Startup Matchmaker

Problem

Startups struggle to find the right investors without a smart matching system that considers both company profiles and historical patterns.

Solution

Developed an intelligent recommendation engine that matches startups with relevant investors using hybrid machine learning. Combines profile similarity with behavioral patterns for accurate matches.

Role

ML engineer | Backend API developer | System integration | Frontend development

Key features

• Hybrid ML Algorithm: Combines content-based and collaborative filtering for better accuracy
• Smart Matching: Analyzes startup profiles and investor preferences for optimal recommendations
• Scoring System: Tunable weights to balance different recommendation factors
• Validated Data: Schema validation ensuring data quality and reliability
• Clean Interface: Intuitive UI with filters, modals, and search options
• AI Assistant: Conversational interface for natural user interaction

Outcome

Built a complete end-to-end ML product demonstrating full pipeline from data preprocessing to API deployment and frontend integration.

Stack