Machine Learning for Customer Segmentation

C

Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset

The correct answer to the question is C. clustering.

Explanation:

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable systems to improve their performance on a specific task over time. There are three types of machine learning: supervised, unsupervised, and reinforcement learning.

Supervised learning involves training a model with labeled data, where the algorithm learns to map input to output. Regression and classification are two examples of supervised learning. In regression, the algorithm predicts a continuous numerical value, while in classification, the algorithm predicts a categorical label or class.

Unsupervised learning, on the other hand, involves training a model on unlabeled data, where the algorithm learns to find patterns or structure in the data. Clustering is an example of unsupervised learning, where the algorithm groups similar data points together based on their attributes or characteristics.

In the given scenario, the objective is to identify groups of people who have similar purchasing habits. This is an example of unsupervised learning as the data is not labeled. Clustering is the appropriate technique to use as it allows us to group similar individuals together based on their purchasing behavior. Once the clusters are identified, businesses can target their marketing efforts towards each cluster, leading to more effective marketing campaigns and increased revenue.

To summarize, the correct type of machine learning to use for identifying groups of people with similar purchasing habits is clustering, which is an example of unsupervised learning.