Resume Screening System

A streamlined AI-powered resume screening system developed by Yash Lade.
Built as a beginner-friendly Python app to automate the process of screening resumes for job applications.
The system allows uploading a candidate’s resume, applies preprocessing and machine-learning logic to evaluate suitability for roles, and produces actionable screening/feedback.

Application live on - https://resume-screening-system-hjv7.onrender.com/

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🚀 Features

• Upload a resume file (PDF/DOCX) and extract key information (skills, experience, education).
• Pre-trained model (based on the dataset included) to classify/resume screening outcome (e.g., “Suitable”, “Needs improvement”, “Not suitable”).
• Web-based front-end using Streamlit (or simple Flask) for intuitive user interaction.
• Easy to use single file deployment (app.py) for starter implementation.
• Dataset and model included (UpdatedResumeDataSet.csv, models/) so you can retrain or extend as a beginner.
• Clear code structure to support understanding of ML pipeline end-to-end (data → feature extraction → model → inference).

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🧑‍💻 Getting Started

Prerequisites

• Python 3.7+ installed.
• (Optional but recommended) A virtual environment (venv/conda) to isolate dependencies.

Installation

1. Clone this repository:

   git clone https://github.com/Yash-Lade/Resume-Screening-System.git
   cd Resume-Screening-System

2. Create and activate a virtual environment (optional but recommended):

   python3 -m venv venv
   

for linux/mac:

source venv/bin/activate

On windows

venv\Scripts\activate

3.Install required packages:

pip install -r requirements.txt

4.Running the App

streamlit run app.py

(or python app.py if Flask/other UI is used) Then open the browser at http://localhost:8501 (or as shown in the terminal) and upload a resume.

🔍 How It Works

• Data ingestion – the dataset UpdatedResumeDataSet.csv contains labelled resume entries for training/validation.
• Feature extraction – the code extracts features like number of years of experience, key skills matched to roles, education level, etc.
• Model training/inference – a classification model stored under models/ is used to predict screening outcome.
• Front-end interface – user uploads a resume file, the back-end runs extraction + model + displays result and key feedback.

🎯 Why This Project?

Traditional resume-screening systems often face limitations: they rely on rigid keyword matching, generic feedback, or expensive commercial solutions. This project aims to democratize resume screening by offering a lightweight, beginner-friendly platform you can extend and customize for different job roles, sectors or languages—without requiring deep ML expertise.

🧠 Use Cases & Extensions

• Customizing the model for specific job-roles (e.g., software engineer, data scientist, UX designer).
• Adding more sophisticated NLP for resume parsing (e.g., named-entity recognition, semantic similarity).
• Building a dashboard to compare candidates, track hiring funnel metrics, visualise screening results.
• Integrating with a recruitment platform or website (via API) to automate end-to-end hiring workflows.