CTR (Click-Through Rate)

CSC 591/791 Algorithms for Data Guided Business Intelligence

Team 10 Members: Ashutosh Chaturvedi, Harshdeep Kaur, Sameer Sharma, Surbhi Gupta, Vipul Kashyap

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. It is commonly used to measure the success of an online advertising campaign for a particular website as well as the effectiveness of email campaigns.

Dataset

Data is available at Kaggle Avazu-CTR-Prediction page: Data Files

File descriptions

• train: Training set. 10 days of click-through data, ordered chronologically. Non-clicks and clicks are subsampled according to different strategies.
• test: Test set. 1 day of ads to for testing your model predictions.

Algorithms Implemented

• Naive Bayes
• Logistic Regression
• FTRL (Follow the Regularized Leader)

Files:

Logistic Regression & Naive Bayes

• base_model.py - Implementation of Naive Bayes and Logistic Regression Algorithm for predicting CTR.
• generate_data_file.py - Data pre-processing to code.

FTRL (Follow the Regularized Leader)

• FTRL.py - Implementation of FTRL Algorithm for CTR.

Usage:

Naive Bayes and Logistic Regression Implementation:

python base_model.py

FTRL Implemenration: Require pypy to run program faster.

python FTRL.py