Predict missing values

hatsjikideehatsjikidee Member Posts: 3 Newbie
edited October 2019 in Help
Hello all,

I have a dataset with about 3000 records of rated songs. About half are rated, the other half is not. I'm trying to build a model that predicts the empty ratings based on what users rated. I have done the following:

My question is, is this correct? Do I need to make adjustments to make it more correct? Because when I for example already change the k I get different values. And another question: how do I show only the values that have been predicted instead of a full overview, including the already filled in values.

Thanks in advance!

Best Answer


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    lionelderkrikorlionelderkrikor Moderator, RapidMiner Certified Analyst, Member Posts: 1,195 Unicorn
    Hi @hatsjikidee,

    If you have some descriptive features of your songs, you can build a model based on your labeled data (your rated songs) and then apply this model to the unlabelled data (the unrated songs).

    To help you further can you share your data ? 

    Hope this helps,



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    hatsjikideehatsjikidee Member Posts: 3 Newbie
    Hi lionel,

    The dataset has 3 attributes:
    Song name - Rating - Name (of the rater)

    Every user has about 40 songs, of which 20 rated and 20 not. So the goal is to predict the missing ones based on what the user rated on the ones he did rate. Hope this gives more clarification.
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    hatsjikideehatsjikidee Member Posts: 3 Newbie
    So from what I understand, as far as it is possible I make good predictions with this process. Thank you both for th help and information!
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