🎬 Movie Recommendation System

This project is a content-based movie recommendation system built using Python and Jupyter Notebook. It suggests movies similar to a selected title based on metadata such as genre, cast, director, and keywords.

📁 Project Structure

movie-recommender/
├── Movie_Recommendation_System.ipynb
├── movies.csv
├── README.md
└── requirements.txt

🚀 Features

• Recommends top N similar movies based on a selected title
• Uses cosine similarity on TF-IDF or CountVectorizer features
• Cleaned and preprocessed movie metadata
• Interactive interface via Jupyter Notebook

🧠 How It Works

1. Load and clean the dataset (movies.csv)
2. Combine relevant features (e.g., genres, cast, director, keywords)
3. Convert text data into numerical vectors using CountVectorizer or TfidfVectorizer
4. Compute similarity scores using cosine similarity
5. Recommend top N movies based on similarity

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/yourusername/movie-recommender.git
   cd movie-recommender

2. Install dependencies:

   pip install -r requirements.txt

3. Launch the notebook:

   jupyter notebook Movie_Recommendation_System.ipynb

📊 Dataset

• Source: Kaggle Movie Dataset
• Columns used: title, genres, keywords, cast, director

✅ Requirements

• Python 3.7+
• pandas
• scikit-learn
• numpy
• Jupyter Notebook

📌 Example

recommend_movies("Inception")

Returns a list of similar movies like "Interstellar", "The Prestige", etc.

📄 License

MIT License

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Let me know if you'd like a version tailored for collaborative GitHub projects, or one that includes Streamlit or Flask deployment instructions!