In my thesis, I tried to build a model that predicts whether the prices of items will be adjusted today.
Analyzing Online Prices by Using Machine Learning Techniques (2018) (master thesis) - ML part source code. Also documented in my medium blog post.
Python 3.6.5
imbalanced_learn == 0.5.0
matplotlib == 3.1.2
numpy == 1.15.4
pandas == 0.23.4
scikit_learn == 0.21.3
seaborn == 0.9.0
As we have known by intuition, the prices in the online supermarket are not often be adjusted. Prices were adjusted only around 2% of the time.
To deal with this imbalanced data, I applied following methods;
- Use a tree-based algorithm
- Resampling
- Use appropriate metrics