Description

Data Science Global Bootcamp Assignment 10

Clustering

Q1: Beer grouping use case
Company X would like to enter the market with a new beer brand.
Before it decides the kind of beer it will launch, it must understand what kinds of products already exist in the market and what kinds of segments the products address.
To understand the segments, the company collects specifications of few samples of beer brands (as given in the Beer
Dataset). Please refer to Beer Grouping dataset on GitHub –
https://github.com/Accelerate-AI/Data-Science-Global-
Bootcamp/blob/main/ClassAssignment/Assignment10/Beer_Grouping.xlsx
Assuming there are 3 segments, use K-Means Algorithm to create three clusters using the Beer Dataset. Once the clusters are finalized, interpret the clusters.

Q2: Distance measures on universities dataset
Please refer to the Universities.xlsx on GitHub: (https://github.com/Accelerate-AI/Data-Science-GlobalBootcamp/blob/main/ClassAssignment/Assignment10/Universities.xlsx ).
a) Find out the Euclidean distance between Stanford and MIT.
b) Find out the Manhattan distance between Stanford and MIT.
c) What difference you can convey for Manhattan distance w.r.t. Euclidean distance measure?

Q3: Distance measures comparison

What is the Euclidean distance between two points (2,4,6) and (3,8)? What is the Manhattan distance in the above case?

Q4: Customer Segmentation use case

One of the large smart phone manufacturing giants is analyzing it’s customer satisfaction and loyalty data. Here is a sample they have captured from a geography for certain period for analysis purposes. As a Data Scientist, you are tasked to analyze the segmentation pattern and recommend your inferences.
The dataset is here on GitHub – https://github.com/Accelerate-AI/Data-Science-GlobalBootcamp/blob/main/ClassAssignment/Assignment10/Satisfaction_Loyalty.csv
Dataset has two features captured here for analysis consideration – Satisfactory index and Loyalty index of customers.
Use clustering methods and provide your inference.