Click-Through Rate Prediction

Khelan Patel (kjpatel4@ncsu.edu) Sonal Patil (sspatil4@ncsu.edu) Darshak Bhatti (dbhatti@ncsu.edu)

Project Description: Click-through rate (CTR) is the ratio of users who click on a specific link to the number of total users who view a page, email, or advertisement. In online advertising, it is a very important metric for evaluating ad performance. As a result, click prediction systems are essential and widely used for sponsored search and real-time bidding.

Learning objective: The aim is to understand revenue dynamics in projecting Adwords to customers. Pre-processing Data: As the size of the dataset is 6GB, we might need to apply some feature selection/reduction techniques. Understanding various data-mining algorithms for binary classification problems. Implement Generalized Linear Models (logistic regression and Logistic Regression Model with SGD and L2 regularization) Comparison of various data-mining models (Using Log-loss and RMSE)

How to obtain data-set : This was part of a Kaggle competition. Dataset can be obtained from here https://www.kaggle.com/c/avazu-ctr-prediction/data