1️⃣ Objective
The objective of this capstone is to develop a functional Recruitment Portal equipped with an advanced Resume Matching Engine. The system will automate the initial screening process by using Natural Language Processing (NLP) techniques to parse resumes, extract key skills and experience, and calculate a numerical matching score against specific job descriptions. This aims to significantly reduce the manual effort of recruiters, minimize subjective bias, and accelerate the time-to-hire by prioritizing the most relevant candidates.
Key Goals:
✨ Develop a resume parsing module capable of extracting structured data (skills, experience, education) from unstructured documents (PDFs, DOCX).
✨ Implement a vectorization technique (e.g., TF-IDF or Word Embeddings) to represent job descriptions and resumes as quantifiable data.
✨ Calculate a similarity score using a metric like Cosine Similarity to rank candidates for a given job.
✨ Build separate dashboards for Applicants (job application) and Recruiters (job posting, candidate ranking).
2️⃣ Problem Statement
Recruiters today are overwhelmed with hundreds of applications per job posting, many of which are unqualified. The process of manually reading and comparing resumes against job requirements is incredibly time-consuming, inefficient, and prone to oversight. This delays hiring decisions and can lead to the accidental rejection of suitable candidates.
This project directly tackles the scalability challenge in recruitment by introducing an intelligent screening layer. The resume matching engine will instantly process and rank applicants based on objective, quantifiable textual similarity, allowing recruiters to focus their time only on the top-ranked, most relevant candidates, thereby streamlining the pipeline and improving the quality of shortlists.
3️⃣ Methodology
The project will follow a specialized data science and software engineering workflow:
✨ Phase 1 — Portal Foundation: Set up the web portal, user authentication (Applicant/Recruiter), and the core database schema for jobs and applications.
✨ Phase 2 — Resume Parsing & Preprocessing: Implement a library (e.g., textract or dedicated API) to extract text from resumes. Clean and tokenize the text, removing stop words and performing stemming/lemmatization.
✨ Phase 3 — Vectorization & Modeling: Apply TF-IDF (Term Frequency-Inverse Document Frequency) to convert both the Job Description and the preprocessed resume text into numerical feature vectors.
✨ Phase 4 — Scoring Engine: Calculate the Cosine Similarity between the job vector and each resume vector. This score is stored as the match percentage for ranking.
✨ Phase 5 — Dashboard and UX: Integrate the scoring engine into the Recruiter Dashboard, allowing them to view candidates ranked by match score and easily filter/manage applications.
4️⃣ Dataset
Core Entities:
✨ Jobs: Job Title, Job Description (raw text), Required Skills (extracted), Posting Date.
✨ Applicants: Personal Info, Application Date, Resume File Path, User ID.
✨ Parsed Resumes: Structured JSON/Text data (Experience list, Skill tags) extracted from the original document.
✨ Applications: Job ID, Applicant ID, Status (Pending, Shortlisted), Match Score.
Patient Records Table (Sample):
5️⃣ Tools and Technologies
6️⃣ Evaluation Metrics
✨ OCR Accuracy (Total Amount): Percentage of receipts where the total amount is correctly extracted (Target: > 90%).
✨ Transaction Time: Time taken from image upload to final expense entry (Target: < 5 seconds).
✨ Auto-Categorization Precision: Accuracy of the ML model in assigning the correct category to new expenses.
✨ Prediction Error: RMSE or similar metric for the simple expense forecasting model’s accuracy.
✨ Usability Score: User feedback on the intuitiveness of the interface and the simplicity of the OCR process.
7️⃣ Deliverables
8️⃣ System Architecture Diagram
Candidate Portal / Submission
Uploads (PDF/DOCX), application form data, and job preference input.
Recruiter Dashboard
Creates/edits job descriptions, sets required skills, and defines scoring weights.
External Job Boards/Sources
Data ingestion pipeline for importing candidate profiles from outside sources.
API Gateway & Application Logic
Handles authentication, data validation, and initiates the matching workflow.
AI/NLP Resume Parsing Service
Extracts structured data (skills, experience) from unstructured resume text.
Candidate Matching & Scoring Engine
Calculates a match percentage between candidate profile and job requirements.
Applicant Tracking Database (PostgreSQL)
Stores candidate history, scoring results, job postings, and interview feedback.
Interview Scheduling Service
Integrates with calendars (Google/Outlook) for automated booking.
Notification Service (Email/SMS)
Automated communication for application confirmation and status updates.
Final Outcome: Reduced Time-to-Hire & Improved Quality of Candidates
Automated initial screening, allowing recruiters to focus on top-ranked matches.
Candidate Portal / Submission
Uploads (PDF/DOCX), application form data, and job preference input.
Recruiter Dashboard
Creates/edits job descriptions, sets required skills, and defines scoring weights.
External Job Boards/Sources
Data ingestion pipeline for importing candidate profiles from outside sources.
API Gateway & Application Logic
Handles authentication, data validation, and initiates the matching workflow.
AI/NLP Resume Parsing Service
Extracts structured data (skills, experience) from unstructured resume text.
Candidate Matching & Scoring Engine
Calculates a match percentage between candidate profile and job requirements.
Applicant Tracking Database (PostgreSQL)
Stores candidate history, scoring results, job postings, and interview feedback.
Interview Scheduling Service
Integrates with calendars (Google/Outlook) for automated booking.
Notification Service (Email/SMS)
Automated communication for application confirmation and status updates.
Final Outcome: Reduced Time-to-Hire & Improved Quality of Candidates
Automated initial screening, allowing recruiters to focus on top-ranked matches.
9️⃣ Expected Outcome
✨ A high-utility application that significantly reduces manual entry time for expense tracking.
✨ A reliable OCR backend capable of extracting financial details from real-world receipts.
✨ A dashboard providing intelligent, data-driven insights into spending habits and budget adherence.
✨ A scalable and well-documented codebase for potential future features like multi-user support or bank integration.