🚀 AdClassifier Pro

AdClassifier Pro is an enterprise-grade Machine Learning application designed to intelligently categorize digital advertisement text. Built with Streamlit and Scikit-learn, it offers a modern SaaS-like experience for both single-text analysis and bulk data processing.

✨ Key Features

📊 Executive Dashboard

• Real-time Metrics: View total ads, unique categories, model accuracy, and versioning.
• Data Visualization: Interactive charts showing the distribution of ad categories in the training dataset.

🔍 Intelligent Classifier

• Real-time Analysis: Paste any job description or ad text to get an instant classification.
• Smart Logic:
  • High Confidence: Displays the predicted category with a visual confidence bar.
  • Low Confidence Handling: If the model's confidence is below 60%, the system flags the input as "Unclear/Random" rather than making a potentially incorrect guess. This ensures reliability for end-users.

📂 Batch Processor

• Bulk Operations: Upload .csv or .xlsx files containing thousands of ad texts.
• Automated Labeling: Processing engine classifies every row.
• Uncertainty Detection: Rows with low confidence scores are explicitly marked as Uncertain/Random.
• Analytics: Auto-generated reports on the distributed categories within your batch.
• Export: Download the fully labeled dataset in one click.

🎨 Premium UI/UX

• Dark Mode: Sleek, high-contrast design for reduced eye strain.
• Responsive: Fully functional on desktop and tablet sizes.
• Interactive Outcomes: clear, card-based result displays.

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🛠️ Tech Stack

• Frontend: Streamlit
• Machine Learning: Scikit-learn
• Data Processing: Pandas & NumPy
• Visualization: Streamlit native charts

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🚀 Installation & Setup

Follow these steps to set up the project locally.

Prerequisites

• Python 3.8 or higher

1. Clone the Repository

git clone <repository-url>
cd Digital-Advertisement-Classification

2. Create a Virtual Environment

It's recommended to use a virtual environment to manage dependencies.

Windows:

python -m venv .venv
.venv\Scripts\activate

Mac/Linux:

python3 -m venv .venv
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Application

streamlit run streamlit_app.py

The app will open automatically in your browser at http://localhost:8501.

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⚙️ Configuration

The application allows for some configuration directly in the code logic.

Confidence Threshold

In streamlit_app.py, you can adjust the sensitivity of the "Smart Logic" by modifying the CONFIDENCE_THRESHOLD constant at the top of the file.

# Default is 60.0%
CONFIDENCE_THRESHOLD = 60.0

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📂 Project Structure

├── data/
│   └── ConcatenatedDigitalAdData.xlsx  # Training dataset
├── model/
│   └── adv_model.sav                   # Pre-trained ML model
├── notebook/
│   └── ...                             # Research notebooks
├── streamlit_app.py                    # Main Application
├── requirements.txt                    # Python dependencies
└── README.md                           # Documentation

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❓ FAQ & Troubleshooting

Q: The Classifier keeps saying "Input unclear". A: This happens when the text provided is too short, gibberish, or completely unrelated to known ad categories. The model confidence is below 60%. Try providing more descriptive text.

Q: "FileNotFoundError" on startup. A: Ensure you are running the streamlit run command from the root directory of the project, so it can find model/adv_model.sav and data/.

Q: Can I train my own model? A: Yes, check the notebook/ directory for the training scripts used to generate the .sav file.