anclet/NISR_Hackaton_2025

Identify Rwanda’s next big export opportunity. Rwanda Export Opportunity Analyzer Purpose: To identify Rwanda’s next export opportunities using real-time global data (Reddit, Google News) and trade statistics. Core Tech: Python with Gradio (UI), Plotly (visualizations), PRAW (Reddit scraping), BeautifulSoup (news scraping), and scikit-learn (ML demand forecasting). Data Sources: Hybrid scraper collects live discussions; trade benchmarks from synthetic global databases (UN Comtrade, World Bank proxies). NLP Engine: Custom rule-based analyzer detects products, markets, buying intent, and demand signals using domain-specific lexicons. ML Models: Ensemble of Linear Regression and Random Forest predicts demand based on intent ratios, market diversity, and signal strength. Key Outputs: Opportunity scores (0–100), readiness levels, real-time alerts, policy briefs, and SME-focused export recommendations. UI Features: Interactive dashboard with filters, dynamic charts, demand prediction tab, trade analytics, and exportable CSV reports. Youth/SME Focus: Includes dedicated tab for actionable SME guidance and export-readiness pathways aligned with national priorities. Deployment: Runs as a Gradio web app (localhost or cloud); requires internet for live scraping; Reddit optional via toggle. Compliance: Fully addresses NISR Hackathon Track 1: export identification, ML prediction, dashboard visualization, policy, and youth/SME engagement.