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Regression is mainly used to make predictions about numbers, such as estimating costs, sales or
grades. It helps in making informed decisions based on past data.

INTRODUCTION TO CLUSTERING

Clustering is a method in AI where data is grouped together based on similarities. Unlike
classification, clustering doesn't need labels to start with. Instead, it looks at patterns and
behaviours in the data, then automatically forms groups based on those similarities.

Imagine you have a photo application that can automatically organise your pictures. It might
group photos by people or locations, such as creating albums for Holiday in Paris or Family Photos,
without needing you to label each picture.

Another example is weather data. Clustering could group days into categories like hot, cold or
rainy based on factors like temperature and rainfall. The app or system doesn't need to know the
labels beforehand but learns to group the data based on patterns it finds.

Clustering helps find hidden patterns in data that may not be obvious at first, making it easier to
understand complex information and organise it more effectively.

DATASETS

Datasets refer to organised collections of data used to train and assess machine learning models.
These datasets provide the necessary information for models to recognise patterns and make
predictions, which are essential for the development of AI and machine learning. They enable
researchers and professionals to address various real-world challenges, including speech, image
recognition, predictive analytics and natural language processing.

Each individual unit within a dataset is known as a data point or record with several attributes or
features. Datasets are usually organised in tables, charts and spreadsheets, allowing easy access
to information and supporting accurate data-driven decision-making.

Types of Datasets

Datasets can be categorised in two main ways:

 Based on data format

10 Artificial Intelligence (CT & AI)-VII