icemeooow / price_recommendation

price_recommendation

Price Recommendation

This project provides an ability to predict the price of product for the next two weeks. Long Short-Term Memory (LSTM) networks were used.

Data

'Breakfast at the Frat' data set are used.

Technology

• python (3.6.2 )
• tensorflow (1.10.0)
• Keras (2.2.2)
• numpy (1.14.5)
• pandas (0.23.4)
• matplotlib (3.0.0)
• seaborn (0.9.0)
• scikit-learn (0.19.2)
• scipy (1.1.0)
• xlrd (1.1.0)

Project structure

• main.py - is used to run the project.
• demand_forecaster.py - allows to predict the demand for a product based on its history.
• price_forecaster.py - allows to predict the price for a product based on its history.
• price_advisor.py - allows to recommend product price for text 2 weeks.
• data_exploration.ipynb - shows data research.
• step_by_step_sample.py - step by step presentation of the process.

How to use

• to recommend product price:

  python main.py
  

Note: User must enter the UPC product code and the name of the store. In addition, the user need to enter promotional information for the next two weeks.

• to see the data research

  jupyter notebook
  

  then open the data_exploration.ipynb file.

• to predict product demand and to see model performance:

  python step_by_step_sample.py