Readme.md.txt

Movie Recommendation System

Recommender System is a system that seeks to predict or filter preferences according to the user's choices. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. Recommender systems produce a list of recommendations in any of the two ways

Collaborative filtering : Collaborative filtering approaches build a model from the user's past behavior (i.e. items purchased or searched by the user) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that users may have an interest in

Content-based filtering: Content based filtering approaches uses a series of discrete characteristics of an item in order to recommend additional items with similar properties. Content based filtering methods are totally based on a description of the item and a profile of the users preferences. It recommends items based on the users past preferences. Lets develop a basic recommendation system using Python and Pandas.

Lets develop a basic recommendation system by suggesting items that are most similar to a particular item, in this case, movies. It just tells what movies/names are most similar to the user's movie choice.