https://github.com/cego669/russianalcoholconsumption
This repository includes a solution to a business problem in which a company wants to discover 10 other regions in Russia that have similar alcohol consumption patterns to Saint Petersburg. Dynamic Time Warping and Principal Component Analysis techniques were used to get to the final list of regions.
https://github.com/cego669/russianalcoholconsumption
alcohol-consumption business customer-segmentation dynamic-time-warping principal-component-analysis python russia similarity time-series time-series-clustering
Last synced: 3 months ago
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This repository includes a solution to a business problem in which a company wants to discover 10 other regions in Russia that have similar alcohol consumption patterns to Saint Petersburg. Dynamic Time Warping and Principal Component Analysis techniques were used to get to the final list of regions.
- Host: GitHub
- URL: https://github.com/cego669/russianalcoholconsumption
- Owner: cego669
- License: mit
- Created: 2024-08-13T21:12:26.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-13T22:41:21.000Z (almost 2 years ago)
- Last Synced: 2025-01-11T12:38:12.961Z (over 1 year ago)
- Topics: alcohol-consumption, business, customer-segmentation, dynamic-time-warping, principal-component-analysis, python, russia, similarity, time-series, time-series-clustering
- Language: Jupyter Notebook
- Homepage: https://www.linkedin.com/in/cego669/
- Size: 3.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ❓ Problem: where should a drinks company run promotions?
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 data
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
## ✅ Solution proposed by this notebook
This notebook ([github page here](https://cego669.github.io/RussianAlcoholConsumption/)) 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)](https://en.wikipedia.org/wiki/Dynamic_time_warping) 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)](https://en.wikipedia.org/wiki/Principal_component_analysis), **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**.
## License
Distributed under the MIT License. See `LICENSE.txt` for more information.
## Contact
Carlos Eduardo Gonçalves de Oliveira - [linkedin](https://www.linkedin.com/in/cego669/) - carlosedgonc@gmail.com
Project Link: [https://github.com/cego669/DirtyCategoriesEncoding](https://github.com/cego669/RussianAlcoholConsumption)