minerva-ml / open-solution-avito-demand-prediction Goto Github PK
View Code? Open in Web Editor NEWOpen solution to the Avito Demand Prediction Challenge
Home Page: https://www.kaggle.com/c/avito-demand-prediction
License: MIT License
Open solution to the Avito Demand Prediction Challenge
Home Page: https://www.kaggle.com/c/avito-demand-prediction
License: MIT License
Explore/add timestamp features:
Then explore/add group-by features with times.
UPDATE (from @Leoniak713):
Add stacking transformer abstraction (pipeline block)
Add tfidf vectorizer likely followed by svd
Build model that is trained:
add simple image features based on intensity statistics and colours
train_active.csv contains info about other user offers at the time. Could be useful
Where can I find necessary requirements for the project?
Val_loss | LB |
---|---|
0.2296 | 0.2352 |
0.2281 | 0.2351 |
0.2294 | 0.2367 |
Add count vectorizer likely followed by svd
How to handle missing values?
Currently replace with 0 +add new binary nan/not nan is used
In general, encode NaN
as a feature. For example missing photo correlated with deal probability.
Add this as Step
.
Train model on images, with targets (multi-output model):
category name
parent category name
Features are two vectors:
category name
(softmax)parent category name
(softmax)Is there any logic by repeting the same code twice? Why you put is_missing
under train_mode
flag? You can declare it out of train_mode
and then in case you need return it...
if train_mode:
is_missing = Step(name='is_missing',
transformer=fe.IsMissing(**config.is_missing),
input_data=['input'],
adapter={'X': ([('input', 'X')])},
cache_dirpath=config.env.cache_dirpath, **kwargs)
is_missing_valid = Step(name='is_missing_valid',
transformer=is_missing,
input_data=['input'],
adapter={'X': ([('input', 'X_valid')])},
cache_dirpath=config.env.cache_dirpath, **kwargs)
return is_missing, is_missing_valid
else:
is_missing = Step(name='is_missing',
transformer=fe.IsMissing(**config.is_missing),
input_data=['input'],
adapter={'X': ([('input', 'X')])},
cache_dirpath=config.env.cache_dirpath, **kwargs)
return is_missing
Explora best/worst results and most important features from solution_1 in the notebooks/devbook.ipynb
Toxic
competition and add them to this challenge.Initialize your participation in the challenge:
master
,periods_train.csv contains information about item_id being on auction. Could be useful
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