AI Hiring Recommendation System
The AI Hiring Recommendation System is a machine learning-driven application designed to automate and optimize the candidate shortlisting process. It analyzes job descriptions and candidate resumes, converts them into numerical feature vectors using NLP, and computes similarity scores to recommend candidates best suited for a given role. This project demonstrates robust data preprocessing, semantic text analysis, weighted scoring, and a modular ML pipeline for practical hiring automation.
Table of Contents
Overview
Features
System Workflow
Technologies Used
Project Structure
Installation
Usage
Input Format
Output Format
Sample Results
Future Enhancements
Contributing
License
Overview
Recruiters spend significant time screening resumes against job descriptions. This system automates that task by using NLP, vectorization, and similarity scoring to produce ranked recommendations. It is designed for HR teams, AI-based recruiting platforms, talent-tech systems, and hiring automation pipelines.
Features
Automated Candidate–Job Matching Vectorizes job descriptions and resumes using TF-IDF or advanced embeddings.
Weighted Scoring Mechanism Prioritizes key skills, experience, and domain requirements.
Ranking Engine Outputs top candidates for each job description based on match score.
Modular Architecture Easy to extend, retrain, or integrate with APIs.
Error Handling & Logging Improves debugging and production readiness.
System Workflow
Data Collection Import candidate profiles and job descriptions.
Preprocessing Cleaning, tokenization, stopword removal, normalization.
Feature Engineering TF-IDF vectorization or transformer embeddings (optional upgrade).
Similarity Computation Cosine similarity or hybrid weighted scoring.
Candidate Ranking Generates scores and sorts candidates for each job.
Technologies Used
Python 3.x
Pandas, NumPy — Data processing
Scikit-learn — TF-IDF modeling, similarity
NLTK / SpaCy — NLP preprocessing
Flask / FastAPI (optional) — API integration
Jupyter Notebook — Experiments
Project Structure AI_Hiring_Recommendation_System/ │── data/ │ ├── candidates.csv │ ├── job_descriptions.csv │ │── src/ │ ├── preprocessing.py │ ├── feature_engineering.py │ ├── scoring.py │ ├── ranking.py │ │── notebooks/ │ ├── experiments.ipynb │ │── results/ │ ├── sample_output.csv │ │── app.py (optional API) │── requirements.txt │── README.md │── .gitignore
Installation
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Clone the Repository git clone https://github.com//AI_Hiring_Recommendation_System.git cd AI_Hiring_Recommendation_System
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Create Virtual Environment python -m venv venv source venv/bin/activate # Mac/Linux venv\Scripts\activate # Windows
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Install Dependencies pip install -r requirements.txt
Usage Run the main script: python app.py
Or run the notebook:
Open notebooks/experiments.ipynb in Jupyter.
Input Format Candidates CSV candidate_id,name,skills,experience,resume_text
Job Description CSV jd_id,role,skills_required,experience_required,jd_text
Output Format
A ranked list of candidate recommendations per job description:
candidate_id | jd_id | match_score
Sample Results
Example output from cosine similarity:
Candidate: A123
Job ID: JD01
Match Score: 0.78
Future Enhancements
Shift to transformer embeddings (BERT, Sentence-BERT).
Add experience weighting with custom domain logic.
Integrate a Flask/FastAPI REST API.
Add visualization dashboards.
Deploy to cloud (AWS/GCP/Azure).
Contributing
Pull requests are welcome. Please open an issue first to discuss proposed changes.
License
This project is open-source under the MIT License.