Predictive models you should know when analyzing data

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When we talk about predictive analytics and the possibilities it offers, it is clear that data analysis has become a common practice in all types of sectors: this resource helps save time by being able to analyze a large amount of information in a short time. In this post we analyze which are the most used predictive models that you should know when analyzing data so that you understand which one is the most suitable for your business.

What are the most common machine learning predictive models?
Predictive models are statistical and machine learning methods designed to predict future events or behaviors based on historical data.

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By identifying patterns and relationships in data, these models allow you to estimate probable results over a given period , saving costs and helping you make better decisions. Let's see which predictive models are available to you.

Classification models
Without a doubt, one of the most widely used predictive jamaica phone number lead models in service companies. These models assign categories or labels to data based on specific characteristics and therefore help to segment customers , which is perfect for creating personalized advertising campaigns for specific targets. They also help to predict the rate of customers who might abandon a product or service.

Regression models
These types of machine learning predictive models analyze the relationship between independent variables and a dependent variable to predict continuous values. For example, they allow predicting future sales of a product based on its seasonality and price over a given period of time.

Time series models
Another of the most widely used predictive models are those based on time series. We are talking about a system capable of analyzing sequential data over time to identify seasonal trends and patterns . This is very useful for certain periods with sales peaks, such as Black Friday or the Christmas campaign , as it could help you prepare a sales forecast .

Clustering models
Predictive clustering models are capable of detecting and grouping sets with similarities , even if they are not in the same category. For example, a bank could group its customers based on their consumption habits to offer personalized financial products.

Decision trees
Predictive models of this type have hierarchical structures that represent decisions and their consequences so that you can classify data or predict outcomes based on multiple variables . Returning to the example above, a bank could quickly analyze user data before granting a loan.

Neural networks
Inspired by the human brain , these predictive models use neural networks capable of recognizing patterns in large volumes of data. This is perfect for all kinds of work related to image recognition, natural language processing, and fraud detection .

As you may have noticed, there are different predictive models, so you will need to choose the option that best suits your needs to boost your business through massive data analysis.

At MASMOVIL NEGOCIOS we hope to have helped you understand which are the most commonly used predictive machine learning models and the possibilities they offer in your business to enhance it and help you in decision-making.