Technologies Used:

• Backend: PHP / Java / Node.js

• Frontend: HTML, CSS, Bootstrap, JavaScript

• ML Tools: Python (for model training), Scikit-learn, Pandas, NumPy

• Database: MySQL / MongoDB

• Visualization: Chart.js / D3.js (for frontend analytics)

Project Objective:

To develop a machine learning-based web application that predicts the likelihood of a user clicking on an online advertisement, based on their demographic data and browsing behavior. This helps advertisers target the right audience and optimize ad placements.

Key Features:

1. User Behavior Tracking:
   Collects data like pages visited, time spent, mouse movement, etc.

2. User Demographics Module:
   Users enter age, gender, location, income, etc. which influence ad targeting.

3. ML Click Prediction Engine:
   Predicts click probability based on historical labeled data using classification algorithms (e.g., Logistic Regression, Random Forest, or XGBoost).

4. Interactive Dashboard (Admin Panel):
   Visualizes ad performance, CTR (Click Through Rate), and predicted vs. actual clicks.

5. Ad Campaign Manager:
   Admin can upload different ad campaigns and monitor predicted reach and success.

6. Real-Time Prediction API:
   As soon as a user views an ad, the system predicts the click probability in real time.

Dataset Used (Sample):

• Features like:

  • Daily Time Spent on Site

  • Age

  • Area Income

  • Ad Topic Line

  • City

  • Male/Female

  • Internet Usage

  • Clicked on Ad (label)

Use public datasets from platforms like Kaggle or simulate your own.

How It Works:

1. Collect and preprocess user interaction and profile data.

2. Train a machine learning classifier (Random Forest / Logistic Regression).

3. Store the trained model.

4. Develop a web interface using HTML/CSS/JS and connect it to the backend (Node.js/Java/PHP).

5. When a user logs in or interacts with a page, the backend predicts the ad click likelihood and stores results.

6. Admin can analyze patterns and optimize ad placements accordingly.

✅ Project Outcomes:

• Helps advertisers improve conversion rates.

• Reduces cost per click (CPC) by targeting potential converters.

• Applies real-world classification and predictive analytics techniques.