Using Jupyter Notebook: Segmentation and Targeting

Using Jupyter Notebook
Segmentation and Targeting (25 points): After a very rough period over the couple years, Disney is hoping to bring more families back to its parks.
Here’s where your team can be a huge help to the company: Disney recently reached out to a large data aggregation company to obtain information about households that might but the park doesn’t know how to begin analyzing it. The dataset is named family_segments.csv. There’s a lot of data here, though! There are more than 9000 observations in the file. Disney is hoping that your team can help them to separate this huge group of people into distinct clusters, and then figure out how to reach out to each of these groups from a marketing perspective.
You may wish to use either k-means or hierarchical clustering for this task. To perform the actual clustering, use only your numeric variables (but when you analyze your clusters, you can include observations about the categorical factors).
Once you have built your clustering model, use anywhere from 4-6 visualizations that help to communicate information about your model. The visualizations should depict information about your clusters that you can clearly explain, and that park management can understand. So stay away from things like PCA and t-SNE!
Name each one of your clusters, and include a few sentences describing/explaining the name that you chose for each cluster.
Finally, for each of your clusters, talk about targeting. In a couple of sentences per group, how should park management reach each of these segments?
Use markdown cells for the cluster names and the targeting section. Also, use a markdown cell to describe the process that you used for arriving at the number of clusters for your model.

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