This project implements a movie recommendation system using cosine similarity to suggest movies based on user preferences. It leverages features such as genres, keywords, tagline, cast, and director to generate recommendations. 🍿
- Introduction
- Technologies Used
- Data Description
- Usage
- How It Works
- Results
- Contributing
- Conclusion
- Contact Info
The movie recommendation system allows users to input their favorite movie and receive a list of similar movies. It uses the cosine similarity metric to evaluate how closely related two movies are based on their descriptive features. 🎥
- Python 🐍
- Pandas 📊
- NumPy ➕
- Scikit-Learn 📚
- TfidfVectorizer 🛠️
- difflib 📖
The dataset used for this project is the movies.csv file, which contains movie information, including:
- genres: The genre(s) of the movie 🎭
- keywords: Keywords associated with the movie 🔑
- tagline: A tagline for the movie 📜
- cast: The main actors in the movie 👥
- director: The director of the movie 🎬
The dataset contains over 4,800 movies, and it is essential for generating accurate recommendations.
To use the Movie Recommendation Engine, simply input your favorite movie title. The engine will then analyze the input and provide a list of recommended movies based on similarity.
The Movie Recommendation Engine utilizes a pre-calculated similarity matrix derived from a dataset containing over 4,800 rows of movie data. The similarity matrix is generated using various features such as genre, director, and cast. When a user inputs a movie title, the engine retrieves its corresponding similarity values and ranks other movies based on their closeness to the input title.
Similarity Calculation: Uses metrics like cosine similarity to determine how closely related movies are.
User Input Handling: Efficiently manages user input errors and provides feedback for invalid entries.
The engine successfully suggests personalized movie recommendations with high accuracy, offering users a seamless experience in discovering films similar to their preferences.
Contributions are welcome! If you would like to contribute to the Movie Recommendation Engine, please fork the repository and submit a pull request. You can also report issues or suggest features in the Issues section.
The Movie Recommendation Engine is designed to enhance the movie-watching experience by suggesting similar films based on user preferences. By employing a pre-calculated similarity matrix and handling user inputs efficiently, it provides personalized and accurate movie recommendations, making it a valuable tool for movie enthusiasts.
If you have any questions or feedback, feel free to reach out:
To run this project, ensure you have Python installed along with the required libraries. You can install the necessary libraries using pip:
pip install pandas numpy scikit-learn