 = pd.DataFrame({'Actual':y_test, 'NN Results':y_pred2, 'LR Results':y_pred1})\n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x265e22fe0e0>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(221)\n",
"plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
"plt.plot(df1['Lr Results'].iloc[0:11],label='Lr Results')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\tTime\tDay\tRush\tSpecial\tCongestion\tDrving Speed\tStops\tWeather\tDistance\tTravel Time"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x265e2510ca0>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(221)\n",
"plt.plot(df1['Actual'].iloc[0:11],label='Actual')\n",
"plt.plot(df2['NN Results'].iloc[0:11],label='NN Results')\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prediction with Current parameters (Predict Time with trained Model)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>40</td>\n",
" <td>14</td>\n",
" <td>23</td>\n",
" <td>14.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Day Special Congestion Drving Speed Stops Weather Distance\n",
"0 6 1 0 8 40 14 23 14.7"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = {'Time':6,\n",
" 'Day':1,\n",
" 'Special':0,\n",
" 'Congestion':8,\n",
" 'Drving Speed':40,\n",
" 'Stops':14,\n",
" 'Weather':23,\n",
" 'Distance':14.7\n",
" }\n",
"df = pd.DataFrame(data,index=[0])\n",
"df\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"linear regression [61.09983093]\n",
"Neural Network [55]\n",
"Actual Travel Time :- 58\n"
]
}
],
"source": [
"new_pred = lr.predict(df)\n",
"NN_pred = NN.predict(df)\n",
"print('linear regression',new_pred)\n",
"print('Neural Network',NN_pred)\n",
"print('Actual Travel Time :- 58')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing With Current Data"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time</th>\n",
" <th>Day</th>\n",
" <th>Special</th>\n",
" <th>Congestion</th>\n",
" <th>Drving Speed</th>\n",
" <th>Stops</th>\n",
" <th>Weather</th>\n",
" <th>Distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>20</td>\n",
" <td>24</td>\n",
" <td>14.7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Day Special Congestion Drving Speed Stops Weather Distance\n",
"0 17 1 0 5 40 20 24 14.7"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = {'Time':17,\n",
" 'Day':1,\n",
" 'Special':0,\n",
" 'Congestion':5,\n",
" 'Drving Speed':40,\n",
" 'Stops':20,\n",
" 'Weather':24,\n",
" 'Distance':14.7\n",
" }\n",
"ddf = pd.DataFrame(data,index=[0])\n",
"ddf"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Travel Time Prediction :- 56 Min\n"
]
}
],
"source": [
"resultsNN = NN.predict(ddf)\n",
"resultsNN\n",
"print('Travel Time Prediction :-',*resultsNN,'Min')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"def GetPrediction(time,date,special,congestion,dspeed,stops,weather,distance):\n",
" data = {'Time':time,\n",
" 'Day':date,\n",
" 'Special':special,\n",
" 'Congestion':congestion,\n",
" 'Drving Speed':dspeed,\n",
" 'Stops':stops,\n",
" 'Weather':weather,\n",
" 'Distance':distance\n",
" }\n",
" ddf = pd.DataFrame(data,index=[0])\n",
" resultsNN = NN.predict(ddf)\n",
" resultsNN\n",
" print('Travel Time Prediction :-',*resultsNN,'Min')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Travel Time Prediction :- 55 Min\n"
]
}
],
"source": [
"def GetDateCode(date):\n",
" if('Sunday' == date ):\n",
" return 0\n",
" elif('Monday' == date):\n",
" return 1\n",
" elif('Tuesday' == date):\n",
" return 2\n",
" elif('Wednesday' == date):\n",
" return 3\n",
" elif('Thursday' == date):\n",
" return 4\n",
" elif('Friday' == date):\n",
" return 5\n",
" elif('Saturday' == date):\n",
" return 6\n",
" else:\n",
" return 9\n",
" \n",
"\n",
" \n",
"time= 6 #this time come from semins data/member of route planning\n",
"day ='Friday' #date come from UI \n",
"date = GetDateCode(day)\n",
"special = 0 # Special Day or Not 1/0\n",
