DSN Pre-Bootcamp Hackathon: Expresso Churn Prediction Challenge by Data Science Nigeria
Predict when an airtime customer will move to another provider
Prize
Knowledge
Time
Ended over 2 years ago
Participants
358 active ยท 671 enrolled
Helping
Nigeria
Prediction
Customer service
Telecom
Description

This is a private hackathon open to Data Science Nigeria (DSN) Pre-Bootcamp hackathon participants. To participate in this hackathon, you must register via this link: https://bit.ly/prezindi2020

Expresso is an African telecommunications company that provides customers with airtime and mobile data bundles. The objective of this challenge is to develop a machine learning model to predict the likelihood of each Expresso customer “churning,” i.e. becoming inactive and not making any transactions for 90 days.

This solution will help Expresso to better serve their customers by understanding which customers are at risk of leaving.

About Expresso:

Expresso is an African telecommunications services company that provides telecommunication services in two African markets: Mauritania and Senegal. Expresso offers a wide range of products and services to meet the needs of customers.

Rules

This is a private hackathon open to Data Science Nigeria participants.

Teams and collaboration

You may participate in this competition as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the highest number of submissions among team members at team formation. Prizes are transferred only to the individual players or to the team leader.

Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be disqualified.

Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).

Datasets and packages

The solution must use publicly-available, open-source packages only.

You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.

You may use pretrained models as long as they are openly available to everyone.

The data used in this competition is the sole property of Zindi and the competition host. You may not transmit, duplicate, publish, redistribute or otherwise provide or make available any competition data to any party not participating in the Competition (this includes uploading the data to any public site such as Kaggle or GitHub). You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.

You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.

Your solution must not infringe the rights of any third party and you must be legally entitled to assign ownership of all rights of copyright in and to the winning solution code to Zindi.

Submissions and winning

You may make a maximum of 10 submissions per day. Your highest-scoring solution on the private leaderboard at the end of the competition will be the one by which you are judged.

Zindi maintains a public leaderboard and a private leaderboard for each competition. The Public Leaderboard includes approximately 50% of the test dataset. While the competition is open, the Public Leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the competition, the Private Leaderboard, which covers the other 50% of the test dataset, will be made public and will constitute the final ranking for the competition.

If your solution places 1st, 2nd, or 3rd in the final ranking, you will be required to submit your winning solution code to us for verification and you thereby agree to share all worldwide rights of copyright in and to such winning solution to Zindi.

You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.

  • Participants may compete individually or in teams of up to four people.
  • The teams will be judged based on their ranking on the dedicated Zindi leaderboard at the time of competition close.
  • All participants in the hackathon must be registered students (undergraduate or graduate) at the university they represent. Lecturers, University staff, and alumni may participate in a mentorship or advisory capacity.
  • Teams cannot collaborate or share information with each other.
  • All solutions must use machine learning, but teams are permitted and encouraged to use exploratory data analysis in building their solutions.
  • All solutions must use publicly-available, open-source packages only.
  • Solutions must use only the allowed and available datasets.
  • Participants caught cheating or breaking any competition rules will be immediately disqualified from the competition.
  • Universities caught cheating or allowing teams to cheat will be immediately disqualified from the competition.
  • The winning code must be submitted to Zindi for review and validation immediately at the close of the competition. In the interest of logistics, code review will take place only after the competition has closed and winners have been announced.

Please refer to the FAQs and Terms of Use for additional rules that may apply to this competition. We reserve the right to update these rules at any time.

Evaluation

The evaluation metric for this challenge is Log Loss.

The values can be between 0 and 1, inclusive. Where 1 indicates the customer churned and 0 indicates the customer stayed with Expresso.

Your submission should look like:

user_id                                      CHURN
00001dbe00e56fc4b1c1b65dda63de2a5ece55f9      0.98
000055d41c8a62052dd426592e8a4a3342bf565d      0.12
000081dd3245e6869a4a9c574c7050e7bb84c2c8      0.37  
Prizes

This is a learning hackathon. Aside from knowledge, there are no prizes for this competition.

Timeline

Competition closes on 22 August 2020.

Final submissions must be received by 11:59 PM WAT (Nigeria time).

We reserve the right to update the contest timeline if necessary.