A Short video of the project is present on the below link https://youtu.be/zE3hiu3pPFc
You can share this video with other students and the report is on this github and is named as Multimodal_Taxi_Demand_Prediction.pdf
wget==3.2
pandas==0.23.4+0.g0409521.dirty
nltk==3.3
Keras==2.2.4
tensorflow==1.12.0
numpy==1.15.4
tabulate==0.8.2
matplotlib==2.2.3
beautifulsoup4==4.6.3
selenium==3.141.0
scikit_learn==0.20.1
mae | rmse | |
---|---|---|
97 | 11.645 | 14.6072 |
25 | 10.0594 | 12.5433 |
181 | 11.8526 | 15.0872 |
189 | 1.67727 | 2.26616 |
mae | rmse | |
---|---|---|
97 | 11.7706 | 14.7235 |
25 | 10.1951 | 12.6717 |
181 | 11.9744 | 15.1028 |
189 | 1.69314 | 2.27517 |
mae | rmse | |
---|---|---|
97 | 12.014 | 15.0118 |
25 | 10.2896 | 12.936 |
181 | 12.1111 | 15.4005 |
189 | 1.75808 | 2.30187 |
mae | rmse | |
---|---|---|
97 | 12.0416 | 15.0563 |
25 | 10.3543 | 12.9647 |
181 | 12.2302 | 15.541 |
189 | 1.75734 | 2.32166 |
mae | rmse | |
---|---|---|
97 | 12.0378 | 15.025 |
25 | 10.4146 | 13.0308 |
181 | 12.1352 | 15.3548 |
189 | 1.82246 | 2.47262 |
mae | rmse | |
---|---|---|
97 | 13.8542 | 17.2312 |
25 | 10.8611 | 13.5539 |
181 | 13.1142 | 16.5431 |
189 | 1.8749 | 2.46243 |
mae | rmse | |
---|---|---|
97 | 13.0931 | 16.2276 |
25 | 10.3735 | 12.7546 |
181 | 14.158 | 18.1432 |
189 | 2.39364 | 3.30199 |
year | month | day | hour | minute | weekday | pickup_no | |
---|---|---|---|---|---|---|---|
count | 26208 | 26208 | 26208 | 26208 | 26208 | 26208 | 26208 |
mean | 2017.33 | 5.52564 | 15.6813 | 11.5 | 0.5 | 4 | 19.3771 |
std | 0.470762 | 3.30677 | 8.77644 | 6.92232 | 0.50001 | 2.00004 | 15.3033 |
min | 2017 | 1 | 1 | 0 | 0 | 1 | 0 |
25% | 2017 | 3 | 8 | 5.75 | 0 | 2 | 6 |
50% | 2017 | 5 | 16 | 11.5 | 0.5 | 4 | 17 |
75% | 2018 | 8 | 23 | 17.25 | 1 | 6 | 29 |
max | 2018 | 12 | 31 | 23 | 1 | 7 | 105 |
year | month | day | hour | minute | weekday | pickup_no | |
---|---|---|---|---|---|---|---|
count | 26208 | 26208 | 26208 | 26208 | 26208 | 26208 | 26208 |
mean | 2017.33 | 5.52564 | 15.6813 | 11.5 | 0.5 | 4 | 16.0918 |
std | 0.470762 | 3.30677 | 8.77644 | 6.92232 | 0.50001 | 2.00004 | 12.1896 |
min | 2017 | 1 | 1 | 0 | 0 | 1 | 0 |
25% | 2017 | 3 | 8 | 5.75 | 0 | 2 | 6 |
50% | 2017 | 5 | 16 | 11.5 | 0.5 | 4 | 14 |
75% | 2018 | 8 | 23 | 17.25 | 1 | 6 | 24 |
max | 2018 | 12 | 31 | 23 | 1 | 7 | 87 |
year | month | day | hour | minute | weekday | pickup_no | |
---|---|---|---|---|---|---|---|
count | 26208 | 26208 | 26208 | 26208 | 26208 | 26208 | 26208 |
mean | 2017.33 | 5.52564 | 15.6813 | 11.5 | 0.5 | 4 | 20.2954 |
std | 0.470762 | 3.30677 | 8.77644 | 6.92232 | 0.50001 | 2.00004 | 16.9635 |
min | 2017 | 1 | 1 | 0 | 0 | 1 | 0 |
25% | 2017 | 3 | 8 | 5.75 | 0 | 2 | 7 |
50% | 2017 | 5 | 16 | 11.5 | 0.5 | 4 | 17 |
75% | 2018 | 8 | 23 | 17.25 | 1 | 6 | 29 |
max | 2018 | 12 | 31 | 23 | 1 | 7 | 154 |
year | month | day | hour | minute | weekday | pickup_no | |
---|---|---|---|---|---|---|---|
count | 26208 | 26208 | 26208 | 26208 | 26208 | 26208 | 26208 |
mean | 2017.33 | 5.52564 | 15.6813 | 11.5 | 0.5 | 4 | 2.73077 |
std | 0.470762 | 3.30677 | 8.77644 | 6.92232 | 0.50001 | 2.00004 | 3.03585 |
min | 2017 | 1 | 1 | 0 | 0 | 1 | 0 |
25% | 2017 | 3 | 8 | 5.75 | 0 | 2 | 1 |
50% | 2017 | 5 | 16 | 11.5 | 0.5 | 4 | 2 |
75% | 2018 | 8 | 23 | 17.25 | 1 | 6 | 4 |
max | 2018 | 12 | 31 | 23 | 1 | 7 | 40 |