"Congestion= 8 # This is depend on the depature time---- heavy traffic =9 / No Traffic = 0 \n",
"drivingspeedAVG = 40 #this is come from GPS data\n",
"stops =14 #total Bus stops/holts between the travel --- data come from the Seminas data/member of route planning \n",
"weather =23 #Get weather in travel area\n",
"\n",
"\n",
"GetPrediction(time,date,special,Congestion,drivingspeedAVG,14,24,14.7)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.7 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.7"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
TravelTimePredictionNN.py 0 → 100644
View file @ 8028c92f
# %%
import pandas as pd
# %%
data = pd.read_csv('BusTravelData.csv')
# %%
data['Day'].unique()
# %%
data['Day'].map({'Sunday':0,'Monday':1,'Tuesday':2})
# %%
data['Day'] = data['Day'].map({'Sunday':0,'Monday':1,'Tuesday':2})
# %%
data.head()
# %%
data['Special'] = data['Special'].map({'No':0,'Yes':1})
# %%
data.tail()
# %%
data['Weather'].unique()
# %%
data.columns
# %%
X = data.drop(['Date','Travel Time'],axis=1)
# %%
y = data['Travel Time']
# %% [markdown]
# Train/test split
#
# 1. split data into two parts
#
# training data set
# testing data set
#
# 2. Train the models on training set
#
# 3. Test the models on testing data set
# %%
from sklearn.model_selection import train_test_split
# %%
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
# %%
y_train
# %%
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
# %%
lr = LinearRegression()
NN = MLPClassifier()
lr.fit(X_train,y_train)
NN.fit(X_train,y_train)
# %%
y_pred1 = lr.predict(X_test)
# %%
df1 = pd.DataFrame({'Actual':y_test, 'Lr Results':y_pred1})
# %%
df1
# %%
y_pred2 = NN.predict(X_test)
df2 = pd.DataFrame({'Actual':y_test, 'NN Results':y_pred2, 'LR Results':y_pred1})
df2
# %%
import matplotlib.pyplot as plt
# %%
plt.subplot(221)
plt.plot(df1['Actual'].iloc[0:11],label='Actual')
plt.plot(df1['Lr Results'].iloc[0:11],label='Lr Results')
plt.legend()
# %% [markdown]
# Time Day Rush Special Congestion Drving Speed Stops Weather Distance Travel Time
# %%
plt.subplot(221)
plt.plot(df1['Actual'].iloc[0:11],label='Actual')
plt.plot(df2['NN Results'].iloc[0:11],label='NN Results')
plt.legend()
# %% [markdown]
# Prediction with Current parameters (Predict Time with trained Model)
# %%
data = {'Time':6,
'Day':1,
'Special':0,
'Congestion':8,
'Drving Speed':40,
'Stops':14,
'Weather':23,
'Distance':14.7
}
df = pd.DataFrame(data,index=[0])
df
# %%
new_pred = lr.predict(df)
NN_pred = NN.predict(df)
# %%
data = {'Time':17,
'Day':1,
'Special':0,
'Congestion':5,
'Drving Speed':40,
'Stops':20,
'Weather':24,
'Distance':14.7
}
ddf = pd.DataFrame(data,index=[0])
ddf
# %%
resultsNN = NN.predict(ddf)
resultsNN
# %%
def GetPrediction(time,date,special,congestion,dspeed,stops,weather,distance):
data = {'Time':time,
'Day':date,
'Special':special,
'Congestion':congestion,
'Drving Speed':dspeed,
'Stops':stops,
'Weather':weather,
'Distance':distance
}
ddf = pd.DataFrame(data,index=[0])
resultsNN = NN.predict(ddf)
resultsNN
print('Travel Time Prediction :-',*resultsNN,'Min')
# %%
def GetDateCode(date):
if('Sunday' == date ):
return 0
elif('Monday' == date):
return 1
elif('Tuesday' == date):
return 2
elif('Wednesday' == date):
return 3
elif('Thursday' == date):
return 4
elif('Friday' == date):
return 5
elif('Saturday' == date):
return 6
else:
return 9
import testingWeather
city = "malabe"
city = city+" weather"
time= 6 #this time come from semins data/member of route planning
day ='Friday' #date come from UI
date = GetDateCode(day)
special = 0 # Special Day or Not 1/0
Congestion= 8 # This is depend on the depature time---- heavy traffic =9 / No Traffic = 0
drivingspeedAVG = 40 #this is come from GPS data
stops =14 #total Bus stops/holts between the travel --- data come from the Seminas data/member of route planning
weather =testingWeather.weather(city) #Get weather in travel area
