arifqodari / expedialearningtorank Goto Github PK
View Code? Open in Web Editor NEWExpedia Learning to Rank Hotels to Maximize Purchases, Kaggle Competition
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baokunguo tvkpz gangyueqi aiden-meister molimomo feliuserra skyjiao yuan39 zjy63562680 alxsoares hyangchun rock999 colinsongf guoyu07 zhouyonglong amimul rszymula ekotti pimnijhuis sytsegroenwoldexpedialearningtorank's Issues
Feature Selection
| Schemes | Content | Progress |
|---|---|---|
| Basic Scheme 1 | Only numeric attributes are kept | Done |
| Basic Scheme 2 | Numeric and Categorical attributes are kept | Done |
| Scheme 3 | Add new average of numeric attributes per prop_id, applied to Scheme 1 and 2 | ? |
What do you think of Scheme 3?
read_data in main.py
I thought I add a bool value to enalbe or disable reading data into memory by chunk, so "chunked' is used as the bool switch.
Categorical attributes to Numerical ones
| Attribute Name | Type | Operation |
|---|---|---|
| country id | Category | Country 37 is dominating, so the country is divided into 37 and not-37 |
| prop location score 1 | Numeric or Category? | 337 different scores, regroup the score according to the min and max scores; or replace it with a new score scheme. |
Should we do in this way? It seems to be lots of work
A wired bug in the preprocessing function of main.py
Data type
To facilitate the calculation, when read csv from file, dype is preferred to be designated.
What do you think of the type?
Category: Str?
Numeric: np.float32?
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