IMDB Top 250 Movie Recommendation

IMDB Top 250 Movie Recommendation is a summary based movie recommendation system over the top 250 IMDB movies. It uses Selenium for web scraping, TF-IDF for text vectorization, and cosine similarity for recommendation calculations.

Key Features

• Efficient Data Acquisition: Utilizes Selenium for precise data extraction from IMDB's top 250 list.
• Text Representation: Implements TF-IDF for numerical representation of movie summaries.
• Semantic Similarity: Computes cosine similarity scores for accurate movie recommendations.

Technologies Utilized

• Selenium for web scraping.
• TF-IDF for text vectorization.
• Cosine similarity for recommendation calculations.
• Python programming language.
• Pandas library for data manipulation.

Usage

To use this system, follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies using pip install -r requirements.txt.
3. Run the main script to scrape data, process summaries, and generate recommendations.
4. Explore the recommended movies and enjoy personalized suggestions.

Contributing

Contributions to this project are welcome. Please fork the repository, make your changes, and submit a pull request.