Resume Classification System 📄🤖

An NLP-powered Resume Classification & Ranking System built with Flask, Scikit-learn, and BERT-based experiments.

The system automatically preprocesses resumes, classifies them into professional domains, and supports resume ranking workflows for intelligent recruitment assistance.

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✨ Features

🧠 NLP Pipeline

• Resume text preprocessing & cleaning
• Tokenization and normalization
• Stopword removal
• Text feature extraction
• Resume classification pipeline
• Resume ranking module

📄 Resume Classification

Automatically classifies resumes into domains such as:

• Data Science
• HR
• Java Developer
• Testing
• DevOps
• Web Designing
• Sales
• Mechanical Engineering

📊 Modeling

• Baseline ML models
• TF-IDF vectorization
• BERT experimentation
• Performance evaluation & comparison
• Notebook-based experimentation workflow

🌐 Web Interface

• Flask application interface
• Resume upload workflow
• Prediction results page
• Ranking visualization

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🗄 Dataset

Dataset includes resumes from multiple professional domains:

Resume.csv
cleaned_resume.csv

Processed datasets are stored after preprocessing for model training and evaluation.

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🛠 Tech Stack

Layer	Technology
Backend	Python · Flask
NLP	NLTK · Scikit-learn
Deep Learning	Transformers / BERT
Data Processing	Pandas · NumPy
Visualization	Matplotlib
Frontend	HTML · CSS · Jinja2

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🚀 Getting Started

Prerequisites

• Python 3.10+
• pip
• Virtual Environment

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1. Clone repository

git clone https://github.com/Youssif71/Resume-Classification-System.git

cd Resume-Classification-System

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2. Create virtual environment

Windows:

python -m venv .venv

.venv\Scripts\activate

Linux / macOS:

python -m venv .venv

source .venv/bin/activate

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3. Install dependencies

pip install -r requirements.txt

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4. Run application

python app.py

Open:

http://127.0.0.1:5000

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📁 Project Structure

Resume-Classification-System/

├── app.py
├── requirements.txt
├── README.md
├── LICENSE
│
├── data/
│   ├── Resume.csv
│   └── cleaned_resume.csv
│
├── docs/
│   ├── NLP Resume Classification Project Report.docx
│   └── NLP Resume Classification Project Report.pdf
│
├── notebooks/
│   ├── preprocessing.ipynb
│   ├── Final_Preprocessing.ipynb
│   ├── baseline_model.ipynb
│   └── bert_model.ipynb
│
├── templates/
│   ├── layout.html
│   ├── index.html
│   └── results.html
│
├── static/
│   └── style.css
│
└── utils/
    ├── preprocessing.py
    ├── inference.py
    ├── ranking.py
    └── __init__.py

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🔬 Experiments

Baseline Models

• Traditional ML classifiers
• TF-IDF representations
• Comparative evaluation

BERT Models

• Transformer experimentation
• Context-aware embeddings
• Performance benchmarking

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📈 Workflow

Resume Input
      ↓
Text Cleaning
      ↓
Preprocessing
      ↓
Feature Extraction
(TF-IDF / BERT)
      ↓
Classification
      ↓
Ranking
      ↓
Prediction Output

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📄 Documentation

Project reports available:

• PDF Report
• DOCX Documentation
• Experimental notebooks

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🔒 Notes

• Built for educational and research purposes
• Supports experimentation with traditional ML and transformer models
• Can be extended for ATS / recruitment systems

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👤 Author

Youssif Magdi Ahmed Assem Adel Akram

Data Science & Artificial Intelligence Student
Zewail City of Science & Technology

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📄 License

Educational use only.