https://github.com/suryaaxc/Movie-Matcher-Flex
High-performance Movie Recommendation System scaling to 32M+ records. Built with Python, TF-IDF Vectorization, and Neon UI. Optimized for real-time cinematic discovery.
https://github.com/suryaaxc/Movie-Matcher-Flex
big-data cosine-similarity data-science-projects git-lfs machine-learning python recommendation-system streamlit tf-idf
Last synced: 2 days ago
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High-performance Movie Recommendation System scaling to 32M+ records. Built with Python, TF-IDF Vectorization, and Neon UI. Optimized for real-time cinematic discovery.
- Host: GitHub
- URL: https://github.com/suryaaxc/Movie-Matcher-Flex
- Owner: suryaaxc
- License: mit
- Created: 2026-03-02T05:02:29.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-06-15T08:02:13.000Z (12 days ago)
- Last Synced: 2026-06-15T11:06:33.169Z (11 days ago)
- Topics: big-data, cosine-similarity, data-science-projects, git-lfs, machine-learning, python, recommendation-system, streamlit, tf-idf
- Language: Python
- Homepage: https://movie-matcher-flex.streamlit.app/
- Size: 76.2 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
🚀 Movie Matcher Flex
Enterprise-Scale Movie Recommendation Engine
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🌐 Live Demo
🚀 Try the application here
👉 https://movie-matcher-flex.streamlit.app/
💻 Source Code
👉 https://github.com/suryaaxc/Movie-Matcher-Flex
----------------------------------------------------------------------------------------
🎬 Project Overview
Movie Matcher Flex is a high-performance content-based movie recommendation system designed to demonstrate how machine learning techniques can efficiently handle large-scale movie datasets.
The system processes a 2.1GB dataset containing millions of movie metadata entries and generates fast similarity-based recommendations using optimized machine learning algorithms.
This project highlights machine learning engineering practices, scalable data processing, and interactive web application design.
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✨ Key Features
🎥 Smart Movie Recommendations
Suggests similar movies using content-based filtering techniques.
⚡ Fast Similarity Matching
Uses Cosine Similarity to quickly compute relationships between movie vectors.
📊 Large Dataset Handling
Efficiently processes 32M+ feature data points.
🧠 TF-IDF Vectorization
Transforms movie metadata into high-dimensional vectors for machine learning analysis.
🎨 Neon-Themed UI
Custom Streamlit interface with neon design for a modern user experience.
☁️ Cloud Deployment
Application deployed on Streamlit Cloud for easy access.
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🧠 Machine Learning Pipeline
The recommendation engine follows a structured machine learning workflow.
Movie Dataset
│
▼
Data Cleaning & Processing
(Pandas / NumPy)
│
▼
TF-IDF Vectorization
│
▼
Cosine Similarity Calculation
│
▼
Recommendation Engine
│
▼
Streamlit Web Interface
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🏗️ System Architecture
┌─────────────────┐
│ Movie Dataset │
│ (2.1GB) │
└────────┬────────┘
│
▼
┌──────────────────┐
│ Data Processing │
│ Pandas / NumPy │
└────────┬─────────┘
│
▼
┌─────────────────────┐
│ TF-IDF Vectorizer │
└────────┬────────────┘
│
▼
┌─────────────────────┐
│ Cosine Similarity │
│ Recommendation Core │
└────────┬────────────┘
│
▼
┌─────────────┐
│ Streamlit UI│
└─────────────┘
----------------------------------------------------------------------------------------
🎨 User Interface
The application includes a custom neon-styled interface designed to make movie discovery engaging and intuitive.
UI Highlights:
🔍 Movie search functionality
🎬 Real-time movie recommendations
🎨 Neon themed interface design
⚡ Fast response time
📊 Dataset Information
Attribute Value
Dataset Size 2.1 GB
Feature Data Points 32M+
Metadata Fields Genres, Keywords, Cast, Overview
⚡ Performance Optimization
To maintain fast performance with large datasets, several optimization techniques were implemented.
✔ Sparse TF-IDF matrices
✔ Efficient NumPy operations
✔ Optimized Pandas data processing
✔ Precomputed similarity vectors
These techniques allow the system to deliver sub-second recommendation responses.
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🛠️ Tech Stack
Backend
Python 3.11
Machine Learning
Scikit-learn
Pandas
NumPy
Frontend
Streamlit
Custom CSS (Neon Theme)
DevOps
GitHub
Git LFS
Streamlit Cloud
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📂 Project Structure
Movie-Matcher-Flex
│
├── web_app
│ └── app.py
│
├── dataset
│ └── movies.csv
│
├── assets
│ └── screenshots
│
├── requirements.txt
├── README.md
└── LICENSE
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🔮 Future Improvements
Possible future upgrades for the project:
Hybrid recommendation system
Deep learning movie embeddings
Collaborative filtering techniques
Movie poster API integration
Faster similarity search using FAISS
----------------------------------------------------------------------------------------
👨💻 Author
Suryakant Kumar
B.E. Computer Science Engineering (AI/ML)
🔗 GitHub
https://github.com/suryaaxc
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📜 License
This project is licensed under the MIT License.
For full license details, see the LICENSE file in this repository.
🔗 https://github.com/suryaaxc/Movie-Matcher-Flex/blob/main/LICENSE