Distance =14.7 #Distance data come from seminas ----- data come from the Seminas data/member of route planning
GetPrediction(time,date,special,Congestion,drivingspeedAVG,stops,weather,Distance)
gps.ino 0 → 100644
View file @ 8028c92f
#include <WiFi.h>
#include <FirebaseESP32.h>
#include <TinyGPS++.h>
#define FIREBASE_HOST "gps-b123c-default-rtdb.firebaseio.com"
#define FIREBASE_AUTH "gEjXkGOWaMEzkZKpKIYqwFH7dQMetih25gsmligT"
#define WIFI_SSID "LAZI 5G"
#define WIFI_PASSWORD "fmrp1530"
FirebaseData firebaseData;
#define RXD2 5
#define TXD2 21
HardwareSerial neogps(1);
String ESP32_API_KEY = "Ad5F10jkBM0";
TinyGPSPlus gps;
void setup(){
Initialization();
WiFiConnection();
neogps.begin(9600, SERIAL_8N1, RXD2, TXD2);
}
float counter2 = 0.5;
void Initialization(){
Serial.begin(115200);
}
void WiFiConnection(){
WiFi.begin(WIFI_SSID, WIFI_PASSWORD);
Serial.print("Connecting to Wi-Fi");
while (WiFi.status() != WL_CONNECTED)
{
Serial.print(".");
delay(300);
}
Serial.println();
Serial.print("Connected with IP: ");
Serial.println(WiFi.localIP());
Serial.println();
Firebase.begin(FIREBASE_HOST, FIREBASE_AUTH);
Firebase.reconnectWiFi(true);
}
void loop(){
boolean newData = false;
for (unsigned long start = millis(); millis() - start < 2000;){
while(neogps.available()){
if(gps.encode(neogps.read())){
if(gps.location.isValid() == 1){
newData = true;
break;
}
}
}
}
//NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
//MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM
//If newData is true
if(true){
newData = false;
//NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
String latitude, longitude;
//float altitude;
//unsigned long date, time, speed, satellites;
latitude = String(gps.location.lat(), 6); // Latitude in degrees (double)
longitude = String(gps.location.lng(), 6); // Longitude in degrees (double)
//altitude = gps.altitude.meters(); // Altitude in meters (double)
//date = gps.date.value(); // Raw date in DDMMYY format (u32)
//time = gps.time.value(); // Raw time in HHMMSSCC format (u32)
//speed = gps.speed.kmph();
//Serial.print("Latitude= ");
//Serial.print(latitude);
//Serial.print(" Longitude= ");
//Serial.println(longitude);
String gps_data;
gps_data = "api_key="+ESP32_API_KEY;
gps_data += "&lat="+latitude;
gps_data += "&lng="+longitude;
Serial.print("gps_data: ");
Serial.println(gps_data);
String x = gps_data;
if(Firebase.setString(firebaseData, "location", x)){
}
if(Firebase.getString(firebaseData, "location")){
if(firebaseData.dataType() == "string"){
Serial.print("data = ");
Serial.println(firebaseData.stringData());
}
}
}
}
\ No newline at end of file
index.php 0 → 100644
View file @ 8028c92f
<!DOCTYPE html>
<html lang="en">
<head>
<!-- Required meta tags -->
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<title>Travelle Dashboard</title>
<!-- Bootstrap CSS -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css">
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.5.1/jquery.min.js" integrity="sha256-9/aliU8dGd2tb6OSsuzixeV4y/faTqgFtohetphbbj0=" crossorigin="anonymous"></script>
</head>
<body>
<div class="container" style="margin-top: 50px;">
<h4 class="text-center">Travelle Dashboard</h4><br>
<h5>Add Option</h5>
<div class="card card-default">
<div class="card-body">
<form id="addUser" class="form-inline" method="POST" action="">
<div class="form-group mb-2">
<label for="name" class="sr-only">Name</label>
<input id="name" type="text" class="form-control" name="name" placeholder="Name"
required autofocus>
</div>
<div class="form-group mx-sm-3 mb-2">
<label for="email" class="sr-only">Cordinates</label>
<input id="email" type="email" class="form-control" name="email" placeholder="Email"
required autofocus>
</div>
<button id="submitUser" type="button" class="btn btn-primary mb-2">Submit</button>
</form>
</div>
</div>
<br>
<h5>Users</h5>
<table class="table table-bordered">
<tr>
<th>Name</th>
<th>Email</th>
<th width="180" class="text-center">Action</th>
