Welcome to the Movie Recommendation System project! 🎥
This project was developed during my internship as a Machine Learning Intern at Bharat Intern. The goal was to create a system that provides personalized movie recommendations based on user input.
The Movie Recommendation System leverages machine learning techniques to suggest movies that are similar to the one provided by the user. The system works by analyzing a movies dataset and utilizing various libraries such as *Numpy, **Pandas, **Difflib, **TfidfVectorizer, and *Cosine Similarity.
- User Input: Enter your favorite movie, and the system will find the closest match.
- Personalized Recommendations: Get a list of movies that are similar to your favorite one.
- Feature Engineering: The system selects relevant features, replaces null values with a null string, and combines these features for analysis.
- Text Vectorization: Converts text data into numerical feature vectors using TfidfVectorizer.
- Similarity Scoring: Measures the similarity between movies using Cosine Similarity.
- Selecting Relevant Features: Identifying the key attributes for movie recommendations.
- Handling Missing Data: Replacing null values with a null string.
- Combining Features: Merging selected features into a single dataset.
- Vectorizing Text: Transforming text data into feature vectors.
- Calculating Similarity: Using Cosine Similarity to determine the closeness between movies.
- User Interaction: Taking input from the user for their favorite movie.
- Finding Closest Match: Searching for the closest match to the user input within the dataset.
- Generating Recommendations: Producing a list of movies similar to the user’s choice.
- Displaying Results: Showing the recommended movies sorted by their similarity scores.
Feel free to contribute to this project by submitting issues or pull requests. Your feedback and contributions are highly appreciated!