What are the most common predictive modeling tools and techniques?

Am example of regression analysis is predicting house prices based on features like square footage, number of bedrooms, and location. In a regression analysis, you'd collect data on these variables for a sample of houses. The dependent variable is the house price, while square footage, bedrooms, and location are independent variables. Using regression, you create an equation that best fits the data, allowing you to estimate coefficients for each variable. This model can then predict house prices for new data, providing insights into how changes in square footage, bedrooms, or location influence house prices. This real-world application showcases regression's predictive power in various industries, aiding decision-making processes.

Global pandemics. Social uprising. Wars. Mass resignations. Cultural shifts in the workplace. Events that in hindsight could have been predicted but weren’t. Recently, we’ve seen a significant amount of change in the world. Change that has a direct impact on you, your business and the people you serve. Such events are known as?Black Swan Events?- an unexpected occurrence that can have significant impacts and consequences. Black Swan Events are typically considered to pop up once in a blue moon. One thing is sure: this is not a matter of ‘if,’ it’s a matter of ‘when’. Here is where regression fails: by focusing only on available data you ignore the missing data. By focusing on the probable you ignore the improbable and flatten the outliers.

Classification and clustering are two very common techniques, especially before the advent of Generative AI. The Classification & Clustering learning are also often referred to as "discriminative" AI as opposed to Generative AI. Classification can be seen as categorization such as credit worthy or not. Classification requires labelled data to train the models. Whereas, Clustering does not require labelled data and can find the "groups" of data elements that are similar to each other. Clustering is often used for anomaly detection by looking for outliers. As nature of data changes over time, these models may start to drift and need re-training. Various measures like Confusion Matrix is used as quality measure for classification.

Imagine predicting customer churn for a telecom company. In decision trees, you'd start with a node (e.g., contract length), split based on factors like contract duration, creating branches for likely outcomes. A random forest extends this by combining multiple decision trees, each trained on a subset of data. It aggregates predictions, reducing overfitting and enhancing accuracy. Decision trees show specific factors influencing churn, while random forests provide robust predictions, collectively illustrating the power of these techniques in data-driven decision-making, like customer retention strategies for the telecom industry.

While prediction is a popular term and hoped for result, we can never truly predict the future. We can only make assumptions about what might happen if past patterns continue in the future. Instead I like to think of it as “perhaps analytics.”