https://github.com/mbbrainz/datamining_expedia-ranking
Expedia Ranking assignment for Datamining techniques. Implemented XGB Ranker Model.
https://github.com/mbbrainz/datamining_expedia-ranking
datamining python ranking-algorithm xgboost
Last synced: 6 days ago
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Expedia Ranking assignment for Datamining techniques. Implemented XGB Ranker Model.
- Host: GitHub
- URL: https://github.com/mbbrainz/datamining_expedia-ranking
- Owner: MbBrainz
- Created: 2022-04-29T07:51:56.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-05-27T09:20:10.000Z (about 4 years ago)
- Last Synced: 2025-10-06T15:47:19.448Z (9 months ago)
- Topics: datamining, python, ranking-algorithm, xgboost
- Language: Python
- Homepage:
- Size: 48 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# dmt-a2-group155
epic stuff
## 2021 winners and their tekkers
the following metric was used for performance
Evaluation metric: Normalized Discounted Cumulative Gain
1st place:
--------------------
model - Gradient Boosting Machines (GBM)
Two types of models
± without EXP features (A)
5000 elementary trees
30 hours to train
± with EXP features (B)
2500 elementary trees
20 hours to train
Most important features:
± Position
± Price
± Location desirability (ver. 2)
Down sampling negative instances improves
training time and predictive performance
2nd place winner:
------------------
model: LambdaMART
LambdaMART is a learning to rank algorithm based
on Multiple Additive Regression Tree (MART).
## Incorporated methods:
1.) Scan through all values which have a Nan count greater than 60% of the total number of rows
https://www.kaggle.com/code/vishalkasa/feature-engineering-k-means
2.)Remove the users who did not booked the hotel
https://www.kaggle.com/code/jiaofenx/expedia-hotel-recommendations
3.) Look at when the booking were made i.e weekdays vs Saturday
4.) Example using K means and various plots for data understanding
https://www.kaggle.com/code/putdejudomthai/expedia-exploratory-data-destination-search
5.) Using chi-squared feature analysis as well as PCA analysis
https://medium.com/@zander.b.tedjo/expedia-hotel-recommendations-using-machine-learning-9a8eccd4ecba
# questions 13-05
how to evaluate scores?
- from sklearn.metrics import ndcg_score
- booking