AI-POWERED RESUME
A N A LY Z E R
Presented by:
1602-22-733-065-M.Abhinash-Rao
1602-22-733-101-K.Dhanush
Problem Statement:
• Job seekers often face overwhelming numbers of job listings
on traditional platforms.
• Lack of personalization leads to inefficiencies in finding
relevant jobs.
• Users spend excessive time manually filtering job results.
Introductio
Proposed Solution:
• Proposed Solutions:
n • Apply machine learning to provide job recommendations
tailored to user profiles.
• Use resume parsing tools to match candidates with suitable
jobs effectively.
• Create an intuitive platform for smooth navigation and
enhanced usability.
Objective:
• The objective is to create an AI-powered job platform offering
personalized recommendations, automated resume matching,
and an intuitive interface for an efficient job search
experience.
Challenges with Traditional Job Search Platforms:
• Overwhelming Number of Job Listings: Job
The seekers face a flood of irrelevant results, making it
difficult to find the right job efficiently.
Challenges of • Lack of Personalization: Users are forced to
manually filter through jobs, as platforms do not tailor
Traditional search results based on individual preferences or past
behavior.
Job Platforms • Inefficient Filtering Options: Basic filtering tools
(like location and job type) are insufficient for
narrowing down job listings in a meaningful way.
• Time-Consuming Process: Users spend a lot of time
applying filters and browsing through large amounts of
irrelevant job postings.
•Enhance Personalization: Deliver
tailored job recommendations based
on user profiles and preferences.
Goals of the •Streamline Job Search: Automate
resume matching and offer advanced
AI-Powered filtering options.
•Improve User Experience: Design a
Resume simple, intuitive interface for easy
platform navigation.
Analyzer •Ensure Real-Time Updates: Provide
accurate, up-to-date job listings for job
seekers.
•Increase Efficiency: Minimize search
time by offering relevant, personalized
job results.
SYSTEM
DESIGN AND
ARCHITECTUR
E
Technology Stack Overview
Frontend Backend AI and Machine Hosting and
API Integration:
Technologies: Technologies: Learning: Deployment:
• HTML/CSS: • Python(Flask • ML- • RESTful APIs: For • Hosting:
Structure and Framework) Models:Random fetching real-time • Flask’s local
style the web Forest, TF-IDF job listings from server (Can
application. (Trained and external portals. extend to
serialized using • Axios or Fetch Heroku/AWS/Googl
pickle). API: For making e Cloud).
• Libraries:PyPDF2 API calls from the
, re, Pandas, frontend. • GitHub: Version
Scikit-learn control and
collaboration.
• Programming Skills:
• Improved proficiency in HTML,CSS and Python for full-stack
development.
• Enhanced Knowledge of handling file uploads (PDF and TXT)
and text extraction using libraries like PyPDF2
• Machine Learning:
Learning • Gained hands-on experience with Random Forest classifiers
for:
Outcomes • Resume Categorization and Job Recommendations.
• Resume Parsing and Data Extraction:
• Learned techniques to clean and preprocess raw text data.
• Learned to use regex for extracting phone numbers , email
addresses.
• Identifying skills from a predefined skills list.
• Detecting educational qualifications using specific keywords.
• Deployment:
• Learned to configure and deploy the application in a local
environment using Flask’s development server.
Future Scope
ADVANCED AI MOBILE IMPROVED RESUME EXPANDED API USER ANALYTICS COMMUNITY
RECOMMENDATION APPLICATION ANALYSIS INTEGRATIONS DASHBOARD FEATURES
S DEVELOPMENT
ENSURE REAL-TIME STREAMLINE JOB
UPDATES SEARCH
