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Download the file Movie Ratings (.csv) at the bottom of the page of chapter 2. You will note that a...
Download the file Movie Ratings (.csv) at the bottom of the page of chapter 2. You will note that a lot of the values a values are NaN (movies rated on a scale of 1-5; a blank means that person didn’t see that movie). for movie rating file open google and search for programmers guide to data mining then you will find a link guidetodatamining.com. and sub link of chapter 2. open the chapter 2 link and scroll down then you will find movie ratings link.open the file.
Build a recommendation system using k-nearest neighbor approach (similar to the one presented in the book). using 2 cases:
- ( case ) You should use cosine similarity for a distance measure instead.(in the book Pearson correlation is used for a distance)
- ( case) Fill in the missing values with mode of the ratings (each column has a different mode ) . Use euclidean distance for a measure
You should submit / upload one IPython Notebook file ( .ipynb )
