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Cdasilver29/AI-Driven-Applicant-Selection-Tool-for-LSETF-PLP-Nigeria-Hackathon

Domain:

education

Record type:

software
Creator:
Cda
Host:
# AI-Driven-Applicant-Selection-Tool-for-LSETF-PLP-Nigeria-Hackathon An intelligent, AI-powered platform that analyzes applicant data and recommends top candidates for LSETF/PLP programs. Built with scalability and seamless LMS integration in mind. ### 🚀 Overview This solution addresses the critical need for efficient, fair, and scalable applicant selection processes. By leveraging advanced machine learning algorithms and natural language processing, the platform automates resume analysis, candidate scoring, and ranking based on customizable criteria. ### Key Features 📄 Multi-format Resume Processing: Supports PDF and DOCX files with intelligent text extraction 🤖 AI-Powered Scoring: Multi-criteria decision analysis based on skills, experience, education, and more 📊 Interactive Dashboard: Real-time analytics and candidate comparison tools 🎯 Customizable Criteria: Adjustable scoring weights to match program requirements 🔌 LMS Integration Ready: Prepared for seamless integration with LSETF's future LMS platform 📱 Responsive Design: Works seamlessly on desktop, tablet, and mobile devices ### 🏗️ Architecture The application follows a modern microservices architecture: text Frontend (React) → Backend (FastAPI) → AI Engine → Database ↑ ↑ ↑ └── Integration Layer ──────────────┘ #### Core Components Frontend Interface: React-based dashboard with Tailwind CSS Backend API: FastAPI RESTful service with comprehensive endpoints AI Processing Engine: NLP-powered resume analysis and scoring algorithms Integration Layer: Prepared for LMS and third-party service integration ### 🛠️ Technology Stack **Frontend** React 18 with Hooks and Context API Vite for fast development and building Tailwind CSS for responsive styling Lucide React for consistent iconography Axios for API communication **Backend** FastAPI with automatic OpenAPI documentation Python 3.9+ with type hints SpaCy for natural language processing scikit-lear …

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