Resume Screening App

A simple web application for automated resume screening and classification using machine learning. This app allows you to upload a PDF or Word (DOCX) resume, extracts and cleans the text, and predicts the most relevant job category using a trained model.

Features

• File Upload: Upload resumes in PDF or DOCX format.
• Text Extraction: Automatically extracts text from uploaded resumes.
• Text Cleaning: Removes unnecessary elements such as hashtags, mentions, URLs, special characters, punctuation, and common stopwords.
• Resume Classification: Uses a pre-trained machine learning model to predict the resume's job category.
• Category Mapping: Maps predicted category codes to human-readable job categories.
• User Interface: Built with Streamlit for a clean and interactive experience.

Supported Categories

Some of the job categories the model can predict:

• Data Science
• HR
• Advocate
• Arts
• Web Designing
• Mechanical Engineer
• Sales
• Health and Fitness
• Civil Engineer
• Java Developer
• Business Analyst
• SAP Developer
• Automation Testing
• Electrical Engineering
• Operations Manager
• Python Developer
• DevOps Engineer
• Network Security Engineer
• PMO
• Database
• Hadoop
• ETL Developer
• DotNet Developer
• Blockchain
• Testing

(See the category_mapping dictionary in app.py for the full list.)

Getting Started

Prerequisites

• Python 3.7+
• pip

Installation

1. Clone this repository and navigate to the project directory.

2. Install dependencies:

   pip install -r requirements.txt

3. Model Files:
   Ensure the following model files are present in the Model/ directory:

   • resume_screening_model.pkl
   • tfid.pkl

   (You can train your own model using the provided Jupyter notebook: Resume_Screening_Model.ipynb.)

Running the App

Activate Venv (Virtual Environment Before Activating) Start the Flask server:

python app.py

Open the displayed local URL in your browser to use the app.

File Structure

• app.py — Main Streamlit application.
• requirements.txt — Project dependencies.
• Resume_Screening_Model.ipynb — Jupyter notebook for data exploration, model training, and experimentation.
• Model/ — Folder containing the trained model (resume_screening_model.pkl) and TF-IDF vectorizer (tfid.pkl).

How It Works

1. Upload: User uploads a PDF or DOCX resume.
2. Extraction: The app extracts text using PyMuPDF for PDFs and python-docx for DOCX files.
3. Cleaning: The text is cleaned by removing noise and stopwords.
4. Vectorization: Clean text is transformed using the pre-trained TF-IDF vectorizer.
5. Prediction: The processed vector is passed into the trained machine learning model for prediction.
6. Result: The predicted job category is displayed to the user.

Example Usage

1. Run the app.
2. Upload a resume (PDF/DOCX).
3. View predicted job category and extracted resume text.

Model Training

To train your own model or inspect the training process:

• See Resume_Screening_Model.ipynb for end-to-end steps including:
  • Loading and visualizing data
  • Text preprocessing
  • Model building and evaluation
  • Saving the trained model and vectorizer

Dependencies

Main dependencies include:

• streamlit
• PyMuPDF
• python-docx
• scikit-learn
• pickle

(See requirements.txt for the complete list.)

License

This project is for educational and demonstration purposes.

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Author: OkYashGajjar