![]() ![]() The head() function returns the specified number of rows and by default the first 5 rows.įrom this dataframe, we need genres (to recommend movies on the basis of category), id (to fetch images in the web app), keywords, overview (movie description), and title. Let’s check what we have in our movies and credits data frames. Pandas library provides the read_csv() method which reads the CSV file and converts it into the dataframe. Read the data: movies = pd.read_csv('tmdb_5000_movies.csv')Ĭredits = pd.read_csv('tmdb_5000_credits.csv') In case you don’t install using: pip install pandas The two main libraries we are using here are pandas and numpy, make sure you have them. In jypyter simply upload the file in the same folder as the ipynb. in google collab under the file section files are uploaded. Let’s import the datasets into the collaboratory or at ide. You can use jupyter notebook or google collab or any corresponding ide. Now, let’s process the data and build our mode. You can download both of the datasets from here. tmdb_5000_credtis.csv: It includes 4 columns- movie_id, title, cast, and crew. tmdb_5000_movies.csv: It includes 20 columns like budget, genres, id, keywords, title, tagline, etc.Ģ. The dataset that we are using here is the TMDB 5000 Movie Dataset. Also, we are making this project as a web app hosted on localhost with the help of streamlit. The content will be the cast, director, category, and movie description which will be combined and the ML model will be built. In this project, we are discussing the movie recommendation system keeping the basis as content. Hybrid: Hybrid is a combination of both, content-based and collaborative filtering-based movie recommendation systems.If user1 watched ‘XYZ’, ‘ABC’ and ‘PQR’ movies and user2 watched ‘ABC’, ‘PQR’, and ‘LMN’ movies then, if the user3 is watching ‘PQR’, the system will recommend the ‘ABC’ movie to the user. Based upon the activities of 2 or more users, it will recommend the movies to the next user. Collaborative filtering: This system is based on the interaction between the movies and users.Based on the similarity score between the attributes the system will recommend the movies. These attributes can be anything like cast, crew, director, movie category, etc. For eg., if a user has watched the ‘XYZ’ actor’s movie then the system will recommend the user that specific actor’s movie. Content-based: This system is based upon the similarity of movie attributes.There are 3 types of recommendation systems: Movie recommendation systems are recommendation systems recommending movies to a person based on their past data or activities. Movie Recommendation System and its types: These systems are really powerful and currently widely used in almost every e-commerce website or OTT or social media app. ![]() Recommendation systems recommend relevant items or content to a user based on his/her past activity or interests. All of these recommendations are nothing but the Machine Learning algorithms forming a system, a recommendation system. Have you come across products on Amazon that is recommended to you or videos on YouTube or how Facebook or LinkedIn recommends new friend/connections? Of course, you must on daily basis.
0 Comments
Leave a Reply. |