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Articles related to "shall"


Clustering Geospatial Data

  • In this article, using Data Science and Python, I will show how different Clustering algorithms can be applied to Geospatial data in order to solve a Retail Rationalization business case.
  • In this article, I’m going to use clustering with geographic data to solve a retail rationalization problem.
  • I shall select a particular geographic area and, in addition to the latitude and longitude provided, I will simulate some business information for each store in the dataset (cost, capacity, staff).
  • Now that it’s all set, I will start by analyzing the business case, then build a clustering model and a rationalization algorithm.
  • Our objective is to close as many high-cost stores (red points) as possible by moving their staff into low-cost stores (green points) with capacity located in the same neighborhood.
  • This article has been a tutorial about how to use Clustering and Geospatial Analysis for a retail business case.

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Clustering Geospatial Data

  • In this article, using Data Science and Python, I will show how different Clustering algorithms can be applied to Geospatial data in order to solve a Retail Rationalization business case.
  • In this article, I’m going to use clustering with geographic data to solve a retail rationalization problem.
  • I shall select a particular geographic area and, in addition to the latitude and longitude provided, I will simulate some business information for each store in the dataset (cost, capacity, staff).
  • Now that it’s all set, I will start by analyzing the business case, then build a clustering model and a rationalization algorithm.
  • Our objective is to close as many high-cost stores (red points) as possible by moving their staff into low-cost stores (green points) with capacity located in the same neighborhood.
  • This article has been a tutorial about how to use Clustering and Geospatial Analysis for a retail business case.

save | comments | report | share on


Clustering Geospatial Data

  • In this article, using Data Science and Python, I will show how different Clustering algorithms can be applied to Geospatial data in order to solve a Retail Rationalization business case.
  • In this article, I’m going to use clustering with geographic data to solve a retail rationalization problem.
  • I shall select a particular geographic area and, in addition to the latitude and longitude provided, I will simulate some business information for each store in the dataset (cost, capacity, staff).
  • Now that it’s all set, I will start by analyzing the business case, then build a clustering model and a rationalization algorithm.
  • Our objective is to close as many high-cost stores (red points) as possible by moving their staff into low-cost stores (green points) with capacity located in the same neighborhood.
  • This article has been a tutorial about how to use Clustering and Geospatial Analysis for a retail business case.

save | comments | report | share on