A clothing recommendation engine startup I worked on with Manasi Vartak and Lauren Clark. Please see: http://asortistyle.com/

This project uses K-Means Clustering to help job-seekers and investors discover startups.

🔗 Startup Recommendation Engine Match startup founders with mentors and service providers using a smart scoring system based on skills, industry, timeline, and startup stage. Includes visual heatmaps and top-5 recommendation outputs. Built with Python, pandas, and fuzzy logic.

This project aims to build an intelligent Recommendation Engine that simulates matchmaking between startup founders and service providers.

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Recommendation Engine for Startups and Businesses

Final submission of Data Analytics Project ( Airbnb Dynamic Pricing Recommendation Engine and Startup Investment Analysis (Shark Tank Data) )

This project involves building a movie recommendation engine for a startup, Ripe Pumpkins, aiming to enhance user experience by providing personalized movie suggestions.

Production-ready recommendation engine: C++ graph traversal (BFS/PageRank) + GraphSAGE (trained on TMDb) + FastAPI + Supabase Auth. Redis cache, JWT auth, genre-aware scoring, instant startup.

Startup Weekend LA 2011-2 project - DrugKick is a recommendation engine for matching potential drug/alcohol rehab candidates with the right facility based on a number of factors

This repository contains a Recommendation Engine for Startup Matching, designed to intelligently connect Founders with the most relevant Service Providers and Mentors, and vice versa. This project simulates the core functionality of a platform, aiming to power real-time decision-making for both parties in the startup ecosystem.

A startup is building a recommendation engine that suggests movies to users based on their preferences. The data is complex, with user ratings, genres, and actors. Project: Implement and analyze the performance of different search algorithms on a complex dataset.

This project applies Multiple Minimum Support Apriori (MSApriori) to real-world music listening data from Last.fm to discover co-listening patterns across 1,892 users and 17,632 artists. The mined association rules feed into a playlist recommendation engine and a listener persona feature for a music startup.