/Supervised-learning-algorithms

This project is going to predict success or failure in downloading the program from the Ads link.Logistic Regression Classifier , SVM , KNN , Decision Tree Classifier , Random Forest Classifier and Naive Bayes algorithms used to predict .

Primary LanguageJupyter NotebookMIT LicenseMIT

Supervised-learning-algorithms

Fraud risk is everywhere, but for companies that advertise online, click fraud can happen at an overwhelming volume, resulting in misleading click data and wasted money. Ad channels can drive up costs by simply clicking on the ad at a large scale. With over 1 billion smart mobile devices in active use every month, China is the largest mobile market in the world and therefore suffers from huge volumes of fradulent traffic.

TalkingData, China’s largest independent big data service platform, covers over 70% of active mobile devices nationwide. They handle 3 billion clicks per day, of which 90% are potentially fraudulent. Their current approach to prevent click fraud for app developers is to measure the journey of a user’s click across their portfolio, and flag IP addresses who produce lots of clicks, but never end up installing apps. With this information, they've built an IP blacklist and device blacklist.