Movie Recommendation System

An intelligent, content-based movie recommendation system built using Python and machine learning techniques.

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🎬 Project Overview

This project simulates a personal AI movie curator. By analyzing plot descriptions of movies, it recommends titles that share narrative similarity with a given input movie using a content-based filtering approach.

The system relies on TF-IDF vectorization and cosine similarity to compare the storyline of movies and generate recommendations based purely on the content — not user ratings or behavior.

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👥 User Experience

• Enter a movie title you like.
• The system analyzes its description and compares it with the rest of the dataset.
• It returns a list of similar movies based on content relevance.

No login, setup, or internet access required — just run and get personalized results.

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🧠 Technologies & Skills Used

💻 Machine Learning & NLP:

• TF-IDF (Term Frequency–Inverse Document Frequency)
• Cosine Similarity for distance measurement between movies

📊 Data Handling:

• Pandas for data manipulation
• Scikit-learn for feature extraction and similarity computation

🐍 Python:

• Efficient and readable scripting
• Modular logic in a clean, functional format

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📂 File Structure

• movie.py: Main file with recommendation logic
• movies.csv: Dataset containing movie titles and plot descriptions

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▶️ How to Run

1. Clone the repository:

   git clone https://github.com/YernintiRevathi/Movie_recommendation.git
   cd Movie_recommendation

2. Install dependencies:

   pip install pandas scikit-learn

3. Run the script:

   python movie.py

4. Input your favorite movie name when prompted and enjoy your curated list!

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✨ Highlights

• Fast and lightweight — no need for deep learning or GPUs
• Easy to understand and extend
• Great for learning about content-based recommendation systems

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📄 License

This project is open-source and available under the MIT License.

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Built with 🎥 and 💻 by Revathi Yerninti