Resume / Candidate Screening System
🔍 About the Task
Hiring teams receive hundreds of resumes for a single job role.
Manually reading each resume is slow, inconsistent, and error-prone.
This is why many companies use Machine Learning–based resume screening systems to:
- shortlist candidates faster
- match skills with job requirements
- identify missing or weak skills
- reduce recruiter workload
In this task, you will build a real ML system that automatically screens, scores, and ranks resumes based on a given job role.
This is a very job-relevant project, commonly used in HR-tech startups, recruitment platforms, and enterprise hiring tools.
🎯 Objective
Your goal is to build an ML system that can:
- Read resume text (PDF/Text datasets)
- Extract skills and relevant keywords
- Compare resumes with a job description
- Rank candidates based on role fit
- Highlight missing or required skills
This mirrors how real resume screening tools work, just on a smaller, beginner-friendly scale.
✅ What You’ll Do
As part of this task, you will:
- Work with unstructured resume text data
- Clean and preprocess text
- Extract skills using NLP techniques
- Build similarity or scoring logic
- Rank candidates based on job relevance
- Explain results clearly for non-technical users
You are learning decision-support ML, not just model training.
🛠️ Tools You’ll Use
This task focuses on Natural Language Processing (NLP).
Core Development Tools
- Python – https://www.python.org
- Jupyter Notebook – https://jupyter.org
- VS Code – https://code.visualstudio.com
- GitHub – https://github.com
NLP & ML Libraries
- spaCy – https://spacy.io
(skill extraction, NLP pipelines) - NLTK – https://www.nltk.org
(text preprocessing & tokenization) - Scikit-learn – https://scikit-learn.org
(vectorization, similarity, ranking logic)
📁 Dataset Guidance (Choose Any)
You may use any dataset that represents resumes, job descriptions, or skill text.
✅ Recommended Working Datasets
📄 Resume Dataset (Kaggle)
🔗 https://www.kaggle.com/datasets/snehaanbhawal/resume-dataset
- Text-based resumes
- Multiple job categories
- Beginner-friendly
📄 Resume Entities & Job Roles Dataset
🔗 https://www.kaggle.com/datasets/ravindrasinghrana/job-description-dataset
- Useful for skill extraction
- Great for NLP practice
📄 Job Descriptions Dataset
🔗 https://www.kaggle.com/datasets/PromptCloudHQ/us-jobs-on-monstercom
- Real job descriptions
- Useful for skill matching & role comparison
⚠️ You may also use:
- simulated resumes
- anonymized student resumes
- custom job descriptions
As long as the data reflects real hiring scenarios, it is valid.
✨ Key Features to Implement
Your solution should include:
✔ Resume text cleaning & preprocessing
✔ Skill extraction using NLP
✔ Job description parsing
✔ Resume-to-role similarity scoring
✔ Candidate ranking based on role fit
✔ Skill gap identification
Optional bonus:
- Weighting important skills
- Visual comparison of candidates
📤 Final Deliverable
You must submit:
- A resume screening & ranking system
- Clear explanation of:
- how resumes are scored
- why certain candidates rank higher
- what skills are missing
- Clean, well-documented code in a public GitHub repository
Your output should feel like something you could confidently show to:
- a recruiter
- an HR manager
- an HR-tech startup
📁 GitHub Inspiration (Verified & Safe)
You may explore these working GitHub topic pages for structure and ideas
(do NOT copy code):
🔹 Resume Parsing Projects
🔗 https://github.com/topics/resume-parser
🔹 NLP Resume Screening Projects
🔗 https://github.com/topics/resume-screening
🔹 Job Description Matching / NLP
🔗 https://github.com/topics/text-similarity
Use these links to understand:
- project structure
- NLP workflow
- scoring logic
Your implementation must be original and explainable.
Showcase Your Work
Once completed:
- Share screenshots or demo videos on LinkedIn
- Explain which business you built it for
- Tag Future Interns
- https://www.linkedin.com/company/future-interns/
This builds visibility, confidence, and credibility.