</tr>
<tbody id="tbody">
</tbody>
</table>
</div>
<!-- Update Model -->
<form action="" method="POST" class="users-update-record-model form-horizontal">
<div id="update-modal" data-backdrop="static" data-keyboard="false" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="custom-width-modalLabel"
aria-hidden="true">
<div class="modal-dialog modal-dialog-centered" style="width:55%;">
<div class="modal-content" style="overflow: hidden;">
<div class="modal-header">
<h4 class="modal-title" id="custom-width-modalLabel">Update</h4>
<button type="button" class="close" data-dismiss="modal"
aria-hidden="true">×
</button>
</div>
<div class="modal-body" id="updateBody">
</div>
<div class="modal-footer">
<button type="button" class="btn btn-light"
data-dismiss="modal">Close
</button>
<button type="button" class="btn btn-success updateUser">Update
</button>
</div>
</div>
</div>
</div>
</form>
<!-- Delete Model -->
<form action="" method="POST" class="users-remove-record-model">
<div id="remove-modal" data-backdrop="static" data-keyboard="false" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="custom-width-modalLabel"
aria-hidden="true" style="display: none;">
<div class="modal-dialog modal-dialog-centered" style="width:55%;">
<div class="modal-content">
<div class="modal-header">
<h4 class="modal-title" id="custom-width-modalLabel">Delete</h4>
<button type="button" class="close remove-data-from-delete-form" data-dismiss="modal" aria-hidden="true">×
</button>
</div>
<div class="modal-body">
<p>Do you want to delete this record?</p>
</div>
<div class="modal-footer">
<button type="button" class="btn btn-default waves-effect remove-data-from-delete-form"
data-dismiss="modal">Close
</button>
<button type="button" class="btn btn-danger waves-effect waves-light deleteRecord">Delete
</button>
</div>
</div>
</div>
</div>
</form>
<!-- The core Firebase JS SDK is always required and must be listed first -->
<script src="https://www.gstatic.com/firebasejs/7.14.6/firebase-app.js"></script>
<!-- TODO: Add SDKs for Firebase products
https://firebase.google.com/docs/web/setup#available-libraries -->
<script src="https://www.gstatic.com/firebasejs/7.14.6/firebase-analytics.js"></script>
<script src="https://www.gstatic.com/firebasejs/7.14.6/firebase-database.js"></script>
<script>
// Initialize Firebase
var config = {
apiKey: "AIzaSyDqcBd6B-l2_rPVslln0lMbw7zPw-8JDd0",
authDomain: "travelle-99619.firebaseapp.com",
projectId: "travelle-99619",
storageBucket: "travelle-99619.appspot.com",
messagingSenderId: "636540305419",
appId: "1:636540305419:web:f0d4c40fe85f03c15b470e",
measurementId: "G-8G2072NEWL"
};
firebase.initializeApp(config);
firebase.analytics();
var database = firebase.database();
var lastIndex = 0;
// Get Data
firebase.database().ref('Users/').on('value', function (snapshot) {
var value = snapshot.val();
var htmls = [];
$.each(value, function (index, value) {
if (value) {
htmls.push('<tr>\
<td>' + value.name + '</td>\
<td>' + value.email + '</td>\
<td><button data-toggle="modal" data-target="#update-modal" class="btn btn-info updateData" data-id="' + index + '">Update</button>\
<button data-toggle="modal" data-target="#remove-modal" class="btn btn-danger removeData" data-id="' + index + '">Delete</button></td>\
</tr>');
}
lastIndex = index;
});
$('#tbody').html(htmls);
$("#submitUser").removeClass('desabled');
});
// Add Data
$('#submitUser').on('click', function () {
var values = $("#addUser").serializeArray();
var name = values[0].value;
var email = values[1].value;
var userID = lastIndex + 1;
console.log(values);
firebase.database().ref('Users/' + userID).set({
name: name,
email: email,
});
// Reassign lastID value
lastIndex = userID;
$("#addUser input").val("");
});
// Update Data
var updateID = 0;
$('body').on('click', '.updateData', function () {
updateID = $(this).attr('data-id');
firebase.database().ref('Users/' + updateID).on('value', function (snapshot) {
