🎓 University Query Priority Classifier

An end-to-end NLP pipeline that automates student support by classifying queries into High, Medium, or Low priority. Built with Scikit-Learn, it features a custom preprocessing engine (stemming, emoji handling, short-form expansion) and a multi-stage ColumnTransformer pipeline for seamless text and categorical data integration.

📝 Project Description

This project implements an end-to-end NLP pipeline designed to automate student support desk operations. It classifies incoming student queries into High, Medium, or Low priority levels based on the query text and the target department.

The system utilizes a custom preprocessing engine and a nested Scikit-Learn Pipeline architecture to handle text vectorization and categorical encoding simultaneously.

🚀 Key Features

• Custom NLP Preprocessor: Handles lowercasing, punctuation removal, short-form expansion (e.g., "asap" ➔ "as soon as possible"), emoji removal, and Porter Stemming.
• Nested Pipeline Architecture: Uses ColumnTransformer to manage text data (TfidfVectorizer) and categorical data (OneHotEncoder) in a single unified object.
• Automated Model Selection: Includes a benchmarking suite for Logistic Regression, Linear SVC, Random Forest, and Naive Bayes with Hyperparameter tuning via GridSearchCV.
• Pickle-Ready: Architecture designed for easy deployment via joblib.

📂 Project Structure

├── data/
│   └── University_Query.csv    # Dataset
├── models/
│   ├── ModelPipeline.pkl  # Trained Pipeline object
│   └── Label_Map.pkl                # Numerical to Label mapping
├── notebooks/
│   └── Pipelining.ipynb           # Data analysis & Model training
|       TextPreprocessing.ipynb
├── src/
│   └── transformers.py              # Custom Preprocess & Flattener classes
├── app.py                           # Streamlit Web Application
├── requirements.txt                 # Dependencies
└── README.md

🛠️ How to Run

1. Clone the Repository

Open your terminal or command prompt and run:

git clone [https://github.com/your-username/university-query-priority.git](https://github.com/your-username/university-query-priority.git)
cd university-query-priority

2. Set Up a Virtual Environment

It is highly recommended to use a virtual environment to avoid dependency conflicts:

Create the environment

python -m venv venv

Activate the environment (Windows)

venv\Scripts\activate

Activate the environment (Mac/Linux)

source venv/bin/activate

3. Install Dependencies

Install all required libraries and download the necessary NLTK data:

pip install -r requirements.txt
python -c "import nltk; nltk.download('stopwords'); nltk.download('punkt')"

4. Run the Web Application

This project uses Streamlit for the frontend. To launch the web interface, run:

streamlit run app.py

5. Training the Model (Optional)

If you wish to retrain the model or explore the data analysis, launch the Jupyter Notebook:

jupyter notebook notebooks/Training_EDA.ipynb