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License: MIT License
Data Science challenge
License: MIT License
keyword: Gradient Boosting Decision Trees
Model A: train a model without user_id to find correlations between (user_gender, user_age, and the other features) to (song's cluster).
Model B: train models for each user's history using Model A and get the result of test sample.
Model B should be temporary and wrap Model A.
This is a nice observation. As the dataset is from one month, maybe we can have as a result only the hour and another column indicating the day.
Create 10 samples on CSV.
Write the options to use listen_type
.
Functionalize preprocess modules
Add another column with a binary status weekday and weekend.
Correct the release date preprocessing.
album
, media
, genre
, media_duration
, and artist
Since there's no data with listen_type=0 in test set, the usage of listen_type is an issue.
Option 1.
Ignore listen_type
Option 2.
Use listen_type=1 only in the training
Option 3.
Use listen_type=0 as training set, listen_type=1 as validation set
Option 4.
Just Use It.
The dataset has more than 7 million rows. I think we can get a sample with around 100 000 rows, (still a lot of data).
My first idea is read the whole dataset and get the sample randomly. Of course, we can do this in a better way, but how O.o?
Stratified sampling is an option: https://en.wikipedia.org/wiki/Stratified_sampling
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