Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore recommendation systems based on Collaborative Filtering and implement simple version of one using Python and the Pandas library. Collaborative filtering is based on the fact that relationships exist between products and people's interests. Many recommendation systems use collaborative filtering to find these relationships and to give an accurate recommendation of a product that the user might like or be interested in. Collaborative filtering has basically two approaches: user-based and item-based. User-based collaborative filtering is based on the user similarity or neighborhood. Item-based collaborative filtering is based on similarity among items. Let's first look at the intuition behind the user-based approach. In user-based collaborative filtering, we have an active user for whom the recommendation is aimed. The collaborative filtering engine first looks for users who are similar. That is users who share the active users rating patterns. Collaborative filtering basis this similarity on things like history, preference, and choices that users make when buying, watching, or enjoying something.