Movie Ratings: Stage 1
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Introduction
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We're going to build a movie rating application in a series of stages to
illustrate the full process of building an app from scratch. It might not be
obvious how we're going to get from 'hello world' in the command line, to a
movie recommendation system on the web, but we'll try anyway and figure it out.

Research has shown that having more data is always better than having a clever
algorithm. The classical example is how to write software that can answer
natural-language questions. To answer "who shot Abraham Lincoln?" we could try
to employ NLP techniques to parse out that our subject is 'Abraham Lincoln',
and someone shot him. We could then use the same techniques to try to
understand the semantics of data found in web pages about Abraham Lincoln to
determine who shot him.

It turns out that a much easier, and much more reliable technique, is to simply
find all texts that match the pattern "____ shot Abraham Lincoln". Sometimes
you will get spurious results, but if the vast majority of matches say "John
Wilkes Booth shot Abraham Lincoln", with a large enough dataset you can say
definitively that it was John Wilkes Booth.

In the same vein, rather than come up with a clever way to predict a person's
movie rating based on tastes, time of year, and so forth, we're instead going
to basically calculate how similar all the movies we know about are to each
other (based on how they're rated by existing uses). Then, we'll use that
movie's similarity to one you've already rated to predict your rating.

Before we can do that though, we need to figure out how to manipulate our seed
data set.


Description
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Included with this project is a directory, ml-100k, which contains 100,000
ratings of movies from 1,000 users representing a cross-section of the
population. You will need to figure out how to read in that data from the files
provided. Included with the files is a README describing how the data is
organized in the files.

Unlike all our previous tasks, this project is open ended: you will not have a
template to work from. You'll have to use everything you've learned up until
now and apply those techniques to build up your app.

For the first stage, you are implement an application that provides a command
line that behaves as such:

    > movie_details 42
    Movie 42: Clerks (1994)
    Comedy

    > average_movie_rating 42
    3.5

    > get_user 845
    Male doctor, age 64

    > user_rating 42 845
    User 845 rated movie 42 at 5 stars

    > rate 42 3
    You have rated movie 42: Clerks (1994) at 3 stars

    > predict 1
    Best guess for movie 1: Toy Story (1995) is 4.5 stars

To do this, you'll need to be able to open several files provided by the
ml-100k dataset: u.user, u.data, u.item, and u.genre.

How you accomplish that is up to you. As general hints to start you off, you
will need to employ the use of several dictionaries and possibly some classes
to make this work. Having a Movie class and a User class seems likely.

If you're really stuck on how to start, try writing a function as follows:

    def movie_details(movie_id):
        """Returns a string containing the movie details for the movie with the
        given id. The format of the output string is as follows:

        Movie id: Movie Name
        Genres, Separated, By, Commas
        """

To make this work, you will first need to figure out how to load the data from
files beforehand. Similarly, you'll probably need a rate function, a predict function,
get_user, etc.

Practice incremental development. If you can't see the solution, just write a
single line of code that moves you even vaguely in the direction you're trying
to go.

In the file correlation.py, you will find the code for a 'pearson similarity',
which tells you how similar two items are to each other, based on the ratings
common between them. See if you can figure out how to use this.