Post by account_disabled on Feb 22, 2024 4:57:08 GMT -5
When we use the word predictive models in an analysis environment, we are referring to a representation of reality based on a descriptive attempt to relate one set of variables to another. Predictive analysis brings together management, information technologies and modeling, focusing on working with large volumes of data and with the aim of contributing to business success. Predictive models Photo credits: Predictive models are the key to being able, through an analytical effort, to detect investment opportunities, know the sales forecast or market share, identify the most profitable consumer segments or the destination markets with the greatest potential. They also play an important role in identifying risks associated with existing products or those that may arise from the implementation of a certain business strategy in the future. What it takes to create predictive models Data scientists involved in building predictive models are multidisciplinary professionals.
In addition to their experience, they must have knowledge in many other areas, such as: Accounting, finance, marketing and management. Information technologies, including data structures, algorithms and object-oriented programming. Statistical modeling, machine learning and mathematical programming. The methodology they apply in their daily work is eclectic since, only in this way can they translate the results of empirical research into words and images; more easily understood by end users, who are the ones who must work with the information, giving it meaning. To do this, they start from predictive models , which represent the relationship between variables. Regression and classification are two Chinese Student Phone Number List common types of predictive models : 1. Regression: consists of predicting a quantifiable response. These types of models address issues such as the number of units of a product sold, the market price or the return on investment. 2. Classification: these predictive models predict a categorical response that responds to an open question, such as the probability that a consumer will become a customer, the existence of fraudulent intent in a transaction or the brand that will be most in demand within the period of time. one year. Predictive models: going deeper into the data. The value generation process Predictive models , in order to carry out their mission, require predictors and the observation of data sets. The greater the number of predictors and the greater the depth of their investigation, the complexity of the analysis will increase. Although this is not the most complicated challenge.
The real challenge for predictive models is to find good subsets of predictors or explanatory variables, that is, to find those that provide the greatest usefulness, those that best fit the data. When considering business problems, you use available data to predict data you don't yet have. It is a process of extrapolating and predicting, with the risks that this implies. Therefore, it is important to keep in mind that the best predictive models , those with the greatest value, are those that provide the highest quality predictions. This data mining technique works by analyzing data of two types: On the one hand, historical: those that the company stored. And, on the other hand, current: those that are collected daily. While the former generally have greater homogeneity, both in their formats and types, and in their sources of origin; When it comes to the latter, variety is the only common denominator. With the intention of generating a model that helps predict future results, in predictive modeling the following actions must be carried out: 1. Data collection: depending on the needs revealed by the statistical model formula itself. It is often necessary to combine information from various sources, internal and external, to gain a more complete perspective. 2. Analysis: Various analytical techniques are applied to the above results to evaluate the probabilities of occurrence of a specific event or behavior. This research also makes it possible to detect data patterns that answer questions about the performance or suitability of an asset, facilitating, among other things, the detection of fraud, one of its most widespread applications. Predictive models in the business environment: the power of decision Predictive models , in their application to business, play a decisive role in: Optimize decision making and minimize risk when it occurs in dynamic conditions Improve customer knowledge and obtain the ability to predict their future actions. Increase consistency in business processes and thus customer satisfaction. Simplify business rules and, at the same time, multiply their effectiveness. Reduce costs and increase profits. Enhance the competitiveness of the company. Specifically, predictive models must be integrated into operational processes and activated during ongoing transactions. In this way, they can analyze historical records and transactional data to isolate patterns that can be detected, for example: A fraudulent transaction, knowing its signs. A risky customer, since certain characteristics are observed that lead one to think that they will soon change suppliers. The type of analysis that predictive models allow assesses the relationship between hundreds of elements to isolate the data that informs about a fact, guiding decision-making along a safe path. One step further are decision models , which have a very similar way of working to predictive models , although they are used in more complex scenarios. This is the most advanced form of predictive analytics and consists of predicting what would happen if a certain action is taken. They are also known as prescriptive models and are based on mapping the relationships between all the elements of a decision.
In addition to their experience, they must have knowledge in many other areas, such as: Accounting, finance, marketing and management. Information technologies, including data structures, algorithms and object-oriented programming. Statistical modeling, machine learning and mathematical programming. The methodology they apply in their daily work is eclectic since, only in this way can they translate the results of empirical research into words and images; more easily understood by end users, who are the ones who must work with the information, giving it meaning. To do this, they start from predictive models , which represent the relationship between variables. Regression and classification are two Chinese Student Phone Number List common types of predictive models : 1. Regression: consists of predicting a quantifiable response. These types of models address issues such as the number of units of a product sold, the market price or the return on investment. 2. Classification: these predictive models predict a categorical response that responds to an open question, such as the probability that a consumer will become a customer, the existence of fraudulent intent in a transaction or the brand that will be most in demand within the period of time. one year. Predictive models: going deeper into the data. The value generation process Predictive models , in order to carry out their mission, require predictors and the observation of data sets. The greater the number of predictors and the greater the depth of their investigation, the complexity of the analysis will increase. Although this is not the most complicated challenge.
The real challenge for predictive models is to find good subsets of predictors or explanatory variables, that is, to find those that provide the greatest usefulness, those that best fit the data. When considering business problems, you use available data to predict data you don't yet have. It is a process of extrapolating and predicting, with the risks that this implies. Therefore, it is important to keep in mind that the best predictive models , those with the greatest value, are those that provide the highest quality predictions. This data mining technique works by analyzing data of two types: On the one hand, historical: those that the company stored. And, on the other hand, current: those that are collected daily. While the former generally have greater homogeneity, both in their formats and types, and in their sources of origin; When it comes to the latter, variety is the only common denominator. With the intention of generating a model that helps predict future results, in predictive modeling the following actions must be carried out: 1. Data collection: depending on the needs revealed by the statistical model formula itself. It is often necessary to combine information from various sources, internal and external, to gain a more complete perspective. 2. Analysis: Various analytical techniques are applied to the above results to evaluate the probabilities of occurrence of a specific event or behavior. This research also makes it possible to detect data patterns that answer questions about the performance or suitability of an asset, facilitating, among other things, the detection of fraud, one of its most widespread applications. Predictive models in the business environment: the power of decision Predictive models , in their application to business, play a decisive role in: Optimize decision making and minimize risk when it occurs in dynamic conditions Improve customer knowledge and obtain the ability to predict their future actions. Increase consistency in business processes and thus customer satisfaction. Simplify business rules and, at the same time, multiply their effectiveness. Reduce costs and increase profits. Enhance the competitiveness of the company. Specifically, predictive models must be integrated into operational processes and activated during ongoing transactions. In this way, they can analyze historical records and transactional data to isolate patterns that can be detected, for example: A fraudulent transaction, knowing its signs. A risky customer, since certain characteristics are observed that lead one to think that they will soon change suppliers. The type of analysis that predictive models allow assesses the relationship between hundreds of elements to isolate the data that informs about a fact, guiding decision-making along a safe path. One step further are decision models , which have a very similar way of working to predictive models , although they are used in more complex scenarios. This is the most advanced form of predictive analytics and consists of predicting what would happen if a certain action is taken. They are also known as prescriptive models and are based on mapping the relationships between all the elements of a decision.