The company owns a chain of stores across Russia that sell a variety of alcoholic drinks. The company recently ran a wine promotion in Saint Petersburg that was very successful. Due to the cost to the business, it isn’t possible to run the promotion in all regions. The marketing team would like to target 10 other regions that have similar buying habits to Saint Petersburg where they would expect the promotion to be similarly successful.
The marketing team has sourced with historical sales volumes per capita for several different drinks types.
- "year" - year (1998-2016)
- "region" - name of a federal subject of Russia. It could be oblast, republic, krai, autonomous okrug, federal city and a single autonomous oblast
- "wine" - sale of wine in litres by year per capita
- "beer" - sale of beer in litres by year per capita
- "vodka" - sale of vodka in litres by year per capita
- "champagne" - sale of champagne in litres by year per capita
- "brandy" - sale of brandy in litres by year per capita
This notebook (github page here) includes an interactive data exploration where you can easily compare alcohol consumption patterns between different regions of Russia. As a solution to the business problem, the Dynamic Time Warping (DTW) technique was used to calculate the distance between time series of the same drink from different regions. In this way, using Principal Component Analysis (PCA), it was possible to project the distances between all regions on a plane, where it is easy to visualize the regions most similar to Saint Petersburg in terms of alcohol consumption pattern.
Distributed under the MIT License. See LICENSE.txt for more information.
Carlos Eduardo Gonçalves de Oliveira - linkedin - carlosedgonc@gmail.com
Project Link: https://github.com/cego669/DirtyCategoriesEncoding