• 

  Recommendation System

🚀 Recommendation System

A Multi-Domain Intelligent Recommendation Web App

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🎯 Overview

This project is a fully functional Recommendation System built using Flask, Machine Learning, and APIs.
It provides personalized suggestions for:

• 📚 Books
• 🎬 Movies
• 🎧 Music
• 👗 Fashion Items

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✨ Features

• 🔍 Personalized smart recommendations
  
• 🎞️ Movie details with posters & ratings via TMDB
• 🎶 Spotify-powered content similarity
• 🎨 Deep learning-based Fashion similarity search
• 🧠 Machine learning + Deep learning hybrid models
• ☁ Google Drive auto-download support for large files

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🧠 Tech Stack

Category	Technology
Backend Framework	Flask
Machine Learning	Scikit-Learn, TensorFlow
Feature Extraction	ResNet50
APIs	Spotify API, TMDB API
Storage	Pickle Models, Google Drive
Frontend	HTML, CSS, JavaScript, Bootstrap

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📁 Project Structure

📦 Recommendation-System
├── Book Recommendation
│   ├── books.pkl
│   ├── similarity.pkl
│   └── app.py
├── Movie Recommendation
│   ├── movies.pkl
│   ├── similarity.pkl
│   └── app.py
├── Music Recommendation
│   ├── spotify_model.pkl
│   ├── data.csv
│   └── app.py
├── Fashion Recommendation
│   ├── images/
│   ├── embeddings.pkl
│   └── app.py
├── static/  (CSS/JS/Images)
├── templates/ (HTML Files)
├── requirements.txt
└── README.md

📦 Dataset & Model Access (Google Drive Support)

Since model files are large, they are stored in Google Drive and downloaded automatically when needed.

🔽 Automatic Download Script

import gdown, os, pickle

os.makedirs("models", exist_ok=True)

files = {
    "books.pkl": "https://drive.google.com/uc?id=FILE_ID_1",
    "similarity.pkl": "https://drive.google.com/uc?id=FILE_ID_2",
    "movies.pkl": "https://drive.google.com/uc?id=FILE_ID_3",
    "spotify_model.pkl": "https://drive.google.com/uc?id=FILE_ID_4",
    "fashion_embeddings.pkl": "https://drive.google.com/uc?id=FILE_ID_5"
}

for filename, url in files.items():
    save_path = f"models/{filename}"
    if not os.path.exists(save_path):
        print(f"📥 Downloading {filename}...")
        gdown.download(url, save_path, quiet=False)

print("✅ All required model files are ready!")

File Name	Type	Purpose
books.pkl	Pickle	Book model
similarity.pkl	Pickle	Book similarity matrix
movies.pkl	Pickle	Movie features
spotify_model.pkl	Pickle	Music recommendation model
fashion_embeddings.pkl	Pickle	Deep learning embeddings

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📥 Download Dataset & Model Files

Since some files are large (datasets + pickle models), you can download them from Google Drive:

🔗 Google Drive Link: 👉 https://drive.google.com/drive/folders/1LLuPREHLQQcBGEInTY60GMmszdNY7V7u?usp=drive_link

After downloading, place the files in the project root directory as shown in structure above.

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🧪 Create a Virtual Environment

▶ Windows

python -m venv venv
venv\Scripts\activate

▶ macOS / Linux

python3 -m venv venv
source venv/bin/activate

🛠️ Installation & Setup

# Clone repository
git clone https://github.com/CARLOX62/Recommendation-Systems.git
cd Recommendation-System

# Install dependencies
pip install -r requirements.txt

# Run app
python app.py

🧪 Create a Virtual Environment

▶ Windows

python -m venv venv
venv\Scripts\activate

▶ macOS / Linux

python3 -m venv venv
source venv/bin/activate

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🎥 Live UI Preview

Home Page	Recommendation Output

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🚀 Future Enhancements

• User login system
  
• Personalized user history tracking
  
• Cloud-based model hosting
  
• Voice-based recommendation search
  

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👨‍💻 Author

👋 Aniket Kumar
Machine Learning Engineer | Python Developer | AI Enthusiast

📌 India 🇮🇳
📫 Email: [email protected]

🌐 Connect With Me

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⭐ Support

If you find this project useful, please consider starring ⭐ the repo — it helps others find it!

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📝 License

This project is licensed under the MIT License.

Built With

• css
• flask
• github
• html
• javascript
• jupyter-notebook
• numpy
• pandas
• pickle
• python
• resnet50
• scikit-learn
• spotify
• tensorflow
• tmdb-api
• vscode