## Welcome to Digital Turbine's Auction Bid Price Prediction!

[Digital Turbine](https://www.digitalturbine.com/) is the leading independent growth & monetization platform, powered by code installed on devices by global carriers and OEMs.

Digital Turbine provides an Ad Exchange for publishers and advertisers - publishers can grow their businesses and monetize their applications, while advertisers get more users installing their apps. The Ad Exchange connects Demand Side Platforms (DSPs) and Supply Side Platforms (SSPs) through [Real Time Bidding](https://en.wikipedia.org/wiki/Real-time_bidding).

 [This image](https://prnt.sc/LvUfS0ukBnmv) helps follow the technical flow of an auction. The steps are as follows:

1. An ad request is sent from a user's device into the mediation platform.
2. The mediation platform initiates an external auction, and sends a bid request to DT Exchange.
3. DT Exchange receives that bid request, initiates an internal auction on its side using DT Exchange, and sends bid request(s) to its DSPs.
4. DT Exchange receives the bid response(s) from its bidders, and chooses the winner (1st price auction).
5. DT Exchange bids accordingly onto the mediation auction (external auction).
6. The mediation then sends a "Win / Loss" auction resolution event to its participants, with winBid (target in this competition).
7. Finally, an ad response will be sent back to the device, present to the user, who will be able to interact with the chosen ad.



As one of the leading exchanges in the world, we operate on a gargantuan scale: we handle 100 billion requests from hundreds of millions of devices daily. One of the most interesting challenges in such Ad Exchange is the pricing problem. Our Ad Exchange is competing with other exchanges and in order to optimize our performance we need to predict what the other exchanges will bid for the request, and what will be the winning bid for the request.

This is your place to shine and to compete!

Given a set of auction attributes (features), as well as a signal which indicates if we (DT) had won that auction or not (has_won), your goal is to predict what bid price would have won us the auction (aka the "winBid").
Meaning - the goal is to predict the winning "winBid" on a given auction.

Note: The problem is a regression problem (as we're interested in knowing what was the winning bid price in $ [CPM](https://www.investopedia.com/terms/c/cpm.asp).

The full dataset description can be found under the "Data" section, you should refer to it for additional information.

## THIS IS ALSO A RECRUITMENT COMPETITION FOR DATA SCIENCE POSITIONS AT DIGITAL TURBINE
We are opening Data Science positions in Warsaw and Petach Tikva.
In case you'd like to apply, please see [this position](https://jobs.lever.co/digitalturbine/c1985c0c-3ac1-44df-b8d4-cc7bc0396bb6).
Participants with high quality contributions (e.g. a high score on the LB, an innovative notebook, an insightful forum discussion) can secure an interview and a significant advantage in our recruitment process.