🎬 NextFlix Movie Recommendation System

A movie recommendation web application inspired by Netflix that suggests movies using Association Rule Mining (Apriori Algorithm). The system analyzes relationships between movies and provides recommendations along with explanation metrics like Support, Confidence, and Lift.

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🎥 Project Demo

Watch the short demo of the project below:

This video demonstrates the workflow of the website, how movie recommendations are generated, and how users can refresh to discover new suggestions.

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🔗 Project Links

• Live Demo: https://nextflix-recommendation-system.onrender.com/
• Portfolio: https://anmolpatel20.github.io/Anmol_Portfolio/
• Certificate [Unstop]: https://unstop.com/certificate-preview/4345a3ef-85cf-458a-9bb5-b7097d374f09
• Youtube Link: https://www.youtube.com/watch?v=fuyraec2YGo

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🚀 Project Overview

NextFlix is a web-based recommendation system where users can:

• Get Top movie recommendations based on a selected movie.
• Analyze the relationship between two movies.
• Understand why a movie is recommended using Apriori rule metrics.

The system is built using:

• Flask for backend
• HTML + CSS for frontend
• Python for recommendation logic
• Apriori rules dataset for movie relationships

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📷 Workflow Diagram

🧠 Recommendation Logic

The system uses Association Rule Mining (Apriori Algorithm) to discover patterns in movie watching behavior.

Key metrics used:

Metric	Meaning
Support	Frequency of two movies appearing together
Confidence	Likelihood that Movie2 is watched when Movie1 is watched
Lift	Strength of association between movies

Example rule:

Movie1 → Movie2
Support: 0.02
Confidence: 0.45
Lift: 3.2

A higher Lift value indicates a stronger recommendation.

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🏗 System Architecture

User Interface (HTML + CSS)
↓
Flask Backend (app.py)
↓
Recommendation Engine (recommender.py)
↓
Apriori Rules Dataset (movie_rules.csv)
↓
Recommendations / Relationship Analysis
↓
Rendered Results (index.html)

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📁 Project Structure

NextFlix_Recommendation_System
│
├── app.py
├── requirements.txt
├── movie_rules.csv
├── netflix_data.csv
│
├── model
│   └── recommender.py
│
├── templates
│   └── index.html
│
├── static
│   ├── css
│   │   └── style.css
│   └── images
│
└── README.md

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🌐 Deployment

The project is deployed using Render.

🖥 Features

✔ Movie recommendation based on association rules
✔ Relationship analysis between two movies
✔ Apriori rule metrics explanation
✔ Clean Flask web interface
✔ Lightweight dataset-based recommendation engine

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🛠 Technologies Used

• Python
• Flask
• Pandas
• Apriori Algorithm
• HTML
• CSS
• Git
• Render (Deployment)

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👨‍💻 Author

Anmol Patel

• Data Science & Machine Learning Enthusiast
• Python Developer

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