AI_Resume_Analyzer_Presentation

1. AI Resume Analyzer
Leveraging NLP & Deep Learning for
Intelligent Resume Screening
By [Your Name] | [Date]

2. Introduction
• - AI Resume Analyzer automates resume
screening using AI.
• - Helps companies find the best candidates
efficiently.
• - Assists job seekers in improving their
resumes.
• - Manual resume screening is time-consuming;
AI ensures accuracy.

3. Objectives
• - Automate resume screening using AI & NLP.
• - Provide resume ranking based on job
requirements.
• - Generate feedback & improvement
suggestions.
• - Improve hiring efficiency and job-seeker
success.

4. Features
• For HR / Recruiters:
• ✔ Upload multiple resumes at once
• ✔ AI-based ranking of resumes
• ✔ Job-specific resume filtering
• ✔ Automated shortlisting
• For Job Seekers:
• ✔ Instant resume feedback
• ✔ Resume Score & Suggestions

5. Technologies Used
• - Frontend: HTML, CSS, JavaScript
• - Backend: Python (Flask/Django)
• - Database: PostgreSQL / MongoDB
• - AI Models: NLP & Deep Learning
• - Libraries: Spacy, NLTK, TensorFlow, PyTorch,
OpenAI GPT / BERT

6. Methodology (Step-by-Step)
• 1. Data Collection: Gather resume datasets
• 2. Preprocessing: Tokenization, Stop-word
removal
• 3. Feature Extraction: TF-IDF, Word
Embeddings
• 4. Model Training: Deep Learning (BERT/GPT)
• 5. Resume Parsing: Extract details using NLP
• 6. Matching Algorithm: Compare resumes with
job descriptions

7. Dataset Used
• - Source: Open-source resume datasets
(Kaggle, LinkedIn, GitHub)
• - Data Includes:
• • Candidate Name, Skills, Experience,
Education
• • Resume Text Content
• • Job Descriptions & Requirements

8. AI Models & Techniques
• - Natural Language Processing (NLP): NER, TF-
IDF, Word Embeddings
• - Deep Learning: BERT, GPT, LSTMs, CNNs
• - Machine Learning: Random Forest, SVM,
Logistic Regression

9. System Architecture
• - User Interface: Web Application (Frontend)
• - Backend Processing: Python Flask/Django API
• - Database: Stores resumes, feedback, job
descriptions
• - AI Processing: NLP & Deep Learning models
analyze data
• - Output: Resume ranking, feedback, and job
matching

10. Workflow Diagram
• Process Flow:
• 1. Upload Resume
• 2. Preprocessing
• 3. AI Analysis
• 4. Scoring
• 5. Feedback Generation
• (Visual representation recommended)

11. Expected Results & Benefits
• For Recruiters:
• ✔ Saves time in shortlisting candidates
• ✔ Increases accuracy in hiring decisions
• For Job Seekers:
• ✔ Personalized resume suggestions
• ✔ Higher chances of getting shortlisted

12. Future Scope & Enhancements
• - Multi-language support for global users
• - Integration with LinkedIn & job portals
• - AI-powered interview preparation
• - Voice-based resume input (Speech-to-Text)

13. Conclusion
• - AI Resume Analyzer automates resume
screening and job matching.
• - Helps HR teams find the best candidates
efficiently.
• - Provides job seekers with actionable
feedback for improvement.

14. Thank You!
• Contact Information: [Your Email, GitHub,
LinkedIn]
• Questions & Discussions