rokzupan1 / customer-segmentation-clustering Goto Github PK

Youtube video: https://www.youtube.com/watchv=iwUli5gIcU0&list=WL&index=3
Tutorial description: The is a data analysis portfolio project that will allow you to  perform customer segmentation on a specific group of mall customers. You will identify the best possible cluster using KMeans unsupervised machine learning algorithm to find the univariate, bivariate and multivariate clusters.  Once these clusters are identified, summary statistics can be perform on these to identify the best marketing group.
				SEGMENT SHOPPING CUSTOMERS
Problem Statement: understand the Target Customers for the marketing team to plan a strategy.
Context: Your boss wants to identify the most important shopping groups based on income, age, and the mall shopping score. He wants the ideal number of groups with a label for each.
Objective Market Segmantation: Divide your mall target market into approachable groups. Create subsets of a market based on demographics behavioral criteria to better understand the target for marketing activities.
Approach: 1. Perform some quick EDA(Exploratory Data Analysis)
	  2. Use KMEANS Clustering Algorithm to create our segments.
	  3. Use Summary Statistic on the clusters
	  4. Visualize
Requirements: 1. Standard Python Installation
	      2. Jupyter Notebook
	      3. PowerPoint
Analysis: Target Cluster -> Target group would be cluster 1 which has a high Spending Score and high income. 60 percent of cluster 1 shoppers are women. We should for a ways to attract these customers using a marketing campaign targeting popular items in the cluster. Cluster 2 presents and interesting opportunity to market to the customers for sales event on popular items.
Analysis Report: Target Cluster -> Target group would be cluster 1 which has a high Spending Score and high income. 54 percent of cluster 1 shoppers are women. We should look for ways to attract these customers using a marketing campaign targeting popular items in this cluster. Cluster 2 presents and interesting opportunity to market to the customers for sales event on popular items.