Experimenting Machine Learning with JavaScript —applied to Movie Ratings


Objective
Predict the rating of movies, based on previous ratings
Method
k-nearest neighbors (KNN) regression
(k-NN is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. In k-NN regression, the output is the average of the values of its k nearest neighbors. More on Wikipedia)
Training Data
380 rated movies (or you can upload your IDMb movie ratings csv)
Features
IMDb Rating; Runtime (mins); Year; Num. Votes
Unlabeled Data
a list of 100 films (or you can upload your IDMb watchlist csv)

1. Training Data

Select the training dataset:


Use the default data set

or Select your IMDb ratings csv

instructions

2. Features

Select the features that could predict a movie rating:

Use these features

    3. Unlabeled Data

    Select unrated movies to predict the rating:


    Use the default data set

    or Select your IMDb watchlist csv

    instructions

    4. K

    Select K (= number of nearest neighbours to look for):

    Define K

    5. Run the Algorithm Once


    TitleIMDB RatingRuntimeYearNum. VotesPrediction

    The xxx nearest neighbors:

    TitleIMDB RatingRuntimeYearNum. VotesYou ratedDistance

    Distance formula = Euclidian distance
    $ \sqrt{(distance_1^2+distance_2^2+...+distance_k^2)} $
    Normalization formula = Feature Scaling
    $ distance_k = \frac{property_k(training) - property_k(unseen)}{range_k} $

    Show all predictions

    6. All Predictions

    TitlePredicted rating
    TitlePredicted rating
    TitlePredicted rating