This project focuses on optimizing web advertisement selection using the Upper Confidence Bound (UCB) algorithm, a powerful Reinforcement Learning technique. The goal is to maximize Click Through Rate (CTR) by intelligently balancing exploration and exploitation.
In digital marketing, selecting the most effective advertisement is crucial. Instead of randomly displaying ads, this project uses Reinforcement Learning to dynamically select the best ad based on user interactions.
- Python 🐍
- Streamlit 📊
- NumPy
- Pandas
- Matplotlib
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The dataset contains user interactions with multiple ads.
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Each column represents an ad.
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Values:
1→ User clicked the ad0→ User did not click
The UCB algorithm selects ads based on:
- Average reward of each ad
- Confidence interval (uncertainty)
It ensures:
- All ads are explored initially
- Best-performing ads are selected more frequently over time
- Interactive Streamlit Web App UI
- Upload your own dataset
- Real-time ad optimization
- Visualization of ad selections
- Best ad identification
- Performance metrics
Run the app locally:
pip install -r requirements.txt streamlit run app.py
- Total reward (CTR performance)
- Best performing ad
- Histogram of ad selections
- Reward distribution per ad
WebAdOptimization_UCB/ │── app.py │── dataset.csv │── requirements.txt │── README.md
- 💼 LinkedIn: https://www.linkedin.com/in/senthamil45
- 🌍 Portfolio: https://senthamill.vercel.app/
- 💻 GitHub: https://github.com/selvan-01/WebAd-Optimization-using-Reinforcement-Learning.git
- Implement Thompson Sampling
- Add real-time data simulation
- Deploy using Streamlit Cloud
- Enhance UI with Plotly dashboards
This project demonstrates how Reinforcement Learning can significantly improve decision-making in digital advertising by maximizing user engagement and revenue.
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