In supervised machine learning, we have a labeled dataset that is used to train the model. For example, we train a model to predict the prices of houses based on features like area, number of bedrooms, and location, etc. In unsupervised machine learning, we do not have a labeled dataset. The goal of unsupervised machine learning is to find patterns and relationships in data. Clustering is one of the most popular techniques used in unsupervised machine learning.

A higher GDP per capita generally means that citizens have more disposable income, which can be used (Corresponding visualization is the first plot in Figure 1) to purchase goods and services that improve their health [Prompt] Generate an engaging caption for a scatter plot and wellbeing. The outlier in this data is Swaziland, titled GDP per capita VS Healthy life expectancy with which has a lower healthy life expectancy than would the x-axis labeled as GDP per capita and the y-axis labeled be expected of its GDP per capita. This is likely due as Healthy life expectancy. Other columns from to the high prevalence of HIV/AIDS in the country, as well as other factors such as poor access to healthcare, the data set include Social support, Perceptions of corruption, sanitation, and nutrition. Generosity, Overall rank, Score, Country or region, and Freedom to make life choices. The range [Added prompt] What is the reason for Swaziland's poor of GDP per capita is 0.0 to 1.684.

Clustering (cluster analysis) is grouping objects based on similarities. Clustering can be used in many areas, including machine learning, computer graphics, pattern recognition, image analysis, information retrieval, bioinformatics, and data compression. Clusters are a tricky concept, which is why there are so many different clustering algorithms. Different cluster models are employed, and for each of these cluster models, different algorithms can be given. Clusters found by one clustering algorithm will definitely be different from clusters found by a different algorithm. Grouping an unlabelled example is called clustering. As the samples are unlabelled, clustering relies on unsupervised machine learning. If the examples are labeled, then it becomes classification. Knowledge of cluster models is fundamental if you want to understand the differences between various cluster algorithms, and in this article, we're going to explore this topic in depth.

Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics. Customer Segmentation can be a powerful means to identify unsatisfied customer needs. Using the above data companies can then outperform the competition by developing uniquely appealing products and services. You are owing a supermarket mall and through membership cards, you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. You want to understand the customers like who are the target customers so that the sense can be given to marketing team and plan the strategy accordingly.

I started with loading all the libraries and dependencies. The columns in the dataset are customer id, gender, age, income and spending score. I dropped the id column as that does not seem relevant to the context. Also I plotted the age frequency of customers. Next I made a box plot of spending score and annual income to better visualize the distribution range. The range of spending score is clearly more than the annual income range.

Clustering is a technique that groups similar objects such that the objects in the same group are more similar to each other than the objects in the other groups. The group of similar objects is called a Cluster. Also called Hierarchical cluster analysis or HCA is an unsupervised clustering algorithm which involves creating clusters that have predominant ordering from top to bottom. For e.g: All files and folders on our hard disk are organized in a hierarchy. The algorithm groups similar objects into groups called clusters.