var values = snapshot.val();
var updateData = '<div class="form-group">\
<label for="first_name" class="col-md-12 col-form-label">Name</label>\
<div class="col-md-12">\
<input id="first_name" type="text" class="form-control" name="name" value="' + values.name + '" required autofocus>\
</div>\
</div>\
<div class="form-group">\
<label for="last_name" class="col-md-12 col-form-label">Email</label>\
<div class="col-md-12">\
<input id="last_name" type="text" class="form-control" name="email" value="' + values.email + '" required autofocus>\
</div>\
</div>';
$('#updateBody').html(updateData);
});
});
$('.updateUser').on('click', function () {
var values = $(".users-update-record-model").serializeArray();
var postData = {
name: values[0].value,
email: values[1].value,
};
var updates = {};
updates['/Users/' + updateID] = postData;
firebase.database().ref().update(updates);
$("#update-modal").modal('hide');
});
// Remove Data
$("body").on('click', '.removeData', function () {
var id = $(this).attr('data-id');
$('body').find('.users-remove-record-model').append('<input name="id" type="hidden" value="' + id + '">');
});
$('.deleteRecord').on('click', function () {
var values = $(".users-remove-record-model").serializeArray();
var id = values[0].value;
firebase.database().ref('Users/' + id).remove();
$('body').find('.users-remove-record-model').find("input").remove();
$("#remove-modal").modal('hide');
});
$('.remove-data-from-delete-form').click(function () {
$('body').find('.users-remove-record-model').find("input").remove();
});
</script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js"></script>
</body>
</html>
routeplanner/AL.py 0 → 100644
View file @ 8028c92f
from helpers import load_map_test, load_map_bus, load_map_train,route_177
import math
val = 0
class PathPlanner():
def __init__(self, M, start=None, goal=None, type=None):
self.map = M
self.start= start
self.goal = goal
self.type = type
self.dis = 0
self.closedSet = self.create_closedSet() if goal != None and start != None else None
self.openSet = self.create_openSet() if goal != None and start != None else None
self.cameFrom = self.create_cameFrom() if goal != None and start != None else None
self.gScore = self.create_gScore() if goal != None and start != None else None
self.fScore = self.create_fScore() if goal != None and start != None else None
self.path = self.run_search() if self.map and self.start != None and self.goal != None else None
def reconstruct_path(self, current):
total_path = [current]
while current in self.cameFrom.keys():
current = self.cameFrom[current]
total_path.append(current)
return total_path
def _reset(self):
self.closedSet = None
self.openSet = None
self.cameFrom = None
self.gScore = None
self.fScore = None
self.path = self.run_search() if self.map and self.start and self.goal else None
def run_search(self):
""" """
if self.map == None:
raise(ValueError, "Must create map before running search")
if self.goal == None:
raise(ValueError, "Must create goal node before running search")
if self.start == None:
raise(ValueError, "Must create start node before running search")
self.closedSet = self.closedSet if self.closedSet != None else self.create_closedSet()
self.openSet = self.openSet if self.openSet != None else self.create_openSet()
self.cameFrom = self.cameFrom if self.cameFrom != None else self.create_cameFrom()
self.gScore = self.gScore if self.gScore != None else self.create_gScore()
self.fScore = self.fScore if self.fScore != None else self.create_fScore()
while not self.is_open_empty():
current = self.get_current_node()
if current == self.goal:
self.path = [x for x in reversed(self.reconstruct_path(current))]
return self.path
else:
self.openSet.remove(current)
self.closedSet.add(current)
for neighbor in self.get_neighbors(current):
if neighbor in self.closedSet:
continue # Ignore the neighbor which is already evaluated.
