So, let us now preprocess our data! Natural Language Processing techniques are our savior when we have to deal with textual data. Since our data cannot be fed to any machine-learning model unless we clean it, that's where NLP comes to play! Let's clean our text data – Firstly, let us create a new column in our dataframe that will hold all necessary keywords required for the model.

In this project, we will generate top 10 movie recommendations for each user as well as generate top 10 user recommendations for each movie. Welcome to this project on creating Movies Recommendation Engine using Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing. That's why I haven't included any purely theoretical lectures in this tutorial: you will learn everything on the way and be able to put it into practice straight away.

Wondered how Google comes up with movies that are similar to the ones you like? After reading this post you will be able to build one such recommendation system for yourself. Now you might be thinking "That's interesting. But, what are the differences between these recommendation engines?". Let me help you out with that.

Makes me feel sad for the rest. Actually, that's a movie ("The Spy that Loved Me") that Netflix recommends for me since I'm a James Bond junkie and Netflix knows that. In fact, Netflix knows a lot about me as it knows a lot about all of its viewers, which is one reason why Netflix is a Wall Street darling and has rewarded its stockholders very well over the past couple of years (see Figure 1). But Netflix isn't doing anything that other organizations cannot do. To replicate Netflix's business success starts with thinking differently about the role of data and analytics in powering the organization's business.

Makes me feel sad for the rest. Actually, that's a movie ("The Spy that Loved Me") that Netflix recommends for me since I'm a James Bond junkie and Netflix knows that. In fact, Netflix knows a lot about me as it knows a lot about all of its viewers, which is one reason why Netflix is a Wall Street darling and has rewarded its stockholders very well over the past couple of years (see Figure 1). But Netflix isn't doing anything that other organizations cannot do. To replicate Netflix's business success starts with thinking differently about the role of data and analytics in powering the organization's business.