Pricing optimization is another valuable use for data scientists.
Pricing is one of toughest challenges for most companies. To begin, there are different pricing methodologies depending on the industry, product, brand power, and so on. And to make things trickier, a product can command drastically different prices depending on the context. Let's take a bottle of water for example. For $2, you could buy a 6-pack of bottled water at a supermarket. However, those same $2 might only afford you one bottle at a movie theatre. As a result, companies will try to find the optimal price, that which maximizes earnings (a.k.a. "gross revenue"), for their market.
The Installation process will get you a copy of the project up and running on your local machine for development and testing purposes
- Clone or download the project into your local machine.
- Unzip the project folder.
- Open the source file Pricing Test Analysis using JypyterNotebook and execute the file.
- Data -- Contains the raw data folder
- Images -- Folder contains the images used in python notebook
If there are any issues in the code, raise them here
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The code and files in this repository is made available for free released under MIT.