if not neighbor in self.openSet: # Discover a new node
self.openSet.add(neighbor)
# The distance from start to a neighbor
if self.getTempGScore(current, neighbor) >= self.get_gScore(neighbor):
continue
# This path is the best until now. Record it!
global val #
val = val + self.dis
self.record_best_path_to(current, neighbor)
print("No Path Found")
self.path = None
return False
def create_closedSet(self):
return set()
def create_openSet(self):
openSet = set()
openSet.add(self.start)
return openSet
def create_cameFrom(self):
cameFrom = {}
return cameFrom
def create_gScore(self):
gScore = {}
for node in self.map.intersections.keys():
if node == self.start:
gScore[node] = 0
else: gScore[node] = float('inf')
return gScore
def create_fScore(self):
if self.start != None:
fScore = {}
for node in self.map.intersections.keys():
if node == self.start:
fScore[node] = self.heuristic_cost_estimate(self.start)
else: fScore[node] = float('inf')
return fScore
raise(ValueError, "Must create start node before creating fScore.")
def set_map(self, M):
"set map attribute "
self._reset(self)
self.start = None
self.goal = None
self.map = M
def set_start(self, start):
" set start attribute "
self._reset(self)
self.start = start
self.goal = None
def set_goal(self, goal):
"set goal attribute "
self._reset(self)
self.goal = goal
#-------------------------------- get information --------------------------------#
def is_open_empty(self):
"returns True if the open set is empty"
return not bool(self.openSet)
def get_current_node(self):
" Returns the node in the open set with the lowest value of f(node)"
current = None
minim = float('inf')
for node in self.openSet:
if self.fScore[node] < minim:
minim = self.fScore[node]
current = node
return current
def get_neighbors(self, node):
"""Returns the neighbors of a node"""
return set(self.map.roads[node])
def get_traveled_distance(self):
#traveled_distance
return self.dis
#-------------------------------- Calculations --------------------------------#
def get_gScore(self, node):
return self.gScore[node]
def getTempGScore(self, current, neighbor):
# distance from the current node to it's neighbors
g_score_current = self.get_gScore(current)
dist_current_neighbor = self.distance(current,neighbor)
return g_score_current+dist_current_neighbor
def heuristic_cost_estimate(self, node):
if self.goal != None:
heuristic_estimate = self.distance(node,self.goal)
return heuristic_estimate
raise(ValueError, "Must create goal node before calculating huristic ")
def calculate_fscore(self, node):
# F = G + H
f_score = self.get_gScore(node) + self.heuristic_cost_estimate(node)
return f_score
# best path
def record_best_path_to(self, current, neighbor):
self.cameFrom[neighbor] = current
self.gScore[neighbor] = self.getTempGScore(current,neighbor)
self.fScore[neighbor] = self.gScore[neighbor] + self.heuristic_cost_estimate(neighbor)
def distance(self, node_1, node_2):
from math import radians, cos, sin, asin, sqrt
#The math module contains a function named
#radians which converts from degrees to radians.
lon1 = radians(self.map.intersections [node_1][0])
lon2 = radians(self.map.intersections [node_2][0])
lat1 = radians(self.map.intersections [node_1][1])
lat2 = radians(self.map.intersections [node_2][1])
# Haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * asin(sqrt(a))
# Radius of earth in kilometers. Use 3956 for miles
r = 6371
# calculate the result
dist = (c * r)
#print (dist)
self.dis = dist
return dist
# Get route
def main( a, node1, node2):
if a == 3:
map = load_map_train()
elif a == 2:
map = load_map_bus()
else:
map = load_map_test()
planner = PathPlanner(map,node1,node2)
print(map.roads)
path = planner.path
print('distance =' ,val)
if path == [5, 16, 37, 12, 34]:
print("code works for test path!!!")
