American Express and Analytics Vidhya presents “AmExpert”. An amazing opportunity to showcase your analytical abilities and talent.
Recent years have witnessed a surge in the number of internet savvy users. Companies in the financial services domain leverage this huge internet traffic arriving at their interface by strategically placing ads/promotions for cross selling of various financial products on a plethora of web pages. The digital analytics unit of Best Cards Company uses cutting edge data science and machine learning for successful promotion of its valuable card products. They believe that a predictive model that forecasts whether a session involves a click on the ad/promotion would help them extract the maximum out of the huge clickstream data that they have collected. You are hired as a consultant to build an efficient model to predict whether a user will click on an ad or not, given the following features:
- Clickstream data/train data for duration: (2nd July 2017 – 7th July 2017)
- Test data for duration: (8th July 2017 – 9th July 2017)
- User features (demographics, user behaviour/activity, buying power etc.)
- Historical transactional data of the previous month with timestamp info (28th May 2017– 1st July 2017). This data contains * actions (views/interest registered) taken by the user historically on the product page via an ad or other sources
- Ad features (product category, webpage, campaign for ad etc.)
- Date time features (exact timestamp of the user session)
A Binary Classification Problem for user ad-clicks, in which data pertaining to User Demographics, User actions and Historical logs of the user are provided for some Product id's.
- Public LB : 35th Rank
- Private LB : 24th Rank
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