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Matthew Jackson

Apictureisworthathousandwords
A complex idea can be conveyed with just a
single still image, namely making it possible
to absorb large amounts of data quickly.

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Talking Data is the largest independent big data service company in China. Their network covers 70% of the mobile services nationwide with 3 billion ad clicks per day. Amongst those clicks, 90% are potentially fraudulent. Click fraud is happening at an overwhelming volume leading to misusage of data and wasting money. Hence, Kaggle (a platform for predictive modeling and analytics competitions from the U.S.) has partnered up with TalkingData to help resolve this issue. This paper is to build predictive analysis models using traditional and Big Data methods to determine whether a smartphone app will be downloaded after clicking an advertisement. We have used data named “TalkingData AdTracking Fraud Detection Challenge”, which is of 7GB and given by a Kaggle competition. Four classification models are implemented with this massive data set in order to predict fraud in both traditional and Big Data methods. We define it fraud when the user clicked on an advertisement without downloading. The traditional platform has a resource limitation to build models with data set over a giga-byte so that we generate a sample data for the traditional models and adopt the full data set for the models in the Big Data Spark ML systems. We also present the accuracy and performance of the models implemented in both traditional and Big Data systems.

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