Movie Recommendation System Using Machine Learning
Movie Recommendation System:
The main objective of 'Movie Recommendation Systems' is to identify and forecast only those movies that are consistent with the user's prior movie tastes. The system makes use of the MovieLens dataset, which includes details on movies like their keywords, genres, cast, directors, etc. That data is preprocessed using Python, and a content-based suggestion model is created. According to how closely they resemble other users, things are recommended to users using the content-based method.
Users can enter their preferred movies into this system to obtain suggestions for other films that are comparable to their favorites. Based on the keywords, genres, cast, and directors of the movies, the system employs a cosine similarity measure to identify related films. The finest recommendations are shown to the user after the recommendations are sorted by similarity score.
The technique creates a collection of recommended films based on how closely they resemble the movies the user entered. The user is shown the best suggestions after the recommendations are sorted by similarity score. The user can choose which movies they want to view after the suggestions are presented in an easy-to-use style.
The offered notebook file takes you step-by-step through the construction of the recommendation system. To aid users in understanding how the system operates, the notebook contains code descriptions and visualizations. A sample of the code to evaluate the recommendation algorithm is also included in the notebook.
Overall, the Movie Recommendation System is a helpful tool for anyone who wants to obtain personalized movie suggestions based on their prior movie tastes. The method is simple to use and adaptable to the requirements of various people.
