🎬 Movie Recommendation System

This is a Content-Based Movie Recommendation System that suggests similar movies using only movie metadata — without needing user history or ratings.

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📌 Project Overview

This system helps users discover similar content by analyzing:

• Genres
• Keywords
• Cast
• Director
• Overview (Plot)

Example: Enter Inception → get back 5 most similar movies based on metadata.

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🔧 Tech Stack

• Python
• Pandas, NumPy
• Scikit-learn (CountVectorizer, Cosine Similarity)
• NLTK (Stemming)
• Jupyter Notebook
• TMDb 5000 Movie Dataset

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💡 Features

• Combines key text-based features into a single tags column
• Applies stemming for noise reduction
• Vectorizes text using CountVectorizer
• Calculates similarity using cosine similarity
• Provides top-5 movie recommendations based on similarity scores

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🧠 How it Works

1. Preprocess movie metadata
2. Merge features (cast, crew, genres, keywords)
3. Stem text to normalize vocabulary
4. Convert tags into vectors using CountVectorizer
5. Compute similarity using cosine_similarity
6. Recommend top similar movies

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🚀 How to Run

1. Clone this repo
2. Open recommendation_system.ipynb in Jupyter
3. Run all cells
4. Call recommend("Movie Name") to get suggestions!

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📂 Dataset

TMDb 5000 Movie Dataset from Kaggle
You must download and place it in the same directory as the notebook

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📃 License

This project is licensed under the MIT License — feel free to use or modify it for educational and learning purposes.

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🙋‍♀️ Author

Shreya Rao
Data Science Undergraduate | Python & ML Enthusiast
GitHub Profile