Goal

• 1 Project Guidelines and Starter Notebook

  • Understanding the Business Problem

  • TalkingData is a Chinese big data company, and one of their areas of expertise is mobile advertisements.

  • In mobile advertisements, click fraud is a major source of losses. Click fraud is the practice of repeatedly clicking on an advertisement hosted on a website with the intention of generating revenue for the host website or draining revenue from the advertiser.

  • In this case, TalkingData happens to be serving the advertisers (their clients). TalkingData cover a whopping approx. 70% of the active mobile devices in China, of which 90% are potentially fraudulent (i.e. the user is actually not going to download the app after clicking).

  • You can imagine the amount of money they can help clients save if they are able to predict whether a given click is fraudulent (or equivalently, whether a given click will result in a download).

  • Their current approach to solve this problem is that they've generated a blacklist of IP addresses - those IPs which produce lots of clicks, but never install any apps. Now, they want to try some advanced techniques to predict the probability of a click being genuine/fraud.

  • In this problem, we will use the features associated with clicks, such as IP address, operating system, device type, time of click etc. to predict the probability of a click being fraud.

    • Note : The data has been provided in the CSV format here