The second part of our AI series focus on Machine Learning.
The most common output from machine learning models is to predict a numeric value, like the amount an individual would likely invest based on their specific parameters. This is called regression analysis.
This post provides examples of four other models.
The first two are based on Supervised learning, ie. the training data contains some sort of outcome, eg did the customer buy the product or not.
The last two examples are based on Unsupervised learning, ie. the training data contains no label parameters.
Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data. In classification, the model is fully trained using the training data, and then it is evaluated on test data before being used to perform prediction on new unseen data.
Where it can be applied:
Email filtering - classification modeling can be used to categorise incoming emails as spam or not spam based on their content features.
Healthcare - assist with diagnosing diseases by classifying patient data into different health conditions or risk categories.
How it works: Classification modeling works by training on a labeled data set where the input features are associated with known class labels, allowing the model to learn patterns and distinguish between different categories. Once trained, the model can then be used to predict the class labels of new, unseen data by identifying which patterns the new inputs closely resemble.
Time to event analysis (also known as survival analysis) is a way to study how long it takes for something to happen, like when a customer makes a purchase after seeing an ad or when a machine breaks down. Instead of just looking at whether the event happens or not, this method focuses on the time it takes for the event to occur.
Where it can be applied:
Customer Churn Prediction – Estimating how long a customer will stay subscribed to a service before canceling.
Product Failure Analysis – Predicting the time until a machine or device breaks down based on usage data.
How it works: Time-to-event analysis tracks how long it takes for a specific event to happen. It uses statistical models to estimate the likelihood of the event occurring over time while also considering cases where the event hasn’t happened yet (called censoring).
Clustering is a type of machine learning that automatically groups similar things together without being told how to do it. It looks at patterns in data and organises items into clusters based on their similarities, like grouping customers with similar shopping habits or sorting news articles by topic. Since it’s unsupervised, the algorithm doesn’t have labeled examples to learn from—it just finds natural patterns in the data. Clustering is useful for things like customer segmentation in marketing, anomaly detection in cyber security, and organising large data sets more efficiently.
Where it can be applied:
Customer Segmentation – Grouping customers based on purchasing behavior to create targeted marketing campaigns.
Image Recognition – Organising similar images together, such as grouping photos of cats, dogs, and cars without prior labels.
How it works: Clustering algorithms analyse data points and measure how similar they are to one another, then group them into clusters where similar items are placed together. The algorithm keeps adjusting the groups until it finds the best way to separate the data into meaningful patterns without any predefined labels.
Association modeling focuses on discovering interesting relationships or patterns among variables in large data sets without having any predefined labels or outcomes. It is often used to identify sets of items that commonly co-occur within transactional data, with the most prevalent application being market basket analysis in retail. This analysis helps businesses understand customer purchasing behaviors by finding frequent item sets or associations between products. The most commonly used techniques in association modeling identify rules such as “If a customer buys bread, they are likely to also buy butter.”
Where it can be applied:
In retail - association modeling can be applied to analyse shopping cart data to determine frequently purchased product combinations, helping to optimise store layout and promotions.
In online streaming services - it can be used to recommend movies or shows by identifying patterns in the viewing habits of users with similar preferences.
How it works: Association modeling works by analysing large data sets to find patterns or relationships between items, often in the form of “if-then” rules, such as “if a person buys item A, they are likely to buy item B.” It does so primarily by identifying frequently occurring pairs or groups of items using algorithms that measure support, confidence, and lift, which represent the strength and significance of these relationships.
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