This project implements a cinema recommendation engine using Python, Pandas, scikit-learn, and ipywidgets. The goal is to help users discover movies based on their preferences and viewing history.
movies.csv: CSV file containing movie data (title, genres, etc.). ratings.csv: CSV file containing movie ratings data (userId, movieId, rating, etc.). cinema_recommendation.ipynb: Jupyter Notebook containing the Python code for the recommendation engine. README.md: Documentation file providing an overview of the project.
pandas scikit-learn numpy ipywidgets
Open the Jupyter Notebook cinema_recommendation.ipynb. Execute the code cells sequentially to load data, preprocess, and build the recommendation engine. Use the interactive widget to search for movies and get recommendations based on user input.
Data cleaning: Removing special characters and irrelevant information from movie titles. Text vectorization: Using TfidfVectorizer to convert movie titles into numerical features. Cosine similarity: Calculating similarity scores between user input and movie titles. Recommendation engine: Analyzing movie ratings to recommend movies based on user preferences. Interactive widget: Creating an interactive search interface for users to discover movies.
Personalization: Adding user profiles for personalized recommendations. Integration: Incorporating external APIs for real-time movie data. User feedback: Implementing a feedback system to improve recommendation accuracy.
Pandas scikit-learn NumPy ipywidgets