🎬 Smart Movie Recommendation System

A content-based movie recommendation system that suggests movies similar to the one selected by the user.
Built using a trained model on the TMDB dataset 🎥📊

✨ Features

• 📌 Get movie recommendations based on a selected movie
• 🎞 Uses a content-based filtering approach
• 📂 Pre-trained model using TMDb dataset
• ⚡ Fast and simple user interface
• 💡 Helps discover new and similar movies

📁 Dataset

• Source: KAGGLE
• The dataset includes information like:
  • Movie title
  • Overview/description
  • Genre
  • Cast & crew
  • Popularity and ratings

⚙️ Tech Stack

• 🐍 Python
• 📊 Pandas, NumPy
• 📈 Scikit-learn (for model training)
• 💻 Streamlit

🚀 How It Works

1. Load the dataset and clean/preprocess it
2. Use text-based features like overview, genres, keywords, cast, and crew
3. Convert them into numerical vectors using TF-IDF / CountVectorizer
4. Calculate similarity scores using cosine similarity
5. Recommend the top N most similar movies to the user

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/rahull0328/nextflick.git

2. Install the dependencies:

   pip install -r requirements.txt

3. Run the project:

   streamlit run app.py

📌 Future Improvements:

• 🎭 Add user ratings and collaborative filtering

• 🌐 Deploy the project online

• 🔍 Add search functionality for any movie

💖 Acknowledgements

• Dataset from The Movie Database TMDB

• Inspired by the concept of content-based recommendation systems