kaggle-taxi-trip-time-prediction

Winning solution for the Taxi-Trip Time Prediction Challenge on Kaggle (https://www.kaggle.com/c/pkdd-15-taxi-trip-time-prediction-ii).

Documentation about the method is available in doc/Taxi_II_Winning_Solution.pdf. Information on how to generate the solution file can be found below.

Generating the solution

Install the dependencies

I used python 2.7.9 with its popular scientific modules, in particular

• numpy for feature processing,
• pandas for data handling, and
• sklearn for model training.

Download the code

To download the code, run:

git clone git://github.com/hochthom/kaggle-taxi-ii.git

Download the training data

Download the data files from Kaggle. Place and extract the files into the data directory.

Create the training and test sets

Run the following scripts in any order.

create_training_set_N1.py

Result: train_pp_N1.csv with size: 1658559 x 8, Duration: 679.9 sec *

create_training_set_N2.py

Result: train_pp_N2.csv with size: 1658559 x 10, Duration: 771.8 sec *

create_training_set_N3.py

Result: train_pp_N3.csv with size: 1658559 x 10, Duration: 819.9 sec *

create_training_set.py

Result: train_pp_RND.csv with size: 1928087 x 17, Duration: 911.5 sec *

create_training_set_Experts.py

Result: 319 Training Sets, with size 3 - 109915 x 17, Duration: 10135.2 sec *
train_pp_TST_0.csv - train_pp_TST_319.csv, no file for trip 125.

*Note: The listed running times were achieved on a 12-core server with 24GByte of ram. The running time may be considerably longer if you use a machine with less ram!

Create the single model predictions

mk_submission.py

creates submissions for models N1, N2, N3, RND; Duration: 5175.5 sec *
Result: my_submission_xx.csv with xx in [N1,N2,N3,RND]

mk_submission_Experts.py

creates the expert model predictions, Duration: 2968.3 sec *
Result: predictions_TVT_experts.pkl

*Note: The listed running times were achieved on a 12-core server with 24GByte of ram. The running time may be considerably longer if you use a machine with less ram!

Create the final (blended) submissions

To generate blended prediction files from all the models, run:

blend_submissions.py

Result: final_submission_1.csv and final_submission_2.csv

Submit predictions

Submit the files on Kaggle to get it scored.

Submission 1 scored better in the private leaderboard than submission 2 (0.5092 vs. 0.5045), whereas for the public leaderboard it was the other way around (0.5253 vs. 0.5354).