# Movie search algorithm: NLP part 1

To be honest, this search algorithm is purely an accident that I mistook the recommendation as search engine. The idea start from that I want to create a recommendation system that based on Natural language processing which is not respond to your implicit data(past movie history, like, review, etc.) but the explicit one.

To be honest, this search algorithm is purely an accident that I mistook the recommendation as search engine. The idea start from that I want to create a recommendation system that based on Natural language processing which is not respond to your implicit data(past movie history, like, review, etc.) but the explicit one. I imagine recommendation that just tell you what movie you would like given the explicit data you give to them, such as “the movie that has alien”, “a lot of fight, no drama”. And what I just describe is NLP search engine, not recommendation system. And this mean this time we will working on the an unstructured data, and partially quote from Wikipedia it is

The information that is not pre-defined data model or does not organize in pre-defined manner.

The sentence simply mean that the unstructured data is a mess that needed transformation into a set of number for us to continue our task.

Imagine that you are about the use data, first thing in mind is that it need to be able to compute and it must be either categorical or numerical value. This is not when you face a sentence, text, paragraph. Those is simply a list of string combined.

## Why we need computer to compute

That is because we are doing Machine learning, and for a computer to learn, it firstly needs to understand the data. Let’s first explain what is machine learning: Machine learning is simply how a computer create a rule without explicitly programmed. The rule we are talking about is how a computer make a decision, in this case, we are trying to make it decide what movie should it search for you when it receive a text.

To transform a text into a quantitative data, we firstly need to transform it to number. This is the same as when we transform categorical variable such as gender of male and female into male {1,0} and female {0,1}, in statistics, this is called Dummy variable, but in programming it is called One-hot encoding. As I mentioned this whole process of transforming unstructured text data into a trainable and processable data is called encoding. However, the simple but significant difference between the encoding in the most case, the encoding of text data go for every the words in your dataset. This vastly and literally change everything because one text paragraph could contain more than 200 characters or 40 unique words. Now, enough with learning preparation, let’s do some programming!

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