Skip to content
#

click-through-rate

Here are 30 public repositories matching this topic...

ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop an ecosystem to experiment, share, reproduce, and deploy in real-world in a smooth and easy way.

  • Updated Apr 8, 2022
  • Python

In this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression. The insights provided recommendations for personalized content, age-targeted ads, and income-level targeting, enhancing marketing strategy.

  • Updated Nov 6, 2023
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the click-through-rate topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the click-through-rate topic, visit your repo's landing page and select "manage topics."

Learn more