🧭 Introduction

Resume Classifier is a backend-focused NLP project that categorizes resumes into relevant job fields using GRU architecture for sequence classification. It’s designed to streamline resume sorting and assist recruiters in identifying suitable candidates by analyzing textual content.

🛠️ Project Type

Backend

🚀 Deplolyed App

Frontend: Not Applicable Backend: Jupyter Notebook Database: Local CSV dataset

📁 Directory Structure

ResumeClassifier/
│
├── data/
│ └── Resume.csv
├── notebooks/
│ └── Resume Classifier.ipynb
├── Visuals/
├── README.md

🎥 Video Walkthrough of the project

🎥 Video Walkthrough of the codebase

✨ Features

• Resume text preprocessing pipeline
• GRU model for job category classification
• Model evaluation using accuracy and confusion matrix

🎯 Design decisions or assumptions

• GRU selected for handling sequential resume text due to efficient memory usage
• Labels encoded based on job category
• CSV format used for data due to project scope
• No database connectivity; data loaded directly into memory

🧪 Installation & Getting started

Clone the repo and install required packages:

git clone https://github.com/Shaileshahire06/Resume-Classifier.git
cd Resume-Classifier

📌 Usage

Example Jupyter notebook usage:

# Open notebook
jupyter notebook notebooks/resume_preprocessing.ipynb

Include screenshots as necessary.

🔐 Credentials

No authentication required in current version.

🌐 APIs Used

No external APIs used — purely self-contained text classification pipeline using Hugging Face and TensorFlow.

📮 API Endpoints

Not applicable for this version.

🧰 Technology Stack

• Python 3.10
• TensorFlow / Keras
• Hugging Face Transformers
• Jupyter Notebooks
• Pandas & NumPy