Explain the use of cluster analysis in data science. Sprockets Corporation designs high-end, specialty machine parts for a variety of industries. You have been hired by Sprockets to assist them with their data analysis needs. Sprockets Corporation management is curious about the leveraging of unstructured data. You are convinced that, if presented with a demonstration of the types of analysis that you can perform and the value added to their bottom line, they will give you more work in this lucrative field. Given a sample data set, you are going to present a cluster analysis using the Python language using two separate techniques: horizontal clustering and vertical clustering (k-means) In a presentation to John Sprocket, CEO and the leadership team at Sprockets Corporation, prepare a presentation showcasing the types of analysis you can perform on their existing data and what the benefits are of said analysis. Include in your presentation all source code created in your analysis. Data Sets https://learning.rasmussen.edu/bbcswebdav/pid-5855340-dt-content-rid-151629593_1/xid-151629593_1 Purchase the answer to view it

Cluster analysis is a powerful technique used in data science to group similar objects or data points together based on their characteristics or attributes. It is commonly used for exploratory data analysis and can provide insights into the underlying structure of the data.

In the case of Sprockets Corporation, cluster analysis can be applied to their data to identify patterns or groupings that may not be immediately apparent. This can help the company better understand their customers, products, or operations, leading to more informed decision-making.

There are several types of cluster analysis techniques available, but for this particular assignment, we will focus on two: horizontal clustering and vertical clustering (k-means).

Horizontal clustering, also known as agglomerative clustering, starts with each data point as a separate cluster and then iteratively merges similar clusters until a desired number of clusters or a specified threshold is reached. This technique is useful when the number of clusters is not known in advance.

Vertical clustering, on the other hand, is a more structured approach that starts with a pre-defined number of clusters (k) and assigns each data point to the nearest cluster centroid. The centroid is the center of a cluster, and the distances between the data points and the cluster centroids are iteratively minimized. This technique is particularly useful when the number of clusters is known or can be estimated beforehand.

To demonstrate these techniques, we will use the Python programming language. Python is a popular choice for data analysis due to its extensive libraries and ease of use.

We will provide a presentation to John Sprocket, CEO of Sprockets Corporation, and his leadership team, showcasing the types of analysis we can perform on their existing data and the benefits of such analysis. In this presentation, we will include all the source code created in our analysis, allowing the company to replicate and extend our findings.

It is important to note that cluster analysis is just one tool in the data scientist’s toolkit. It should be used in conjunction with other techniques and domain knowledge to gain a comprehensive understanding of the data and make well-informed decisions.

In conclusion, cluster analysis is a valuable technique in data science that can help uncover hidden patterns or groupings in data. By applying cluster analysis to the data of Sprockets Corporation, we can provide insights that will benefit their bottom line and drive future work in the field of data analysis.

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