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Machine LearningCase Study·87% Match

Movie Recommendation — Cosine Similarity & Collaborative Filtering

Movie recommendation engine deployed on AWS EC2. FastAPI backend serves cosine-similarity recommendations computed from a PostgreSQL-backed dataset. Streamlit frontend (Dockerfile.streamlit) renders results with Pandas/Plotly. Docker Compose manages the backend, Streamlit app, and database services. Pre-processed data loaded via sql_load.py.

PythonFlaskMachine LearningCollaborative FilteringCosine SimilarityStreamlitAWS EC2
ProductionAWS EC2DeployedFastAPIBackendCosineSimilarityDockerCompose
AWS EC2
Deployed
FastAPI
Backend
Cosine
Similarity
Docker
Compose
🔴 The Problem
▸

Simple genre-based filters produce poor personalisation

▸

Most tutorial recommendation engines never run in a real environment

✅ The Solution
▸

Cosine-similarity on TF-IDF feature vectors served via FastAPI for sub-50ms responses

▸

Docker Compose bundles FastAPI backend, Streamlit frontend, and PostgreSQL in one command

📈 Impact & Results
▸

Live on AWS EC2 — publicly accessible demo

▸

Recommendations return in under 50ms at the API layer

▸

Pre-processed similarity matrix loaded at startup eliminates per-request compute

Full Tech Stack
PythonFlaskMachine LearningCollaborative FilteringCosine SimilarityStreamlitAWS EC2AWS S3

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