print(path)
else:
print("Genarated path")
print(path)
# for x in path:
# if routeNo
# print (x)
#for each in path:
main( 1, 2, 14)
\ No newline at end of file
routeplanner/helpers.py 0 → 100644
View file @ 8028c92f
import networkx as nx
import pickle
from chart_studio import plotly as py
import random
from plotly.graph_objs import *
from plotly.offline import init_notebook_mode, plot, iplot
#init_notebook_mode(connected=True)
route_177 = {
1: 34,
2: 42,
3: 54,
4: 67,
5: 80,
6: 92,
7: 105,
8: 109,
9: 117,
10: 126,
11: 134,
12: 140,
13: 149,
14: 157,
15: 163,
}
map_test = {
0: {'pos': (6.911034627182109, 79.84918916006576), 'connections': [1], 'name' : 'Kollupitiya','type': 0,'routeNo': 177, 'hValue': 1},
1: {'pos': (6.911751932322411, 79.86194701315071), 'connections': [0,2], 'name' : 'Viharamahadevi Park', 'type': 0, 'routeNo': 177, 'hValue': 1},
2: {'pos': (6.911385550864001, 79.87682791026592), 'connections': [1,3], 'name' : 'House Of Fashion', 'type': 0, 'routeNo': 177, 'hValue': 1},
3: {'pos': (6.911031363415147, 79.88498429384545), 'connections': [2,4], 'name' : 'Castle Street', 'type': 0, 'routeNo': 177, 'hValue': 1},
4: {'pos': (6.908462881966912, 79.89338919261249), 'connections': [3,5], 'name' : 'Rajagiriya', 'type': 0, 'routeNo': 177, 'hValue': 1},
5: {'pos': (6.906663916178293, 79.90205413217801), 'connections': [4,6], 'name' : 'Hsbc Rajagiriya', 'type': 0, 'routeNo': 177, 'hValue': 1},
6: {'pos': (6.90333333857459, 79.90747047529919), 'connections': [5,7], 'name' : 'Ethulkotte New', 'type': 0, 'routeNo': 177, 'hValue': 1},
7: {'pos': (6.903185701293392, 79.91168232658337), 'connections': [6,8], 'name' : 'Parliament Junction', 'type': 0, 'routeNo': 177, 'hValue': 1},
8: {'pos': (6.902102452411621, 79.91741047710077), 'connections': [7,9], 'name' : 'Battaramulla Junction', 'type': 0, 'routeNo': 177, 'hValue': 1},
9: {'pos': (6.90431022366131, 79.92400480782847), 'connections': [8,10], 'name' : 'Ganahena', 'type': 0, 'routeNo': 177, 'hValue': 1},
10: {'pos': (6.906173952520986, 79.92862087153598), 'connections': [9,11], 'name' : 'Koswatta', 'type': 0, 'routeNo': 177, 'hValue': 1},
11: {'pos': (6.9084657304543455, 79.9383708894408), 'connections': [10,12], 'name' : 'Kotte-Bope', 'type': 0, 'routeNo': 177, 'hValue': 1},
12: {'pos': (6.9085023402918155, 79.94425286198016), 'connections': [11,13], 'name' : 'Thalahena Junction', 'type': 0, 'routeNo': 177, 'hValue': 1},
13: {'pos': (6.903962700873259, 79.95430199947661), 'connections': [12,14], 'name' : 'Malabe', 'type': 0, 'routeNo': 177, 'hValue': 1},
14: {'pos': (6.933539686667202, 79.85005389798111), 'connections': [15], 'name' : 'Fort', 'type': 1, 'routeNo': 8717, 'hValue': 1},
15: {'pos': (6.923619774894732, 79.84965509086771), 'connections': [14,16], 'name' : 'Kompannavidiya', 'type': 1, 'routeNo': 8717, 'hValue': 1},
16: {'pos': (6.911282166657041, 79.84821747831087), 'connections': [15,1,17], 'name' : 'Kollupitiya', 'type': 1, 'routeNo': 8717, 'hValue': 1},
17: {'pos': (6.893710110662125, 79.85300590028642), 'connections': [16,18], 'name' : 'Bambalapitiya', 'type': 1, 'routeNo': 8717, 'hValue': 1},
18: {'pos': (6.875030063532378, 79.85744793142631), 'connections': [17,19], 'name' : 'Wellawatte', 'type': 1, 'routeNo': 8717, 'hValue': 1},
19: {'pos': (6.850915755796403, 79.8620969312958), 'connections': [18], 'name' : 'Dehiwala ', 'type': 1, 'routeNo': 8717, 'hValue': 1},
