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PROBLEM STATEMENT

Given the telematics data for each trip and the label if the trip is tagged as dangerous driving, derive a model that can detect dangerous driving trips.

SUBMISSION DEADLINE

Please submit the final repository including documentation by or before 17 June 2019,
6.00pm (SGT)
.

Grab has been proactively pushing to make transportation in SEA safer. As part of the effort, we want to identify dangerous drivings in a timely manner.

The given dataset contains telematics data during trips (bookingID). Each trip will be assigned with label 1 or 0 in a separate label file to indicate dangerous driving. Pls take note that dangerous drivings are labelled per trip, while each trip could contain thousands of telematics data points. participants are supposed to create the features based on the telematics data before training models.

Field

Description

bookingID

Accuracy

Bearing

acceleration_x

acceleration_y

acceleration_z

gyro_x

gyro_y

gyro_z

second

Speed

trip id

accuracy inferred by GPS in meters

GPS bearing in degree

accelerometer reading at x axis (m/s2)

accelerometer reading at y axis (m/s2)

accelerometer reading at z axis (m/s2)

gyroscope reading in x axis (rad/s)

gyroscope reading in y axis (rad/s)

gyroscope reading in z axis (rad/s)

time of the record by number of seconds

speed measured by GPS in m/s

You will be judged on the following criteria:

Code Quality

Code Quality, also known as Software Quality, is generally defined in two ways:
 

  • How well does the code conform to the functional specifications and requirements
    of a project.

  • Structural quality, which relates to the maintainability and robustness of the code.

Creativity in Problem-solving

Creativity speaks volumes about your capability to make sense of given data, derive tangible results relevant to the business needs of an organization and present the findings. All this, while keeping in mind the problem statements.

 

Check out our thought process behind these challenges in our short film!

Feature Engineering

 

Feature Engineering also referred to as pre-processing, refers to the process of selecting and transforming variables when creating a data model for a given problem statement. While you will be given a general dataset which relates to the problem statement, you need to create “features” that make the models and algorithms work as intended.

 

Note that your code should be able to automatically create your desired features, that can be used in the evaluation of the Hold-out test set.

Model Performance

Model performance determines how a model represents the data and how well the chosen model will work. In this challenge, we will be performing a Hold-out model evaluation. For this problem, you are given a training data set, and our evaluators will have a test data set (not seen by the model). This test dataset will assess the likely future performance of the model.

 

Model will be evaluated based on the AUC-ROC curve. The model produces the highest area on test dataset is preferred.

QUALIFICATION CRITERIA

  • Submit the correct link to your repository

  • Make sure your repository includes the complete codebase (all the commits are done, documentation, complete, etc)

  • Solve only one of the challenges mentioned on the website

  • Do not plagiarise the code. That will be grounds for instant disqualification

  • The link to your repository must be publicly accessibly from the time of submission.

SUBMISSION GUIDELINES

You can submit the code (either as a codebase or a Jupyter notebook) by uploading it to a public Github or similar repository. The instructions to submit the repository link will be sent to you via email once you accept the challenge on https://www.aiforsea.com/