Many businesses today aim to provide useful product suggestions to online users in order to increase their consumption on websites. People usually choose or buy a new product based on the recommendations of friends, comparisons of similar products, or feedback from other users. A recommender system must be implemented in order for all of these tasks to be completed automatically. Recommender systems are tools that provide suggestions that best suit the client's needs, even if the client is unaware of it.Personalized content offers are based on past behaviour, and they entice customers to return to the website. A web series recommendation mechanism for Netflix/Prime/Disney plus Hotstar will be built in this paper. The dataset used in this study contains over 5 K web series and 500 K+ customers. Popularity, Collaborative Filtering, Content-based Filtering, and Hybrid Approaches are the four main types of recommender algorithms. This paper will introduce all of them. We will choose the algorithms that best fit the data, implement them, and compare them.