AI-Powered Job Relevance Analyzer

Overview

The AI-Powered Job Relevance Analyzer is an innovative tool designed to optimize job searches and assist in career growth. By leveraging AI and ML technologies, it provides tailored job recommendations, skill-gap analysis, and actionable career guidance. The platform integrates user inputs like resumes, LinkedIn profiles, and keywords to match job opportunities from multiple platforms while offering personalized feedback for improvement.

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Features

1. Cross-Platform Job Aggregation

• Aggregates job postings from LinkedIn, CareerJet, and other platforms using RapidAPI and web scraping.
• Displays tailored job recommendations based on extracted user skills and preferences.

2. AI-Based Interview Feedback

• Allows users to record interviews for feedback.
• Converts speech to text using Whisper and analyzes performance.
• Provides actionable suggestions for improvement.

3. Skill Matching

• Compares user skills with job descriptions to calculate a compatibility score for each posting.
• Generates custom recruiter messages for selected job postings.
• Includes filters for top matches and specific platforms.

4. User Profiling

• Extracts LinkedIn profile data to analyze strengths, weaknesses, skills, and experience.
• Offers insights categorized using the Gemini API.

5. Career Guidance Bot

• Interactive chatbot to provide career advice and resolve user queries.

6. Performance Metrics

• Model accuracy: 90%
• Precision: 85%
• Recall: 92%
• Word loss in Whisper transcription: 16%

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Technology Stack

Frontend

• ReactJS: Responsive user interface.

Backend

• Flask: API handling and backend processing.

APIs

• RapidAPI: Job aggregation.
• Gemini API: Profile and career insights.

Libraries

• SpaCy: NLP for resume parsing and skill extraction.
• Whisper: Speech-to-text for interview analysis.
• Matplotlib/Plotly: Visualization of user insights.

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Here's a README.md file format for your project:

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AI-Powered Job Relevance Analyzer

Overview

The AI-Powered Job Relevance Analyzer is an innovative tool designed to optimize job searches and assist in career growth. By leveraging AI and ML technologies, it provides tailored job recommendations, skill-gap analysis, and actionable career guidance. The platform integrates user inputs like resumes, LinkedIn profiles, and keywords to match job opportunities from multiple platforms while offering personalized feedback for improvement.

---

Features

1. Cross-Platform Job Aggregation

• Aggregates job postings from LinkedIn, CareerJet, and other platforms using RapidAPI and web scraping.
• Displays tailored job recommendations based on extracted user skills and preferences.

2. AI-Based Interview Feedback

• Allows users to record interviews for feedback.
• Converts speech to text using Whisper and analyzes performance.
• Provides actionable suggestions for improvement.

3. Skill Matching

• Compares user skills with job descriptions to calculate a compatibility score for each posting.
• Generates custom recruiter messages for selected job postings.
• Includes filters for top matches and specific platforms.

4. User Profiling

• Extracts LinkedIn profile data to analyze strengths, weaknesses, skills, and experience.
• Offers insights categorized using the Gemini API.

5. Career Guidance Bot

• Interactive chatbot to provide career advice and resolve user queries.

6. Performance Metrics

• Model accuracy: 90%
• Precision: 85%
• Recall: 92%
• Word loss in Whisper transcription: 16%

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Technology Stack

Frontend

• ReactJS: Responsive user interface.

Backend

• Flask: API handling and backend processing.

APIs

• RapidAPI: Job aggregation.
• Gemini API: Profile and career insights.

Libraries

• SpaCy: NLP for resume parsing and skill extraction.
• Whisper: Speech-to-text for interview analysis.
• Matplotlib/Plotly: Visualization of user insights.

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Installation Guide

1. Clone the Repository

git clone https://github.com/your-repo/job-relevance-analyzer.git
cd job-relevance-analyzer
2. Set Up Virtual Environment
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python -m venv env
source env/bin/activate  # For Linux/Mac
env\Scripts\activate     # For Windows
3. Install Dependencies
bash
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pip install -r requirements.txt
npm install  # For frontend dependencies
4. API Keys
Create a .env file in the project root and add your API keys:

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RAPID_API_KEY=your_rapidapi_key
GEMINI_API_KEY=your_geminiapi_key
5. Run the Application
Backend
bash
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python app.py
Frontend
bash
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npm start
Access the application at http://localhost:3000.

Usage Guide
Input Options: Upload a resume, enter a LinkedIn URL, or search by keywords.
Job Search: View and filter job postings tailored to your profile.
Skill Matching: Get compatibility scores and custom recruiter messages.
Interview Analysis: Receive AI-based feedback on recorded interviews.
Profile Insights: Understand strengths and areas for improvement.
Chatbot Assistance: Use the bot for career-related queries.
Architecture

Challenges Tackled
Data Handling: Preprocessed diverse inputs for consistent analysis.
Hyperparameter Tuning: Achieved high performance through optimization.
Speech Accuracy: Reduced word-loss in transcription using Whisper.
Scalability: Built for handling large datasets and platform integrations.
Future Enhancements
Integrate additional job platforms (e.g., Glassdoor, Indeed).
Provide personalized learning paths for skill gaps.
Develop mobile applications for accessibility.
Enhance chatbot with multilingual support.

Contact
For any queries or support, contact us at [email protected].

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### *Steps to Use*
1. Replace placeholders like your-repo and your-repo-name with actual values.
2. Add your *Architecture Diagram* in the mentioned section.
3. Update the *Contributors* section with team members' names and roles. 

This README.md will help showcase your project clearly and professionally!