Advanced 
✅ LLM Resume Review 
✅ Interview Question Generator 
✅ Candidate Summary Generation 
✅ Hiring Recommendation Engine

▎📂 Project Structure

• resumes/: Directory to store uploaded resumes in various formats (PDF, DOCX, etc.).
• job_descriptions/: Directory to store job descriptions that will be used for screening.
• app.py: Main application file where the Streamlit app is run.
• parser.py: Script for parsing resumes and extracting relevant information.
• scorer.py: Script for scoring resumes based on ATS criteria and ranking candidates.
• requirements.txt: List of dependencies required to run the project.
• README.md: Documentation of the project, including setup instructions and usage.
• screenshots/: Folder to store screenshots of the application interface for documentation purposes.

▎💼 Resume Project Description

AI Resume Screening System 
Developed an AI-powered Resume Screening System using NLP, TF-IDF, Cosine Similarity, Python, and Streamlit. The system automates candidate evaluation through:

• Resume Parsing: Extracting key information from resumes using NLP techniques.
• Skill Extraction: Identifying relevant skills from both resumes and job descriptions.
• ATS Scoring: Evaluating resumes based on Applicant Tracking System (ATS) criteria.
• Candidate Ranking: Ranking candidates based on their fit for the job description.
• LLM-based Feedback Generation: Providing personalized feedback to candidates based on their resumes.

▎🎯 Mini Challenge Features

1. Resume Ranking Dashboard: Visualize candidate rankings and scores in an interactive dashboard using Streamlit's charting capabilities.
2. Interview Question Generation: Create tailored interview questions based on the skills and experiences highlighted in the resumes.
3. PDF Report Generation: Generate comprehensive reports in PDF format summarizing candidate evaluations and scores for easy sharing with hiring teams.
4. Multi-resume Bulk Screening: Enable bulk upload of multiple resumes for simultaneous screening and evaluation.
5. Semantic Matching Using Embeddings: Implement embeddings (e.g., BERT, Word2Vec) to improve semantic matching between resumes and job descriptions beyond simple keyword matching.

▎🔍 Interview Questions From This Project

Q1. What is TF-IDF? 
Answer: TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure that evaluates the importance of a word in a document relative to a collection of documents (corpus). It increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus, helping to highlight words that are unique to specific documents.

Q2. What is Cosine Similarity? 
Answer: Cosine Similarity is a metric used to measure how similar two vectors are by calculating the cosine of the angle between them. In the context of text analysis, it quantifies how closely related two pieces of text are based on their vector representations. A cosine similarity closer to 1 indicates high similarity, while a value closer to 0 indicates low similarity.

Q3. Why use embeddings instead of keywords? 
Answer: Embeddings allow for a deeper understanding of text by capturing semantic meaning rather than relying solely on exact keyword matches. This means that related skills or concepts can be recognized even if different terminology is used, thereby improving the accuracy of candidate evaluations.

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