}
class Map:
def __init__(self, G):
self._graph = G
self.intersections = nx.get_node_attributes(G, "pos")
self.routeNo = nx.get_node_attributes(G, "routeNo")
self.type = nx.get_node_attributes(G, "type")
self.roads = [list(G[node]) for node in G.nodes()]
def save(self, filename):
with open(filename, 'wb') as f:
pickle.dump(self._graph, f)
def load_map_graph(map_dict):
G = nx.Graph()
for node in map_dict.keys():
G.add_node(node, pos=map_dict[node]['pos'], type=map_dict[node]['type'],routeNo=map_dict[node]['routeNo'])
for node in map_dict.keys():
for con_node in map_dict[node]['connections']:
G.add_edge(node, con_node)
return G
def load_map_b(map_dict):
# G = nx.Graph()
# for node in map_dict.keys():
# #nodeValue =
# if map_dict[node]['type'] != 1:
# continue
# else:
# G.add_node(node, pos=map_dict[node]['pos'], type=map_dict[node]['type'],routeNo=map_dict[node]['routeNo'])
G = nx.Graph()
for node in map_dict.keys():
G.add_node(node, pos=map_dict[node]['pos'], type=map_dict[node]['type'],routeNo=map_dict[node]['routeNo'])
# for node in map_dict.keys():
# for con_node in map_dict[node]['connections']:
# if map_dict[con_node]['type'] != 1:
# continue
# else:
# G.add_edge(node, con_node)
for node in map_dict.keys():
for con_node in map_dict[node]['connections']:
if map_dict[node]['type'] != 1:
continue
elif map_dict[con_node]['type'] != 1:
continue
else:
G.add_edge(node, con_node)
return G
#map for train
def load_map_t(map_dict):
G = nx.Graph()
for node in map_dict.keys():
G.add_node(node, pos=map_dict[node]['pos'], type=map_dict[node]['type'],routeNo=map_dict[node]['routeNo'])
for node in map_dict.keys():
for con_node in map_dict[node]['connections']:
if map_dict[node]['type'] == 1:
continue
elif map_dict[con_node]['type'] == 1:
continue
else:
G.add_edge(node, con_node)
return G
def load_map_test():
G = load_map_graph(map_test)
return Map(G)
def load_map_bus():
G = load_map_b(map_test)
return Map(G)
def load_map_train():
G = load_map_t(map_test)
return Map(G)
routeplanner/test.py 0 → 100644
View file @ 8028c92f
from helpers import load_map_40
MAP_40_ANSWERS = [
(5, 34, [5, 16, 37, 12, 34]),
(5, 5, [5]),
(8, 24, [8, 14, 16, 37, 12, 17, 10, 24])
]
def test(shortest_path_function):
map_40 = load_map_40()
correct = 0
for start, goal, answer_path in MAP_40_ANSWERS:
path = shortest_path_function(map_40, start, goal).path
if path == answer_path:
correct += 1
print("For start:", start,
"Goal: ", goal,
"Your path:", path,
"Correct: ", answer_path)
else:
print("Error Testing faild !!!" )
if correct == len(MAP_40_ANSWERS):
print("All tests pass ")
else:
print("Only passed", correct, "/", len(MAP_40_ANSWERS), "test cases")
\ No newline at end of file
testingWeather.py 0 → 100644
View file @ 8028c92f
from bs4 import BeautifulSoup
import requests
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
def weather(city):
city = city.replace(" ", "+")
res = requests.get(
f'https://www.google.com/search?q={city}&oq={city}&aqs=chrome.0.35i39l2j0l4j46j69i60.6128j1j7&sourceid=chrome&ie=UTF-8', headers=headers)
soup = BeautifulSoup(res.text, 'html.parser')
location = soup.select('#wob_loc')[0].getText().strip()
time = soup.select('#wob_dts')[0].getText().strip()
info = soup.select('#wob_dc')[0].getText().strip()
weather = soup.select('#wob_tm')[0].getText().strip()
return weather
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